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thomasfdtaigmips-1.0/examples/treeminer/miner_main_temp.cpp0000755002402200254040000001535012520407730021322 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include #include #include #include "mips/patternMining/tree/networkTreeMiner_temp.hpp" #include "mips/core/utils.hpp" #include "mips/core/tree/enum/extenderRootedTree.hpp" #include "mips/core/graph/graphAdjList.hpp" using namespace std; using namespace mips::pattern_mining; /* ******************************************************************** * ================= * Non-class stuff * ================= * ******************************************************************** */ float freq_thresh=-1; float success_prob=0.2; //int kill_mins = 10*60; // 10 mins in secs int pattern_size = -1; char *results_file = (char *) ""; char *stats_file = (char *) ""; char *graph_file = (char *) ""; bool runInBatch = false; bool useVF2 = false; bool doHomomorphism=true; bool doSampling=false; bool useUllman = false; int batchSize = 100; short debug_level=0; int sampleSize = 10; void printUsage() { cerr << "Usage: NetworkTreeMinerWithSampling -g -k -f " << "[-s -r [-v | -u | -b] -p --batch-size --sample-size ]" << endl; // -w ]"; cerr << "\t-v uses VF2 algorithm for isomorphism test (instead of Williams)" << endl << "\t-u uses ullman's algorithm for isomorphism test (instead of Williams or VF2)" << endl << "\t-b uses batch-mode of Williams method (-v and -u are ignored if -b is set," << " whereas -p is ignored if either -v or -u is set)" << endl << "\t-n skips homomorphism check in William's method" << endl << "\t-t does random sampling and statistical test during homomorphism check in Williams method (so we skip further checking, if statistically a pattern is deemed in frequent)" << endl; } bool parseOption(char* cmd_arg, char* param) { string arg(cmd_arg); if(arg == "-b") { if(useVF2 or useUllman) return false; runInBatch = true; return true; } if(arg == "-v") { if(runInBatch or useUllman) return false; useVF2 = true; return true; } if(arg == "-u") { if(runInBatch or useVF2) return false; useUllman = true; return true; } if(arg == "-n") { doHomomorphism = false; return true; } if(arg == "-t") { doSampling = true; return true; } //All the rest of the args should have a param if(string(param)=="") { return false; } if(arg == "-d") { istringstream convert(param); if ( !(convert >> debug_level) ) return false; } else if(arg == "-k") { istringstream convert(param); if( !(convert >> pattern_size) ) return false; } else if(arg == "-f") { istringstream convert(param); if( !(convert >> freq_thresh) ) return false; } else if(arg == "-p") { istringstream convert(param); if( !(convert >> success_prob) ) return false; } /* else if(arg == "-w") { istringstream convert(param); if ( !(convert >> kill_mins) ) return false; kill_mins *= 60; }*/ else if(arg == "-s") { stats_file = param; } else if(arg == "-r") { results_file = param; } else if(arg == "-g") { graph_file = param; } else if(arg == "--batch-size") { istringstream convert(param); if( !(convert >> batchSize) ) return false; } else if(arg == "--sample-size") { istringstream convert(param); if( !(convert >> sampleSize) ) return false; } else { cerr << "Unknown option " << arg << ' ' << endl; return false; } return true; } int main(int argc, char* argv[]) { int a=1; //parse options while( a < argc ) { //cout << "current: " << a << ',' << argv[a] << ',' << argv[a]+1 << endl; /* if(argv[a][1]=='b') { cout << "In here ..." << endl; runInBatch = true; a++; continue; } */ //else parse the rest of options //if(argc <= (a+1)) { // printUsage(); // return -1; //} //most options require a parameter if(not parseOption(argv[a], (a+1) < argc ? argv[a+1]:(char *)"")) { //since an arg with no param can be last in the list printUsage(); return -1; } if((argv[a][1]=='b' or argv[a][1]=='v' or argv[a][1]=='u' or argv[a][1]=='n' or argv[a][1]=='t') and argv[a][2]=='\0') a++; else a+=2; } //check all required options were present if(pattern_size==-1 or graph_file=="" or freq_thresh<0) { printUsage(); return -1; } if(debug_level>1) cout << "Following options set (some can be default values):" << endl << "patternsize: " << pattern_size << endl << "freq_thresh: " << freq_thresh << endl << "success_prob: " << success_prob << endl << "useVf2: " << useVF2 << endl << "useUllman: " << useUllman << endl << "homomorphism-check: " << doHomomorphism << endl << "homomorphism-statistical-check: " << doSampling << endl << "batchsize: " << batchSize << endl << "samplesize: " << sampleSize << endl //<< "wait time (in secs): " << kill_mins << endl << "debug_level: " << debug_level << endl << "graph_file: " << graph_file << endl << "results_file: " << results_file << endl; ifstream infile; //ofstream outfile; // let minePattern() take care of this infile.open(graph_file, ifstream::in); if(!infile) { cerr << "Error opening graph file " << graph_file << "." << endl; printUsage(); return -1; } //if(results_file!="") // outfile.open(results_file, ios_base::openmode mode=ios_base::app) if(debug_level>0) cout << "======== Doing file " << graph_file << " =========" << endl; char c; float th = freq_thresh; NetworkTreeMiner_temp miner; miner.setDebugLevel(debug_level); miner.setStatsFile(stats_file); miner.setResultsFile(results_file); while(infile.good()) { c = infile.get(); if(infile.eof()) continue; else infile.putback(c); GraphAdjList g; infile >> g; //cout << "Read in the following graph:" << endl; //cout << g << endl; if(th<1) th = round_float(freq_thresh*num_vertices(g), 0); //cout << "graph g is:\n" << g << endl; cout << "converted threshold is: " << th << endl; miner.minePatterns_temp(g, pattern_size, th, success_prob, (useUllman or useVF2), useUllman, doHomomorphism, doSampling, runInBatch, batchSize, sampleSize); } } mips-1.0/examples/treeminer/miner_main.cpp0000755002402200254040000001561612520407730020302 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include #include #include #include "mips/patternMining/tree/networkTreeMiner.hpp" #include "mips/core/utils.hpp" #include "mips/core/tree/enum/extenderRootedTree.hpp" #include "mips/core/graph/graphAdjList.hpp" using namespace std; using namespace mips::pattern_mining; /* ******************************************************************** * ================= * Non-class stuff * ================= * ******************************************************************** */ float freq_thresh=-1; float success_prob=0.2; //int kill_mins = 10*60; // 10 mins in secs -- we do this external to C++ code int pattern_size = -1; char *results_file = (char *) ""; char *stats_file = (char *) ""; char *graph_file = (char *) ""; bool runInBatch = false; bool useVF2 = false; bool doHomomorphism=true; bool doFourier=true; bool doSampling=false; bool useUllman = false; int batchSize = 100; short debug_level=0; void printUsage() { cerr << "Usage: NetworkTreeMiner -g -k -f " << "[-s -r [-v | -u | -b] -p --batch-size ]" << endl; // -w ]"; cerr << "\t-v uses VF2 algorithm for isomorphism test (instead of Williams)" << endl << "\t-u uses ullman's algorithm for isomorphism test (instead of Williams or VF2)" << endl << "\t-b uses batch-mode of Williams method (-v and -u are ignored if -b is set," << " whereas -p is ignored if either -v or -u is set)" << endl << "\t-n skips homomorphism check in William's method" << endl << "\t-m skips 'memory light' fourier trick for William's method" << endl << "\t-t does random sampling and statistical test during homomorphism check in Williams method (so we skip further checking, if statistically a pattern is deemed in frequent)" << endl; } bool parseOption(char* cmd_arg, char* param) { string arg(cmd_arg); if(arg == "-b") { if(useVF2 or useUllman) return false; runInBatch = true; return true; } if(arg == "-v") { if(runInBatch or useUllman) return false; useVF2 = true; return true; } if(arg == "-u") { if(runInBatch or useVF2) return false; useUllman = true; return true; } if(arg == "-n") { doHomomorphism = false; return true; } if(arg == "-m") { doFourier = false; return true; } if(arg == "-t") { doSampling = true; return true; } //All the rest of the args should have a param if(string(param)=="") { return false; } if(arg == "-d") { istringstream convert(param); if ( !(convert >> debug_level) ) return false; } else if(arg == "-k") { istringstream convert(param); if( !(convert >> pattern_size) ) return false; } else if(arg == "-f") { istringstream convert(param); if( !(convert >> freq_thresh) ) return false; } else if(arg == "-p") { istringstream convert(param); if( !(convert >> success_prob) ) return false; } /* else if(arg == "-w") { istringstream convert(param); if ( !(convert >> kill_mins) ) return false; kill_mins *= 60; }*/ else if(arg == "-s") { stats_file = param; } else if(arg == "-r") { results_file = param; } else if(arg == "-g") { graph_file = param; } else if(arg == "--batch-size") { istringstream convert(param); if( !(convert >> batchSize) ) return false; } else { cerr << "Unknown option " << arg << ' ' << endl; return false; } return true; } int main(int argc, char* argv[]) { int a=1; //parse options while( a < argc ) { //cout << "current: " << a << ',' << argv[a] << ',' << argv[a]+1 << endl; /* if(argv[a][1]=='b') { cout << "In here ..." << endl; runInBatch = true; a++; continue; } */ //else parse the rest of options //if(argc <= (a+1)) { // printUsage(); // return -1; //} //most options require a parameter if(not parseOption(argv[a], (a+1) < argc ? argv[a+1]:(char *)"")) { //since an arg with no param can be last in the list printUsage(); return -1; } if((argv[a][1]=='b' or argv[a][1]=='v' or argv[a][1]=='u' or argv[a][1]=='n' or argv[a][1]=='m' or argv[a][1]=='t') and argv[a][2]=='\0') a++; else a+=2; } //check all required options were present if(pattern_size==-1 or graph_file=="" or freq_thresh<0) { printUsage(); return -1; } if(debug_level>1) cout << "Following options set (some can be default values):" << endl << "patternsize: " << pattern_size << endl << "freq_thresh: " << freq_thresh << endl << "success_prob: " << success_prob << endl << "useVf2: " << useVF2 << endl << "useUllman: " << useUllman << endl << "homomorphism-check: " << doHomomorphism << endl << "homomorphism-statistical-check: " << doSampling << endl << "batchsize: " << batchSize << endl //<< "wait time (in secs): " << kill_mins << endl << "useFourier: " << doFourier << endl << "debug_level: " << debug_level << endl << "graph_file: " << graph_file << endl << "results_file: " << results_file << endl; ifstream infile; //ofstream outfile; // let minePattern() take care of this infile.open(graph_file, ifstream::in); if(!infile) { cerr << "Error opening graph file " << graph_file << "." << endl; printUsage(); return -1; } //if(results_file!="") // outfile.open(results_file, ios_base::openmode mode=ios_base::app) if(debug_level>0) { cout << "======== Loading file " << graph_file; cout.flush(); } //if(debug_level>0) { cout << "*** Loading file " << graph_file << endl << " .... ";} char c; float th = freq_thresh; NetworkTreeMiner miner; miner.setDebugLevel(debug_level); miner.setStatsFile(stats_file); miner.setResultsFile(results_file); while(infile.good()) { c = infile.get(); if(infile.eof()) continue; else infile.putback(c); GraphAdjList g; infile >> g; //cout << "Read in the following graph:" << endl; //cout << g << endl; if(debug_level>0) cout << " ... done. =========" << endl; if(th<1) th = round_float(freq_thresh*num_vertices(g), 0); //cout << "graph g is:\n" << g << endl; cout << "converted threshold is: " << th << endl; miner.minePatterns(g, pattern_size, th, success_prob, (useUllman or useVF2), useUllman, doHomomorphism, doSampling, doFourier, runInBatch, batchSize); } } mips-1.0/examples/treeminer/embeddings_counter.cpp0000755002402200254040000001176712520407730022027 0ustar thomasfdtaig #include "mips/patternMining/frequency/subgraphExactFrequency.hpp" #include "mips/core/graph/internalLabeledGraph.hpp" #include "mips/core/utils.hpp" #include #include using namespace std; using namespace boost; using namespace mips::pattern_mining; void printUsage() { cerr << "Usage: EmbeddingsCounter -g -p [-m -r -d -i -u [-t | -c]]" << endl; } /* * For Testing this class */ int main(int argc, char* argv[]) { //cout << "max rand is " << RAND_MAX << endl; int repeats = 1; float success_prob = 0.7; int thresh = 0; float tmp_thresh=thresh; int debug_level=0; bool rootImages=false; bool useUllman=false; bool countFreq=false; char *graph_file = (char *) ""; char *pattern_file = (char *) ""; char *map_file = (char *) ""; int a=1; while( a < argc ) { string arg(argv[a]); if(argc <= (a+1) and arg!="-i" and arg!="-u" and arg!="-c") { printUsage(); return -1; } //all options require a parameter if(arg=="-g") graph_file = argv[a+1]; else if(arg=="-p") pattern_file = argv[a+1]; else if(arg=="-m") map_file = argv[a+1]; else if(arg== "-r") repeats = atoi(argv[a+1]); else if(arg == "-t") tmp_thresh = atof(argv[a+1]); else if(arg == "-d") debug_level = atoi(argv[a+1]); else if(arg == "-i") { rootImages=true; a++; continue; } else if(arg == "-c") { countFreq=true; a++; continue; } else if(arg == "-u") { useUllman=true; a++; continue; } else { printUsage(); return -1; } a += 2; } //if(debug_level>0) debug_level += 1; if(debug_level>1) { cout << "thresh: " << tmp_thresh << endl << "debug: " << debug_level << endl << "rootImages: " << rootImages << endl << "countFreq: " << countFreq << endl << "useUllman: " << useUllman << endl << "graph_file: " << graph_file << endl << "pattern_file: " << pattern_file << endl << "map_file: " << map_file << endl; } GraphAdjList G, P; if(graph_file=="" or pattern_file=="") { printUsage(); return -1; } ifstream gfile; gfile.open(graph_file, ifstream::in); if(!gfile) { cerr << "Error opening graph file " << graph_file << endl; return -1; } gfile >> G; gfile.close(); ifstream pfile(pattern_file); if(!pfile) { cerr << "Error opening pattern file " << pattern_file << endl; return -1; } pfile >> P; pfile.close(); if(debug_level>3) { cout << "P: " << endl << P << endl; cout << "G: " << endl << G << endl; } if(tmp_thresh < 1) thresh = (int) round_float(num_vertices(G) * tmp_thresh, 0); else thresh = (int) round_float(tmp_thresh, 0); if(debug_level>1 and tmp_thresh<1) { cout << "converted thresh: " << thresh << endl; } //Commented out by SR //SubgraphExactFrequency counter; mips::core::InternalLabeledGraph labeled_G(G); mips::core::InternalLabeledGraph labeled_P(P); SubgraphExactFrequency,mips::core::InternalLabeledGraph > counter; if(useUllman) counter.setUseUllman(true); counter.setDebugLevel(debug_level); if(map_file!="") counter.setMapOut(string(map_file)+"_TMP.TXT"); //cout << "Initialising .. " << counter.isFrequent(&P, &G, thresh, success_prob) << endl; cout.flush(); for(int i=0; i> "+mapfile+" && echo ']' >> "+mapfile; string cmd = "sed s/'Occurrence [0-9]\\+: \\['// < "+mapfile+"_TMP.TXT | sed s/',\\]'// | sed s/','/' '/g >> "+mapfile; system(cmd.c_str()); } } mips-1.0/examples/treeminer/#miner_main.cpp#0000755002402200254040000001561712520407730020411 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include #include #include #include "mips/patternMining/tree/networkTreeMiner.hpp" #include "mips/core/utils.hpp" #include "mips/core/tree/enum/extenderRootedTree.hpp" #include "mips/core/graph/graphAdjList.hpp" using namespace std; using namespace mips::pattern_mining; /* ******************************************************************** * ================= * Non-class stuff * ================= * ******************************************************************** */ float freq_thresh=-1; float success_prob=0.2; //int kill_mins = 10*60; // 10 mins in secs -- we do this external to C++ code int pattern_size = -1; char *results_file = (char *) ""; char *stats_file = (char *) ""; char *graph_file = (char *) ""; bool runInBatch = false; bool useVF2 = false; bool doHomomorphism=true; bool doFourier=true; bool doSampling=false; bool useUllman = false; int batchSize = 100; short debug_level=0; void printUsage() { cerr << "Usage: NetworkTreeMiner -g -k -f " << "[-s -r [-v | -u | -b] -p --batch-size ]" << endl; // -w ]"; cerr << "\t-v uses VF2 algorithm for isomorphism test (instead of Williams)" << endl << "\t-u uses ullman's algorithm for isomorphism test (instead of Williams or VF2)" << endl << "\t-b uses batch-mode of Williams method (-v and -u are ignored if -b is set," << " whereas -p is ignored if either -v or -u is set)" << endl << "\t-n skips homomorphism check in William's method" << endl << "\t-m skips 'memory light' fourier trick for William's method" << endl << "\t-t does random sampling and statistical test during homomorphism check in Williams method (so we skip further checking, if statistically a pattern is deemed in frequent)" << endl; } bool parseOption(char* cmd_arg, char* param) { string arg(cmd_arg); if(arg == "-b") { if(useVF2 or useUllman) return false; runInBatch = true; return true; } if(arg == "-v") { if(runInBatch or useUllman) return false; useVF2 = true; return true; } if(arg == "-u") { if(runInBatch or useVF2) return false; useUllman = true; return true; } if(arg == "-n") { doHomomorphism = false; return true; }: if(arg == "-m") { doFourier = false; return true; } if(arg == "-t") { doSampling = true; return true; } //All the rest of the args should have a param if(string(param)=="") { return false; } if(arg == "-d") { istringstream convert(param); if ( !(convert >> debug_level) ) return false; } else if(arg == "-k") { istringstream convert(param); if( !(convert >> pattern_size) ) return false; } else if(arg == "-f") { istringstream convert(param); if( !(convert >> freq_thresh) ) return false; } else if(arg == "-p") { istringstream convert(param); if( !(convert >> success_prob) ) return false; } /* else if(arg == "-w") { istringstream convert(param); if ( !(convert >> kill_mins) ) return false; kill_mins *= 60; }*/ else if(arg == "-s") { stats_file = param; } else if(arg == "-r") { results_file = param; } else if(arg == "-g") { graph_file = param; } else if(arg == "--batch-size") { istringstream convert(param); if( !(convert >> batchSize) ) return false; } else { cerr << "Unknown option " << arg << ' ' << endl; return false; } return true; } int main(int argc, char* argv[]) { int a=1; //parse options while( a < argc ) { //cout << "current: " << a << ',' << argv[a] << ',' << argv[a]+1 << endl; /* if(argv[a][1]=='b') { cout << "In here ..." << endl; runInBatch = true; a++; continue; } */ //else parse the rest of options //if(argc <= (a+1)) { // printUsage(); // return -1; //} //most options require a parameter if(not parseOption(argv[a], (a+1) < argc ? argv[a+1]:(char *)"")) { //since an arg with no param can be last in the list printUsage(); return -1; } if((argv[a][1]=='b' or argv[a][1]=='v' or argv[a][1]=='u' or argv[a][1]=='n' or argv[a][1]=='m' or argv[a][1]=='t') and argv[a][2]=='\0') a++; else a+=2; } //check all required options were present if(pattern_size==-1 or graph_file=="" or freq_thresh<0) { printUsage(); return -1; } if(debug_level>1) cout << "Following options set (some can be default values):" << endl << "patternsize: " << pattern_size << endl << "freq_thresh: " << freq_thresh << endl << "success_prob: " << success_prob << endl << "useVf2: " << useVF2 << endl << "useUllman: " << useUllman << endl << "homomorphism-check: " << doHomomorphism << endl << "homomorphism-statistical-check: " << doSampling << endl << "batchsize: " << batchSize << endl //<< "wait time (in secs): " << kill_mins << endl << "useFourier: " << doFourier << endl << "debug_level: " << debug_level << endl << "graph_file: " << graph_file << endl << "results_file: " << results_file << endl; ifstream infile; //ofstream outfile; // let minePattern() take care of this infile.open(graph_file, ifstream::in); if(!infile) { cerr << "Error opening graph file " << graph_file << "." << endl; printUsage(); return -1; } //if(results_file!="") // outfile.open(results_file, ios_base::openmode mode=ios_base::app) if(debug_level>0) { cout << "======== Loading file " << graph_file; cout.flush(); } //if(debug_level>0) { cout << "*** Loading file " << graph_file << endl << " .... ";} char c; float th = freq_thresh; NetworkTreeMiner miner; miner.setDebugLevel(debug_level); miner.setStatsFile(stats_file); miner.setResultsFile(results_file); while(infile.good()) { c = infile.get(); if(infile.eof()) continue; else infile.putback(c); GraphAdjList g; infile >> g; //cout << "Read in the following graph:" << endl; //cout << g << endl; if(debug_level>0) cout << " ... done. =========" << endl; if(th<1) th = round_float(freq_thresh*num_vertices(g), 0); //cout << "graph g is:\n" << g << endl; cout << "converted threshold is: " << th << endl; miner.minePatterns(g, pattern_size, th, success_prob, (useUllman or useVF2), useUllman, doHomomorphism, doSampling, doFourier, runInBatch, batchSize); } } mips-1.0/examples/treeminer/rule_miner_main.cpp0000755002402200254040000001527412520407730021331 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include #include #include #include #include //#include "mips/core/tree/enum/extenderRootedTree.hpp" #include using namespace std; using namespace mips::pattern_mining; /* ******************************************************************** * ================= * Non-class stuff * ================= * ******************************************************************** */ //float freq_thresh=-1; float confidence = -1; float success_prob=0.2; //int kill_mins = 10*60; // 10 mins in secs -- we do this external to C++ code int pattern_size = -1; char *rules_file = (char *) ""; char *stats_file = (char *) ""; char *graph_file = (char *) ""; bool runInBatch = false; bool useVF2 = false; bool doHomomorphism=true; bool doSampling=false; bool useUllman = false; int batchSize = 100; short debug_level=0; void printUsage() { cerr << "Usage: NetworkRuleMiner -g -a -k -c " << "[[-v | -u | -b] -p --batch-size ]" << endl; // -w ]"; cerr << "\t-v uses VF2 algorithm for isomorphism test (instead of Williams)" << endl << "\t-u uses ullman's algorithm for isomorphism test (instead of Williams or VF2)" << endl << "\t-b uses batch-mode of Williams method (-v and -u are ignored if -b is set," << " whereas -p is ignored if either -v or -u is set)" << endl << "\t-n skips homomorphism check in William's method" //<< endl //<< "\t-t does random sampling and statistical test during homomorphism check in Williams method (so we skip further checking, if statistically a pattern is deemed in frequent)" << endl; } bool parseOption(char* cmd_arg, char* param) { string arg(cmd_arg); if(arg == "-b") { if(useVF2 or useUllman) return false; runInBatch = true; return true; } if(arg == "-v") { if(runInBatch or useUllman) return false; useVF2 = true; return true; } if(arg == "-u") { if(runInBatch or useVF2) return false; useUllman = true; return true; } if(arg == "-n") { doHomomorphism = false; return true; } if(arg == "-t") { doSampling = true; return true; } //All the rest of the args should have a param if(string(param)=="") { return false; } if(arg == "-d") { istringstream convert(param); if ( !(convert >> debug_level) ) return false; } else if(arg == "-k") { istringstream convert(param); if( !(convert >> pattern_size) ) return false; } else if(arg == "-c") { istringstream convert(param); if( !(convert >> confidence) ) return false; } else if(arg == "-p") { istringstream convert(param); if( !(convert >> success_prob) ) return false; } /* else if(arg == "-w") { istringstream convert(param); if ( !(convert >> kill_mins) ) return false; kill_mins *= 60; }*/ else if(arg == "-s") { stats_file = param; } else if(arg == "-a") { rules_file = param; } else if(arg == "-g") { graph_file = param; } else if(arg == "--batch-size") { istringstream convert(param); if( !(convert >> batchSize) ) return false; } else { cerr << "Unknown option " << arg << ' ' << endl; return false; } return true; } int main(int argc, char* argv[]) { int a=1; //parse options while( a < argc ) { //cout << "current: " << a << ',' << argv[a] << ',' << argv[a]+1 << endl; /* if(argv[a][1]=='b') { cout << "In here ..." << endl; runInBatch = true; a++; continue; } */ //else parse the rest of options //if(argc <= (a+1)) { // printUsage(); // return -1; //} //most options require a parameter if(not parseOption(argv[a], (a+1) < argc ? argv[a+1]:(char *)"")) { //since an arg with no param can be last in the list printUsage(); return -1; } if((argv[a][1]=='b' or argv[a][1]=='v' or argv[a][1]=='u' or argv[a][1]=='n' or argv[a][1]=='t') and argv[a][2]=='\0') a++; else a+=2; } //check all required options were present if(pattern_size==-1 or graph_file=="" or confidence<0) { printUsage(); return -1; } if(debug_level>1) cout << "Following options set (some can be default values):" << endl << "patternsize: " << pattern_size << endl << "confidence: " << confidence << endl << "success_prob: " << success_prob << endl << "useVf2: " << useVF2 << endl << "useUllman: " << useUllman << endl << "homomorphism-check: " << doHomomorphism << endl << "homomorphism-statistical-check: " << doSampling << endl << "batchsize: " << batchSize << endl //<< "wait time (in secs): " << kill_mins << endl << "debug_level: " << debug_level << endl << "graph_file: " << graph_file << endl << "rules_file: " << rules_file << endl; ifstream infile; //ofstream outfile; // let minePattern() take care of this infile.open(graph_file, ifstream::in); if(!infile) { cerr << "Error opening graph file " << graph_file << "." << endl; printUsage(); return -1; } //if(results_file!="") // outfile.open(results_file, ios_base::openmode mode=ios_base::app) if(debug_level>0) { cout << "======== Loading file " << graph_file; cout.flush(); } //if(debug_level>0) { cout << "*** Loading file " << graph_file << endl << " .... ";} //float th = freq_thresh; NetworkRuleMiner miner; miner.setDebugLevel(debug_level); miner.setStatsFile(stats_file); miner.setAssocFile(rules_file); char c; while(infile.good()) { c = infile.get(); if(infile.eof()) continue; else infile.putback(c); GraphAdjList g; infile >> g; //cout << "Read in the following graph:" << endl; //cout << g << endl; if(debug_level>0) cout << " ... done. =========" << endl; //if(th<1) th = round_float(freq_thresh*num_vertices(g), 0); //cout << "graph g is:\n" << g << endl; //cout << "converted threshold is: " << th << endl; miner.mineRules(g, pattern_size, confidence, success_prob, (useUllman or useVF2), useUllman, doHomomorphism, doSampling, runInBatch, batchSize); } } mips-1.0/examples/treeminer/CMakeLists.txt0000755002402200254040000000051612520407730020211 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) CREATE_EXAMPLE(NAME NetworkTreeMiner SOURCES miner_main.cpp) CREATE_EXAMPLE(NAME NetworkTreeMinerWithSampling SOURCES miner_main_temp.cpp) CREATE_EXAMPLE(NAME NetworkRuleMiner SOURCES rule_miner_main.cpp) CREATE_EXAMPLE(NAME EmbeddingsCounter SOURCES embeddings_counter.cpp) mips-1.0/examples/prediction/0000755002402200254040000000000012520407730015612 5ustar thomasfdtaigmips-1.0/examples/prediction/decisionTree/0000755002402200254040000000000012520407730020227 5ustar thomasfdtaigmips-1.0/examples/prediction/decisionTree/boston.cpp0000644002402200254040000001024512520407730022241 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include #include #include #include "data.hpp" #include "../common/boston.hpp" template std::vector calculateFeatureThresholds(const T & exampleContainer, unsigned int featurePosition) { typedef typename T::const_iterator iterator; std::vector values; // extract the features for(iterator it = exampleContainer.begin(); it != exampleContainer.end(); ++it) values.push_back(it->features[featurePosition]); // sort them std::sort(values.begin(), values.end()); // and take middle between two values as threshold std::vector thresholds; for(unsigned int pos = 1; pos < values.size(); ++pos) if(values[pos-1] < values[pos]) thresholds.push_back((values[pos-1] + values[pos])/2); return thresholds; } int main() { typedef mips::utilities::DataFormatStreamReader DataReader; // read the files std::ifstream boston_training_file (DATA_FILE_boston_trainingdata, std::ifstream::in); std::ifstream boston_test_file (DATA_FILE_boston_testdata, std::ifstream::in); if( ! boston_training_file.is_open() || ! boston_test_file.is_open()) { std::cout << "Error reading file!" << std::endl; exit(-1); } // construct a data reader_NominalType DataReader boston_training_data(&boston_training_file); DataReader boston_test_data(&boston_test_file); typedef mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits< ThresholdTest, // the test type BostonExample, // the example type TargetAttributeExtractor > LearnerTraits; // time to create the learner LearnerTraits::Learner treeLearner; // and fill in the examples treeLearner.exampleContainer().assign(boston_training_data.current(), boston_training_data.end()); // and now find the right threshold values for each feature for(unsigned int featurePos = 0; featurePos < BostonExample::NrFeatures - 1; ++featurePos) { // find the thresholds for each feature std::vector thresholds = calculateFeatureThresholds(treeLearner.exampleContainer(), featurePos); // now create and add all the tests for(unsigned int pos = 0; pos < thresholds.size(); ++pos) treeLearner.testContainer().push_back(ThresholdTest(featurePos, thresholds[pos])); } // let's learn a tree LearnerTraits::Model tree = treeLearner(); // and write it to std::out LearnerTraits::ModelPrinter treePrinter; treePrinter(tree, std::cout); // We serialize the tree to a temporary buffer std::stringstream stream; { boost::archive::binary_oarchive oa(stream); oa << tree; } // and deserialize in another tree LearnerTraits::Model tree2; { boost::archive::binary_iarchive ia(stream); ia >> tree2; } // classify an external dataset LearnerTraits::Classifier predictor(&tree2); mips::core::Variance variance; unsigned int count = 0; float mse = 0.0; for(DataReader::iterator it = boston_test_data.current(); it != boston_test_data.end(); ++it) { const BostonExample & example = *it; float realValue = example.features[BostonExample::NrFeatures-1]; float predValue = predictor(example); variance += realValue; mse += (realValue - predValue)*(realValue - predValue); ++count; } std::cout << "dataset variance: " << variance.value() << std::endl; std::cout << "mean squared error:" << mse/count << std::endl; } mips-1.0/examples/prediction/decisionTree/iris.cpp0000755002402200254040000000706112520407730021710 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include //! [tree_serialz] #include //! [tree_serialz] #include #include #include #include #include "data.hpp" #include "../common/iris.hpp" using namespace mips::utilities; //! [Iris main] int main() { typedef DataFormatStreamReader DataReader; // read the files std::ifstream iris_training_file (DATA_FILE_iris_trainingdata, std::ifstream::in); std::ifstream iris_test_file (DATA_FILE_iris_testdata, std::ifstream::in); if( ! iris_training_file.is_open() || ! iris_test_file.is_open()) { std::cout << "Error reading file!" << std::endl; exit(-1); } // construct a data reader_NominalType DataReader iris_training_data(&iris_training_file); DataReader iris_test_data(&iris_test_file); typedef mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits< ThresholdTest *, // the test type, polymorphic thus a pointer IrisExample, // the example type TargetAttributeExtractorRanking, // the target attribute extractor mips::prediction::decision_tree::NoActionPruner // we do not want to have minimum leaf size > LearnerTraits; // time to create the learner LearnerTraits::Learner treeLearner; // and fill in the tests for(double threshold = 0.0; threshold <= 7.0; threshold += 0.1) { treeLearner.testContainer().push_back(new TestSepalLength(threshold)); treeLearner.testContainer().push_back(new TestSepalWidth(threshold)); treeLearner.testContainer().push_back(new TestPetalLength(threshold)); treeLearner.testContainer().push_back(new TestPetalWidth(threshold)); } // now the examples treeLearner.exampleContainer().assign(iris_training_data.current(), iris_training_data.end()); // let's learn a tree LearnerTraits::Model tree = treeLearner(); // Serialize it //! [tree_ser_out] std::stringstream stream; { boost::archive::binary_oarchive oa(stream); oa << tree; } //! [tree_ser_out] // and deserialize to do the classifcation //! [tree_ser_in] LearnerTraits::Model tree2; { boost::archive::binary_iarchive ia(stream); ia >> tree2; } //! [tree_ser_in] // and write it to std::out LearnerTraits::ModelPrinter treePrinter; treePrinter(tree, std::cout); // classify an external dataset LearnerTraits::Classifier prediction(&tree2); for(DataReader::iterator it = iris_test_data.current(); it != iris_test_data.end(); ++it) { const IrisExample & iris = *it; IrisExample::IrisType predictedType = prediction(iris); std::cout << " Actual Category: " << categoryToLabel(iris.irisClass) << std::endl << " -> Predicated category: " << categoryToLabel(predictedType) << std::endl << std::endl; } return 0; } //! [Iris main] mips-1.0/examples/prediction/decisionTree/CMakeLists.txt0000755002402200254040000000045112520407730022772 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) CREATE_EXAMPLE(NAME iris SOURCES iris.cpp HEADERS ../common/iris.hpp DATA_FILES iris_testdata iris_trainingdata) CREATE_EXAMPLE(NAME boston SOURCES boston.cpp HEADERS ../common/boston.hpp DATA_FILES boston_testdata boston_trainingdata) mips-1.0/examples/prediction/decisionForest/0000755002402200254040000000000012520407730020572 5ustar thomasfdtaigmips-1.0/examples/prediction/decisionForest/iris_rf.cpp0000755002402200254040000000604512520407730022743 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include #include #include #include #include "data.hpp" #include "../common/iris.hpp" using namespace mips::utilities; int main() { typedef DataFormatStreamReader DataReader; // read the files std::ifstream iris_training_file (DATA_FILE_iris_trainingdata, std::ifstream::in); std::ifstream iris_test_file (DATA_FILE_iris_testdata, std::ifstream::in); if( ! iris_training_file.is_open() || ! iris_test_file.is_open()) { std::cout << "Error reading file!" << std::endl; exit(-1); } // construct a data reader DataReader iris_training_data(&iris_training_file); DataReader iris_test_data(&iris_test_file); typedef mips::prediction::decision_forest::RandomFractionForestLearnerTraits< ThresholdTest *, // the test type, polymorphic thus a pointer IrisExample, // the example type TargetAttributeExtractorRanking, // the target attribute extractor mips::prediction::decision_tree::NoActionPruner // we do not want to have minimum leaf size > LearnerTraits; // time to create the learner LearnerTraits::Learner treeLearner; // and fill in the tests for(double threshold = 0.0; threshold <= 7.0; threshold += 0.1) { treeLearner.testContainer().push_back(new TestSepalLength(threshold)); treeLearner.testContainer().push_back(new TestSepalWidth(threshold)); treeLearner.testContainer().push_back(new TestPetalLength(threshold)); treeLearner.testContainer().push_back(new TestPetalWidth(threshold)); } // now the examples treeLearner.exampleContainer().assign(iris_training_data.current(), iris_training_data.end()); // let's learn a tree LearnerTraits::Model tree = treeLearner(); // and write it to std::out LearnerTraits::ModelPrinter treePrinter; treePrinter(tree, std::cout); // classify an external dataset LearnerTraits::Classifier prediction(&tree); for(DataReader::iterator it = iris_test_data.current(); it != iris_test_data.end(); ++it) { const IrisExample & iris = *it; IrisExample::IrisType predictedType = prediction(iris); std::cout << " Actual Category: " << categoryToLabel(iris.irisClass) << std::endl << " -> Predicated category: " << categoryToLabel(predictedType) << std::endl << std::endl; } return 0; } mips-1.0/examples/prediction/decisionForest/iris_ps.cpp0000755002402200254040000000603512520407730022755 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include #include #include #include #include #include "data.hpp" #include "../common/iris.hpp" using namespace mips::utilities; int main() { typedef DataFormatStreamReader DataReader; // read the files std::ifstream iris_training_file (DATA_FILE_iris_trainingdata, std::ifstream::in); std::ifstream iris_test_file (DATA_FILE_iris_testdata, std::ifstream::in); if( ! iris_training_file.is_open() || ! iris_test_file.is_open()) { std::cout << "Error reading file!" << std::endl; exit(-1); } // construct a data reader DataReader iris_training_data(&iris_training_file); DataReader iris_test_data(&iris_test_file); typedef mips::prediction::decision_forest::ProbabilisticForestTraits< ThresholdTest *, // the test type, polymorphic thus a pointer IrisExample, // the example type TargetAttributeExtractorRanking, // the target attribute extractor mips::prediction::decision_tree::NoActionPruner // we do not want to have minimum leaf size > LearnerTraits; // time to create the learner LearnerTraits::Learner treeLearner; // and fill in the tests for(double threshold = 0.0; threshold <= 7.0; threshold += 0.1) { treeLearner.testContainer().push_back(new TestSepalLength(threshold)); treeLearner.testContainer().push_back(new TestSepalWidth(threshold)); treeLearner.testContainer().push_back(new TestPetalLength(threshold)); treeLearner.testContainer().push_back(new TestPetalWidth(threshold)); } // now the examples treeLearner.exampleContainer().assign(iris_training_data.current(), iris_training_data.end()); // let's learn a tree LearnerTraits::Model tree = treeLearner(); // and write it to std::out LearnerTraits::ModelPrinter treePrinter; treePrinter(tree, std::cout); // classify an external dataset LearnerTraits::Classifier prediction(&tree); for(DataReader::iterator it = iris_test_data.current(); it != iris_test_data.end(); ++it) { const IrisExample & iris = *it; IrisExample::IrisType predictedType = prediction(iris); std::cout << " Actual Category: " << categoryToLabel(iris.irisClass) << std::endl << " -> Predicated category: " << categoryToLabel(predictedType) << std::endl << std::endl; } return 0; } mips-1.0/examples/prediction/decisionForest/boston_ps.cpp0000644002402200254040000001035412520407730023307 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #define MIPS_NO_LOG #include #include #include #include #include #include #include "data.hpp" #include "../common/boston.hpp" template std::vector calculateFeatureThresholds(const T & exampleContainer, unsigned int featurePosition) { typedef typename T::const_iterator iterator; std::vector values; // extract the features for(iterator it = exampleContainer.begin(); it != exampleContainer.end(); ++it) values.push_back(it->features[featurePosition]); // sort them std::sort(values.begin(), values.end()); // and take middle between two values as threshold std::vector thresholds; for(unsigned int pos = 1; pos < values.size(); ++pos) if(values[pos-1] < values[pos]) thresholds.push_back((values[pos-1] + values[pos])/2); return thresholds; } int main() { typedef mips::utilities::DataFormatStreamReader DataReader; // read the files std::ifstream boston_training_file (DATA_FILE_boston_trainingdata, std::ifstream::in); std::ifstream boston_test_file (DATA_FILE_boston_testdata, std::ifstream::in); if( ! boston_training_file.is_open() || ! boston_test_file.is_open()) { std::cout << "Error reading file!" << std::endl; exit(-1); } // construct a data reader_NominalType DataReader boston_training_data(&boston_training_file); DataReader boston_test_data(&boston_test_file); typedef mips::prediction::decision_forest::ProbabilisticForestTraits< ThresholdTest, // the test type BostonExample, // the example type TargetAttributeExtractor > LearnerTraits; // time to create the learner LearnerTraits::Learner treeLearner; // and fill in the examples treeLearner.exampleContainer().assign(boston_training_data.current(), boston_training_data.end()); // and now find the right threshold values for each feature for(unsigned int featurePos = 0; featurePos < BostonExample::NrFeatures - 1; ++featurePos) { // find the thresholds for each feature std::vector thresholds = calculateFeatureThresholds(treeLearner.exampleContainer(), featurePos); // now create and add all the tests for(unsigned int pos = 0; pos < thresholds.size(); ++pos) treeLearner.testContainer().push_back(ThresholdTest(featurePos, thresholds[pos])); } // let's learn a tree LearnerTraits::Model tree = treeLearner(); // and write it to std::out LearnerTraits::ModelPrinter treePrinter; treePrinter(tree, std::cout); // serialize it std::stringstream stream; { boost::archive::binary_oarchive oa(stream); oa << tree; } LearnerTraits::Model tree2; { boost::archive::binary_iarchive ia(stream); ia >> tree2; } // classify an external dataset LearnerTraits::Classifier predictor(&tree2); mips::core::Variance variance; unsigned int count = 0; float mse = 0.0; for(DataReader::iterator it = boston_test_data.current(); it != boston_test_data.end(); ++it) { const BostonExample & example = *it; float realValue = example.features[BostonExample::NrFeatures-1]; float predValue = predictor(example); variance += realValue; mse += (realValue - predValue)*(realValue - predValue); ++count; } // calculate the dataset variance std::cout << "dataset variance: " << variance.value() << std::endl; std::cout << "mean squared error:" << mse/count << std::endl; } mips-1.0/examples/prediction/decisionForest/boston_rf.cpp0000644002402200254040000001035312520407730023273 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #define MIPS_NO_LOG #include #include #include #include #include #include #include "data.hpp" #include "../common/boston.hpp" template std::vector calculateFeatureThresholds(const T & exampleContainer, unsigned int featurePosition) { typedef typename T::const_iterator iterator; std::vector values; // extract the features for(iterator it = exampleContainer.begin(); it != exampleContainer.end(); ++it) values.push_back(it->features[featurePosition]); // sort them std::sort(values.begin(), values.end()); // and take middle between two values as threshold std::vector thresholds; for(unsigned int pos = 1; pos < values.size(); ++pos) if(values[pos-1] < values[pos]) thresholds.push_back((values[pos-1] + values[pos])/2); return thresholds; } int main() { typedef mips::utilities::DataFormatStreamReader DataReader; // read the files std::ifstream boston_training_file (DATA_FILE_boston_trainingdata, std::ifstream::in); std::ifstream boston_test_file (DATA_FILE_boston_testdata, std::ifstream::in); if( ! boston_training_file.is_open() || ! boston_test_file.is_open()) { std::cout << "Error reading file!" << std::endl; exit(-1); } // construct a data reader_NominalType DataReader boston_training_data(&boston_training_file); DataReader boston_test_data(&boston_test_file); typedef mips::prediction::decision_forest::RandomFractionForestLearnerTraits< ThresholdTest, // the test type BostonExample, // the example type TargetAttributeExtractor > LearnerTraits; // time to create the learner LearnerTraits::Learner treeLearner; // and fill in the examples treeLearner.exampleContainer().assign(boston_training_data.current(), boston_training_data.end()); // and now find the right threshold values for each feature for(unsigned int featurePos = 0; featurePos < BostonExample::NrFeatures - 1; ++featurePos) { // find the thresholds for each feature std::vector thresholds = calculateFeatureThresholds(treeLearner.exampleContainer(), featurePos); // now create and add all the tests for(unsigned int pos = 0; pos < thresholds.size(); ++pos) treeLearner.testContainer().push_back(ThresholdTest(featurePos, thresholds[pos])); } // let's learn a tree LearnerTraits::Model tree = treeLearner(); // and write it to std::out LearnerTraits::ModelPrinter treePrinter; treePrinter(tree, std::cout); // serialize it std::stringstream stream; { boost::archive::binary_oarchive oa(stream); oa << tree; } LearnerTraits::Model tree2; { boost::archive::binary_iarchive ia(stream); ia >> tree2; } // classify an external dataset LearnerTraits::Classifier predictor(&tree2); mips::core::Variance variance; unsigned int count = 0; float mse = 0.0; for(DataReader::iterator it = boston_test_data.current(); it != boston_test_data.end(); ++it) { const BostonExample & example = *it; float realValue = example.features[BostonExample::NrFeatures-1]; float predValue = predictor(example); variance += realValue; mse += (realValue - predValue)*(realValue - predValue); ++count; } // calculate the dataset variance std::cout << "dataset variance: " << variance.value() << std::endl; std::cout << "mean squared error:" << mse/count << std::endl; } mips-1.0/examples/prediction/decisionForest/CMakeLists.txt0000755002402200254040000000114612520407730023337 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) CREATE_EXAMPLE(NAME iris_randomFraction SOURCES iris_rf.cpp HEADERS ../common/iris.hpp DATA_FILES iris_testdata iris_trainingdata) CREATE_EXAMPLE(NAME iris_probabilisticSplit SOURCES iris_ps.cpp HEADERS ../common/iris.hpp DATA_FILES iris_testdata iris_trainingdata) CREATE_EXAMPLE(NAME boston_randomFraction SOURCES boston_rf.cpp HEADERS ../common/boston.hpp DATA_FILES boston_testdata boston_trainingdata) CREATE_EXAMPLE(NAME boston_probabilisticSplit SOURCES boston_ps.cpp HEADERS ../common/boston.hpp DATA_FILES boston_testdata boston_trainingdata) mips-1.0/examples/prediction/common/0000755002402200254040000000000012520407730017102 5ustar thomasfdtaigmips-1.0/examples/prediction/common/boston.hpp0000644002402200254040000000670112520407730021123 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_EXAMPLES_PREDICTION_COMMON_BOSTON_HPP #define MIPS_EXAMPLES_PREDICTION_COMMON_BOSTON_HPP #include #include #include #include #include #include // This class represents an example struct BostonExample { const static unsigned int NrFeatures = 14; float features[NrFeatures]; }; inline std::ostream & operator<<(std::ostream & stream, const BostonExample & example) { stream << "Example ["; for(unsigned int i = 0; i < BostonExample::NrFeatures; ++i) { if(i != 0) stream << ";"; stream << example.features[i]; } stream << "]"; return stream; } // The class for a test class ThresholdTest { public: ThresholdTest(unsigned int nrFeature = 0, float threshold = 0.0f) : nrFeature_(nrFeature), threshold_(threshold) { } bool operator()(const BostonExample & example) const { assert(nrFeature_ < BostonExample::NrFeatures); return example.features[nrFeature_] <= threshold_; } void setThreshold(float value) { threshold_ = value; } void setNrFeature(unsigned int value) { nrFeature_ = value; } unsigned int nrFeature() const { return nrFeature_; } float threshold() const { return threshold_; } template void serialize(Archive & ar, unsigned int) { ar & nrFeature_ & threshold_; } private: unsigned int nrFeature_; float threshold_; }; inline bool operator==(const ThresholdTest & lhs, const ThresholdTest & rhs) { return lhs.nrFeature() == rhs.nrFeature() && lhs.threshold() == rhs.threshold(); } inline bool operator<(const ThresholdTest & lhs, const ThresholdTest & rhs) { if(lhs.nrFeature() < rhs.nrFeature()) return true; else if(lhs.nrFeature() == rhs.nrFeature()) return lhs.threshold() < rhs.threshold(); else return false; } inline std::ostream & operator<<(std::ostream & stream, const ThresholdTest & test) { stream << "Test [ example.feature[" << test.nrFeature() << "] <= " << test.threshold() << "]"; return stream; } // The target attribute exrtractor struct TargetAttributeExtractor : public std::unary_function { typedef mips::prediction::NumericalAttributeType AttributeType; float operator()(const BostonExample & example) const { return example.features[BostonExample::NrFeatures - 1]; } }; // reader for the boston data class DataFormatReader_BostonFilePolicy { public: typedef BostonExample DataFormat; bool read(std::istream & is, DataFormat & obj) { std::string line; if(!getline(is,line)) return false; std::vector svec; boost::split(svec, line, boost::is_any_of(";")); if(svec.size() != BostonExample::NrFeatures) return false; for(unsigned int pos = 0; pos < svec.size(); ++pos) obj.features[pos] = boost::lexical_cast(svec[pos]); return true; } }; #endif // MIPS_EXAMPLES_PREDICTION_COMMON_BOSTON_HPP mips-1.0/examples/prediction/common/iris.hpp0000755002402200254040000001562212520407730020572 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_EXAMPLES_PREDICTION_COMMON_IRIS_HPP #define MIPS_EXAMPLES_PREDICTION_COMMON_IRIS_HPP //! [ar_serialz] #include #include //! [ar_serialz] //! [poly_header] #include //! [poly_header] //! [test_header] #include #include //! [test_header] #include #include #include #include // This class represents an example struct IrisExample { enum IrisType { Iris_setosa = 1, Iris_versicolor = 2, Iris_virginica= 3, Invalid = 4 }; float sepalLength; float sepalWidth; float petalLength; float petalWidth; IrisType irisClass; }; // It needs to be streamable for debug purposes inline std::ostream & operator<<(std::ostream & stream, const IrisExample & iris) { stream << "[ Sepal Length = " << iris.sepalLength << ", Sepal Width = " << iris.sepalWidth << ", Petal Length = " << iris.petalLength << ", Petal Width = " << iris.petalWidth << ", Classification = " << iris.irisClass << "]"; return stream; } // The base class for a test class ThresholdTest { public: ThresholdTest(float val = 0.0f) : _val(val) {} virtual ~ThresholdTest() {} // the necessary function for a test bool operator()(const IrisExample & example) const { return extractAttribute(example) <= threshold(); } // the abstract functions each subclass should implement virtual float extractAttribute(const IrisExample & example) const = 0; virtual std::string name() const = 0; float threshold() const { return _val; } // necessary for boost::serialization template void serialize(Archive & ar, const unsigned /*version*/) { ar & _val; } private: float _val; }; // A test should also be streamable for debug purposes inline std::ostream & operator<<(std::ostream & stream, const ThresholdTest & test) { stream < void serialize(Archive & ar, const unsigned /*version*/) { ar & boost::serialization::base_object(*this); } }; // implementations of the test class class TestSepalWidth : public ThresholdTest { public: TestSepalWidth(float val = 0.0f) : ThresholdTest(val) {} virtual float extractAttribute(const IrisExample & example) const { return example.sepalWidth; } std::string name() const { return "sepalWidth"; } // necessary for boost::serialization template void serialize(Archive & ar, const unsigned /*version*/) { ar & boost::serialization::base_object(*this); } }; // implementations of the test class class TestPetalLength : public ThresholdTest { public: TestPetalLength(float val = 0.0f) : ThresholdTest(val) {} virtual float extractAttribute(const IrisExample & example) const { return example.petalLength; } std::string name() const { return "petalLength"; } // necessary for boost::serialization template void serialize(Archive & ar, const unsigned /*version*/) { ar & boost::serialization::base_object(*this); } }; // implementations of the test class class TestPetalWidth : public ThresholdTest { public: TestPetalWidth(float val = 0.0f) : ThresholdTest(val) {} virtual float extractAttribute(const IrisExample & example) const { return example.petalWidth; } std::string name() const { return "petalWidth"; } // necessary for boost::serialization template void serialize(Archive & ar, const unsigned /*version*/) { ar & boost::serialization::base_object(*this); } }; // necessary for boost::serialization when serializing polymorphic pointers //! [ poly_line] BOOST_CLASS_EXPORT_GUID(TestSepalLength, "TestSepalLength") //! [ poly_line] BOOST_CLASS_EXPORT_GUID(TestSepalWidth, "TestSepalWidth") BOOST_CLASS_EXPORT_GUID(TestPetalLength, "TestPetalLength") BOOST_CLASS_EXPORT_GUID(TestPetalWidth, "TestPetalWidth") // The target attribute exrtractor struct TargetAttributeExtractorRanking : public std::unary_function { typedef mips::prediction::NominalAttributeType AttributeType; IrisExample::IrisType operator()(const IrisExample & example) const { return example.irisClass; } }; // reader for the iris data class DataFormatReader_IrisFilePolicy { public: typedef IrisExample DataFormat; bool read(std::istream & is, DataFormat & obj) { static const char seperator = ','; std::string irisType; std::string line; if(!getline(is,line)) return false; std::stringstream tmp(line); tmp >> obj.sepalLength; tmp.ignore(1,seperator); tmp >> obj.sepalWidth; tmp.ignore(1,seperator); tmp >> obj.petalLength; tmp.ignore(1,seperator); tmp >> obj.petalWidth; tmp.ignore(1,seperator); tmp >> irisType; if(irisType.compare("Iris-setosa") == 0 ) obj.irisClass = DataFormat::Iris_setosa; else if( irisType.compare("Iris-versicolor") == 0 ) obj.irisClass = DataFormat::Iris_versicolor; else obj.irisClass = DataFormat::Iris_virginica; return true; } }; inline std::string categoryToLabel(const IrisExample::IrisType & irisType) { if(irisType == IrisExample::Iris_setosa) return "Iris_setosa"; else if(irisType == IrisExample::Iris_versicolor) return "Iris_versicolor"; else if(irisType == IrisExample::Iris_virginica) return "Iris_virginica"; else return "unknown"; } #endif // MIPS_EXAMPLES_PREDICTION_COMMON_IRIS_HPP mips-1.0/examples/prediction/CMakeLists.txt0000755002402200254040000000017512520407730020360 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) add_subdirectory(decisionTree) add_subdirectory(decisionForest) mips-1.0/examples/data/0000755002402200254040000000000012520407730014363 5ustar thomasfdtaigmips-1.0/examples/data/iris_test.csv0000755002402200254040000000052012520407730017105 0ustar thomasfdtaig5.6,3.0,4.1,1.3,Iris-versicolor 5.5,2.5,4.0,1.3,Iris-versicolor 5.5,2.6,4.4,1.2,Iris-versicolor 6.1,3.0,4.6,1.4,Iris-versicolor 5.8,2.6,4.0,1.2,Iris-versicolor 4.6,3.4,1.4,0.3,Iris-setosa 5.0,3.4,1.5,0.2,Iris-setosa 4.4,2.9,1.4,0.2,Iris-setosa 6.9,3.1,5.1,2.3,Iris-virginica 5.8,2.7,5.1,1.9,Iris-virginica 6.8,3.2,5.9,2.3,Iris-virginicamips-1.0/examples/data/boston_test.csv0000644002402200254040000000775312520407730017457 0ustar thomasfdtaig0.62739;0.00;8.140;0;0.5380;5.8340;56.50;4.4986;4;307.0;21.00;395.62;8.47;19.90 0.61154;20.00;3.970;0;0.6470;8.7040;86.90;1.8010;5;264.0;13.00;389.70;5.12;50.00 0.08308;0.00;2.460;0;0.4880;5.6040;89.80;2.9879;3;193.0;17.80;391.00;13.98;26.40 0.03359;75.00;2.950;0;0.4280;7.0240;15.80;5.4011;3;252.0;18.30;395.62;1.98;34.90 0.63796;0.00;8.140;0;0.5380;6.0960;84.50;4.4619;4;307.0;21.00;380.02;10.26;18.20 0.21038;20.00;3.330;0;0.4429;6.8120;32.20;4.1007;5;216.0;14.90;396.90;4.85;35.10 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16.81180;0.00;18.100;0;0.7000;5.2770;98.10;1.4261;24;666.0;20.20;396.90;30.81;7.20 0.97617;0.00;21.890;0;0.6240;5.7570;98.40;2.3460;4;437.0;21.20;262.76;17.31;15.60 0.03445;82.50;2.030;0;0.4150;6.1620;38.40;6.2700;2;348.0;14.70;393.77;7.43;24.10 0.10000;34.00;6.090;0;0.4330;6.9820;17.70;5.4917;7;329.0;16.10;390.43;4.86;33.10 19.60910;0.00;18.100;0;0.6710;7.3130;97.90;1.3163;24;666.0;20.20;396.90;13.44;15.00 0.25356;0.00;9.900;0;0.5440;5.7050;77.70;3.9450;4;304.0;18.40;396.42;11.50;16.20 11.57790;0.00;18.100;0;0.7000;5.0360;97.00;1.7700;24;666.0;20.20;396.90;25.68;9.70 0.12816;12.50;6.070;0;0.4090;5.8850;33.00;6.4980;4;345.0;18.90;396.90;8.79;20.90 0.06617;0.00;3.240;0;0.4600;5.8680;25.80;5.2146;4;430.0;16.90;382.44;9.97;19.30 5.58107;0.00;18.100;0;0.7130;6.4360;87.90;2.3158;24;666.0;20.20;100.19;16.22;14.30 0.01301;35.00;1.520;0;0.4420;7.2410;49.30;7.0379;1;284.0;15.50;394.74;5.49;32.70 0.17899;0.00;9.690;0;0.5850;5.6700;28.80;2.7986;6;391.0;19.20;393.29;17.60;23.10 0.10084;0.00;10.010;0;0.5470;6.7150;81.60;2.6775;6;432.0;17.80;395.59;10.16;22.80 0.19657;22.00;5.860;0;0.4310;6.2260;79.20;8.0555;7;330.0;19.10;376.14;10.15;20.50 0.12083;0.00;2.890;0;0.4450;8.0690;76.00;3.4952;2;276.0;18.00;396.90;4.21;38.70 1.34284;0.00;19.580;0;0.6050;6.0660;100.00;1.7573;5;403.0;14.70;353.89;6.43;24.30 0.35114;0.00;7.380;0;0.4930;6.0410;49.90;4.7211;5;287.0;19.60;396.90;7.70;20.40 0.03738;0.00;5.190;0;0.5150;6.3100;38.50;6.4584;5;224.0;20.20;389.40;6.75;20.70 0.05644;40.00;6.410;1;0.4470;6.7580;32.90;4.0776;4;254.0;17.60;396.90;3.53;32.40 0.03537;34.00;6.090;0;0.4330;6.5900;40.40;5.4917;7;329.0;16.10;395.75;9.50;22.00 37.66190;0.00;18.100;0;0.6790;6.2020;78.70;1.8629;24;666.0;20.20;18.82;14.52;10.90 0.09103;0.00;2.460;0;0.4880;7.1550;92.20;2.7006;3;193.0;17.80;394.12;4.82;37.90 6.44405;0.00;18.100;0;0.5840;6.4250;74.80;2.2004;24;666.0;20.20;97.95;12.03;16.10 0.17446;0.00;10.590;1;0.4890;5.9600;92.10;3.8771;4;277.0;18.60;393.25;17.27;21.70 0.07013;0.00;13.890;0;0.5500;6.6420;85.10;3.4211;5;276.0;16.40;392.78;9.69;28.70 5.73116;0.00;18.100;0;0.5320;7.0610;77.00;3.4106;24;666.0;20.20;395.28;7.01;25.00 0.22438;0.00;9.690;0;0.5850;6.0270;79.70;2.4982;6;391.0;19.20;396.90;14.33;16.80 0.14150;0.00;6.910;0;0.4480;6.1690;6.60;5.7209;3;233.0;17.90;383.37;5.81;25.30 0.22876;0.00;8.560;0;0.5200;6.4050;85.40;2.7147;5;384.0;20.90;70.80;10.63;18.60 1.51902;0.00;19.580;1;0.6050;8.3750;93.90;2.1620;5;403.0;14.70;388.45;3.32;50.00 0.01381;80.00;0.460;0;0.4220;7.8750;32.00;5.6484;4;255.0;14.40;394.23;2.97;50.00 0.57529;0.00;6.200;0;0.5070;8.3370;73.30;3.8384;8;307.0;17.40;385.91;2.47;41.70 0.08370;45.00;3.440;0;0.4370;7.1850;38.90;4.5667;5;398.0;15.20;396.90;5.39;34.90 12.04820;0.00;18.100;0;0.6140;5.6480;87.60;1.9512;24;666.0;20.20;291.55;14.10;20.80 2.14918;0.00;19.580;0;0.8710;5.7090;98.50;1.6232;5;403.0;14.70;261.95;15.79;19.40 0.75026;0.00;8.140;0;0.5380;5.9240;94.10;4.3996;4;307.0;21.00;394.33;16.30;15.60 0.15038;0.00;25.650;0;0.5810;5.8560;97.00;1.9444;2;188.0;19.10;370.31;25.41;17.30 1.20742;0.00;19.580;0;0.6050;5.8750;94.60;2.4259;5;403.0;14.70;292.29;14.43;17.40 4.34879;0.00;18.100;0;0.5800;6.1670;84.00;3.0334;24;666.0;20.20;396.90;16.29;19.90 0.37578;0.00;10.590;1;0.4890;5.4040;88.60;3.6650;4;277.0;18.60;395.24;23.98;19.30 0.11329;30.00;4.930;0;0.4280;6.8970;54.30;6.3361;6;300.0;16.60;391.25;11.38;22.00 11.16040;0.00;18.100;0;0.7400;6.6290;94.60;2.1247;24;666.0;20.20;109.85;23.27;13.40 0.12269;0.00;6.910;0;0.4480;6.0690;40.00;5.7209;3;233.0;17.90;389.39;9.55;21.20 0.15445;25.00;5.130;0;0.4530;6.1450;29.20;7.8148;8;284.0;19.70;390.68;6.86;23.30 5.20177;0.00;18.100;1;0.7700;6.1270;83.40;2.7227;24;666.0;20.20;395.43;11.48;22.70 0.59005;0.00;21.890;0;0.6240;6.3720;97.90;2.3274;4;437.0;21.20;385.76;11.12;23.00 0.04337;21.00;5.640;0;0.4390;6.1150;63.00;6.8147;4;243.0;16.80;393.97;9.43;20.50 0.06664;0.00;4.050;0;0.5100;6.5460;33.10;3.1323;5;296.0;16.60;390.96;5.33;29.40 0.05561;70.00;2.240;0;0.4000;7.0410;10.00;7.8278;5;358.0;14.80;371.58;4.74;29.00 7.40389;0.00;18.100;0;0.5970;5.6170;97.90;1.4547;24;666.0;20.20;314.64;26.40;17.20 mips-1.0/examples/data/CMakeLists.txt0000755002402200254040000000036612520407730017133 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/macro.txt) ADD_DATA_FILE(IRIS_TESTDATA iris_test.csv) ADD_DATA_FILE(IRIS_TRAININGDATA iris_training.csv) ADD_DATA_FILE(BOSTON_TESTDATA boston_test.csv) ADD_DATA_FILE(BOSTON_TRAININGDATA boston_training.csv) mips-1.0/examples/graphstats/0000755002402200254040000000000012520407730015632 5ustar thomasfdtaigmips-1.0/examples/graphstats/graphStatistics.cpp0000755002402200254040000001002512520407730021513 0ustar thomasfdtaig#include #include #include #include "mips/core/graph/graphAdjList.hpp" #include #include #include using namespace std; using namespace boost; int DEBUG_LEVEL=0; string graph_file = ""; string output_file = ""; int HIST_SIZE = 10000; bool parseOption(char* cmd_arg, char* param) { string arg(cmd_arg); if(arg == "-d") { istringstream convert(param); if ( !(convert >> DEBUG_LEVEL) ) return false; } else if(arg == "-i") { graph_file = param; } else if(arg == "-o") { output_file = param; } else { cerr << "Unknown option " << arg << ' ' << endl; return false; } return true; } void printUsage() { cout << "Usage: testRootedTreeEnum -i [-d -o ]" << endl; } int main(int argc, char* argv[]) { int a=1; //parse options while( a < argc ) { if(argc <= (a+1)) { printUsage(); return -1; } //all options require a parameter if(not parseOption(argv[a], argv[a+1])) { printUsage(); return -1; } a += 2; } //check all required options were present if(graph_file=="") { printUsage(); return -1; } GraphAdjList G; ifstream infile; infile.open(graph_file.c_str(), ifstream::in); infile >> G; int num_v = num_vertices(G), vidx=0; //vector degree_dist(num_v); map degree_dist; GraphAdjList::vertex_iterator vi, vend; property_map::type vindex_map = get(vertex_index, G); cout << "===>>" << graph_file << "<<===" << endl; cout.flush(); //cout << "Num Vertices: " << num_v << endl; float avg = 0.0; int max, min, tmp; unsigned long num_e = 0; max = min = out_degree(*vertices(G).first, G); //G.adj_list[0].size(); for(tie(vi,vend)=vertices(G); vi!=vend; vi++) { tmp = out_degree(*vi, G); //G.adj_list[i].size(); avg += tmp; //G.adj_list[i].size(); if(tmp < min) min = tmp; if(tmp > max) max = tmp; if(degree_dist.size() < HIST_SIZE) // we don't want too big a map degree_dist[tmp]++; //By C++ standard default initialization of int (in degree_dist[tmp]) should be 0 //edge count /* for(int j=0; j i) num_e++; } */ num_e += out_degree(*vi, G); //G.adj_list[i].size(); vidx = get(vindex_map, *vi); if(vidx%100==0 or vidx>num_v-50) { cout << "counted " << (vidx+1) << " / " << num_v << '\r'; cout.flush(); } } cout << endl; cout << "Num Vertices/Edges: " << num_v << "/" << num_e/2.0 << endl; cout << "Labelset Vertices/Edges: " << get_property(G, graph_vertex_labelset).size() << "/" << get_property(G, graph_edge_labelset).size() << endl; cout << "Min. Degree: "; cout << min << endl; cout << "Max. Degree: "; cout << max << endl; cout << "Avg. Degree: "; cout << avg/num_v << endl; cout << "Degree bins: " << degree_dist.size() << endl; cout << "Degree distribution: ["; map::iterator it; int i; for(it = degree_dist.begin(), i=0; it != degree_dist.end() && i<100; it++, i++) { if(i>0) cout << ", "; cout << it->first << ":" << it->second; //degree_dist[i]; } if(i::reverse_iterator rit; cout << "Sorted degree distribution: ["; for(rit=degree_dist.rbegin(), i=0; rit != degree_dist.rend() && i<100; rit++, i++) { if(i>0) cout << ", "; cout << rit->first << ":" << rit->second; //degree_dist[i]; } if(ifirst << "," << it->second << endl; } outfile.close(); } } mips-1.0/examples/graphstats/sanityChecks.cpp0000755002402200254040000002240112520407730020770 0ustar thomasfdtaig#include #include #include #include #include /* For graphAdjList use */ #include "mips/core/graph/graphAdjList.hpp" /* For non graphAdjList use */ //#include "mips/core/graph/format.hpp" //#include //typedef char glabel_type; //inline glabel_type getDefaultLabel() { return '0'; } //#include //namespace boost { // enum graph_vertex_labelset_t { graph_vertex_labelset }; // enum graph_edge_labelset_t { graph_edge_labelset }; // enum graph_format_t { graph_format }; // enum graph_numv_t { graph_numv }; // enum graph_nume_t { graph_nume }; // enum graph_id_t { graph_id }; // //enum graph_vertexsortlist_t { graph_vertexsortlist }; // //enum graph_v_perlabel_idx_t { graph_v_perlabel_idx }; // BOOST_INSTALL_PROPERTY(graph, id); // BOOST_INSTALL_PROPERTY(graph, numv); // BOOST_INSTALL_PROPERTY(graph, nume); // BOOST_INSTALL_PROPERTY(graph, format); // BOOST_INSTALL_PROPERTY(graph, vertex_labelset); // BOOST_INSTALL_PROPERTY(graph, edge_labelset); // //BOOST_INSTALL_PROPERTY(graph, vertexsortlist); // //BOOST_INSTALL_PROPERTY(graph, v_perlabel_idx); //typedef boost::property VertexProperty; //typedef boost::property EdgeProperty; //typedef std::vector vertex_labelset_t; //typedef std::vector edge_labelset_t; //typedef property set1; //typedef property set2; //typedef property set3; //typedef property set4; //typedef property set5; //typedef property GraphProperty; //} //typedef boost::adjacency_list BoostGraphAdjList2; //typedef BoostGraphAdjList2 GraphAdjList; using namespace std; using namespace boost; int DEBUG_LEVEL=0; string graph_file = ""; void printDot(GraphAdjList &g, ostream &f, bool print_labels=false) { property_map::type vlabel_map = get(vertex_label, g); property_map::type elabel_map = get(edge_label, g); property_map::type index_map = get(vertex_index, g); GraphAdjList::vertex_iterator vi, vend; GraphAdjList::out_edge_iterator gj, gend; f << "graph g {" << endl; for(tie(vi,vend)=vertices(g); vi!=vend; vi++) { if(print_labels) f << get(index_map, *vi) << " [label=\"" << get(vlabel_map, *vi) << "\"];" << endl; else f << get(index_map, *vi) << ';' << endl; } for(tie(vi,vend)=vertices(g); vi!=vend; vi++) { //i in xrange(len(adj)): for(tie(gj,gend)=out_edges(*vi, g); gj *vi) //NO SOLUTION FOR THIS STATEMENT YET -- when looked at from boost perspective of abstraction!! if(print_labels) f << get(index_map, *vi) << "--" << get(index_map, target(*gj, g)) << " [label=\"" << get(elabel_map, *gj) << "\"];" << endl; else f << get(index_map, *vi) << "--" << get(index_map, target(*gj, g)) << ';' << endl; } f << '}' << endl; } //end printDot bool parseOption(char* cmd_arg, char* param) { string arg(cmd_arg); if(arg == "-d") { istringstream convert(param); if ( !(convert >> DEBUG_LEVEL) ) return false; } else if(arg == "-i") { graph_file = param; } else { cerr << "Unknown option " << arg << ' ' << endl; return false; } return true; } void printUsage() { cout << "Usage: testRootedTreeEnum -i [-d ]" << endl; } int main(int argc, char* argv[]) { int a=1; //parse options while( a < argc ) { if(argc <= (a+1)) { printUsage(); return -1; } //all options require a parameter if(not parseOption(argv[a], argv[a+1])) { printUsage(); return -1; } a += 2; } //check all required options were present if(graph_file=="") { printUsage(); return -1; } GraphAdjList G; ifstream infile; infile.open(graph_file.c_str(), ifstream::in); cout << "Loading graph file ... " << endl; cout.flush(); infile >> G; property_map::type elabel_map = get(edge_label, G); // property_map::type vlabel_map = get(vertex_label, G); // mips::graph::io::readGraph(infile, G, vlabel_map, elabel_map); cout << " ... done." << endl; cout.flush(); int num_v = num_vertices(G); GraphAdjList::vertex_iterator vi, vend; GraphAdjList::out_edge_iterator gj, gend; // property_map::const_type elabel_map = get(edge_label, (const GraphAdjList) G); property_map::const_type vindex_map = get(vertex_index, (const GraphAdjList) G); cout << "===>>" << graph_file << "<<===" << endl; cout.flush(); //cout << "Num Vertices: " << num_v << endl; float avg = 0.0; int max, min, tmp; unsigned long num_e = 0; max = min = out_degree(*vertices(G).first, G); //G.adj_list[0].size(); for(tie(vi,vend)=vertices(G); vi!=vend; vi++) { tmp = out_degree(*vi, G); //G.adj_list[i].size(); avg += tmp; //G.adj_list[i].size(); if(tmp < min) min = tmp; if(tmp > max) max = tmp; //edge count for(tie(gj,gend)=out_edges(*vi, G); gj != gend; gj++) { //GraphAdjListEdge e = *gj; //G.adj_list[i][j]; if(target(*gj, G) > *vi) //THIS MAY CAUSE PROBLEMS with non-int vertex_descriptors, but no solution for it yet num_e++; } } cout << "Num Vertices/Edges: " << num_v << "/" << num_e << endl; cout << "Labelset Vertices/Edges: " << get_property(G, graph_vertex_labelset).size() << "/" << get_property(G, graph_edge_labelset).size() << endl; cout << "Min. Degree: "; cout << min << endl; cout << "Max. Degree: "; cout << max << endl; cout << "Avg. Degree: "; cout << avg/num_v << endl; cout << "Performing multiple edges check ..." << endl; GraphAdjList::vertex_descriptor *edges = new GraphAdjList::vertex_descriptor[num_v]; //THIS MAY CAUSE PROBLEMS with non-int vertex_descriptors fill(edges, edges+num_v, GraphAdjList::null_vertex()); vector< pair > multi_edges; //int num_e=0; int vidx=-1; for(tie(vi,vend)=vertices(G); vi!=vend; vi++) { vidx = get(vindex_map, *vi); if(vidx>(num_v-100) or vidx%(num_v/10)==0) cout << "\t" << vidx+1 << "/" << num_v << "\t\t\r"; for(tie(gj,gend)=out_edges(*vi, G); gj != gend; gj++) { //GraphAdjListEdge e = *gj; //G.adj_list[i][j]; //edge count //if(e.destNode > i) // num_e++; //multiple edge count GraphAdjList::vertex_descriptor vtmp = target(*gj, G); if(edges[vtmp] == *vi) multi_edges.push_back( make_pair(*vi, vtmp) ); edges[vtmp] = *vi; } } cout << endl << "... " << multi_edges.size() << " multiple edges found." << endl; cout << "multi_edges examples: "; for(int i=0; i<5 && i p; vector< pair > dir_exs, bad_exs; GraphAdjList::out_edge_iterator gj2, gj2end, itr_tmp; for(tie(vi,vend)=vertices(G); vi!=vend; vi++) { vidx = get(vindex_map, *vi); if(vidx>(num_v-100) or vidx%(num_v/10)==0) cout << "\t" << vidx+1 << "/" << num_v << "\t\t\r"; for(tie(gj,gend)=out_edges(*vi,G); gj != gend; gj++) { //int idx=0; idx i ) { while(gj2 != gj2end && !(boost::target(*gj2,G) == *vi)) ++gj2; if( gj2 == gj2end ) { dir_no++; dir_exs.push_back( make_pair(*vi, ev) ); //if(i==0) { // cout << endl << i << ": "; for(int l=0; l i } } cout << endl << "... " << dir_no << " directed edges found"; cout << endl << "... and " << bad_lbls << " bad labels found." << endl; cout << "directed examples: "; for(int i=0; i<5 && i0) cout << "Writing to file" << outfname << endl; //printDot(G, outfile, true); } mips-1.0/examples/graphstats/CMakeLists.txt0000755002402200254040000000027012520407730020374 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) CREATE_EXAMPLE(NAME sanityChecks SOURCES sanityChecks.cpp) CREATE_EXAMPLE(NAME graphStatistics SOURCES graphStatistics.cpp) mips-1.0/examples/CMakeLists.txt0000755002402200254040000000022112520407730016210 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) add_subdirectory(prediction) add_subdirectory(treeminer) add_subdirectory(graphstats) mips-1.0/data/0000755002402200254040000000000012520407730012545 5ustar thomasfdtaigmips-1.0/data/iris_test.csv0000755002402200254040000000052012520407730015267 0ustar thomasfdtaig5.6,3.0,4.1,1.3,Iris-versicolor 5.5,2.5,4.0,1.3,Iris-versicolor 5.5,2.6,4.4,1.2,Iris-versicolor 6.1,3.0,4.6,1.4,Iris-versicolor 5.8,2.6,4.0,1.2,Iris-versicolor 4.6,3.4,1.4,0.3,Iris-setosa 5.0,3.4,1.5,0.2,Iris-setosa 4.4,2.9,1.4,0.2,Iris-setosa 6.9,3.1,5.1,2.3,Iris-virginica 5.8,2.7,5.1,1.9,Iris-virginica 6.8,3.2,5.9,2.3,Iris-virginicamips-1.0/data/REFERENCES0000644002402200254040000000054012520407730014110 0ustar thomasfdtaigiris: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. boston: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. mips-1.0/data/boston_test.csv0000644002402200254040000000775312520407730015641 0ustar thomasfdtaig0.62739;0.00;8.140;0;0.5380;5.8340;56.50;4.4986;4;307.0;21.00;395.62;8.47;19.90 0.61154;20.00;3.970;0;0.6470;8.7040;86.90;1.8010;5;264.0;13.00;389.70;5.12;50.00 0.08308;0.00;2.460;0;0.4880;5.6040;89.80;2.9879;3;193.0;17.80;391.00;13.98;26.40 0.03359;75.00;2.950;0;0.4280;7.0240;15.80;5.4011;3;252.0;18.30;395.62;1.98;34.90 0.63796;0.00;8.140;0;0.5380;6.0960;84.50;4.4619;4;307.0;21.00;380.02;10.26;18.20 0.21038;20.00;3.330;0;0.4429;6.8120;32.20;4.1007;5;216.0;14.90;396.90;4.85;35.10 0.01778;95.00;1.470;0;0.4030;7.1350;13.90;7.6534;3;402.0;17.00;384.30;4.45;32.90 0.04113;25.00;4.860;0;0.4260;6.7270;33.50;5.4007;4;281.0;19.00;396.90;5.29;28.00 20.08490;0.00;18.100;0;0.7000;4.3680;91.20;1.4395;24;666.0;20.20;285.83;30.63;8.80 0.06911;45.00;3.440;0;0.4370;6.7390;30.80;6.4798;5;398.0;15.20;389.71;4.69;30.50 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iris_test.csv) ADD_DATA_FILE(iris_trainingdata iris_training.csv) ADD_DATA_FILE(boston_testdata boston_test.csv) ADD_DATA_FILE(boston_trainingdata boston_training.csv) mips-1.0/COPYRIGHT0000644002402200254040000000037612520407731013136 0ustar thomasfdtaigCopyright (C) 2010-2015 DTAI - KULeuven mips@cs.kuleuven.be The development of the MIgrant Prototype System (MIPS) was supported by ERC Starting Grant 240186 "MiGraNT: Mining Graphs and Networks: a Theory-based approach". License: see file LICENSE. mips-1.0/doc/0000755002402200254040000000000012520407731012402 5ustar thomasfdtaigmips-1.0/doc/Doxyfile.in0000644002402200254040000020017312520407731014520 0ustar thomasfdtaig# Doxyfile 1.6.3 # This file describes the settings to be used by the documentation system # doxygen (www.doxygen.org) for a project # # All text after a hash (#) is considered a comment and will be ignored # The format is: # TAG = value [value, ...] # For lists items can also be appended using: # TAG += value [value, ...] # Values that contain spaces should be placed between quotes (" ") #--------------------------------------------------------------------------- # Project related configuration options #--------------------------------------------------------------------------- # This tag specifies the encoding used for all characters in the config file # that follow. The default is UTF-8 which is also the encoding used for all # text before the first occurrence of this tag. Doxygen uses libiconv (or the # iconv built into libc) for the transcoding. See # http://www.gnu.org/software/libiconv for the list of possible encodings. DOXYFILE_ENCODING = UTF-8 # The PROJECT_NAME tag is a single word (or a sequence of words surrounded # by quotes) that should identify the project. PROJECT_NAME = MIPS # The PROJECT_NUMBER tag can be used to enter a project or revision number. # This could be handy for archiving the generated documentation or # if some version control system is used. PROJECT_NUMBER = # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) # base path where the generated documentation will be put. # If a relative path is entered, it will be relative to the location # where doxygen was started. If left blank the current directory will be used. OUTPUT_DIRECTORY = # If the CREATE_SUBDIRS tag is set to YES, then doxygen will create # 4096 sub-directories (in 2 levels) under the output directory of each output # format and will distribute the generated files over these directories. # Enabling this option can be useful when feeding doxygen a huge amount of # source files, where putting all generated files in the same directory would # otherwise cause performance problems for the file system. CREATE_SUBDIRS = NO # The OUTPUT_LANGUAGE tag is used to specify the language in which all # documentation generated by doxygen is written. Doxygen will use this # information to generate all constant output in the proper language. # The default language is English, other supported languages are: # Afrikaans, Arabic, Brazilian, Catalan, Chinese, Chinese-Traditional, # Croatian, Czech, Danish, Dutch, Esperanto, Farsi, Finnish, French, German, # Greek, Hungarian, Italian, Japanese, Japanese-en (Japanese with English # messages), Korean, Korean-en, Lithuanian, Norwegian, Macedonian, Persian, # Polish, Portuguese, Romanian, Russian, Serbian, Serbian-Cyrilic, Slovak, # Slovene, Spanish, Swedish, Ukrainian, and Vietnamese. OUTPUT_LANGUAGE = English # If the BRIEF_MEMBER_DESC tag is set to YES (the default) Doxygen will # include brief member descriptions after the members that are listed in # the file and class documentation (similar to JavaDoc). # Set to NO to disable this. BRIEF_MEMBER_DESC = NO # If the REPEAT_BRIEF tag is set to YES (the default) Doxygen will prepend # the brief description of a member or function before the detailed description. # Note: if both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the # brief descriptions will be completely suppressed. REPEAT_BRIEF = YES # This tag implements a quasi-intelligent brief description abbreviator # that is used to form the text in various listings. Each string # in this list, if found as the leading text of the brief description, will be # stripped from the text and the result after processing the whole list, is # used as the annotated text. Otherwise, the brief description is used as-is. # If left blank, the following values are used ("$name" is automatically # replaced with the name of the entity): "The $name class" "The $name widget" # "The $name file" "is" "provides" "specifies" "contains" # "represents" "a" "an" "the" ABBREVIATE_BRIEF = # If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then # Doxygen will generate a detailed section even if there is only a brief # description. ALWAYS_DETAILED_SEC = NO # If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all # inherited members of a class in the documentation of that class as if those # members were ordinary class members. Constructors, destructors and assignment # operators of the base classes will not be shown. INLINE_INHERITED_MEMB = NO # If the FULL_PATH_NAMES tag is set to YES then Doxygen will prepend the full # path before files name in the file list and in the header files. If set # to NO the shortest path that makes the file name unique will be used. FULL_PATH_NAMES = YES # If the FULL_PATH_NAMES tag is set to YES then the STRIP_FROM_PATH tag # can be used to strip a user-defined part of the path. Stripping is # only done if one of the specified strings matches the left-hand part of # the path. The tag can be used to show relative paths in the file list. # If left blank the directory from which doxygen is run is used as the # path to strip. STRIP_FROM_PATH = # The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of # the path mentioned in the documentation of a class, which tells # the reader which header file to include in order to use a class. # If left blank only the name of the header file containing the class # definition is used. Otherwise one should specify the include paths that # are normally passed to the compiler using the -I flag. STRIP_FROM_INC_PATH = # If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter # (but less readable) file names. This can be useful is your file systems # doesn't support long names like on DOS, Mac, or CD-ROM. SHORT_NAMES = NO # If the JAVADOC_AUTOBRIEF tag is set to YES then Doxygen # will interpret the first line (until the first dot) of a JavaDoc-style # comment as the brief description. If set to NO, the JavaDoc # comments will behave just like regular Qt-style comments # (thus requiring an explicit @brief command for a brief description.) JAVADOC_AUTOBRIEF = NO # If the QT_AUTOBRIEF tag is set to YES then Doxygen will # interpret the first line (until the first dot) of a Qt-style # comment as the brief description. If set to NO, the comments # will behave just like regular Qt-style comments (thus requiring # an explicit \brief command for a brief description.) QT_AUTOBRIEF = NO # The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make Doxygen # treat a multi-line C++ special comment block (i.e. a block of //! or /// # comments) as a brief description. This used to be the default behaviour. # The new default is to treat a multi-line C++ comment block as a detailed # description. Set this tag to YES if you prefer the old behaviour instead. MULTILINE_CPP_IS_BRIEF = NO # If the INHERIT_DOCS tag is set to YES (the default) then an undocumented # member inherits the documentation from any documented member that it # re-implements. INHERIT_DOCS = YES # If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce # a new page for each member. If set to NO, the documentation of a member will # be part of the file/class/namespace that contains it. SEPARATE_MEMBER_PAGES = NO # The TAB_SIZE tag can be used to set the number of spaces in a tab. # Doxygen uses this value to replace tabs by spaces in code fragments. TAB_SIZE = 8 # This tag can be used to specify a number of aliases that acts # as commands in the documentation. An alias has the form "name=value". # For example adding "sideeffect=\par Side Effects:\n" will allow you to # put the command \sideeffect (or @sideeffect) in the documentation, which # will result in a user-defined paragraph with heading "Side Effects:". # You can put \n's in the value part of an alias to insert newlines. ALIASES = # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C # sources only. Doxygen will then generate output that is more tailored for C. # For instance, some of the names that are used will be different. The list # of all members will be omitted, etc. OPTIMIZE_OUTPUT_FOR_C = NO # Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java # sources only. Doxygen will then generate output that is more tailored for # Java. For instance, namespaces will be presented as packages, qualified # scopes will look different, etc. OPTIMIZE_OUTPUT_JAVA = NO # Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran # sources only. Doxygen will then generate output that is more tailored for # Fortran. OPTIMIZE_FOR_FORTRAN = NO # Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL # sources. Doxygen will then generate output that is tailored for # VHDL. OPTIMIZE_OUTPUT_VHDL = NO # Doxygen selects the parser to use depending on the extension of the files it parses. # With this tag you can assign which parser to use for a given extension. # Doxygen has a built-in mapping, but you can override or extend it using this tag. # The format is ext=language, where ext is a file extension, and language is one of # the parsers supported by doxygen: IDL, Java, Javascript, C#, C, C++, D, PHP, # Objective-C, Python, Fortran, VHDL, C, C++. For instance to make doxygen treat # .inc files as Fortran files (default is PHP), and .f files as C (default is Fortran), # use: inc=Fortran f=C. Note that for custom extensions you also need to set FILE_PATTERNS otherwise the files are not read by doxygen. EXTENSION_MAPPING = # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want # to include (a tag file for) the STL sources as input, then you should # set this tag to YES in order to let doxygen match functions declarations and # definitions whose arguments contain STL classes (e.g. func(std::string); v.s. # func(std::string) {}). This also make the inheritance and collaboration # diagrams that involve STL classes more complete and accurate. BUILTIN_STL_SUPPORT = NO # If you use Microsoft's C++/CLI language, you should set this option to YES to # enable parsing support. CPP_CLI_SUPPORT = NO # Set the SIP_SUPPORT tag to YES if your project consists of sip sources only. # Doxygen will parse them like normal C++ but will assume all classes use public # instead of private inheritance when no explicit protection keyword is present. SIP_SUPPORT = NO # For Microsoft's IDL there are propget and propput attributes to indicate getter # and setter methods for a property. Setting this option to YES (the default) # will make doxygen to replace the get and set methods by a property in the # documentation. This will only work if the methods are indeed getting or # setting a simple type. If this is not the case, or you want to show the # methods anyway, you should set this option to NO. IDL_PROPERTY_SUPPORT = YES # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC # tag is set to YES, then doxygen will reuse the documentation of the first # member in the group (if any) for the other members of the group. By default # all members of a group must be documented explicitly. DISTRIBUTE_GROUP_DOC = NO # Set the SUBGROUPING tag to YES (the default) to allow class member groups of # the same type (for instance a group of public functions) to be put as a # subgroup of that type (e.g. under the Public Functions section). Set it to # NO to prevent subgrouping. Alternatively, this can be done per class using # the \nosubgrouping command. SUBGROUPING = YES # When TYPEDEF_HIDES_STRUCT is enabled, a typedef of a struct, union, or enum # is documented as struct, union, or enum with the name of the typedef. So # typedef struct TypeS {} TypeT, will appear in the documentation as a struct # with name TypeT. When disabled the typedef will appear as a member of a file, # namespace, or class. And the struct will be named TypeS. This can typically # be useful for C code in case the coding convention dictates that all compound # types are typedef'ed and only the typedef is referenced, never the tag name. TYPEDEF_HIDES_STRUCT = NO # The SYMBOL_CACHE_SIZE determines the size of the internal cache use to # determine which symbols to keep in memory and which to flush to disk. # When the cache is full, less often used symbols will be written to disk. # For small to medium size projects (<1000 input files) the default value is # probably good enough. For larger projects a too small cache size can cause # doxygen to be busy swapping symbols to and from disk most of the time # causing a significant performance penality. # If the system has enough physical memory increasing the cache will improve the # performance by keeping more symbols in memory. Note that the value works on # a logarithmic scale so increasing the size by one will rougly double the # memory usage. The cache size is given by this formula: # 2^(16+SYMBOL_CACHE_SIZE). The valid range is 0..9, the default is 0, # corresponding to a cache size of 2^16 = 65536 symbols SYMBOL_CACHE_SIZE = 0 #--------------------------------------------------------------------------- # Build related configuration options #--------------------------------------------------------------------------- # If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in # documentation are documented, even if no documentation was available. # Private class members and static file members will be hidden unless # the EXTRACT_PRIVATE and EXTRACT_STATIC tags are set to YES EXTRACT_ALL = NO # If the EXTRACT_PRIVATE tag is set to YES all private members of a class # will be included in the documentation. EXTRACT_PRIVATE = YES # If the EXTRACT_STATIC tag is set to YES all static members of a file # will be included in the documentation. EXTRACT_STATIC = NO # If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) # defined locally in source files will be included in the documentation. # If set to NO only classes defined in header files are included. EXTRACT_LOCAL_CLASSES = NO # This flag is only useful for Objective-C code. When set to YES local # methods, which are defined in the implementation section but not in # the interface are included in the documentation. # If set to NO (the default) only methods in the interface are included. EXTRACT_LOCAL_METHODS = NO # If this flag is set to YES, the members of anonymous namespaces will be # extracted and appear in the documentation as a namespace called # 'anonymous_namespace{file}', where file will be replaced with the base # name of the file that contains the anonymous namespace. By default # anonymous namespace are hidden. EXTRACT_ANON_NSPACES = NO # If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all # undocumented members of documented classes, files or namespaces. # If set to NO (the default) these members will be included in the # various overviews, but no documentation section is generated. # This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_MEMBERS = YES # If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all # undocumented classes that are normally visible in the class hierarchy. # If set to NO (the default) these classes will be included in the various # overviews. This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_CLASSES = YES # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all # friend (class|struct|union) declarations. # If set to NO (the default) these declarations will be included in the # documentation. HIDE_FRIEND_COMPOUNDS = NO # If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any # documentation blocks found inside the body of a function. # If set to NO (the default) these blocks will be appended to the # function's detailed documentation block. HIDE_IN_BODY_DOCS = NO # The INTERNAL_DOCS tag determines if documentation # that is typed after a \internal command is included. If the tag is set # to NO (the default) then the documentation will be excluded. # Set it to YES to include the internal documentation. INTERNAL_DOCS = NO # If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate # file names in lower-case letters. If set to YES upper-case letters are also # allowed. This is useful if you have classes or files whose names only differ # in case and if your file system supports case sensitive file names. Windows # and Mac users are advised to set this option to NO. CASE_SENSE_NAMES = YES # If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen # will show members with their full class and namespace scopes in the # documentation. If set to YES the scope will be hidden. HIDE_SCOPE_NAMES = NO # If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen # will put a list of the files that are included by a file in the documentation # of that file. SHOW_INCLUDE_FILES = YES # If the FORCE_LOCAL_INCLUDES tag is set to YES then Doxygen # will list include files with double quotes in the documentation # rather than with sharp brackets. FORCE_LOCAL_INCLUDES = NO # If the INLINE_INFO tag is set to YES (the default) then a tag [inline] # is inserted in the documentation for inline members. INLINE_INFO = YES # If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen # will sort the (detailed) documentation of file and class members # alphabetically by member name. If set to NO the members will appear in # declaration order. SORT_MEMBER_DOCS = YES # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the # brief documentation of file, namespace and class members alphabetically # by member name. If set to NO (the default) the members will appear in # declaration order. SORT_BRIEF_DOCS = NO # If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the (brief and detailed) documentation of class members so that constructors and destructors are listed first. If set to NO (the default) the constructors will appear in the respective orders defined by SORT_MEMBER_DOCS and SORT_BRIEF_DOCS. This tag will be ignored for brief docs if SORT_BRIEF_DOCS is set to NO and ignored for detailed docs if SORT_MEMBER_DOCS is set to NO. SORT_MEMBERS_CTORS_1ST = YES # If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the # hierarchy of group names into alphabetical order. If set to NO (the default) # the group names will appear in their defined order. SORT_GROUP_NAMES = NO # If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be # sorted by fully-qualified names, including namespaces. If set to # NO (the default), the class list will be sorted only by class name, # not including the namespace part. # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. # Note: This option applies only to the class list, not to the # alphabetical list. SORT_BY_SCOPE_NAME = NO # The GENERATE_TODOLIST tag can be used to enable (YES) or # disable (NO) the todo list. This list is created by putting \todo # commands in the documentation. GENERATE_TODOLIST = YES # The GENERATE_TESTLIST tag can be used to enable (YES) or # disable (NO) the test list. This list is created by putting \test # commands in the documentation. GENERATE_TESTLIST = YES # The GENERATE_BUGLIST tag can be used to enable (YES) or # disable (NO) the bug list. This list is created by putting \bug # commands in the documentation. GENERATE_BUGLIST = YES # The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or # disable (NO) the deprecated list. This list is created by putting # \deprecated commands in the documentation. GENERATE_DEPRECATEDLIST= YES # The ENABLED_SECTIONS tag can be used to enable conditional # documentation sections, marked by \if sectionname ... \endif. ENABLED_SECTIONS = # The MAX_INITIALIZER_LINES tag determines the maximum number of lines # the initial value of a variable or define consists of for it to appear in # the documentation. If the initializer consists of more lines than specified # here it will be hidden. Use a value of 0 to hide initializers completely. # The appearance of the initializer of individual variables and defines in the # documentation can be controlled using \showinitializer or \hideinitializer # command in the documentation regardless of this setting. MAX_INITIALIZER_LINES = 30 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated # at the bottom of the documentation of classes and structs. If set to YES the # list will mention the files that were used to generate the documentation. SHOW_USED_FILES = YES # If the sources in your project are distributed over multiple directories # then setting the SHOW_DIRECTORIES tag to YES will show the directory hierarchy # in the documentation. The default is NO. SHOW_DIRECTORIES = NO # Set the SHOW_FILES tag to NO to disable the generation of the Files page. # This will remove the Files entry from the Quick Index and from the # Folder Tree View (if specified). The default is YES. SHOW_FILES = YES # Set the SHOW_NAMESPACES tag to NO to disable the generation of the # Namespaces page. # This will remove the Namespaces entry from the Quick Index # and from the Folder Tree View (if specified). The default is YES. SHOW_NAMESPACES = YES # The FILE_VERSION_FILTER tag can be used to specify a program or script that # doxygen should invoke to get the current version for each file (typically from # the version control system). Doxygen will invoke the program by executing (via # popen()) the command , where is the value of # the FILE_VERSION_FILTER tag, and is the name of an input file # provided by doxygen. Whatever the program writes to standard output # is used as the file version. See the manual for examples. FILE_VERSION_FILTER = # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed by # doxygen. The layout file controls the global structure of the generated output files # in an output format independent way. The create the layout file that represents # doxygen's defaults, run doxygen with the -l option. You can optionally specify a # file name after the option, if omitted DoxygenLayout.xml will be used as the name # of the layout file. LAYOUT_FILE = DoxygenLayout.xml #--------------------------------------------------------------------------- # configuration options related to warning and progress messages #--------------------------------------------------------------------------- # The QUIET tag can be used to turn on/off the messages that are generated # by doxygen. Possible values are YES and NO. If left blank NO is used. QUIET = NO # The WARNINGS tag can be used to turn on/off the warning messages that are # generated by doxygen. Possible values are YES and NO. If left blank # NO is used. WARNINGS = YES # If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings # for undocumented members. If EXTRACT_ALL is set to YES then this flag will # automatically be disabled. WARN_IF_UNDOCUMENTED = YES # If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for # potential errors in the documentation, such as not documenting some # parameters in a documented function, or documenting parameters that # don't exist or using markup commands wrongly. WARN_IF_DOC_ERROR = YES # This WARN_NO_PARAMDOC option can be abled to get warnings for # functions that are documented, but have no documentation for their parameters # or return value. If set to NO (the default) doxygen will only warn about # wrong or incomplete parameter documentation, but not about the absence of # documentation. WARN_NO_PARAMDOC = NO # The WARN_FORMAT tag determines the format of the warning messages that # doxygen can produce. The string should contain the $file, $line, and $text # tags, which will be replaced by the file and line number from which the # warning originated and the warning text. Optionally the format may contain # $version, which will be replaced by the version of the file (if it could # be obtained via FILE_VERSION_FILTER) WARN_FORMAT = "$file:$line: $text" # The WARN_LOGFILE tag can be used to specify a file to which warning # and error messages should be written. If left blank the output is written # to stderr. WARN_LOGFILE = #--------------------------------------------------------------------------- # configuration options related to the input files #--------------------------------------------------------------------------- # The INPUT tag can be used to specify the files and/or directories that contain # documented source files. You may enter file names like "myfile.cpp" or # directories like "/usr/src/myproject". Separate the files or directories # with spaces. INPUT = @CMAKE_CURRENT_SOURCE_DIR@/../mips/ @CMAKE_CURRENT_SOURCE_DIR@/dox # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is # also the default input encoding. Doxygen uses libiconv (or the iconv built # into libc) for the transcoding. See http://www.gnu.org/software/libiconv for # the list of possible encodings. INPUT_ENCODING = UTF-8 # If the value of the INPUT tag contains directories, you can use the # FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank the following patterns are tested: # *.c *.cc *.cxx *.cpp *.c++ *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh *.hxx # *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.py *.f90 FILE_PATTERNS = *.cpp *.hpp *.dox # The RECURSIVE tag can be used to turn specify whether or not subdirectories # should be searched for input files as well. Possible values are YES and NO. # If left blank NO is used. RECURSIVE = YES # The EXCLUDE tag can be used to specify files and/or directories that should # excluded from the INPUT source files. This way you can easily exclude a # subdirectory from a directory tree whose root is specified with the INPUT tag. EXCLUDE = # The EXCLUDE_SYMLINKS tag can be used select whether or not files or # directories that are symbolic links (a Unix filesystem feature) are excluded # from the input. EXCLUDE_SYMLINKS = NO # If the value of the INPUT tag contains directories, you can use the # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude # certain files from those directories. Note that the wildcards are matched # against the file with absolute path, so to exclude all test directories # for example use the pattern */test/* EXCLUDE_PATTERNS = # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names # (namespaces, classes, functions, etc.) that should be excluded from the # output. The symbol name can be a fully qualified name, a word, or if the # wildcard * is used, a substring. Examples: ANamespace, AClass, # AClass::ANamespace, ANamespace::*Test EXCLUDE_SYMBOLS = # The EXAMPLE_PATH tag can be used to specify one or more files or # directories that contain example code fragments that are included (see # the \include command). EXAMPLE_PATH = @CMAKE_CURRENT_SOURCE_DIR@/../examples/ # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank all files are included. EXAMPLE_PATTERNS = *.cpp # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be # searched for input files to be used with the \include or \dontinclude # commands irrespective of the value of the RECURSIVE tag. # Possible values are YES and NO. If left blank NO is used. EXAMPLE_RECURSIVE = YES # The IMAGE_PATH tag can be used to specify one or more files or # directories that contain image that are included in the documentation (see # the \image command). IMAGE_PATH = # The INPUT_FILTER tag can be used to specify a program that doxygen should # invoke to filter for each input file. Doxygen will invoke the filter program # by executing (via popen()) the command , where # is the value of the INPUT_FILTER tag, and is the name of an # input file. Doxygen will then use the output that the filter program writes # to standard output. # If FILTER_PATTERNS is specified, this tag will be # ignored. INPUT_FILTER = # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern # basis. # Doxygen will compare the file name with each pattern and apply the # filter if there is a match. # The filters are a list of the form: # pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further # info on how filters are used. If FILTER_PATTERNS is empty, INPUT_FILTER # is applied to all files. FILTER_PATTERNS = # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using # INPUT_FILTER) will be used to filter the input files when producing source # files to browse (i.e. when SOURCE_BROWSER is set to YES). FILTER_SOURCE_FILES = NO #--------------------------------------------------------------------------- # configuration options related to source browsing #--------------------------------------------------------------------------- # If the SOURCE_BROWSER tag is set to YES then a list of source files will # be generated. Documented entities will be cross-referenced with these sources. # Note: To get rid of all source code in the generated output, make sure also # VERBATIM_HEADERS is set to NO. SOURCE_BROWSER = NO # Setting the INLINE_SOURCES tag to YES will include the body # of functions and classes directly in the documentation. INLINE_SOURCES = NO # Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct # doxygen to hide any special comment blocks from generated source code # fragments. Normal C and C++ comments will always remain visible. STRIP_CODE_COMMENTS = YES # If the REFERENCED_BY_RELATION tag is set to YES # then for each documented function all documented # functions referencing it will be listed. REFERENCED_BY_RELATION = NO # If the REFERENCES_RELATION tag is set to YES # then for each documented function all documented entities # called/used by that function will be listed. REFERENCES_RELATION = NO # If the REFERENCES_LINK_SOURCE tag is set to YES (the default) # and SOURCE_BROWSER tag is set to YES, then the hyperlinks from # functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will # link to the source code. # Otherwise they will link to the documentation. REFERENCES_LINK_SOURCE = YES # If the USE_HTAGS tag is set to YES then the references to source code # will point to the HTML generated by the htags(1) tool instead of doxygen # built-in source browser. The htags tool is part of GNU's global source # tagging system (see http://www.gnu.org/software/global/global.html). You # will need version 4.8.6 or higher. USE_HTAGS = NO # If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen # will generate a verbatim copy of the header file for each class for # which an include is specified. Set to NO to disable this. VERBATIM_HEADERS = YES #--------------------------------------------------------------------------- # configuration options related to the alphabetical class index #--------------------------------------------------------------------------- # If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index # of all compounds will be generated. Enable this if the project # contains a lot of classes, structs, unions or interfaces. ALPHABETICAL_INDEX = NO # If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then # the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns # in which this list will be split (can be a number in the range [1..20]) COLS_IN_ALPHA_INDEX = 5 # In case all classes in a project start with a common prefix, all # classes will be put under the same header in the alphabetical index. # The IGNORE_PREFIX tag can be used to specify one or more prefixes that # should be ignored while generating the index headers. IGNORE_PREFIX = #--------------------------------------------------------------------------- # configuration options related to the HTML output #--------------------------------------------------------------------------- # If the GENERATE_HTML tag is set to YES (the default) Doxygen will # generate HTML output. GENERATE_HTML = YES # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `html' will be used as the default path. HTML_OUTPUT = html # The HTML_FILE_EXTENSION tag can be used to specify the file extension for # each generated HTML page (for example: .htm,.php,.asp). If it is left blank # doxygen will generate files with .html extension. HTML_FILE_EXTENSION = .html # The HTML_HEADER tag can be used to specify a personal HTML header for # each generated HTML page. If it is left blank doxygen will generate a # standard header. HTML_HEADER = # The HTML_FOOTER tag can be used to specify a personal HTML footer for # each generated HTML page. If it is left blank doxygen will generate a # standard footer. HTML_FOOTER = # The HTML_STYLESHEET tag can be used to specify a user-defined cascading # style sheet that is used by each HTML page. It can be used to # fine-tune the look of the HTML output. If the tag is left blank doxygen # will generate a default style sheet. Note that doxygen will try to copy # the style sheet file to the HTML output directory, so don't put your own # stylesheet in the HTML output directory as well, or it will be erased! HTML_STYLESHEET = # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML # page will contain the date and time when the page was generated. Setting # this to NO can help when comparing the output of multiple runs. HTML_TIMESTAMP = YES # If the HTML_ALIGN_MEMBERS tag is set to YES, the members of classes, # files or namespaces will be aligned in HTML using tables. If set to # NO a bullet list will be used. HTML_ALIGN_MEMBERS = YES # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML # documentation will contain sections that can be hidden and shown after the # page has loaded. For this to work a browser that supports # JavaScript and DHTML is required (for instance Mozilla 1.0+, Firefox # Netscape 6.0+, Internet explorer 5.0+, Konqueror, or Safari). HTML_DYNAMIC_SECTIONS = NO # If the GENERATE_DOCSET tag is set to YES, additional index files # will be generated that can be used as input for Apple's Xcode 3 # integrated development environment, introduced with OSX 10.5 (Leopard). # To create a documentation set, doxygen will generate a Makefile in the # HTML output directory. Running make will produce the docset in that # directory and running "make install" will install the docset in # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find # it at startup. # See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html for more information. GENERATE_DOCSET = NO # When GENERATE_DOCSET tag is set to YES, this tag determines the name of the # feed. A documentation feed provides an umbrella under which multiple # documentation sets from a single provider (such as a company or product suite) # can be grouped. DOCSET_FEEDNAME = "Doxygen generated docs" # When GENERATE_DOCSET tag is set to YES, this tag specifies a string that # should uniquely identify the documentation set bundle. This should be a # reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen # will append .docset to the name. DOCSET_BUNDLE_ID = org.doxygen.Project # If the GENERATE_HTMLHELP tag is set to YES, additional index files # will be generated that can be used as input for tools like the # Microsoft HTML help workshop to generate a compiled HTML help file (.chm) # of the generated HTML documentation. GENERATE_HTMLHELP = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can # be used to specify the file name of the resulting .chm file. You # can add a path in front of the file if the result should not be # written to the html output directory. CHM_FILE = # If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can # be used to specify the location (absolute path including file name) of # the HTML help compiler (hhc.exe). If non-empty doxygen will try to run # the HTML help compiler on the generated index.hhp. HHC_LOCATION = # If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag # controls if a separate .chi index file is generated (YES) or that # it should be included in the master .chm file (NO). GENERATE_CHI = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING # is used to encode HtmlHelp index (hhk), content (hhc) and project file # content. CHM_INDEX_ENCODING = # If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag # controls whether a binary table of contents is generated (YES) or a # normal table of contents (NO) in the .chm file. BINARY_TOC = NO # The TOC_EXPAND flag can be set to YES to add extra items for group members # to the contents of the HTML help documentation and to the tree view. TOC_EXPAND = YES # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and QHP_VIRTUAL_FOLDER # are set, an additional index file will be generated that can be used as input for # Qt's qhelpgenerator to generate a Qt Compressed Help (.qch) of the generated # HTML documentation. GENERATE_QHP = NO # If the QHG_LOCATION tag is specified, the QCH_FILE tag can # be used to specify the file name of the resulting .qch file. # The path specified is relative to the HTML output folder. QCH_FILE = # The QHP_NAMESPACE tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#namespace QHP_NAMESPACE = org.doxygen.Project # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#virtual-folders QHP_VIRTUAL_FOLDER = doc # If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to add. # For more information please see # http://doc.trolltech.com/qthelpproject.html#custom-filters QHP_CUST_FILTER_NAME = # The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the custom filter to add.For more information please see # Qt Help Project / Custom Filters. QHP_CUST_FILTER_ATTRS = # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this project's # filter section matches. # Qt Help Project / Filter Attributes. QHP_SECT_FILTER_ATTRS = # If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can # be used to specify the location of Qt's qhelpgenerator. # If non-empty doxygen will try to run qhelpgenerator on the generated # .qhp file. QHG_LOCATION = # If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files # will be generated, which together with the HTML files, form an Eclipse help # plugin. To install this plugin and make it available under the help contents # menu in Eclipse, the contents of the directory containing the HTML and XML # files needs to be copied into the plugins directory of eclipse. The name of # the directory within the plugins directory should be the same as # the ECLIPSE_DOC_ID value. After copying Eclipse needs to be restarted before the help appears. GENERATE_ECLIPSEHELP = NO # A unique identifier for the eclipse help plugin. When installing the plugin # the directory name containing the HTML and XML files should also have # this name. ECLIPSE_DOC_ID = org.doxygen.Project # The DISABLE_INDEX tag can be used to turn on/off the condensed index at # top of each HTML page. The value NO (the default) enables the index and # the value YES disables it. DISABLE_INDEX = NO # This tag can be used to set the number of enum values (range [1..20]) # that doxygen will group on one line in the generated HTML documentation. ENUM_VALUES_PER_LINE = 4 # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index # structure should be generated to display hierarchical information. # If the tag value is set to YES, a side panel will be generated # containing a tree-like index structure (just like the one that # is generated for HTML Help). For this to work a browser that supports # JavaScript, DHTML, CSS and frames is required (i.e. any modern browser). # Windows users are probably better off using the HTML help feature. GENERATE_TREEVIEW = YES # By enabling USE_INLINE_TREES, doxygen will generate the Groups, Directories, # and Class Hierarchy pages using a tree view instead of an ordered list. USE_INLINE_TREES = NO # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be # used to set the initial width (in pixels) of the frame in which the tree # is shown. TREEVIEW_WIDTH = 250 # Use this tag to change the font size of Latex formulas included # as images in the HTML documentation. The default is 10. Note that # when you change the font size after a successful doxygen run you need # to manually remove any form_*.png images from the HTML output directory # to force them to be regenerated. FORMULA_FONTSIZE = 10 # When the SEARCHENGINE tag is enabled doxygen will generate a search box for the HTML output. The underlying search engine uses javascript # and DHTML and should work on any modern browser. Note that when using HTML help (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET) there is already a search function so this one should # typically be disabled. For large projects the javascript based search engine # can be slow, then enabling SERVER_BASED_SEARCH may provide a better solution. SEARCHENGINE = YES # When the SERVER_BASED_SEARCH tag is enabled the search engine will be implemented using a PHP enabled web server instead of at the web client using Javascript. Doxygen will generate the search PHP script and index # file to put on the web server. The advantage of the server based approach is that it scales better to large projects and allows full text search. The disadvances is that it is more difficult to setup # and does not have live searching capabilities. SERVER_BASED_SEARCH = NO #--------------------------------------------------------------------------- # configuration options related to the LaTeX output #--------------------------------------------------------------------------- # If the GENERATE_LATEX tag is set to YES (the default) Doxygen will # generate Latex output. GENERATE_LATEX = YES # The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `latex' will be used as the default path. LATEX_OUTPUT = latex # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be # invoked. If left blank `latex' will be used as the default command name. # Note that when enabling USE_PDFLATEX this option is only used for # generating bitmaps for formulas in the HTML output, but not in the # Makefile that is written to the output directory. LATEX_CMD_NAME = latex # The MAKEINDEX_CMD_NAME tag can be used to specify the command name to # generate index for LaTeX. If left blank `makeindex' will be used as the # default command name. MAKEINDEX_CMD_NAME = makeindex # If the COMPACT_LATEX tag is set to YES Doxygen generates more compact # LaTeX documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_LATEX = NO # The PAPER_TYPE tag can be used to set the paper type that is used # by the printer. Possible values are: a4, a4wide, letter, legal and # executive. If left blank a4wide will be used. PAPER_TYPE = a4wide # The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX # packages that should be included in the LaTeX output. EXTRA_PACKAGES = # The LATEX_HEADER tag can be used to specify a personal LaTeX header for # the generated latex document. The header should contain everything until # the first chapter. If it is left blank doxygen will generate a # standard header. Notice: only use this tag if you know what you are doing! LATEX_HEADER = # If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated # is prepared for conversion to pdf (using ps2pdf). The pdf file will # contain links (just like the HTML output) instead of page references # This makes the output suitable for online browsing using a pdf viewer. PDF_HYPERLINKS = YES # If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of # plain latex in the generated Makefile. Set this option to YES to get a # higher quality PDF documentation. USE_PDFLATEX = YES # If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode. # command to the generated LaTeX files. This will instruct LaTeX to keep # running if errors occur, instead of asking the user for help. # This option is also used when generating formulas in HTML. LATEX_BATCHMODE = NO # If LATEX_HIDE_INDICES is set to YES then doxygen will not # include the index chapters (such as File Index, Compound Index, etc.) # in the output. LATEX_HIDE_INDICES = NO # If LATEX_SOURCE_CODE is set to YES then doxygen will include source code with syntax highlighting in the LaTeX output. Note that which sources are shown also depends on other settings such as SOURCE_BROWSER. LATEX_SOURCE_CODE = NO #--------------------------------------------------------------------------- # configuration options related to the RTF output #--------------------------------------------------------------------------- # If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output # The RTF output is optimized for Word 97 and may not look very pretty with # other RTF readers or editors. GENERATE_RTF = NO # The RTF_OUTPUT tag is used to specify where the RTF docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `rtf' will be used as the default path. RTF_OUTPUT = rtf # If the COMPACT_RTF tag is set to YES Doxygen generates more compact # RTF documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_RTF = NO # If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated # will contain hyperlink fields. The RTF file will # contain links (just like the HTML output) instead of page references. # This makes the output suitable for online browsing using WORD or other # programs which support those fields. # Note: wordpad (write) and others do not support links. RTF_HYPERLINKS = NO # Load stylesheet definitions from file. Syntax is similar to doxygen's # config file, i.e. a series of assignments. You only have to provide # replacements, missing definitions are set to their default value. RTF_STYLESHEET_FILE = # Set optional variables used in the generation of an rtf document. # Syntax is similar to doxygen's config file. RTF_EXTENSIONS_FILE = #--------------------------------------------------------------------------- # configuration options related to the man page output #--------------------------------------------------------------------------- # If the GENERATE_MAN tag is set to YES (the default) Doxygen will # generate man pages GENERATE_MAN = NO # The MAN_OUTPUT tag is used to specify where the man pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `man' will be used as the default path. MAN_OUTPUT = man # The MAN_EXTENSION tag determines the extension that is added to # the generated man pages (default is the subroutine's section .3) MAN_EXTENSION = .3 # If the MAN_LINKS tag is set to YES and Doxygen generates man output, # then it will generate one additional man file for each entity # documented in the real man page(s). These additional files # only source the real man page, but without them the man command # would be unable to find the correct page. The default is NO. MAN_LINKS = NO #--------------------------------------------------------------------------- # configuration options related to the XML output #--------------------------------------------------------------------------- # If the GENERATE_XML tag is set to YES Doxygen will # generate an XML file that captures the structure of # the code including all documentation. GENERATE_XML = NO # The XML_OUTPUT tag is used to specify where the XML pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `xml' will be used as the default path. XML_OUTPUT = xml # The XML_SCHEMA tag can be used to specify an XML schema, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_SCHEMA = # The XML_DTD tag can be used to specify an XML DTD, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_DTD = # If the XML_PROGRAMLISTING tag is set to YES Doxygen will # dump the program listings (including syntax highlighting # and cross-referencing information) to the XML output. Note that # enabling this will significantly increase the size of the XML output. XML_PROGRAMLISTING = YES #--------------------------------------------------------------------------- # configuration options for the AutoGen Definitions output #--------------------------------------------------------------------------- # If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will # generate an AutoGen Definitions (see autogen.sf.net) file # that captures the structure of the code including all # documentation. Note that this feature is still experimental # and incomplete at the moment. GENERATE_AUTOGEN_DEF = NO #--------------------------------------------------------------------------- # configuration options related to the Perl module output #--------------------------------------------------------------------------- # If the GENERATE_PERLMOD tag is set to YES Doxygen will # generate a Perl module file that captures the structure of # the code including all documentation. Note that this # feature is still experimental and incomplete at the # moment. GENERATE_PERLMOD = NO # If the PERLMOD_LATEX tag is set to YES Doxygen will generate # the necessary Makefile rules, Perl scripts and LaTeX code to be able # to generate PDF and DVI output from the Perl module output. PERLMOD_LATEX = NO # If the PERLMOD_PRETTY tag is set to YES the Perl module output will be # nicely formatted so it can be parsed by a human reader. # This is useful # if you want to understand what is going on. # On the other hand, if this # tag is set to NO the size of the Perl module output will be much smaller # and Perl will parse it just the same. PERLMOD_PRETTY = YES # The names of the make variables in the generated doxyrules.make file # are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. # This is useful so different doxyrules.make files included by the same # Makefile don't overwrite each other's variables. PERLMOD_MAKEVAR_PREFIX = #--------------------------------------------------------------------------- # Configuration options related to the preprocessor #--------------------------------------------------------------------------- # If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will # evaluate all C-preprocessor directives found in the sources and include # files. ENABLE_PREPROCESSING = YES # If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro # names in the source code. If set to NO (the default) only conditional # compilation will be performed. Macro expansion can be done in a controlled # way by setting EXPAND_ONLY_PREDEF to YES. MACRO_EXPANSION = YES # If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES # then the macro expansion is limited to the macros specified with the # PREDEFINED and EXPAND_AS_DEFINED tags. EXPAND_ONLY_PREDEF = YES # If the SEARCH_INCLUDES tag is set to YES (the default) the includes files # in the INCLUDE_PATH (see below) will be search if a #include is found. SEARCH_INCLUDES = YES # The INCLUDE_PATH tag can be used to specify one or more directories that # contain include files that are not input files but should be processed by # the preprocessor. INCLUDE_PATH = # You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard # patterns (like *.h and *.hpp) to filter out the header-files in the # directories. If left blank, the patterns specified with FILE_PATTERNS will # be used. INCLUDE_FILE_PATTERNS = # The PREDEFINED tag can be used to specify one or more macro names that # are defined before the preprocessor is started (similar to the -D option of # gcc). The argument of the tag is a list of macros of the form: name # or name=definition (no spaces). If the definition and the = are # omitted =1 is assumed. To prevent a macro definition from being # undefined via #undef or recursively expanded use the := operator # instead of the = operator. PREDEFINED = MIPS_DOXYGEN_IGNORE # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then # this tag can be used to specify a list of macro names that should be expanded. # The macro definition that is found in the sources will be used. # Use the PREDEFINED tag if you want to use a different macro definition. EXPAND_AS_DEFINED = CONVERTER_INIT_SQLPARAMS \ CONVERTER_CLASS_SQLPARAMS \ CONVERTER_SQLPARAMS # If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then # doxygen's preprocessor will remove all function-like macros that are alone # on a line, have an all uppercase name, and do not end with a semicolon. Such # function macros are typically used for boiler-plate code, and will confuse # the parser if not removed. SKIP_FUNCTION_MACROS = YES #--------------------------------------------------------------------------- # Configuration::additions related to external references #--------------------------------------------------------------------------- # The TAGFILES option can be used to specify one or more tagfiles. # Optionally an initial location of the external documentation # can be added for each tagfile. The format of a tag file without # this location is as follows: # # TAGFILES = file1 file2 ... # Adding location for the tag files is done as follows: # # TAGFILES = file1=loc1 "file2 = loc2" ... # where "loc1" and "loc2" can be relative or absolute paths or # URLs. If a location is present for each tag, the installdox tool # does not have to be run to correct the links. # Note that each tag file must have a unique name # (where the name does NOT include the path) # If a tag file is not located in the directory in which doxygen # is run, you must also specify the path to the tagfile here. TAGFILES = # When a file name is specified after GENERATE_TAGFILE, doxygen will create # a tag file that is based on the input files it reads. GENERATE_TAGFILE = # If the ALLEXTERNALS tag is set to YES all external classes will be listed # in the class index. If set to NO only the inherited external classes # will be listed. ALLEXTERNALS = NO # If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed # in the modules index. If set to NO, only the current project's groups will # be listed. EXTERNAL_GROUPS = YES # The PERL_PATH should be the absolute path and name of the perl script # interpreter (i.e. the result of `which perl'). PERL_PATH = /usr/bin/perl #--------------------------------------------------------------------------- # Configuration options related to the dot tool #--------------------------------------------------------------------------- # If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will # generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base # or super classes. Setting the tag to NO turns the diagrams off. Note that # this option is superseded by the HAVE_DOT option below. This is only a # fallback. It is recommended to install and use dot, since it yields more # powerful graphs. CLASS_DIAGRAMS = YES # You can define message sequence charts within doxygen comments using the \msc # command. Doxygen will then run the mscgen tool (see # http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the # documentation. The MSCGEN_PATH tag allows you to specify the directory where # the mscgen tool resides. If left empty the tool is assumed to be found in the # default search path. MSCGEN_PATH = # If set to YES, the inheritance and collaboration graphs will hide # inheritance and usage relations if the target is undocumented # or is not a class. HIDE_UNDOC_RELATIONS = YES # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is # available from the path. This tool is part of Graphviz, a graph visualization # toolkit from AT&T and Lucent Bell Labs. The other options in this section # have no effect if this option is set to NO (the default) HAVE_DOT = YES # By default doxygen will write a font called FreeSans.ttf to the output # directory and reference it in all dot files that doxygen generates. This # font does not include all possible unicode characters however, so when you need # these (or just want a differently looking font) you can specify the font name # using DOT_FONTNAME. You need need to make sure dot is able to find the font, # which can be done by putting it in a standard location or by setting the # DOTFONTPATH environment variable or by setting DOT_FONTPATH to the directory # containing the font. DOT_FONTNAME = FreeSans # The DOT_FONTSIZE tag can be used to set the size of the font of dot graphs. # The default size is 10pt. DOT_FONTSIZE = 10 # By default doxygen will tell dot to use the output directory to look for the # FreeSans.ttf font (which doxygen will put there itself). If you specify a # different font using DOT_FONTNAME you can set the path where dot # can find it using this tag. DOT_FONTPATH = # If the CLASS_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect inheritance relations. Setting this tag to YES will force the # the CLASS_DIAGRAMS tag to NO. CLASS_GRAPH = YES # If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect implementation dependencies (inheritance, containment, and # class references variables) of the class with other documented classes. COLLABORATION_GRAPH = NO # If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen # will generate a graph for groups, showing the direct groups dependencies GROUP_GRAPHS = YES # If the UML_LOOK tag is set to YES doxygen will generate inheritance and # collaboration diagrams in a style similar to the OMG's Unified Modeling # Language. UML_LOOK = NO # If set to YES, the inheritance and collaboration graphs will show the # relations between templates and their instances. TEMPLATE_RELATIONS = NO # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDE_GRAPH, and HAVE_DOT # tags are set to YES then doxygen will generate a graph for each documented # file showing the direct and indirect include dependencies of the file with # other documented files. INCLUDE_GRAPH = NO # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDED_BY_GRAPH, and # HAVE_DOT tags are set to YES then doxygen will generate a graph for each # documented header file showing the documented files that directly or # indirectly include this file. INCLUDED_BY_GRAPH = YES # If the CALL_GRAPH and HAVE_DOT options are set to YES then # doxygen will generate a call dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable call graphs # for selected functions only using the \callgraph command. CALL_GRAPH = NO # If the CALLER_GRAPH and HAVE_DOT tags are set to YES then # doxygen will generate a caller dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable caller # graphs for selected functions only using the \callergraph command. CALLER_GRAPH = NO # If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen # will graphical hierarchy of all classes instead of a textual one. GRAPHICAL_HIERARCHY = NO # If the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are set to YES # then doxygen will show the dependencies a directory has on other directories # in a graphical way. The dependency relations are determined by the #include # relations between the files in the directories. DIRECTORY_GRAPH = YES # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images # generated by dot. Possible values are png, jpg, or gif # If left blank png will be used. DOT_IMAGE_FORMAT = png # The tag DOT_PATH can be used to specify the path where the dot tool can be # found. If left blank, it is assumed the dot tool can be found in the path. DOT_PATH = # The DOTFILE_DIRS tag can be used to specify one or more directories that # contain dot files that are included in the documentation (see the # \dotfile command). DOTFILE_DIRS = # The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of # nodes that will be shown in the graph. If the number of nodes in a graph # becomes larger than this value, doxygen will truncate the graph, which is # visualized by representing a node as a red box. Note that doxygen if the # number of direct children of the root node in a graph is already larger than # DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note # that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH. DOT_GRAPH_MAX_NODES = 50 # The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the # graphs generated by dot. A depth value of 3 means that only nodes reachable # from the root by following a path via at most 3 edges will be shown. Nodes # that lay further from the root node will be omitted. Note that setting this # option to 1 or 2 may greatly reduce the computation time needed for large # code bases. Also note that the size of a graph can be further restricted by # DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction. MAX_DOT_GRAPH_DEPTH = 0 # Set the DOT_TRANSPARENT tag to YES to generate images with a transparent # background. This is disabled by default, because dot on Windows does not # seem to support this out of the box. Warning: Depending on the platform used, # enabling this option may lead to badly anti-aliased labels on the edges of # a graph (i.e. they become hard to read). DOT_TRANSPARENT = NO # Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output # files in one run (i.e. multiple -o and -T options on the command line). This # makes dot run faster, but since only newer versions of dot (>1.8.10) # support this, this feature is disabled by default. DOT_MULTI_TARGETS = YES # If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will # generate a legend page explaining the meaning of the various boxes and # arrows in the dot generated graphs. GENERATE_LEGEND = YES # If the DOT_CLEANUP tag is set to YES (the default) Doxygen will # remove the intermediate dot files that are used to generate # the various graphs. DOT_CLEANUP = YES mips-1.0/doc/DoxygenLayout.xml0000644002402200254040000001360512520407731015744 0ustar thomasfdtaig mips-1.0/doc/dox/0000755002402200254040000000000012520407731013174 5ustar thomasfdtaigmips-1.0/doc/dox/modules/0000755002402200254040000000000012520407731014644 5ustar thomasfdtaigmips-1.0/doc/dox/modules/labeledGraph/0000755002402200254040000000000012520407731017216 5ustar thomasfdtaigmips-1.0/doc/dox/modules/labeledGraph/status.dox0000644002402200254040000000144112520407731021255 0ustar thomasfdtaig/*! \page lg_status Labeled Graph status Labeled Graph - Description: - Main responsable: Thomas Fannes - Documentation - Doxygen status + date: Not done - External documentation review: Not done - Code - Namespace/folder: - Main class(es): - Main directories/files: - Unit tests: - Code level + date: Can be one of the following: - Under development: Code is garanteed not to work or/and to change - Experimental: Code will compile but not garanteed to work - Normal: Code should work, any malfunctions should be reported - Mature: Code is used in many example, malfunctions are most likely not happening anymore - External review: Not done - Examples & applications - Examples: - Applications: */ mips-1.0/doc/dox/modules/labeledGraph/overview.dox0000644002402200254040000000016412520407731021601 0ustar thomasfdtaig/*! \page lg_module Labeled Graph \subpage lg_status \tableofcontents \section lg_overview Labeled Graph */ mips-1.0/doc/dox/modules/decisionforestLearner/0000755002402200254040000000000012520407731021175 5ustar thomasfdtaigmips-1.0/doc/dox/modules/decisionforestLearner/forestLearner.dox0000644002402200254040000000720512520407731024530 0ustar thomasfdtaig/*! \page fl_module Decision Forest Learner \tableofcontents \section dfl_overview Overview A decision forest learner uses decision tree learning, but in a randomized way. There are two decision forest learners available: - \link forest_random_fraction Random Fraction Forest Learner\endlink: Learns a forest with a random fraction of the test collecion. - \link probabilistic_forest Probabilistic Forest Learner\endlink: Learns a forest where the test are chosen proportionally to their heuristic score. If you have not read the information from the decision tree learner, please do this \ref tl_module "now". \subsection forest_base Basic Forest Learner The basic forest tree learner is defined in \link mips::prediction::decision_forest::DepthFirstForestLearner mips/classifier/decisionForest/depthFirstForestLearner.hpp \endlink and is defined completely similar as decision tree learner, but instead of learning one tree, the learner learns a predefined number of trees: \subsection forest_random_fraction Random Fraction Forest Learner The random fraction forest learner choses for each node in each tree a random fraction of the tests, and the best node in that fraction is selected to expand the tree. \subsection probabilistic_forest Probabilistic Forest Learner The probabilistic forest learner choses for each node in each tree a test proportionally to its heuristic value. \subsection dt_iris Using a classification forest We will adapt the iris example to use a classification forest. We will no longer be using the decisionTree2/defaultLearner.hpp but now we will be using the decisionForest/randomFractionForest.hpp \code #include \endcode All the test, target attribute extractor, etc, can be reused. The only thing that changes is the traits class, instead of mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits, we will use mips::prediction::decision_forest::RandomFractionForestLearnerTraits. \code typedef mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits< ThresholdTest *, // the test type, polymorphic thus a pointer IrisExample, // the example type TargetAttributeExtractor, // the target attribute extractor mips::prediction::decision_tree::NoPrePruning // we do not want to have minimum leaf size > LearnerTraits; \endcode becomes \code typedef mips::prediction::decision_forest::RandomFractionForestLearnerTraits< ThresholdTest *, // the test type, polymorphic thus a pointer IrisExample, // the example type TargetAttributeExtractor, // the target attribute extractor mips::prediction::decision_tree::NoPrePruning // we do not want to have minimum leaf size > LearnerTraits; \endcode and with these small changes, we have an up and running decision forest. Changing randomFractionForest.hpp to probabilisticForest.hpp and mips::prediction::decision_forest::RandomFractionForestLearnerTraits to mips::prediction::decision_forest::ProbabilisticForestLearnerTraits will change the way how the forest is learned. \section reading Further reading Take a look at the mips::prediction::decision_forest namespace for more information. */ mips-1.0/doc/dox/modules/complexGroupAlgebra/0000755002402200254040000000000012520407731020606 5ustar thomasfdtaigmips-1.0/doc/dox/modules/complexGroupAlgebra/status.dox0000644002402200254040000000237012520407731022647 0ustar thomasfdtaig/*! \page cga_status Complex group algebra status Complex group algebra - Description: This module provides an implementation of a finite field, comprising of the set of polynomials of degree at most n-1, modulo an irreducible polynomial of degree n. Further it also includes implementation of the ring, when the finite field is combined with a group comprising of k-bit binary vectors with component-wise addition modulo 2 as the group operation. The module is mainly used in \link ntm_module tree mining\endlink, in Koutis & Williams algorithm. - Main responsable: Ashraf - Documentation - Documentation-page: \link cga_module Documentation \endlink - Doxygen status: In progress (30 Apr 2015) - External review: Not done - Code - Namespace/folder: mips::core::group - Main class(es): mips::core::group::Field, mips::core::group::GF2l_Z2k - Unit tests: mips/core/group/test/testGF2l_Z2k.cpp, mips/core/group/test/testGF2Labels.cpp - Code level: - Mature: Finite fields - Under development: Rings - External review: Not done - Examples & applications - Examples: None - Applications: None */ mips-1.0/doc/dox/modules/complexGroupAlgebra/overview.dox0000644002402200254040000002301412520407731023170 0ustar thomasfdtaig/*! \page cga_module Complex group algebra The status: \subpage cga_status \section cga_overview Overview The module basically provides: - an implementation of finite field (Galois Fields, GF) of characterisitic 2 and length 2^l, realised as a set of binary polynomials of degree at most l-1 modulo an irreducible polynomial of degree l. - An implementation of a ring, when the above finite field is combined with a group of k-bit binary vectors, with component-wise addition modulo 2 as their group operation (GF2l_Z2k). The complex group algebra module is mainly used in \link patternMatching Pattern Matching \endlink algorithm of Koutis & Williams. \section field_use Using Finite Field To use the finite field import: \code #include \endcode Objects of this class can then be created as follows (field has length 2^l): \code Field f = Field(l); \endcode All values of the field are of type Field::value (which itself is of type 'typedef uint8_t value;'). To request objects of Field::value, use: \code Field::value fval1 = f.zero(); //or Field::value fval2; f.zero(fval2); \endcode Arithmetic on Field::value's is performed using the following methods: \code Field::value add(Field::value a, Field::value b); Field::value subtract(Field::value a, Field::value b); Field::value multiply(Field::value val1, Field::value val2); \endcode Note, that: - subtraction in a characteristic 2 field is the same as addition - we do not have a division method implmented yet In addition to the above, some utility printing methods are also provided for introspection: \code //if we have a polynomial (x^2+x+1), it is: std::string toStrInt(value val) const; //printed as 7 std::string toStrBinary(value val) const; //printed as 0111 std::string toStrPoly(value val) const; //printed as x^2+x+1 //e.g. cout << "fval: " << f.toStrPoly(fval) << endl; \endcode \section rings_use Using the Ring (GF2l_Z2k) To use ring import: \code #include \endcode There are two implementations of this ring: - one is static; and - the other dynamic (so its size is determined at runtime) Objects of the above two implementations (for field length 2^l and vectors of lenght k) can be requested by : \code GF2l_Z2k_Static gf; // or GF2l_Z2k_Dynamic gf(k,l); \endcode All objects of the above ring are of type 'GF::value' (where GF is either GF2l_Z2k_Static or GF2l_Z2k_Dynamic). The neutral elements can be requested using the methods zero() or one(), or random elements using the random() method. For e.g.: \code // for static GF2l_Z2k_Static<3, 2>::value gval; // or for dynamic GF2l_Z2k_Dynamic::value gval; gf.zero(gval); //sets the value to 0 // or gf.one(gval); //sets the value to 1xW_0, where W_0 is the zero vector \endcode *IMPORTANT*: Objects of this class MUST be initialized properly with the zero(.) or one(.) methods before use. Arithmetic operations for this ring can be performed using the following methods: \code void add(value& v, const value& a, const value& b); //v=a+b void add(value& v, const value& a); //v=v+a; void subtract(value& v, const value& a, const value& b); //v=a-b void subtract(value& v, const value& a); //v=v-a; void multiply(value& v, const value& a, const value& b); //v=a+b \endcode Random elements from this ring can be requested through the following methods: \code gval = gf.random(); //returns a value 1xW_0 + 1xVi - where Vi is a random k-bit vector. // or gf.zero(gval); gf.random(gval); //sets the value to 1xW_0 + 1x_Vi (we need to initialize gval with zero() before use) //scalars gf::fieldVal fval; fval = gf.randomScalar(); // returns a random coefficient (polynomial) from the GF field gf.multiplyScalar(gval, fval); //sets the value to fvalxW_0 + fvalxV_i, e.g. to (x^2+x+1)xW_0 + (x^2+x+1)xV_i, if fval=(x^2+x+1) \endcode Note, that: - subtraction in this ring is also the same as addition - we do not have division method implmented yet In addition to the above, some utility and printing methods are also provided for introspection: \code //**Printing //if we have a polynomial (x+1)[011] + (x)[110], std::string toStrInt(const value& v) const; //prints it like: (3)[3] + (2)[6] std::string toStrBinary(const value& v) const; //prints it like: (011)[011] + (010)[110] std::string toStrPoly(const value& v) const; //prints it like: (x+1)[3] + (x)[6] std::string toStrPolyBinary(const value& v) const;//prints it like: (x+1)[011] + (x)[110] //e.g. cout << "gval: " << gf.toStrPolyBinary(gval) << endl; //**Other utility methods void setSeed(unsigned seed); //sets the random number seed unsigned int getSeed() const; //gives the random number seed ///**only available in GF2l_Z2k_Dynamic unsigned getFieldSize() const; //returns l unsigned getVectorSize() const; //returns k \endcode \section optimized_version Optimized GF2l_Z2 ring A specialized class GF2l_Z2k_Dynamic_LabelOptimized \code template< typename _RandEng = boost::random::mt11213b, typename _RandDist = boost::random::uniform_int_distribution > class GF2l_Z2k_Dynamic_LabelOptimized; \endcode for GF2l_Z2k_Dynamic is also available. Its main purpose is to allow for optimizations involving different sized GF2l_Z2k values (for e.g. in graph matching, in case of colored pattern graph, we can use < k sized values for different sets of colored vertices, thereby reducing complexity of GF2l_Z2k algebraic operations). GF2l_Z2k_Dynamic_LabelOptimized allows to: - Request smaller GF2l_Z2k random values (smaller than k) - Add/multiply differently sized GF2l_Z2K values (with differing k) - Resize values GF2l_Z2k values to make them smaller/bigger - Clear bits in bit vectors of a GF2l_Z2k value - Permute bits in the bit vectors of a GF2l_Z2k value (move bits around in value 'a' - to match bit positions (of different colored vertex sets) with 'another' GF2l_Z2k value 'b') Specialized methods in >GF2l_Z2k_Dynamic_LabelOptimized: \code //**Get random values with bits at most k (supplied paramater is 2^k) value random(gflong max_2k) const; //returns a new value 1xW_0 + 1xV_i, where Vi is a random k-bit vector. void random(value& v, gflong max_2k) const; //sets a value as 1xW_0 + 1xV_i // Resizes a GF2l_Z2k value -- size is assumed to be new 2^k, NOT new k void resize(value &result, gflong size); //Transforms a *newly* drawn random value, i.e. performs a single shift to shift the bits to their //correct/needed position. // E.g.: val = gf.random(8); //assume this gives (W_0 + Vi) with Vi=[111] // gf.transform(val, 2); //111 --> 11100 void transform(value &result, unsigned shift); //Clears bits. // @param size - number of bits to clear / zero // @param startpos - start position (least significant bit) to clear from // E.g.: gfd.clearbits(val, 2, 2); //1111 --> 0011 void clearbits(value &result, gflong size, gflong startpos); //Clears bits (as above), but takes a list/vector of positions to clear. // @param to clear - vector containing a list of (std::pair(size, startpos)) sizes and start positions to clear. // E.g.: //11111 --> 10111 --> 10101 // vector< pair > toclear; // toclear.push_back(pair(1,3)); toclear.push_back(pair(1,1)); // gf.clearbits(val, toclear); // void clearbits(value &result, const std::vector< std::pair > &toclear); // Struct used below struct GF2Permute { int size; int oldpos; int newpos; GF2Permute(int s, int o, int n) : size(s), oldpos(o), newpos(n) {} ... }; //Permute a given value (2^k vectors) according to the given permutation. // @param permutation - vector containing permute objects specifying // which bit segments/blocks should move where. // E.g.: // 01 11 11 --> 11 11 01 (shift 0 to 4 and 4 to 0 -- don't touch middle) // std::vector< GF2Permute > permutations; // permutations.push_back( GF2Permute(2, 0, 4) ); // permutations.push_back( GF2Permute(2, 4, 0) ); // gf.permuteBlocks(val1, permutations); void permuteBlocks(value &result, const std::vector &permutation) const; //Permute a given value (as above). // @param permutation - vector specifying which *individual* bit positions should be move where. // E.g. 1: // // 01 11 11 --> 11 11 01 (shift 2 bits from 0 to 4 and 4 to 0 -- don't touch middle) // std::vector permutations; permutations.resize(6); // permutations[0]=4; permutations[1]=5; // permutations[2]=2; permutations[3]=3; // permutations[4]=0; permutations[5]=1; // gfd.permuteBits(val, permutations); // E.g. 2: // // 00 01 11 --> 11 00 01 (shift 2 bits from 0 to 4 and 2 to 0) // permutations.clear(); permutations.resize(6); // permutations[0]=2; permutations[1]=3; // permutations[2]=-1; permutations[3]=-1; //leave these as zero (i.e. don't need to specify all bits) // permutations[4]=0; permutations[5]=1; // gfd.permuteBits(val1, permutations); void permuteBits(value &result, const std::vector &permutation) const; //Shifts *all* bits to the left or right. Can be used //after clearbits(), to shift the remaining bits to 'pack' them together in a block // // @param shiftfrom - old position to move from // @param shiftto - new position to move to // Example1: // 10101 --> 01011 //gfd.shiftAfterClear(val, 2, 1); // 01011 --> 00111 //gfd.shiftAfterClear(val, 3, 2); // Example2: // 110011 --> 1111 //gfd.shiftAfterClear(val1, 4, 2); // void shiftAfterClear(value &result, unsigned shiftfrom, unsigned shiftto) {} \endcode */ mips-1.0/doc/dox/modules/decisionTreeLearner/0000755002402200254040000000000012520407731020572 5ustar thomasfdtaigmips-1.0/doc/dox/modules/decisionTreeLearner/decisionTreeLearner.dox0000644002402200254040000004404012520407731025236 0ustar thomasfdtaig/*! \page tl_module Decision Tree Learner \tableofcontents \section dt_introduction Introduction Decision tree learning is a method commonly used in data mining. The goal is to create a model that predicts a target value for an example based on a set of labeled examples. The learned model is a tree, where target value prediction is assured by following a path on the tree towards a leaf node, and where the leaf node decides the target value of that example. The path on the tree is decided by a test stored at each node, which results into fast prediction of new examples. The decision tree module uses different layers: The bottom layer, the generic learner, is highly flexible. On the downside, the flexibility demands the use of correct concepts, template C++ programming knowledge and a steep learning curve. Therefore, on top of the generic learner, there is a default decision tree learner, which is still generic, but it imposes a few restrictions. We advise all users to first look at the documentation of the default decision tree learner before trying to use the generic learner. \section dt_DefaultDecisionTreeLearner The default decision tree learner \subsection dt_ddtl_overview Overview A default decision tree learner is created by using the mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits class, which will learn a decision tree depth-first. The modularity is defined by the template parameters which the user can specify (or use the default value). In this overview we only consider the first 4 template parameters. More information on the other 6 template parameters can be found \link mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits here\endlink. | Template position | Name | Description | | - | - | - | | 1 | Test | Should follow the \link concepts::mips::prediction::decision_tree::Test Test\endlink concept. This type defines the base test type. Note that polymorphic tests are allowed, it is possible to use pointers to objects as a test. In this case the test type should be the pointer type. | 2 | Example | The example type. | 3 | TargetAttributeExtractor | Extract the target value from an example. This class should follow the \link concepts::mips::prediction::AttributeExtractor AttributeExtractor\endlink-concept. Especially the AttributeType is important, it defines the default heuristic to be used. | 4 | PrePruner | The prepruner is used to prune tests while constructing the tree. This class should follow the \link concepts::mips::prediction::decision_tree::PrePruner PrePruner\endlink-concept. By default the mips::prediction::decision_tree::MinimumLeafSizePruner is used and this is set to a minimum leaf size of 10. Altough these template parameters allow for a generic approach, the impose a few restriction on the decision trees: - The decision trees are limited to binary trees, where each node has a "yes" and a "no" branch. If the test evaluates to true on the example the "yes" branch is followed, otherwise the "no" branch is followed. - Based on the target value, we learn two types of decision trees: If the target value is nominal, (e.g., red, green or blue), we learn a classification decision tree and use the information gain splitting criterium. If the target value is ordinal (e.g., small, medium, large) or numerical (2.3, -17, 22), we learn a regression decision tree and use variance reduction. In this document, the iris section learns a classification decision tree and the boston section learns a regression decision tree. \subsection dt_ddtl_structure Structure The default tree learner traits class exposes the following types: - Learner: The decision tree learner - Model: The actual decision tree - Classifier: A functor to predict the target value for an (unseen) example - ModelPrinter: A functor for printing out a human-readable form of the model. This types will expose the following associated types and/or methods: - **Learner** | Associated types || | - | - | | Model | The model learned by the learner | | TestContainer | A std::vector like container | | ExampleContainer | A std::vector like container | | PrePruner | The prepruner as passed on to the learner traits | | TargetValueExtractor | The target value extractor type | | Associated methods || | ------ | ------------- | | *Constructors* || | Learner() | Default constructable | | *Property methods* || | TestContainer & testContainer() | Allows access to the container containing the tests | | const TestContainer & testContainer() const | Allows acces to the container containing the tests | | ExampleContainer & exampleContainer()| Allows access to the container for examples | | const ExampleContainer & exampleContainer() const | Allows access to the container for examples | | const PrePruner & prePruner() const | Allows access to the pre pruner | | PrePruner & prePruner() | Allows access to the pre pruner | | int maxNumberOfExamples() const | Gets the maximum number of examples to process (e.g., when learning from an infinite stream). The default value of -1 means all the examples | | void setMaxNumberOfExamples(int newMax) | Sets the maximum number of examples to process (e.g., when learning from an infinite stream). A value of -1 means all the examples. This is the default value. | | *Learning method* || | Model operator()() const | Learns a model on the supplied tests and examples with a default constructed target value extractor | | Model operator()(const TargetValueExtractor & extractor) const | Learns a model on the supplied tests and examples with the specified target value extractor | - **Model** | Associated methods || | - | - | | boost::archive & operator&(boost::archive &, const Model & model) | Uses boost serialization to write out a serialized version of the model | | boost::archive & operator&(boost::archive &, Model & model) | Uses boost serialization to read in a serialized version of the model | See \ref serialz for more information on serialization - **Classifier** | Associated types || | ------------------ | ----------- | | result_type | The output of a prediction | | Associated methods || | ------- | ------------ | | Classifier(const Model * model) | A classifier is constructed by passing a const pointer to the model. The classifier does not take ownership of the model. | | result_type operator()(cont Example & example) const | Predicts the target value for an example. | - **ModelPrinter** | Associated methods || | ------- | ------------ | | ModelPrinter() | Default constructable | | void operator()(const Model & model, std::ostream & stream) | Writes a human readable representation of the model to the supplied stream | In the next section, we show how to configure, learn and use, both classification and regression,decision trees based on the decision tree module. Building of the decision tree can be broken down into different parts: defining the tests, the target attribute, and learning the tree. \subsection dt_ddtl_iris Building classification trees This section will show how to construct a classification tree. To start include the following headers \code #include #include \endcode The default learner contains a default learner configuration class. By default this class uses the minimumLeafSizePruner, but as we don't want any pruning, we will also need the noPrePruning class. Next we will define our example. In this case we have as example lengths and width from the leaves of an iris, and we want to predict which type of iris this is: \code struct IrisExample { enum IrisType { Iris_setosa = 1, Iris_versicolor = 2, Iris_virginica= 3, Invalid = 4 }; float sepalLength; float sepalWidth; float petalLength; float petalWidth; IrisType irisClass; }; \endcode __Implementing the tests__ As we want different types of test (polymorphism), we will start by creating a base class. \code class ThresholdTest { public: ThresholdTest(float val = 0.0f) : _val(val) {} // the necessary function for a test bool operator()(const IrisExample & example) const { return extractAttribute(example) <= threshold(); } // the abstract functions each subclass should implement virtual float extractAttribute(const IrisExample & example) const = 0; virtual std::string name() const = 0; float threshold() const { return _val; } private: float _val; }; \endcode Inheriting from the base class, we implement the actual tests: \code class TestSepalLength : public ThresholdTest { public: TestSepalLength(float val = 0.0f) : ThresholdTest(val) {} virtual float extractAttribute(const IrisExample & example) const { return example.sepalLength; } std::string name() const { return "sepalLength"; } }; class TestSepalWidth : public ThresholdTest { public: TestSepalWidth(float val = 0.0f) : ThresholdTest(val) {} virtual float extractAttribute(const IrisExample & example) const { return example.sepalWidth; } std::string name() const { return "sepalWidth"; } }; class TestPetalLength : public ThresholdTest { public: TestPetalLength(float val = 0.0f) : ThresholdTest(val) {} virtual float extractAttribute(const IrisExample & example) const { return example.petalLength; } std::string name() const { return "petalLength"; } }; class TestPetalWidth : public ThresholdTest { public: TestPetalWidth(float val = 0.0f) : ThresholdTest(val) {} virtual float extractAttribute(const IrisExample & example) const { return example.petalWidth; } std::string name() const { return "petalWidth"; } }; \endcode __Implementing the target attribute extractor__ We have now defined our example, our test class and nextly we will define a target attribute extractor. \code struct TargetAttributeExtractor : public std::unary_function { typedef mips::prediction::NominalAttributeType AttributeType; IrisExample::IrisType operator()(const IrisExample & example) const { return example.irisClass; } }; \endcode As we want to learn the iris-type, the target attribute is Nominal, and therefore we define AttributeType of type mips::prediction::NominalAttributeType. __Running the decision tree__ Okay, now it is time to put everything together: \code int main() { // get two data readers for training and test data // ... (code not shown) DataReader iris_training_data(&iris_training_file); DataReader iris_test_data(&iris_test_file); typedef mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits< ThresholdTest *, // the test type, polymorphic thus a pointer IrisExample, // the example type TargetAttributeExtractor, // the target attribute extractor mips::prediction::decision_tree::NoPrePruning // we do not want to have minimum leaf size > LearnerTraits; \endcode We defined the traits class with our test (as a polymorphic pointer), our example type, the target attribute extractor. Furthermore, as we don't have a huge amount of data, we will disable prepruning. \code // time to create the learner LearnerTraits::Learner treeLearner; // and fill in the tests for(double threshold = 0.0; threshold <= 7.0; threshold += 0.1) { treeLearner.testContainer().push_back(new TestSepalLength(threshold)); treeLearner.testContainer().push_back(new TestSepalWidth(threshold)); treeLearner.testContainer().push_back(new TestPetalLength(threshold)); treeLearner.testContainer().push_back(new TestPetalWidth(threshold)); } // now the examples treeLearner.exampleContainer().assign(iris_training_data.current(), iris_training_data.end()); \endcode The components of a decision tree can be accessed through the component function. We access the internal containers to set the tests and the examples In the next part we will learn a tree and print out the tree to std::out \code // let's learn a tree LearnerTraits::Model tree = treeLearner(); // and write it to std::out LearnerTraits::ModelPrinter treePrinter; treePrinter(tree, std::cout); \endcode And now we will use the classifier to classify an unseen example using the tree \code // classify an external dataset LearnerTraits::Classifier classifier(&tree); for(DataReader::iterator it = iris_test_data.current(); it != iris_test_data.end(); ++it) { const IrisExample & iris = *it; IrisExample::IrisType predictedType = classifier(iris); std::cout << " Actual Category: " << categoryToLabel(iris.irisClass) << std::endl << " -> Predicated category: " << categoryToLabel(predictedType) << std::endl << std::endl; } return 0; } \endcode \subsection boston Building a regression tree \dontinclude "prediction/common/boston.hpp" The previous example showed how to build a decision tree for nominal data. In this example we show how to build a decision tree for numerical data using the [Boston data set](https://archive.ics.uci.edu/ml/datasets/Housing). We would like to learn a decision tree for predicting the median value of owner-occupied homes. Before, we can use our decision tree we first need to configure it. __Configuring the decision tree__ To configure the decision tree module we need to provide it with the type of the container that will hold an example(training data), how to access the target attribute( the feature we are trying to predict ) and the tests for each feature that will be used to split the dataset. We start by first declaring a container for holding each example in the dataset. \skipline BostonExample \line BostonExample \until } Second, we implement the tests needed for separating the data. \skipline ThresholdTest \until }; Third, we implement the target attribute extractor. \skipline TargetAttributeExtractor \until }; Note, as our target attribute is numerical we have defined the AttributeType to be mips::prediction::NumericalAttributeType. Before we implement the main function to build and run our decision tree we digress a bit to implement a helper function. Given a feature, the function will compute the threshold for the corresponding test. \dontinclude "prediction/decisionTree/boston.cpp" \skipline calculateFeatureThresholds \until } Finally, we build and run the decision tree. \skipline main \skipline &boston_training_file \skipline &boston_test_file \until LearnerTraits; We define a set of DataReaders to read in the data set from file and we configure the decision tree learner class. Next, we create an object of the learner and add the data and the tests. \skipline Learner \until } We are done! __Learning the tree__ We can now learn the tree on our training data and print it out. \skipline Model \until treePrinter(tree, std::cout); __Using the tree__ Now, we can make predictions using the learned tree. \skipline Classifier \until } \until } \section serialz Serialization It is wasteful to relearn a decision tree every time we need to classify a new set of unseen data; especially if the time taken to learn the tree is large. Serialization allows a reversible deconstruction of the decision tree such that the tree can be stored and retrieved as required. A serialized tree is written to and read from an *archive*. \subsection tree_serialz Serializing a decision tree. To enable serialization support for a decision tree we need to include the following headers: \snippet iris.cpp tree_serialz \snippet iris.cpp ar_serialz The first include enables writing to an archive while the second, enables reading from an archive. To write a decision tree to a given stream, we do the following : \snippet iris.cpp tree_ser_out Similary, to read in a serialized tree, we do: \snippet iris.cpp tree_ser_in \subsection test_serialize Serializing tests. As the nodes of a decision tree are the implemented tests, we show, in this section, how to use the boost::serialization module to support serialization. We need to include the following headers: \snippet iris.cpp test_header Then implementing serialization support in a test is as simple as inserting a line in your implementation: \dontinclude iris.cpp \skipline ThresholdTest \until { \skipline serialize \skipline } \skipline TestSepalLength \until { \skipline serialize \skipline } It is important to note that we added serialization support to both the base *and* the derived class. \subsection poly_serialize Serializing inherited tests. In the last section, we added serialization support to a test. But if there are multiple test classes inheriting from a common base class then the above change is not enough. We need to let the serialization framework know the difference between the different derived classes. To achieve this we need to include additionally the following header. \snippet iris.cpp poly_header and finally implement the following line for each test class inheriting from the base. \skipline BOOST_CLASS This would make the test class serializable. Please refer to the [BOOST serialization] (http://www.boost.org/doc/libs/1_55_0/libs/serialization/doc/index.html) documentation for more information. \section apps Applications This section lists applications generated using the decision tree framework developed above. - DTLoG \subsection neighnorJoining Probability tree */ mips-1.0/doc/dox/modules/storagePolicy/0000755002402200254040000000000012520407731017470 5ustar thomasfdtaigmips-1.0/doc/dox/modules/storagePolicy/status.dox0000644002402200254040000000143212520407731021527 0ustar thomasfdtaig/*! \page sp_status Storage Policy status Storage Policy - Description: The storage policy is there to allow the code-user and not the code-developper to decide the way how to store elements. - Main responsable: Thomas Fannes - Documentation - Doxygen status + date: Up to date (August 19, 2014) - External documentation review: Not done - Code - Namespace/folder: mips/utilies/storeBy*.hpp - Main class(es): mips::utilies::storeByValue, mips::utilies::storeBySmartPointer, mips::utilies::storeByPointer, mips::utilies::storeByInheritance - Main directories/files: mips/utilities - Unit tests: - Code level + date: Mature (August 19, 2014) - External review: Not done - Examples & applications - Examples: - Applications: */ mips-1.0/doc/dox/modules/storagePolicy/overview.dox0000644002402200254040000001113412520407731022052 0ustar thomasfdtaig/*! \page sp_module Storage Policy \subpage sp_status \section sp_overview Storage Policy The storage policy concept and the different implementations are brought to life to deal with differences in, on one hand storing, and, on the other hand, using data. Imagine a generic algorithm that needs access to a certain data type. Depending on the data type, the user wants to decide to store this data bye value, share over different elements, etc. This choice should not be a part of the algorithm, but rather a choice of the user. The storage policy concept and the different implementations of this concept are there to overcome this problem. ## The storage policy concept ## A storage policy is a templated class, which has the following methods/typedefs: template struct StoragePolicy; Name | Description -----|------------ value_type | The stored value type StoragePolicy() | A default constructor StoragePolicy(...) | A constructor for directly initializing the storage. This is implementation specific, see below for more information. static StoragePolicy SafeInitialise() | Constructs a StoragePolicy object and tries to initialise it as safe as possible without causing memory leaks. value_type & get() | A mutable accessor to the stored object const value_type & get() const | A non-mutable accessor to the stored object void set(...) | A setter, the type is implementation specific, see below for more information. ## Implementation specifics ## There are four implementations for a storage policy: - \ref mips::utilities::StoreByValue StoreByvalue: The element type is stored by value - \ref mips::utilities::StoreByInheritance StoreByInheritance: The storage inherits publicly from the supplied element type - \ref mips::utilities::StoreByPointer StoreByPointer: The element type is stored as a pointer (or a const pointer), no memory management. When supplying a const type to the storeByPointer class, the mutable accessor method is not available. - \ref mips::utilities::StoreBySmartPointer StoreBySmartPointer: The elements is stored as a smart pointer, and thus includes memory management. Type | Accepted types | Results --|--|-- StoreByValue | none | Calls the default constructor of value_type, should be available   | const value_type & | Copies the value_type, copy constructor should be available StoreByInheritance | none | Calls the default constructor of value_type, should be available   | const value_type & | Copies the value_type, copy constructor should be available StoreByPointer | none | Sets the pointer to a null pointer (get() will result int undefined behaviour )   | value_type * | Sets the pointer value (no memory management) StoreBySmartPointer | none | Sets the smart pointer to a null pointer (get() will result int undefined behaviour )   | boost::shared_pointer & | Sets the smart pointer to the supplied value   | const value_type & | The smart pointer will point to a newly copy-constructed variable (from the supplied value) on the heap The safe initialise method has different effects depending the used concept implementation: Concept implementation | SafeInitialise action ---|--- StoreByValue | Sets the stored element to the default constructed value StoreByInheritance | Sets the stored element to the default constructed value StoreByPointer | Sets the stored element to a null pointer StoreBySmartPointer | Sets the stored element to a newly allocated element on the heap by using the default constructed value ## Recommended usage ## When using a storage policy in an algorithm we recommend the following usage: - make it clear in a template parameter that you are using the storage policy. Also, provide a default storage policy implementation. Normally StoreByValue or StoreBySmartPointer are good choices. \code template < ..., template class DataBankStoragePolicy = ... > struct UsageExample { ... private: DataBankStoragePolicy dataBank_; }; \endcode - Make it clear in the documentation that which type of initialisation you are using: The storage policy default constructor or SafeInitialise. - Passing the right type to the storage policy class: \code template void setDataBank(const DatabaseSetType & databank) { dataBank_.set(databank); } \endcode - If necessary for the algorithm also have templated constructor \code UsageExample() : dataBank_(DataBankStoragePolicy::SafeInitialise()) { } template UsageExample(const DatabaseSetType & databank) : databank_(databank) { } \endcode **/ mips-1.0/doc/dox/modules/overview.dox0000644002402200254040000000233012520407731017224 0ustar thomasfdtaig/*! \page modules_overiew Module overview \tableofcontents \section mod_overview_main Module Overview The following modules are documented in mips: Core data types and methods - \subpage cga_module Core graph functionality - \subpage lg_module Miners - \subpage ntm_module Classifiers - \subpage tl_module - \subpage fl_module Utilies - \subpage sp_module \section mod_default_empty Adding new documentation for modules We use a default structure to store the module. A module template can be be created by running the python file create_module_doc.py in the directory mips/doc/dox, with one argument, i.e., the long name of the module. For example, when creating documentation for a new module "Combining Spam and Bacon in logarithmic time", you do the following: - cd mips/doc/dox - python create_module_doc.py "Combining Spam and Bacon in logarithmic time" and afterwards you have to edit the files in ~/programming/mips/doc/dox/modules/combiningSpamAndBaconInLogarithmicTime, update status.dox and overview.dox. Also do not forget to add the file to the module be means of a subpage, this keeps everything nice and structured. */ mips-1.0/doc/dox/modules/networkTreeMiner/0000755002402200254040000000000012520407731020150 5ustar thomasfdtaigmips-1.0/doc/dox/modules/networkTreeMiner/status.dox0000644002402200254040000000131512520407731022207 0ustar thomasfdtaig/*! \page ntm_status Network Tree Miner status Network Tree Miner - Description: This module mines frequent rooted trees in large networks (as frequent patterns and association rules). - Main responsable: ashraf - Documentation - Doxygen status: Up-to-date (30 Apr 2015) - External documentation review: Not done - Code - Namespace/folder: mips::pattern_mining - Main class(es): mips::pattern_mining::NetworkTreeMiner and mips::pattern_mining::NetworkRuleMiner - Unit tests: mips/patternMining/frequency/test/testTreesInNetwork.cpp - Code level: Normal - External review: Not done - Examples & applications - Applications: NetworkTreeMiner & NetworkRuleMiner */ mips-1.0/doc/dox/modules/networkTreeMiner/networkTreeMiner.dox0000644002402200254040000001537212520407731024200 0ustar thomasfdtaig/*! \page ntm_module Network Tree Miner \subpage ntm_status \tableofcontents \section ntm_overview Network Tree Miner This module allows for mining frequent rooted trees in large networks. There are two types of miners implemented: - \link tree_miner NetworkTreeMiner\endlink: Mines frequent rooted tree patterns in a network. - \link rule_miner NetworkRuleMiner\endlink: Mines association rules (where antecedant and consquent are rooted trees) in a network. \subsection tree_miner Mining Frequent Rooted Trees To mine frequent rooted trees include the following header: \code #include "mips/patternMining/tree/networkTreeMiner.hpp" \endcode Frequent patterns by mined by the following class defined in this header: \code // _Graph is any boost compliant graph class template class NetworkTreeMiner { ... NetworkTreeMiner(); ~NetworkTreeMiner() { } // outputs extra information on the screen while mining patterns for d>0 // amount of information increases with increasing d void setDebugLevel(short d); // if set, writes the frequent patterns found to the specified file std::string getResultsFile(); void setResultsFile(std::string); // if set writes statistics (runtime, freq. patterns found etc), to the specified file std::string getStatsFile(); void setStatsFile(std::string fname); //maing mining method void minePatterns(_Graph &graph, int pattern_size, float threshold, float success_prob, bool doExactCount, bool useUllman, bool doHomomorphism, bool doSampling, bool doFourier, bool runInBatch, int batchSize=100); ... } \endcode Example usage is as follows: \code .... Graph graph; NetworkTreeMiner miner; miner.setResultsFile(results_file); //optional miner.setStatsFile(stats_file); //optional miner.setDebugLevel(debug_level); //optional .... miner.minePatterns(graph, pattern_size, threshold, success_prob, doExactCount, useUllman, doHomomorphism, doSampling, doFourier, runInBatch, batchSize); \endcode The parameters required by the main mining method 'minePatterns' are as follows: - graph - Graph/network to mine patterns in - pattern_size - maximum size (no. of vertices) of trees to mine. - threshold - frequency threshold, mines all trees with frequency >= threshold. - success_prob - The success probability for Koutis&Williams method (the method is repeated sufficient number of times to achieve this success_prob) - doExactCount - If true, use a deterministic ismorphism method, VF2 (or Ullmans if useUllman below is true). If false, use Kouties&Willams randmized complex algebra based isomorphism method. - useUllman - If true, use Ullmans method instead of VF2 (if doExactCount is true). - doHomomorphism - If true, do cheaper homomorphism test first before more expensive in Kouties&Willams. - doSampling - If true, does random sampling and statistical test during homomorphism test in Koutis&Williams method (we skip further isomorphism test for a candidate pattern, if statistically a pattern is deemed infrequent) - doFourier - Use a fourier like transform Koutis&Williams (uses less memory) - runInBatch - If true, run batchmode for Kouties&Willams method. - batchSize - Size of the batches (used if runInBatch is set to true). For further details see our paper in \link reading Further reading\endlink. \subsection rule_miner Mining Tree Association Rules To mine frequent association rules (where both the antecedant and the consquent are rooted trees) include the following header: \code #include "mips/patternMining/tree/networkRuleMiner.hpp" \endcode The following class defined in this header mines the association rules: \code // _Graph is any boost compliant graph class class NetworkRuleMiner: public NetworkTreeMiner<_Graph> { .... NetworkRuleMiner(); ~NetworkRuleMiner() {} // if set, writes the association rules found to the specified file void setAssocFile(std::string fname); std::string getAssocFile(); // main mining method void mineRules(_Graph &G, int pattern_size, float thresh, float successprob, bool exactCount, bool useUllman, bool doHomomorphism, bool doSampling, bool runInBatch, int batchSize=100); ... } \endcode The parameters required by the main mining method 'mineRules' are as follows: - graph - Graph/network to mine association rules in - pattern_size - maximum size (no. of vertices) of trees (in consquent of the rule) to mine. - threshold - mines all rules with support greater than or equal to this threshold. - success_prob - The success probability for Koutis&Williams method (the method is repeated sufficient number of times to achieve this success_prob) - doExactCount - If true, use a deterministic ismorphism method, VF2 (or Ullmans if useUllman below is true). If false, use Kouties&Willams randmized complex algebra based isomorphism method. - useUllman - If true, use Ullmans method instead of VF2 (if doExactCount is true). - doHomomorphism - If true, do cheaper homomorphism test first before more expensive in Kouties&Willams. - doSampling - If true, does random sampling and statistical test during homomorphism test in Koutis&Williams method (we skip further isomorphism test for a candidate pattern, if statistically a pattern is deemed infrequent) - runInBatch - If true, run batchmode for Kouties&Willams method. - batchSize - Size of the batches (used if runInBatch is set to true). \section applications Applications Example front end applications for both \link tree_miner pattern miner\endlink and \link rule_miner association rule miner\endlink can be found under examples folder. They are NetworkTreeMiner and NetworkRuleMiner respectively. Further example applications include: - sanityChecks - Performs and reports consistency checks on a given graph/network. - graphStatistics - Reports various statistics on a given graph/network. - NetworkTreeMinerWithSampling - Same as NetworkTreeMiner, but allows sampline a specified number freq. patterns at each level of mining (see \link reading paper\endlink below). - EmbeddingsCounter - Counts and reports all ismorphic embeddings for a specified pattern graph in a specified network (uses VF2 or Ullmans). All of the above applications offer basic command line help to the user. \section reading Further reading For details see: A. Kibriya and J. Ramon, “Nearly exact mining of frequent trees in large networks”, Data Mining and Knowledge Discovery, vol. 27, no. 3, pp. 478–504, 2013. */ mips-1.0/doc/dox/references.dox0000644002402200254040000000056012520407731016032 0ustar thomasfdtaig/*! \page references References \section ref_dataset Datasets The following datasets are used in mips: - iris dataset [1] - boston dataset [1] \section ref_references References 1. Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. */ mips-1.0/doc/dox/status.dox.template0000644002402200254040000000142212520407731017044 0ustar thomasfdtaig/*! \page $SMALLNAME$_status $BIGNAME$ status $BIGNAME$ - Description: - Main responsable: $USER$ - Documentation - Doxygen status + date: Not done - External documentation review: Not done - Code - Namespace/folder: - Main class(es): - Main directories/files: - Unit tests: - Code level + date: Can be one of the following: - Under development: Code is garanteed not to work or/and to change - Experimental: Code will compile but not garanteed to work - Normal: Code should work, any malfunctions should be reported - Mature: Code is used in many example, malfunctions are most likely not happening anymore - External review: Not done - Examples & applications - Examples: - Applications: */ mips-1.0/doc/dox/create_module_doc.py0000644002402200254040000000327612520407731017213 0ustar thomasfdtaig#!/usr/bin/python import sys import subprocess import io import os def completeReplace(line, translation): for key,val in translation.items(): line = line.replace(key, val) return line def fillInTemplateFile(template, outStream, translation): for line in template: line = completeReplace(line, translation) outStream.write(line) def main(argv=None): if argv is None: argv = sys.argv if len(argv) != 2: print "Please supply the as argument the long, human readable module name, between quotes. Also run this program should be run in the dox directory" return 2 name = argv[1] camelName = name.split()[0].lower() + "".join(word.title() for word in name.split()[1:]) small = "".join(word[0].lower() for word in name.split()) user = subprocess.check_output("git config user.name", shell=True) translation = {'$USER$': user, '$BIGNAME$': name, '$SMALLNAME$' : small} #read in the two template files statusfile = open('status.dox.template', 'r') overviewfile = open('overview.dox.template', 'r') #create subdirectories dirname = "modules/"+camelName if os.path.exists(dirname): print "Directory \'" + dirname + "\' already exists, doing nothing" return 2 os.makedirs(dirname) #write the two files fillInTemplateFile(statusfile, open(dirname+"/status.dox", 'w'), translation) print "Status file created: \'" + dirname + "/status.dox\'" fillInTemplateFile(overviewfile, open(dirname+"/overview.dox", 'w'), translation) print "Overview file created: \'" + dirname + ".overview.dox\'" print "Now edit the file modules/overview.dox and add the following line in the right section:" print " \\subpage " + small + "_module" if __name__ == "__main__": sys.exit(main()) mips-1.0/doc/dox/overview.dox.template0000644002402200254040000000020712520407731017367 0ustar thomasfdtaig/*! \page $SMALLNAME$_module $BIGNAME$ \subpage $SMALLNAME$_status \tableofcontents \section $SMALLNAME$_overview $BIGNAME$ */ mips-1.0/doc/CMakeLists.txt0000644002402200254040000000167112520407731015147 0ustar thomasfdtaig# check if doxygen is even installed find_package(Doxygen) if (DOXYGEN_FOUND STREQUAL "NO") message(FATAL_ERROR "Doxygen not found. Please get a copy http://www.doxygen.org") endif (DOXYGEN_FOUND STREQUAL "NO") # add a target to generate API documentation with Doxygen find_package(Doxygen) if(DOXYGEN_FOUND) configure_file(${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY) add_custom_command(OUTPUT DoxygenLayout.xml COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/DoxygenLayout.xml ${CMAKE_CURRENT_BINARY_DIR}/DoxygenLayout.xml ) add_custom_target(doc COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/DoxygenLayout.xml ${CMAKE_CURRENT_BINARY_DIR}/DoxygenLayout.xml COMMAND ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} COMMENT "Generating API documentation with Doxygen" VERBATIM ) endif(DOXYGEN_FOUND) mips-1.0/LICENSE0000644002402200254040000007725012520407731012655 0ustar thomasfdtaig GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program. 13. Use with the GNU Affero General Public License. Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such. 14. Revised Versions of this License. The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. 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EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 16. Limitation of Liability. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. mips-1.0/script/0000755002402200254040000000000012520407731013141 5ustar thomasfdtaigmips-1.0/script/modules/0000755002402200254040000000000012520407731014611 5ustar thomasfdtaigmips-1.0/script/modules/FindMYSQL.cmake0000755002402200254040000000233312520407731017325 0ustar thomasfdtaig# - Find mysqlclient # Find the native MySQL includes and library # # MYSQL_INCLUDE_DIR - where to find mysql.h, etc. # MYSQL_LIBRARIES - List of libraries when using MySQL. # MYSQL_FOUND - True if MySQL found. IF (MYSQL_INCLUDE_DIR) # Already in cache, be silent SET(MYSQL_FIND_QUIETLY TRUE) ENDIF (MYSQL_INCLUDE_DIR) FIND_PATH(MYSQL_INCLUDE_DIR mysql.h /usr/local/include/mysql /usr/include/mysql ) SET(MYSQL_NAMES mysqlclient mysqlclient_r) FIND_LIBRARY(MYSQL_LIBRARY NAMES ${MYSQL_NAMES} PATHS /usr/lib64 /usr/lib /usr/local/lib PATH_SUFFIXES mysql ) IF (MYSQL_INCLUDE_DIR AND MYSQL_LIBRARY) SET(MYSQL_FOUND TRUE) SET( MYSQL_LIBRARIES ${MYSQL_LIBRARY} ) ELSE (MYSQL_INCLUDE_DIR AND MYSQL_LIBRARY) SET(MYSQL_FOUND FALSE) SET( MYSQL_LIBRARIES ) ENDIF (MYSQL_INCLUDE_DIR AND MYSQL_LIBRARY) IF (MYSQL_FOUND) IF (NOT MYSQL_FIND_QUIETLY) MESSAGE(STATUS "Found MySQL: ${MYSQL_LIBRARY}") ENDIF (NOT MYSQL_FIND_QUIETLY) ELSE (MYSQL_FOUND) IF (MYSQL_FIND_REQUIRED) MESSAGE(STATUS "Looked for MySQL libraries named ${MYSQL_NAMES}.") MESSAGE(FATAL_ERROR "Could NOT find MySQL library") ENDIF (MYSQL_FIND_REQUIRED) ENDIF (MYSQL_FOUND) MARK_AS_ADVANCED( MYSQL_LIBRARY MYSQL_INCLUDE_DIR ) mips-1.0/script/modules/FindPackageHandleStandardArgs.cmake0000644002402200254040000002631112520407731023364 0ustar thomasfdtaig# FIND_PACKAGE_HANDLE_STANDARD_ARGS( ... ) # # This function is intended to be used in FindXXX.cmake modules files. # It handles the REQUIRED, QUIET and version-related arguments to FIND_PACKAGE(). # It also sets the _FOUND variable. # The package is considered found if all variables ... listed contain # valid results, e.g. valid filepaths. # # There are two modes of this function. The first argument in both modes is # the name of the Find-module where it is called (in original casing). # # The first simple mode looks like this: # FIND_PACKAGE_HANDLE_STANDARD_ARGS( (DEFAULT_MSG|"Custom failure message") ... ) # If the variables to are all valid, then _FOUND # will be set to TRUE. # If DEFAULT_MSG is given as second argument, then the function will generate # itself useful success and error messages. You can also supply a custom error message # for the failure case. This is not recommended. # # The second mode is more powerful and also supports version checking: # FIND_PACKAGE_HANDLE_STANDARD_ARGS(NAME [REQUIRED_VARS ...] # [VERSION_VAR # [CONFIG_MODE] # [FAIL_MESSAGE "Custom failure message"] ) # # As above, if through are all valid, _FOUND # will be set to TRUE. # After REQUIRED_VARS the variables which are required for this package are listed. # Following VERSION_VAR the name of the variable can be specified which holds # the version of the package which has been found. If this is done, this version # will be checked against the (potentially) specified required version used # in the find_package() call. The EXACT keyword is also handled. The default # messages include information about the required version and the version # which has been actually found, both if the version is ok or not. # Use the option CONFIG_MODE if your FindXXX.cmake module is a wrapper for # a find_package(... NO_MODULE) call, in this case all the information # provided by the config-mode of find_package() will be evaluated # automatically. # Via FAIL_MESSAGE a custom failure message can be specified, if this is not # used, the default message will be displayed. # # Example for mode 1: # # FIND_PACKAGE_HANDLE_STANDARD_ARGS(LibXml2 DEFAULT_MSG LIBXML2_LIBRARY LIBXML2_INCLUDE_DIR) # # LibXml2 is considered to be found, if both LIBXML2_LIBRARY and # LIBXML2_INCLUDE_DIR are valid. Then also LIBXML2_FOUND is set to TRUE. # If it is not found and REQUIRED was used, it fails with FATAL_ERROR, # independent whether QUIET was used or not. # If it is found, success will be reported, including the content of . # On repeated Cmake runs, the same message won't be printed again. # # Example for mode 2: # # FIND_PACKAGE_HANDLE_STANDARD_ARGS(BISON REQUIRED_VARS BISON_EXECUTABLE # VERSION_VAR BISON_VERSION) # In this case, BISON is considered to be found if the variable(s) listed # after REQUIRED_VAR are all valid, i.e. BISON_EXECUTABLE in this case. # Also the version of BISON will be checked by using the version contained # in BISON_VERSION. # Since no FAIL_MESSAGE is given, the default messages will be printed. # # Another example for mode 2: # # FIND_PACKAGE(Automoc4 QUIET NO_MODULE HINTS /opt/automoc4) # FIND_PACKAGE_HANDLE_STANDARD_ARGS(Automoc4 CONFIG_MODE) # In this case, FindAutmoc4.cmake wraps a call to FIND_PACKAGE(Automoc4 NO_MODULE) # and adds an additional search directory for automoc4. # The following FIND_PACKAGE_HANDLE_STANDARD_ARGS() call produces a proper # success/error message. #============================================================================= # Copyright 2007-2009 Kitware, Inc. # # Distributed under the OSI-approved BSD License (the "License"); # see accompanying file Copyright.txt for details. # # This software is distributed WITHOUT ANY WARRANTY; without even the # implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the License for more information. #============================================================================= # (To distribute this file outside of CMake, substitute the full # License text for the above reference.) INCLUDE(FindPackageMessage) INCLUDE(CMakeParseArguments) # internal helper macro MACRO(_FPHSA_FAILURE_MESSAGE _msg) IF (${_NAME}_FIND_REQUIRED) MESSAGE(FATAL_ERROR "${_msg}") ELSE (${_NAME}_FIND_REQUIRED) IF (NOT ${_NAME}_FIND_QUIETLY) MESSAGE(STATUS "${_msg}") ENDIF (NOT ${_NAME}_FIND_QUIETLY) ENDIF (${_NAME}_FIND_REQUIRED) ENDMACRO(_FPHSA_FAILURE_MESSAGE _msg) # internal helper macro to generate the failure message when used in CONFIG_MODE: MACRO(_FPHSA_HANDLE_FAILURE_CONFIG_MODE) # _CONFIG is set, but FOUND is false, this means that some other of the REQUIRED_VARS was not found: IF(${_NAME}_CONFIG) _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: missing: ${MISSING_VARS} (found ${${_NAME}_CONFIG} ${VERSION_MSG})") ELSE(${_NAME}_CONFIG) # If _CONSIDERED_CONFIGS is set, the config-file has been found, but no suitable version. # List them all in the error message: IF(${_NAME}_CONSIDERED_CONFIGS) SET(configsText "") LIST(LENGTH ${_NAME}_CONSIDERED_CONFIGS configsCount) MATH(EXPR configsCount "${configsCount} - 1") FOREACH(currentConfigIndex RANGE ${configsCount}) LIST(GET ${_NAME}_CONSIDERED_CONFIGS ${currentConfigIndex} filename) LIST(GET ${_NAME}_CONSIDERED_VERSIONS ${currentConfigIndex} version) SET(configsText "${configsText} ${filename} (version ${version})\n") ENDFOREACH(currentConfigIndex) _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE} ${VERSION_MSG}, checked the following files:\n${configsText}") ELSE(${_NAME}_CONSIDERED_CONFIGS) # Simple case: No Config-file was found at all: _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: found neither ${_NAME}Config.cmake nor ${_NAME_LOWER}-config.cmake ${VERSION_MSG}") ENDIF(${_NAME}_CONSIDERED_CONFIGS) ENDIF(${_NAME}_CONFIG) ENDMACRO(_FPHSA_HANDLE_FAILURE_CONFIG_MODE) FUNCTION(FIND_PACKAGE_HANDLE_STANDARD_ARGS _NAME _FIRST_ARG) # set up the arguments for CMAKE_PARSE_ARGUMENTS and check whether we are in # new extended or in the "old" mode: SET(options CONFIG_MODE) SET(oneValueArgs FAIL_MESSAGE VERSION_VAR) SET(multiValueArgs REQUIRED_VARS) SET(_KEYWORDS_FOR_EXTENDED_MODE ${options} ${oneValueArgs} ${multiValueArgs} ) LIST(FIND _KEYWORDS_FOR_EXTENDED_MODE "${_FIRST_ARG}" INDEX) IF(${INDEX} EQUAL -1) SET(FPHSA_FAIL_MESSAGE ${_FIRST_ARG}) SET(FPHSA_REQUIRED_VARS ${ARGN}) SET(FPHSA_VERSION_VAR) ELSE(${INDEX} EQUAL -1) CMAKE_PARSE_ARGUMENTS(FPHSA "${options}" "${oneValueArgs}" "${multiValueArgs}" ${_FIRST_ARG} ${ARGN}) IF(FPHSA_UNPARSED_ARGUMENTS) MESSAGE(FATAL_ERROR "Unknown keywords given to FIND_PACKAGE_HANDLE_STANDARD_ARGS(): \"${FPHSA_UNPARSED_ARGUMENTS}\"") ENDIF(FPHSA_UNPARSED_ARGUMENTS) IF(NOT FPHSA_FAIL_MESSAGE) SET(FPHSA_FAIL_MESSAGE "DEFAULT_MSG") ENDIF(NOT FPHSA_FAIL_MESSAGE) ENDIF(${INDEX} EQUAL -1) # now that we collected all arguments, process them IF("${FPHSA_FAIL_MESSAGE}" STREQUAL "DEFAULT_MSG") SET(FPHSA_FAIL_MESSAGE "Could NOT find ${_NAME}") ENDIF("${FPHSA_FAIL_MESSAGE}" STREQUAL "DEFAULT_MSG") # In config-mode, we rely on the variable _CONFIG, which is set by find_package() # when it successfully found the config-file, including version checking: IF(FPHSA_CONFIG_MODE) LIST(INSERT FPHSA_REQUIRED_VARS 0 ${_NAME}_CONFIG) LIST(REMOVE_DUPLICATES FPHSA_REQUIRED_VARS) SET(FPHSA_VERSION_VAR ${_NAME}_VERSION) ENDIF(FPHSA_CONFIG_MODE) IF(NOT FPHSA_REQUIRED_VARS) MESSAGE(FATAL_ERROR "No REQUIRED_VARS specified for FIND_PACKAGE_HANDLE_STANDARD_ARGS()") ENDIF(NOT FPHSA_REQUIRED_VARS) LIST(GET FPHSA_REQUIRED_VARS 0 _FIRST_REQUIRED_VAR) STRING(TOUPPER ${_NAME} _NAME_UPPER) STRING(TOLOWER ${_NAME} _NAME_LOWER) # collect all variables which were not found, so they can be printed, so the # user knows better what went wrong (#6375) SET(MISSING_VARS "") SET(DETAILS "") SET(${_NAME_UPPER}_FOUND TRUE) # check if all passed variables are valid FOREACH(_CURRENT_VAR ${FPHSA_REQUIRED_VARS}) IF(NOT ${_CURRENT_VAR}) SET(${_NAME_UPPER}_FOUND FALSE) SET(MISSING_VARS "${MISSING_VARS} ${_CURRENT_VAR}") ELSE(NOT ${_CURRENT_VAR}) SET(DETAILS "${DETAILS}[${${_CURRENT_VAR}}]") ENDIF(NOT ${_CURRENT_VAR}) ENDFOREACH(_CURRENT_VAR) # version handling: SET(VERSION_MSG "") SET(VERSION_OK TRUE) SET(VERSION ${${FPHSA_VERSION_VAR}} ) IF (${_NAME}_FIND_VERSION) IF(VERSION) IF(${_NAME}_FIND_VERSION_EXACT) # exact version required IF (NOT "${${_NAME}_FIND_VERSION}" VERSION_EQUAL "${VERSION}") SET(VERSION_MSG "Found unsuitable version \"${VERSION}\", but required is exact version \"${${_NAME}_FIND_VERSION}\"") SET(VERSION_OK FALSE) ELSE (NOT "${${_NAME}_FIND_VERSION}" VERSION_EQUAL "${VERSION}") SET(VERSION_MSG "(found suitable exact version \"${VERSION}\")") ENDIF (NOT "${${_NAME}_FIND_VERSION}" VERSION_EQUAL "${VERSION}") ELSE(${_NAME}_FIND_VERSION_EXACT) # minimum version specified: IF ("${${_NAME}_FIND_VERSION}" VERSION_GREATER "${VERSION}") SET(VERSION_MSG "Found unsuitable version \"${VERSION}\", but required is at least \"${${_NAME}_FIND_VERSION}\"") SET(VERSION_OK FALSE) ELSE ("${${_NAME}_FIND_VERSION}" VERSION_GREATER "${VERSION}") SET(VERSION_MSG "(found suitable version \"${VERSION}\", required is \"${${_NAME}_FIND_VERSION}\")") ENDIF ("${${_NAME}_FIND_VERSION}" VERSION_GREATER "${VERSION}") ENDIF(${_NAME}_FIND_VERSION_EXACT) ELSE(VERSION) # if the package was not found, but a version was given, add that to the output: IF(${_NAME}_FIND_VERSION_EXACT) SET(VERSION_MSG "(Required is exact version \"${${_NAME}_FIND_VERSION}\")") ELSE(${_NAME}_FIND_VERSION_EXACT) SET(VERSION_MSG "(Required is at least version \"${${_NAME}_FIND_VERSION}\")") ENDIF(${_NAME}_FIND_VERSION_EXACT) ENDIF(VERSION) ELSE (${_NAME}_FIND_VERSION) IF(VERSION) SET(VERSION_MSG "(found version \"${VERSION}\")") ENDIF(VERSION) ENDIF (${_NAME}_FIND_VERSION) IF(VERSION_OK) SET(DETAILS "${DETAILS}[v${VERSION}(${${_NAME}_FIND_VERSION})]") ELSE(VERSION_OK) SET(${_NAME_UPPER}_FOUND FALSE) ENDIF(VERSION_OK) # print the result: IF (${_NAME_UPPER}_FOUND) FIND_PACKAGE_MESSAGE(${_NAME} "Found ${_NAME}: ${${_FIRST_REQUIRED_VAR}} ${VERSION_MSG}" "${DETAILS}") ELSE (${_NAME_UPPER}_FOUND) IF(FPHSA_CONFIG_MODE) _FPHSA_HANDLE_FAILURE_CONFIG_MODE() ELSE(FPHSA_CONFIG_MODE) IF(NOT VERSION_OK) _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE}: ${VERSION_MSG} (found ${${_FIRST_REQUIRED_VAR}})") ELSE(NOT VERSION_OK) _FPHSA_FAILURE_MESSAGE("${FPHSA_FAIL_MESSAGE} (missing: ${MISSING_VARS}) ${VERSION_MSG}") ENDIF(NOT VERSION_OK) ENDIF(FPHSA_CONFIG_MODE) ENDIF (${_NAME_UPPER}_FOUND) SET(${_NAME_UPPER}_FOUND ${${_NAME_UPPER}_FOUND} PARENT_SCOPE) ENDFUNCTION(FIND_PACKAGE_HANDLE_STANDARD_ARGS _FIRST_ARG) mips-1.0/script/modules/FindYAP.cmake0000755002402200254040000002107212520407731017052 0ustar thomasfdtaig# - find YAP # YAP_INCLUDE_DIR - Where to find YAP header files (directory) # YAP_LIBRARY - YAP library # YAP_FOUND - Set to TRUE if we found everything (library, includes and executable) IF( YAP_INCLUDE_DIR AND YAP_LIBRARY ) SET( YAP_FIND_QUIETLY TRUE ) ENDIF( YAP_INCLUDE_DIR AND YAP_LIBRARY ) FIND_PATH( YAP_INCLUDE_DIR NAMES YapInterface.h HINTS /home/samrat/include PATH_SUFFIXES Yap NO_DEFAULT_PATH ) FIND_LIBRARY( YAP_LIBRARY NAMES libYap.a HINTS /home/samrat/lib NO_DEFAULT_PATH ) IF(YAP_DEBUG) MESSAGE( STATUS "Looking for YAP required libraries in the following directories : ") IF(CMAKE_PREFIX_PATH) MESSAGE( STATUS "Cmake user provided path prefix = ${CMAKE_PREFIX_PATH}" ) ELSE() MESSAGE( STATUS "Cmake system provided path prefix = ${CMAKE_SYSTEM_PREFIX_PATH}") ENDIF() IF(CMAKE_LIBRARY_PATH) MESSAGE( STATUS "Cmake user provided library path = ${CMAKE_LIBRARY_PATH}" ) ELSE() MESSAGE( STATUS "Cmake system provided library path = ${CMAKE_SYSTEM_LIBRARY_PATH}") ENDIF() MESSAGE( STATUS "NOTE: Library names will be appended with \"${CMAKE_FIND_LIBRARY_PREFIXES}\" and \"${CMAKE_FIND_LIBRARY_SUFFIXES}\"") IF(CMAKE_INCLUDE_PATH) MESSAGE( STATUS "Cmake user provided include path = ${CMAKE_INCLUDE_PATH}" ) ELSE() MESSAGE( STATUS "Cmake system provided include path = ${CMAKE_SYSTEM_INCLUDE_PATH}") ENDIF() IF(CMAKE_FRAMEWORK_PATH) MESSAGE( STATUS "Cmake user provided framework path = ${CMAKE_FRAMEWORK_PATH}" ) ELSE() MESSAGE( STATUS "Cmake system provided framework path = ${CMAKE_SYSTEM_FRAMEWORK_PATH}") ENDIF() ENDIF() #FUNCTION (_Yap_Get_Version _Output_Version) # EXEC_PROGRAM("yap -dump-runtime-variables" OUTPUT_VARIABLE _Yap_Version ) # STRING(REGEX MATCH "YAP_VERSION=[0-9]+" _YAP_VERSION_LINE ${_Yap_Version}) # STRING(REGEX REPLACE "YAP_VERSION=([0-9]+)" "\\1" YAP_VERSION_NUMBER ${_YAP_VERSION_LINE}) # SET(_Output_Version ${YAP_VERSION_NUMBER} PARENT_SCOPE) #ENDFUNCTION() #IF(YAP_INCLUDE_DIR) # Extract YAP_VERSION from YAP.hpp # Read the whole file: # # SET(YAP_VERSION 0) # SET(YAP_LIB_VERSION "") # _Yap_Get_Version(_Output_Version) # SET(YAP_VERSION ${_Output_Version}) # SET(YAP_VERSION ${YAP_VERSION} CACHE INTERNAL "The version number for YAP library") # IF(NOT "${YAP_VERSION}" STREQUAL "0") # MATH(EXPR YAP_MAJOR_VERSION "${YAP_VERSION} / 10000") # MATH(EXPR YAP_MINOR_VERSION "${YAP_VERSION} / 100 % 100") # MATH(EXPR YAP_SUBMINOR_VERSION "${YAP_VERSION} % 100") #set(YAP_ERROR_REASON # "${YAP_ERROR_REASON}\nFound YAP ${YAP_MAJOR_VERSION}.${YAP_MINOR_VERSION}.${YAP_SUBMINOR_VERSION} in ${YAP_INCLUDE_DIR}.") # ENDIF(NOT "${YAP_VERSION}" STREQUAL "0") # if(YAP_DEBUG) # message(STATUS "[ ${CMAKE_CURRENT_LIST_FILE}:${CMAKE_CURRENT_LIST_LINE} ] " # "YAP.hpp reveals YAP " # "${YAP_MAJOR_VERSION}.${YAP_MINOR_VERSION}.${YAP_SUBMINOR_VERSION}") # endif(YAP_DEBUG) # ELSE(YAP_INCLUDE_DIR) # set(YAP_ERROR_REASON # "${YAP_ERROR_REASON}Unable to find the header files.") # ENDIF(YAP_INCLUDE_DIR) # SET(YAP_FOUND FALSE) # IF(YAP_INCLUDE_DIR) # SET( YAP_FOUND TRUE ) # Check the version of YAP against the requested version. # if (YAP_FIND_VERSION AND NOT YAP_FIND_VERSION_MINOR) # message(SEND_ERROR "When requesting a specific version of YAP, you must provide at least the major and minor version numbers, e.g., 1.34") # endif (YAP_FIND_VERSION AND NOT YAP_FIND_VERSION_MINOR) # if(YAP_MAJOR_VERSION LESS "${YAP_FIND_VERSION_MAJOR}" ) # set( YAP_FOUND FALSE ) # set(_YAP_VERSION_AGE "old") # elseif(YAP_MAJOR_VERSION EQUAL "${YAP_FIND_VERSION_MAJOR}" ) # if(YAP_MINOR_VERSION LESS "${YAP_FIND_VERSION_MINOR}" ) # set( YAP_FOUND FALSE ) # set(_YAP_VERSION_AGE "old") # elseif(YAP_MINOR_VERSION EQUAL "${YAP_FIND_VERSION_MINOR}" ) # if( YAP_FIND_VERSION_PATCH AND YAP_SUBMINOR_VERSION LESS "${YAP_FIND_VERSION_PATCH}" ) # set( YAP_FOUND FALSE ) # set(_YAP_VERSION_AGE "old") # endif( YAP_FIND_VERSION_PATCH AND YAP_SUBMINOR_VERSION LESS "${YAP_FIND_VERSION_PATCH}" ) # endif( YAP_MINOR_VERSION LESS "${YAP_FIND_VERSION_MINOR}" ) # endif( YAP_MAJOR_VERSION LESS "${YAP_FIND_VERSION_MAJOR}" ) # if (YAP_FOUND AND YAP_FIND_VERSION_EXACT) # If the user requested an exact version of YAP, check # that. We already know that the YAP version we have is >= the # requested version. # set(_YAP_VERSION_AGE "new") # If the user didn't specify a patchlevel, it's 0. # if (NOT YAP_FIND_VERSION_PATCH) # set(YAP_FIND_VERSION_PATCH 0) # endif (NOT YAP_FIND_VERSION_PATCH) # We'll set YAP_FOUND true again if we have an exact version match. # set(YAP_FOUND FALSE) # if(YAP_MAJOR_VERSION EQUAL "${YAP_FIND_VERSION_MAJOR}" ) # if(YAP_MINOR_VERSION EQUAL "${YAP_FIND_VERSION_MINOR}" ) # if(YAP_SUBMINOR_VERSION EQUAL "${YAP_FIND_VERSION_PATCH}" ) # set( YAP_FOUND TRUE ) # endif(YAP_SUBMINOR_VERSION EQUAL "${YAP_FIND_VERSION_PATCH}" ) # endif( YAP_MINOR_VERSION EQUAL "${YAP_FIND_VERSION_MINOR}" ) # endif( YAP_MAJOR_VERSION EQUAL "${YAP_FIND_VERSION_MAJOR}" ) # endif (YAP_FOUND AND YAP_FIND_VERSION_EXACT) # if(NOT YAP_FOUND) # State that we found a version of YAP that is too new or too old. # set(YAP_ERROR_REASON # "${YAP_ERROR_REASON}${YAP_MAJOR_VERSION}.${YAP_MINOR_VERSION}.${YAP_SUBMINOR_VERSION} < ${YAP_FIND_VERSION_MAJOR}.${YAP_FIND_VERSION_MINOR}") # if (YAP_FIND_VERSION_PATCH) # set(YAP_ERROR_REASON # "${YAP_ERROR_REASON}.${YAP_FIND_VERSION_PATCH}") # endif (YAP_FIND_VERSION_PATCH) # if (NOT YAP_FIND_VERSION_EXACT) # set(YAP_ERROR_REASON "${YAP_ERROR_REASON}") # endif (NOT YAP_FIND_VERSION_EXACT) # set(YAP_ERROR_REASON "${YAP_ERROR_REASON}.") # endif (NOT YAP_FOUND) # ELSE(YAP_INCLUDE_DIR) # SET( YAP_FOUND FALSE) # ENDIF(YAP_INCLUDE_DIR) FIND_LIBRARY(YAP_EXTRA_LIBS_GMP NAMES gmp NO_CMAKE_ENVIRONMENT_PATH NO_SYSTEM_ENVIRONMENT_PATH) FIND_LIBRARY(YAP_EXTRA_LIBS_GMP NAMES gmp NO_CMAKE_ENVIRONMENT_PATH ) FIND_LIBRARY(YAP_EXTRA_LIBS_GMP NAMES gmp ) IF(YAP_DEBUG) MESSAGE( STATUS "YAP_EXTRA_LIBS_GMP = ${YAP_EXTRA_LIBS_GMP}") ENDIF() IF(YAP_EXTRA_LIBS_GMP) SET(YAP_DEPENDENCIES_FOUND TRUE) ELSE(YAP_EXTRA_LIBS_GMP) SET(YAP_DEPENDENCIES_FOUND FALSE) SET(YAP_ERROR_REASON "${YAP_ERROR_REASON}\nCould not find Gmp library.") ENDIF(YAP_EXTRA_LIBS_GMP) FIND_LIBRARY(YAP_EXTRA_LIBS_READLINE NAMES readline NO_CMAKE_ENVIRONMENT_PATH NO_SYSTEM_ENVIRONMENT_PATH ) FIND_LIBRARY(YAP_EXTRA_LIBS_READLINE NAMES readline NO_CMAKE_ENVIRONMENT_PATH ) FIND_LIBRARY(YAP_EXTRA_LIBS_READLINE NAMES readline ) IF(YAP_EXTRA_LIBS_READLINE) SET(YAP_DEPENDENCIES_FOUND TRUE) ELSE(YAP_EXTRA_LIBS_READLINE) SET(YAP_DEPENDENCIES_FOUND FALSE) SET(YAP_ERROR_REASON "${YAP_ERROR_REASON}\nCould not find readline libary.") ENDIF(YAP_EXTRA_LIBS_READLINE) IF(YAP_DEBUG) MESSAGE( STATUS "YAP_EXTRA_LIBS_READLING = ${YAP_EXTRA_LIBS_READLINE}") ENDIF() FIND_LIBRARY(YAP_EXTRA_LIBS_DL NAMES dl NO_CMAKE_ENVIRONMENT_PATH NO_SYSTEM_ENVIRONMENT_PATH) FIND_LIBRARY(YAP_EXTRA_LIBS_DL NAMES dl NO_CMAKE_ENVIRONMENT_PATH) FIND_LIBRARY(YAP_EXTRA_LIBS_DL NAMES dl ) IF(YAP_EXTRA_LIBS_DL) SET(YAP_DEPENDENCIES_FOUND TRUE) ELSE(YAP_EXTRA_LIBS_DL) SET(YAP_DEPENDENCIES_FOUND FALSE) SET(YAP_ERROR_REASON "${YAP_ERROR_REASON}\nCould not find dynamic linking libary.") ENDIF() IF(YAP_DEBUG) MESSAGE( STATUS "YAP_EXTRA_LIBS_DL = ${YAP_EXTRA_LIBS_DL}") ENDIF() IF( YAP_INCLUDE_DIR AND YAP_LIBRARY AND YAP_DEPENDENCIES_FOUND ) SET(YAP_LIBRARY ${YAP_LIBRARY} ${YAP_EXTRA_LIBS_GMP} ${YAP_EXTRA_LIBS_READLINE} ${YAP_EXTRA_LIBS_DL}) SET(YAP_FOUND TRUE) ELSE(YAP_INCLUDE_DIR AND YAP_LIBRARY AND YAP_DEPENDENCIES_FOUND) SET(YAP_FOUND FALSE) ENDIF( YAP_INCLUDE_DIR AND YAP_LIBRARY AND YAP_DEPENDENCIES_FOUND ) IF( YAP_FOUND ) #IF( NOT YAP_FIND_QUIETLY ) MESSAGE( STATUS "YAP Version : ${YAP_MAJOR_VERSION}.${YAP_MINOR_VERSION}.${YAP_SUBMINOR_VERSION}" ) MESSAGE( STATUS "Found YAP library: ${YAP_LIBRARY}") #ENDIF( NOT YAP_FIND_QUIETLY ) ELSE( YAP_FOUND ) IF( YAP_FIND_REQUIRED ) MESSAGE( FATAL_ERROR "Could not find YAP. ( ${YAP_ERROR_REASON} )" ) ELSE( YAP_FIND_REQUIRED ) MESSAGE( STATUS "Optional package YAP was not found ( ${YAP_ERROR_REASON} )" ) ENDIF( YAP_FIND_REQUIRED ) ENDIF( YAP_FOUND ) MARK_AS_ADVANCED( YAP_INCLUDE_DIR YAP_LIBRARY ) mips-1.0/script/modules/FindGLPK.cmake0000755002402200254040000000235512520407731017161 0ustar thomasfdtaig# - find GLPK # input: hints for FindGLPK # GLPK_INCLUDEDIR - where the GLPK include files can be located # GLPK_LIBRARYDIR - where the GLPK library files can be located # GLPK_ROOT - root directory, GLPK_ROOT/include is the include directory, GLPK_ROOT/lib is the library directory # output # GLPK_INCLUDE_DIR - Where to find GLPK header files (directory) # GLPK_LIBRARY - GLPK library # GLPK_FOUND - Set to TRUE if we found everything (library, includes and executable) # Written by Thomas Fannes, 2012 IF( GLPK_INCLUDE_DIR AND GLPK_LIBRARY ) SET( GPLK_FIND_QUIETLY TRUE ) ENDIF( GLPK_INCLUDE_DIR AND GLPK_LIBRARY ) FIND_PATH( GLPK_INCLUDE_DIR NAMES glpk.h HINTS ${GLPK_INCLUDEDIR} ${GLPK_ROOT}/include PATHS /usr/include /usr/local/include ) FIND_LIBRARY( GLPK_LIBRARY NAMES glpk HINTS ${GLPK_LIBRARYDIR} ${GLPK_ROOT}/lib PATH /usr/local/lib ) IF(GLPK_INCLUDE_DIR AND GLPK_LIBRARY) SET(GLPK_FOUND TRUE) message("GLPK found header at ${GLP_INCLUDE_DIR} and library at ${GLPK_LIBRARY}") ELSE() SET(GLPK_FOUND False) message("GLPK not found") ENDIF(GLPK_INCLUDE_DIR AND GLPK_LIBRARY) MARK_AS_ADVANCED( SQLITE_INCLUDE_DIR SQLITE_LIBRARY ) mips-1.0/script/modules/FindDoxygen.cmake0000644002402200254040000001173712520407731020042 0ustar thomasfdtaig# - This module looks for Doxygen and the path to Graphviz's dot # Doxygen is a documentation generation tool. Please see # http://www.doxygen.org # # This module accepts the following optional variables: # # DOXYGEN_SKIP_DOT = If true this module will skip trying to find Dot # (an optional component often used by Doxygen) # DOXYGEN_EXECUTABLE_PATH = An optional location to search the doxygen executable # # This modules defines the following variables: # # DOXYGEN_EXECUTABLE = The path to the doxygen command. # DOXYGEN_FOUND = Was Doxygen found or not? # # DOXYGEN_DOT_EXECUTABLE = The path to the dot program used by doxygen. # DOXYGEN_DOT_FOUND = Was Dot found or not? # DOXYGEN_DOT_PATH = The path to dot not including the executable # # #============================================================================= # Copyright 2001-2009 Kitware, Inc. # # Distributed under the OSI-approved BSD License (the "License"); # see accompanying file Copyright.txt for details. # # This software is distributed WITHOUT ANY WARRANTY; without even the # implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the License for more information. #============================================================================= # (To distribute this file outside of CMake, substitute the full # License text for the above reference.) # For backwards compatibility support IF(Doxygen_FIND_QUIETLY) SET(DOXYGEN_FIND_QUIETLY TRUE) ENDIF(Doxygen_FIND_QUIETLY) # ===== Rationale for OS X AppBundle mods below ===== # With the OS X GUI version, Doxygen likes to be installed to /Applications and # it contains the doxygen executable in the bundle. In the versions I've # seen, it is located in Resources, but in general, more often binaries are # located in MacOS. # # NOTE: The official Doxygen.app that is distributed for OS X uses non-standard # conventions. Instead of the command-line "doxygen" tool being placed in # Doxygen.app/Contents/MacOS, "Doxywizard" is placed there instead and # "doxygen" is placed in Contents/Resources. This is most likely done # so that something happens when people double-click on the Doxygen.app # package. Unfortunately, CMake gets confused by this as when it sees the # bundle it uses "Doxywizard" as the executable to use instead of # "doxygen". Therefore to work-around this issue we temporarily disable # the app-bundle feature, just for this CMake module: if(APPLE) # Save the old setting SET(TEMP_DOXYGEN_SAVE_CMAKE_FIND_APPBUNDLE ${CMAKE_FIND_APPBUNDLE}) # Disable the App-bundle detection feature SET(CMAKE_FIND_APPBUNDLE "NEVER") endif() # FYI: # In the older versions of OS X Doxygen, dot was included with the # Doxygen bundle. But the new versions require you to download # Graphviz.app which contains "dot" in it's bundle. # ============== End OSX stuff ================ # # Find Doxygen... # FIND_PROGRAM(DOXYGEN_EXECUTABLE NAMES doxygen PATHS "[HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\doxygen_is1;Inno Setup: App Path]/bin" /Applications/Doxygen.app/Contents/Resources /Applications/Doxygen.app/Contents/MacOS HINTS ${DOXYGEN_EXECUTABLE_PATH} DOC "Doxygen documentation generation tool (http://www.doxygen.org)" ) include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake) FIND_PACKAGE_HANDLE_STANDARD_ARGS(Doxygen DEFAULT_MSG DOXYGEN_EXECUTABLE) # # Find Dot... # IF(NOT DOXYGEN_SKIP_DOT) FIND_PROGRAM(DOXYGEN_DOT_EXECUTABLE NAMES dot PATHS "$ENV{ProgramFiles}/Graphviz 2.21/bin" "C:/Program Files/Graphviz 2.21/bin" "$ENV{ProgramFiles}/ATT/Graphviz/bin" "C:/Program Files/ATT/Graphviz/bin" [HKEY_LOCAL_MACHINE\\SOFTWARE\\ATT\\Graphviz;InstallPath]/bin /Applications/Graphviz.app/Contents/MacOS /Applications/Doxygen.app/Contents/Resources /Applications/Doxygen.app/Contents/MacOS DOC "Graphviz Dot tool for using Doxygen" ) if(DOXYGEN_DOT_EXECUTABLE) set(DOXYGEN_DOT_FOUND TRUE) # The Doxyfile wants the path to Dot, not the entire path and executable get_filename_component(DOXYGEN_DOT_PATH "${DOXYGEN_DOT_EXECUTABLE}" PATH CACHE) endif() endif(NOT DOXYGEN_SKIP_DOT) # # Backwards compatibility... # if(APPLE) # Restore the old app-bundle setting setting SET(CMAKE_FIND_APPBUNDLE ${TEMP_DOXYGEN_SAVE_CMAKE_FIND_APPBUNDLE}) endif() # Maintain the _FOUND variables as "YES" or "NO" for backwards compatibility # (allows people to stuff them directly into Doxyfile with configure_file()) if(DOXYGEN_FOUND) set(DOXYGEN_FOUND "YES") else() set(DOXYGEN_FOUND "NO") endif() if(DOXYGEN_DOT_FOUND) set(DOXYGEN_DOT_FOUND "YES") else() set(DOXYGEN_DOT_FOUND "NO") endif() # For backwards compatibility support SET (DOXYGEN ${DOXYGEN_EXECUTABLE} ) SET (DOT ${DOXYGEN_DOT_EXECUTABLE} ) MARK_AS_ADVANCED( DOXYGEN_EXECUTABLE DOXYGEN_DOT_EXECUTABLE DOXYGEN_DOT_PATH ) mips-1.0/script/modules/FindSQLite3.cmake0000755002402200254040000001611112520407731017643 0ustar thomasfdtaig# - find SQLite # SQLITE_INCLUDE_DIR - Where to find SQLite header files (directory) # SQLITE_LIBRARY - SQLite library # SQLITE_FOUND - Set to TRUE if we found everything (library, includes and executable) # Written by Samrat Roy, 2012 IF( SQLITE_INCLUDE_DIR AND SQLITE_LIBRARY ) SET( SQLite3_FIND_QUIETLY TRUE ) ENDIF( SQLITE_INCLUDE_DIR AND SQLITE_LIBRARY ) FIND_PATH( SQLITE_INCLUDE_DIR NAMES sqlite3.h HINTS /home/samrat/include /usr/include /usr/local/include PATH_SUFFIXES sqlite3 ) FIND_LIBRARY( SQLITE_LIBRARY NAMES sqlite3 HINTS /home/samrat/lib /usr/lib /usr/local/lib ) IF(SQLITE_INCLUDE_DIR AND EXISTS ${SQLITE_INCLUDE_DIR} ) # Extract SQLITE_VERSION from sqlite3.hpp # Read the whole file: # SET(SQLITE_VERSION 0) SET(SQLITE_LIB_VERSION "") FILE(READ "${SQLITE_INCLUDE_DIR}/sqlite3.h" _sqlite_HPP_CONTENTS) if(SQLITE_DEBUG) message(STATUS "[ ${CMAKE_CURRENT_LIST_FILE}:${CMAKE_CURRENT_LIST_LINE} ] " "location of sqlite3.hpp: ${SQLITE_INCLUDE_DIR}/sqlite3.hpp") endif() STRING(REGEX REPLACE ".*#define SQLITE_VERSION_NUMBER ([0-9]+).*" "\\1" SQLITE_VERSION "${_sqlite_HPP_CONTENTS}") SET(SQLITE_VERSION ${SQLITE_VERSION} CACHE INTERNAL "The version number for Sqlite3 library") IF(NOT "${SQLITE_VERSION}" STREQUAL "0") MATH(EXPR SQLITE_MAJOR_VERSION "${SQLITE_VERSION} / 1000000") MATH(EXPR SQLITE_MINOR_VERSION "${SQLITE_VERSION} / 1000 % 1000") MATH(EXPR SQLITE_SUBMINOR_VERSION "${SQLITE_VERSION} % 100") #set(SQLITE_ERROR_REASON # "${SQLITE_ERROR_REASON}\nFound SQLite ${SQLITE_MAJOR_VERSION}.${SQLITE_MINOR_VERSION}.${SQLITE_SUBMINOR_VERSION} in ${SQLITE_INCLUDE_DIR}.") ENDIF(NOT "${SQLITE_VERSION}" STREQUAL "0") if(SQLITE_DEBUG) message(STATUS "[ ${CMAKE_CURRENT_LIST_FILE}:${CMAKE_CURRENT_LIST_LINE} ] " "sqlite3.hpp reveals sqlite " "${SQLITE_MAJOR_VERSION}.${SQLITE_MINOR_VERSION}.${SQLITE_SUBMINOR_VERSION}") endif(SQLITE_DEBUG) ELSE(SQLITE_INCLUDE_DIR AND EXISTS ${SQLITE_INCLUDE_DIR}) IF( EXISTS ${SQLITE_INCLUDE_DIR} ) SET(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON}Unable to find the header files in ${SQLITE_INCLUDE_DIR}") ELSE( EXISTS ${SQLITE_INCLUDE_DIR} ) SET(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON} ${SQLITE_INCLUDE_DIR} does not exist!") ENDIF( EXISTS ${SQLITE_INCLUDE_DIR} ) ENDIF(SQLITE_INCLUDE_DIR AND EXISTS ${SQLITE_INCLUDE_DIR}) SET(SQLITE_FOUND FALSE) IF(SQLITE_INCLUDE_DIR AND EXISTS ${SQLITE_INCLUDE_DIR}) SET( SQLITE_FOUND TRUE ) # Check the version of SQLITE against the requested version. if (SQLite3_FIND_VERSION AND NOT SQLite3_FIND_VERSION_MINOR) message(SEND_ERROR "When requesting a specific version of Sqlite3, you must provide at least the major and minor version numbers, e.g., 1.34") endif (SQLite3_FIND_VERSION AND NOT SQLite3_FIND_VERSION_MINOR) if(SQLITE_MAJOR_VERSION LESS "${SQLite3_FIND_VERSION_MAJOR}" ) set( SQLITE_FOUND FALSE ) set(_SQLITE_VERSION_AGE "old") elseif(SQLITE_MAJOR_VERSION EQUAL "${SQLite3_FIND_VERSION_MAJOR}" ) if(SQLITE_MINOR_VERSION LESS "${SQLite3_FIND_VERSION_MINOR}" ) set( SQLITE_FOUND FALSE ) set(_SQLITE_VERSION_AGE "old") elseif(SQLITE_MINOR_VERSION EQUAL "${SQLite3_FIND_VERSION_MINOR}" ) if( SQLite3_FIND_VERSION_PATCH AND SQLITE_SUBMINOR_VERSION LESS "${SQLite3_FIND_VERSION_PATCH}" ) set( SQLITE_FOUND FALSE ) set(_SQLITE_VERSION_AGE "old") endif( SQLite3_FIND_VERSION_PATCH AND SQLITE_SUBMINOR_VERSION LESS "${SQLite3_FIND_VERSION_PATCH}" ) endif( SQLITE_MINOR_VERSION LESS "${SQLite3_FIND_VERSION_MINOR}" ) endif( SQLITE_MAJOR_VERSION LESS "${SQLite3_FIND_VERSION_MAJOR}" ) if (SQLITE_FOUND AND SQLite3_FIND_VERSION_EXACT) # If the user requested an exact version of SQLITE, check # that. We already know that the SQLITE version we have is >= the # requested version. set(_SQLITE_VERSION_AGE "new") # If the user didn't specify a patchlevel, it's 0. if (NOT SQLite3_FIND_VERSION_PATCH) set(SQLite3_FIND_VERSION_PATCH 0) endif (NOT SQLite3_FIND_VERSION_PATCH) # We'll set SQLITE_FOUND true again if we have an exact version match. set(SQLITE_FOUND FALSE) if(SQLITE_MAJOR_VERSION EQUAL "${SQLite3_FIND_VERSION_MAJOR}" ) if(SQLITE_MINOR_VERSION EQUAL "${SQLite3_FIND_VERSION_MINOR}" ) if(SQLITE_SUBMINOR_VERSION EQUAL "${SQLite3_FIND_VERSION_PATCH}" ) set( SQLITE_FOUND TRUE ) endif(SQLITE_SUBMINOR_VERSION EQUAL "${SQLite3_FIND_VERSION_PATCH}" ) endif( SQLITE_MINOR_VERSION EQUAL "${SQLite3_FIND_VERSION_MINOR}" ) endif( SQLITE_MAJOR_VERSION EQUAL "${SQLite3_FIND_VERSION_MAJOR}" ) endif (SQLITE_FOUND AND SQLite3_FIND_VERSION_EXACT) if(NOT SQLITE_FOUND) # State that we found a version of Sqlite that is too new or too old. set(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON}${SQLITE_MAJOR_VERSION}.${SQLITE_MINOR_VERSION}.${SQLITE_SUBMINOR_VERSION} < ${SQLite3_FIND_VERSION_MAJOR}.${SQLite3_FIND_VERSION_MINOR}") if (SQLite3_FIND_VERSION_PATCH) set(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON}.${SQLite3_FIND_VERSION_PATCH}") endif (SQLite3_FIND_VERSION_PATCH) if (NOT SQLite3_FIND_VERSION_EXACT) set(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON}") endif (NOT SQLite3_FIND_VERSION_EXACT) set(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON}.") endif (NOT SQLITE_FOUND) ELSE(SQLITE_INCLUDE_DIR AND EXISTS ${SQLITE_INCLUDE_DIR}) SET( SQLITE_FOUND FALSE) ENDIF(SQLITE_INCLUDE_DIR AND EXISTS ${SQLITE_INCLUDE_DIR}) IF( SQLITE_FOUND AND NOT (SQLITE_LIBRARY) ) SET( SQLITE_FOUND FALSE ) SET( SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON}Unable to find library.") ELSEIF( SQLITE_FOUND AND NOT EXISTS ${SQLITE_LIBRARY} ) SET( SQLITE_FOUND FALSE ) SET(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON} ${SQLITE_LIBRARY} does not exist.") ELSEIF( SQLITE_FOUND AND EXISTS ${SQLITE_LIBRARY} AND IS_DIRECTORY ${SQLITE_LIBRARY} ) SET( SQLITE_FOUND FALSE ) SET(SQLITE_ERROR_REASON "${SQLITE_ERROR_REASON} ${SQLITE_LIBRARY} is directory. Please give the path to libsqlite3.") ENDIF( SQLITE_FOUND AND NOT (SQLITE_LIBRARY) ) IF( SQLITE_FOUND ) #IF( NOT SQLite3_FIND_QUIETLY ) MESSAGE( STATUS "SQLite3 version : ${SQLITE_MAJOR_VERSION}.${SQLITE_MINOR_VERSION}.${SQLITE_SUBMINOR_VERSION}") MESSAGE( STATUS "Found SQLite library: ${SQLITE_LIBRARY}") #ENDIF( NOT SQLite3_FIND_QUIETLY ) ELSE( SQLITE_FOUND ) IF( SQLite3_FIND_REQUIRED ) MESSAGE( FATAL_ERROR "Failed to find required module SQLite3.\n${SQLITE_ERROR_REASON}" ) ELSE( SQLite3_FIND_REQUIRED ) MESSAGE( STATUS "Failed to find optional module SQLite3.( ${SQLITE_ERROR_REASON} )" ) ENDIF( SQLite3_FIND_REQUIRED ) ENDIF( SQLITE_FOUND ) MARK_AS_ADVANCED( SQLITE_INCLUDE_DIR SQLITE_LIBRARY ) mips-1.0/script/modules/FindSQLite.cmake0000755002402200254040000000524312520407731017564 0ustar thomasfdtaig# - find Sqlite 3 # SQLITE3_INCLUDE_DIR - Where to find Sqlite 3 header files (directory) # SQLITE3_LIBRARIES - Sqlite 3 libraries # SQLITE3_LIBRARY_RELEASE - Where the release library is # SQLITE3_LIBRARY_DEBUG - Where the debug library is # SQLITE3_FOUND - Set to TRUE if we found everything (library, includes and executable) # Copyright (c) 2010 Pau Garcia i Quiles, # # Redistribution and use is allowed according to the terms of the BSD license. # For details see the accompanying COPYING-CMAKE-SCRIPTS file. # # Generated by CModuler, a CMake Module Generator - http://gitorious.org/cmoduler IF( SQLITE3_INCLUDE_DIR AND SQLITE3_LIBRARY_RELEASE AND SQLITE3_LIBRARY_DEBUG ) SET(SQLITE3_FIND_QUIETLY TRUE) ENDIF( SQLITE3_INCLUDE_DIR AND SQLITE3_LIBRARY_RELEASE AND SQLITE3_LIBRARY_DEBUG ) FIND_PATH( SQLITE3_INCLUDE_DIR sqlite3.h ) FIND_LIBRARY(SQLITE3_LIBRARY_RELEASE NAMES sqlite3 ) FIND_LIBRARY(SQLITE3_LIBRARY_DEBUG NAMES sqlite3 sqlite3d HINTS /usr/lib/debug/usr/lib/ ) IF( SQLITE3_LIBRARY_RELEASE OR SQLITE3_LIBRARY_DEBUG AND SQLITE3_INCLUDE_DIR ) SET( SQLITE3_FOUND TRUE ) ENDIF( SQLITE3_LIBRARY_RELEASE OR SQLITE3_LIBRARY_DEBUG AND SQLITE3_INCLUDE_DIR ) IF( SQLITE3_LIBRARY_DEBUG AND SQLITE3_LIBRARY_RELEASE ) # if the generator supports configuration types then set # optimized and debug libraries, or if the CMAKE_BUILD_TYPE has a value IF( CMAKE_CONFIGURATION_TYPES OR CMAKE_BUILD_TYPE ) SET( SQLITE3_LIBRARIES optimized ${SQLITE3_LIBRARY_RELEASE} debug ${SQLITE3_LIBRARY_DEBUG} ) ELSE( CMAKE_CONFIGURATION_TYPES OR CMAKE_BUILD_TYPE ) # if there are no configuration types and CMAKE_BUILD_TYPE has no value # then just use the release libraries SET( SQLITE3_LIBRARIES ${SQLITE3_LIBRARY_RELEASE} ) ENDIF( CMAKE_CONFIGURATION_TYPES OR CMAKE_BUILD_TYPE ) ELSEIF( SQLITE3_LIBRARY_RELEASE ) SET( SQLITE3_LIBRARIES ${SQLITE3_LIBRARY_RELEASE} ) ELSE( SQLITE3_LIBRARY_DEBUG AND SQLITE3_LIBRARY_RELEASE ) SET( SQLITE3_LIBRARIES ${SQLITE3_LIBRARY_DEBUG} ) ENDIF( SQLITE3_LIBRARY_DEBUG AND SQLITE3_LIBRARY_RELEASE ) IF( SQLITE3_FOUND ) IF( NOT SQLITE3_FIND_QUIETLY ) MESSAGE( STATUS "Found Sqlite3 header file in ${SQLITE3_INCLUDE_DIR}") MESSAGE( STATUS "Found Sqlite3 libraries: ${SQLITE3_LIBRARIES}") ENDIF( NOT SQLITE3_FIND_QUIETLY ) ELSE(SQLITE3_FOUND) IF( SQLITE3_FIND_REQUIRED) MESSAGE( FATAL_ERROR "Could not find Sqlite3" ) ELSE( SQLITE3_FIND_REQUIRED) MESSAGE( STATUS "Optional package Sqlite3 was not found" ) ENDIF( SQLITE3_FIND_REQUIRED) ENDIF(SQLITE3_FOUND) mips-1.0/script/modules/FindMatlab.cmake0000755002402200254040000000100012520407731017606 0ustar thomasfdtaig# - Find Matlab # # MATLAB_BIN - should be set to the right location # # # MATLAB_INCLUDE - where to find the includes for matlab # MATLAB_LIB - where to find the libraries for matlab # MATLAB_FOUND - True if Matlab found. IF(MATLAB_BIN) set(MATLAB_INCLUDE ${MATLAB_BIN}/extern/include) set(MATLAB_LIB ${MATLAB_BIN}/runtime/glnxa64) set(MATLAB_BUILD ${CMAKE_BINARY_DIR}/MATLAB) set(MATLAB_FOUND true) file(MAKE_DIRECTORY ${MATLAB_BUILD}) endif(MATLAB_BIN) mips-1.0/script/cmake/0000755002402200254040000000000012520407731014221 5ustar thomasfdtaigmips-1.0/script/cmake/FindMIPS_release.cmake0000755002402200254040000001202412520407731020276 0ustar thomasfdtaig# Finds the mips release library # # There are three ways how to use the mips_release library: # 1) mips_release is installed: # - MIPS_INSTALLED_ROOT: Specifies the root directory, where ${MIPS_ROOT}/include is the include directory and ${MIPS_ROOT}/lib is the library directory # - MIPS_INSTALLED_LIBRARYDIR: The directory where the mips library can be located # - MIPS_INSTALLED_INCLUDEDIR: The directory where the mips include files can be located # - MIPS_INSTALLED_SKIP: If this flag is set, cmake will not look for the installed library, but rather check for option 2 or 3. Note that, even if none of the MIPS_INSTALL* flags are set, # cmake will still look in the default directories (.e.g, /usr/local) for an installed mips library. The skip flag can be used when one wants to explictly skip the automatic detection of an # installed mips library. # # 2) there is a local clone of the mips_release repository: # - MIPS_LOCAL_REPO: The directory where the local repository can be found # - MIPS_LOCAL_SKIP: If this flag is set, cmake will not look for a local repository, but rather go for option 3. Note that not setting MIPS_LOCAL_REPO will have the same result has setting # MIPS_LOCAL_SKIP # # 3) mips_release should be obtained through a central repository: # - MIPS_GIT_REPO: The URL where the git repository can be found, if not set, https://mips-build.cs.kuleuven.be/gerrit/mips is used. # - MIPS_GIT_REFSPEC: The ref spec to checkout, by default origin/release is used. # # After executing the script, the following targets and variables are set: # # - MIPS_RELEASE_FOUND - set to TRUE if cmake found everything # - MIPS_RELEASE_INCLUDE_DIR - Where to find the MIPS header files (include directory) # - MIPS_RELEASE_LIBRARY - A direct target to the mips library # - MIPS_RELEASE_RELEASE - A target on which your library should dependent on. # Written by Thomas Fannes, 2014 macro(APPEND_OPTION LISTVAR NAME) if(${NAME}) set(INNAME "${${NAME}}") set(VAR "-D${NAME}=${${NAME}}") set(${LISTVAR} ${${LISTVAR}} ${VAR}) endif() endmacro() IF( MIPS_RELEASE_FOUND AND MIPS_RELEASE_INCLUDE_DIR AND MIPS_RELEASE_LIBRARY ) SET( MIPS_RELEASE_FIND_QUIETLY TRUE ) ENDIF() # check for an installed mips version IF (NOT MIPS_INSTALLED_SKIP AND NOT MIPS_RELEASE_FIND_QUIETLY AND NOT MIPS_RELEASE_FOUND) FIND_PATH(MIPS_RELEASE_INCLUDE_DIR NAMES mips/mips.hpp HINTS ${MIPS_INSTALLED_INCLUDEDIR} ${MIPS_INSTALLED_ROOT}/include PATHS /usr/include /usr/local/include ) FIND_LIBRARY( MIPS_RELEASE_LIBRARY NAMES mips HINTS ${MIPS_INSTALLED_LIBRARYDIR} ${MIPS_INSTALLED_ROOT}/lib PATH /usr/local/lib ) IF(MIPS_RELEASE_INCLUDE_DIR AND MIPS_RELEASE_LIBRARY) SET(MIPS_RELEASE_FOUND TRUE) message("Installed mips found header at ${MIPS_RELEASE_INCLUDE_DIR}") mark_as_advanced( MIPS_INCLUDE_DIR MIPS_LIBRARY MIPS_FOUND) add_custom_target(MIPS_RELEASE COMMENT "mips_release (INSTALLED_VERSION)") ELSE() IF(MIPS_INSTALLED_ROOT OR MIPS_INSTALLED_INCLUDEDIR OR MIPS_INSTALLED_LIBRARYDIR) message(FATAL_ERROR "Unable to find installed mips library") ENDIF() ENDIF() ENDIF() IF(NOT MIPS_RELEASE_FOUND) include(${CMAKE_CURRENT_LIST_DIR}/ExternalProject.cmake) # set the right flags for building mips set(MIPS_ROOT ${CMAKE_BINARY_DIR}/mips_release/local) set(MIPS_RELEASE_CMAKE_ARGS "-DCMAKE_INSTALL_PREFIX:PATH=${MIPS_ROOT}") APPEND_OPTION(MIPS_RELEASE_CMAKE_ARGS BOOST_ROOT) APPEND_OPTION(MIPS_RELEASE_CMAKE_ARGS BOOST_INCLUDEDIR) APPEND_OPTION(MIPS_RELEASE_CMAKE_ARGS BOOST_LIBRARYDIR) APPEND_OPTION(MIPS_RELEASE_CMAKE_ARGS CMAKE_BUILD_TYPE) # check for a local repository IF(NOT MIPS_LOCAL_SKIP AND MIPS_LOCAL_REPO) ExternalProject_Add(MIPS_RELEASE SOURCE_DIR "${MIPS_LOCAL_REPO}" CMAKE_ARGS "${MIPS_RELEASE_CMAKE_ARGS}" ) set(MIPS_RELEASE_INCLUDE_DIR ${MIPS_ROOT}/include) set(MIPS_RELEASE_LIBRARY ${MIPS_ROOT}/lib/libmips.so) SET(MIPS_RELEASE_FOUND TRUE) message("local mips repository found at ${MIPS_LOCAL_REPO}") mark_as_advanced( MIPS_RELEASE_INCLUDE_DIR MIPS_RELEASE_LIBRARY MIPS_RELEASE_FOUND) # download central repository ELSE() IF(NOT MIPS_GIT_REPO) set(MIPS_GIT_REPO "https://mips-build.cs.kuleuven.be/gerrit/mips") ENDIF() IF(NOT MIPS_GIT_REFSPEC) set(MIPS_GIT_REFSPEC "origin/master") ENDIF() ExternalProject_Add(MIPS_RELEASE GIT_REPOSITORY "${MIPS_GIT_REPO}" GIT_TAG "${MIPS_GIT_REFSPEC}" CMAKE_ARGS "${MIPS_RELEASE_CMAKE_ARGS}" ) set(MIPS_RELEASE_INCLUDE_DIR ${MIPS_ROOT}/include) set(MIPS_RELEASE_LIBRARY ${MIPS_ROOT}/lib/libmips.so) SET(MIPS_RELEASE_FOUND TRUE) message("central mips repository from ${MIPS_GIT_REPO}") mark_as_advanced( MIPS_RELEASE_INCLUDE_DIR MIPS_RELEASE_LIBRARY MIPS_RELEASE_FOUND) ENDIF() ENDIF() mips-1.0/script/cmake/ExternalProject.cmake0000644002402200254040000017273412520407731020352 0ustar thomasfdtaig# - Create custom targets to build projects in external trees # The 'ExternalProject_Add' function creates a custom target to drive # download, update/patch, configure, build, install and test steps of an # external project: # ExternalProject_Add( # Name for custom target # [DEPENDS projects...] # Targets on which the project depends # [PREFIX dir] # Root dir for entire project # [LIST_SEPARATOR sep] # Sep to be replaced by ; in cmd lines # [TMP_DIR dir] # Directory to store temporary files # [STAMP_DIR dir] # Directory to store step timestamps # #--Download step-------------- # [DOWNLOAD_NAME fname] # File name to store (if not end of URL) # [DOWNLOAD_DIR dir] # Directory to store downloaded files # [DOWNLOAD_COMMAND cmd...] # Command to download source tree # [CVS_REPOSITORY cvsroot] # CVSROOT of CVS repository # [CVS_MODULE mod] # Module to checkout from CVS repo # [CVS_TAG tag] # Tag to checkout from CVS repo # [SVN_REPOSITORY url] # URL of Subversion repo # [SVN_REVISION rev] # Revision to checkout from Subversion repo # [SVN_USERNAME john ] # Username for Subversion checkout and update # [SVN_PASSWORD doe ] # Password for Subversion checkout and update # [SVN_TRUST_CERT 1 ] # Trust the Subversion server site certificate # [GIT_REPOSITORY url] # URL of git repo # [GIT_TAG tag] # Git branch name, commit id or tag # [HG_REPOSITORY url] # URL of mercurial repo # [HG_TAG tag] # Mercurial branch name, commit id or tag # [URL /.../src.tgz] # Full path or URL of source # [URL_HASH ALGO=value] # Hash of file at URL # [URL_MD5 md5] # Equivalent to URL_HASH MD5=md5 # [TLS_VERIFY bool] # Should certificate for https be checked # [TLS_CAINFO file] # Path to a certificate authority file # [TIMEOUT seconds] # Time allowed for file download operations # #--Update/Patch step---------- # [UPDATE_COMMAND cmd...] # Source work-tree update command # [PATCH_COMMAND cmd...] # Command to patch downloaded source # #--Configure step------------- # [SOURCE_DIR dir] # Source dir to be used for build # [CONFIGURE_COMMAND cmd...] # Build tree configuration command # [CMAKE_COMMAND /.../cmake] # Specify alternative cmake executable # [CMAKE_GENERATOR gen] # Specify generator for native build # [CMAKE_GENERATOR_TOOLSET t] # Generator-specific toolset name # [CMAKE_ARGS args...] # Arguments to CMake command line # [CMAKE_CACHE_ARGS args...] # Initial cache arguments, of the form -Dvar:string=on # #--Build step----------------- # [BINARY_DIR dir] # Specify build dir location # [BUILD_COMMAND cmd...] # Command to drive the native build # [BUILD_IN_SOURCE 1] # Use source dir for build dir # #--Install step--------------- # [INSTALL_DIR dir] # Installation prefix # [INSTALL_COMMAND cmd...] # Command to drive install after build # #--Test step------------------ # [TEST_BEFORE_INSTALL 1] # Add test step executed before install step # [TEST_AFTER_INSTALL 1] # Add test step executed after install step # [TEST_COMMAND cmd...] # Command to drive test # #--Output logging------------- # [LOG_DOWNLOAD 1] # Wrap download in script to log output # [LOG_UPDATE 1] # Wrap update in script to log output # [LOG_CONFIGURE 1] # Wrap configure in script to log output # [LOG_BUILD 1] # Wrap build in script to log output # [LOG_TEST 1] # Wrap test in script to log output # [LOG_INSTALL 1] # Wrap install in script to log output # #--Custom targets------------- # [STEP_TARGETS st1 st2 ...] # Generate custom targets for these steps # ) # The *_DIR options specify directories for the project, with default # directories computed as follows. # If the PREFIX option is given to ExternalProject_Add() or the EP_PREFIX # directory property is set, then an external project is built and installed # under the specified prefix: # TMP_DIR = /tmp # STAMP_DIR = /src/-stamp # DOWNLOAD_DIR = /src # SOURCE_DIR = /src/ # BINARY_DIR = /src/-build # INSTALL_DIR = # Otherwise, if the EP_BASE directory property is set then components # of an external project are stored under the specified base: # TMP_DIR = /tmp/ # STAMP_DIR = /Stamp/ # DOWNLOAD_DIR = /Download/ # SOURCE_DIR = /Source/ # BINARY_DIR = /Build/ # INSTALL_DIR = /Install/ # If no PREFIX, EP_PREFIX, or EP_BASE is specified then the default # is to set PREFIX to "-prefix". # Relative paths are interpreted with respect to the build directory # corresponding to the source directory in which ExternalProject_Add is # invoked. # # If SOURCE_DIR is explicitly set to an existing directory the project # will be built from it. # Otherwise a download step must be specified using one of the # DOWNLOAD_COMMAND, CVS_*, SVN_*, or URL options. # The URL option may refer locally to a directory or source tarball, # or refer to a remote tarball (e.g. http://.../src.tgz). # # The 'ExternalProject_Add_Step' function adds a custom step to an external # project: # ExternalProject_Add_Step( # Names of project and custom step # [COMMAND cmd...] # Command line invoked by this step # [COMMENT "text..."] # Text printed when step executes # [DEPENDEES steps...] # Steps on which this step depends # [DEPENDERS steps...] # Steps that depend on this step # [DEPENDS files...] # Files on which this step depends # [ALWAYS 1] # No stamp file, step always runs # [WORKING_DIRECTORY dir] # Working directory for command # [LOG 1] # Wrap step in script to log output # ) # The command line, comment, and working directory of every standard # and custom step is processed to replace tokens # , # , # , # and # with corresponding property values. # # Any builtin step that specifies a "_COMMAND cmd..." or custom # step that specifies a "COMMAND cmd..." may specify additional command # lines using the form "COMMAND cmd...". At build time the commands will # be executed in order and aborted if any one fails. For example: # ... BUILD_COMMAND make COMMAND echo done ... # specifies to run "make" and then "echo done" during the build step. # Whether the current working directory is preserved between commands # is not defined. Behavior of shell operators like "&&" is not defined. # # The 'ExternalProject_Get_Property' function retrieves external project # target properties: # ExternalProject_Get_Property( [prop1 [prop2 [...]]]) # It stores property values in variables of the same name. # Property names correspond to the keyword argument names of # 'ExternalProject_Add'. # # The 'ExternalProject_Add_StepTargets' function generates custom targets for # the steps listed: # ExternalProject_Add_StepTargets( [step1 [step2 [...]]]) # # If STEP_TARGETS is set then ExternalProject_Add_StepTargets is automatically # called at the end of matching calls to ExternalProject_Add_Step. Pass # STEP_TARGETS explicitly to individual ExternalProject_Add calls, or # implicitly to all ExternalProject_Add calls by setting the directory property # EP_STEP_TARGETS. # # If STEP_TARGETS is not set, clients may still manually call # ExternalProject_Add_StepTargets after calling ExternalProject_Add or # ExternalProject_Add_Step. # # This functionality is provided to make it easy to drive the steps # independently of each other by specifying targets on build command lines. # For example, you may be submitting to a sub-project based dashboard, where # you want to drive the configure portion of the build, then submit to the # dashboard, followed by the build portion, followed by tests. If you invoke # a custom target that depends on a step halfway through the step dependency # chain, then all the previous steps will also run to ensure everything is # up to date. # # For example, to drive configure, build and test steps independently for each # ExternalProject_Add call in your project, write the following line prior to # any ExternalProject_Add calls in your CMakeLists file: # # set_property(DIRECTORY PROPERTY EP_STEP_TARGETS configure build test) #============================================================================= # Copyright 2008-2012 Kitware, Inc. # # Distributed under the OSI-approved BSD License (the "License"); # see accompanying file Copyright.txt for details. # # This software is distributed WITHOUT ANY WARRANTY; without even the # implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the License for more information. #============================================================================= # (To distribute this file outside of CMake, substitute the full # License text for the above reference.) # Pre-compute a regex to match documented keywords for each command. math(EXPR _ep_documentation_line_count "${CMAKE_CURRENT_LIST_LINE} - 16") file(STRINGS "${CMAKE_CURRENT_LIST_FILE}" lines LIMIT_COUNT ${_ep_documentation_line_count} REGEX "^# ( \\[[A-Z0-9_]+ [^]]*\\] +#.*$|[A-Za-z0-9_]+\\()") foreach(line IN LISTS lines) if("${line}" MATCHES "^# [A-Za-z0-9_]+\\(") if(_ep_func) set(_ep_keywords_${_ep_func} "${_ep_keywords_${_ep_func}})$") endif() string(REGEX REPLACE "^# ([A-Za-z0-9_]+)\\(.*" "\\1" _ep_func "${line}") #message("function [${_ep_func}]") set(_ep_keywords_${_ep_func} "^(") set(_ep_keyword_sep) else() string(REGEX REPLACE "^# \\[([A-Z0-9_]+) .*" "\\1" _ep_key "${line}") #message(" keyword [${_ep_key}]") set(_ep_keywords_${_ep_func} "${_ep_keywords_${_ep_func}}${_ep_keyword_sep}${_ep_key}") set(_ep_keyword_sep "|") endif() endforeach() if(_ep_func) set(_ep_keywords_${_ep_func} "${_ep_keywords_${_ep_func}})$") endif() # Save regex matching supported hash algorithm names. set(_ep_hash_algos "MD5|SHA1|SHA224|SHA256|SHA384|SHA512") set(_ep_hash_regex "^(${_ep_hash_algos})=([0-9A-Fa-f]+)$") function(_ep_parse_arguments f name ns args) # Transfer the arguments to this function into target properties for the # new custom target we just added so that we can set up all the build steps # correctly based on target properties. # # We loop through ARGN and consider the namespace starting with an # upper-case letter followed by at least two more upper-case letters, # numbers or underscores to be keywords. set(key) foreach(arg IN LISTS args) set(is_value 1) if(arg MATCHES "^[A-Z][A-Z0-9_][A-Z0-9_]+$" AND NOT ((arg STREQUAL "${key}") AND (key STREQUAL "COMMAND")) AND NOT arg MATCHES "^(TRUE|FALSE)$") if(_ep_keywords_${f} AND arg MATCHES "${_ep_keywords_${f}}") set(is_value 0) endif() endif() if(is_value) if(key) # Value if(NOT arg STREQUAL "") set_property(TARGET ${name} APPEND PROPERTY ${ns}${key} "${arg}") else() get_property(have_key TARGET ${name} PROPERTY ${ns}${key} SET) if(have_key) get_property(value TARGET ${name} PROPERTY ${ns}${key}) set_property(TARGET ${name} PROPERTY ${ns}${key} "${value};${arg}") else() set_property(TARGET ${name} PROPERTY ${ns}${key} "${arg}") endif() endif() else() # Missing Keyword message(AUTHOR_WARNING "value '${arg}' with no previous keyword in ${f}") endif() else() set(key "${arg}") endif() endforeach() endfunction() define_property(DIRECTORY PROPERTY "EP_BASE" INHERITED BRIEF_DOCS "Base directory for External Project storage." FULL_DOCS "See documentation of the ExternalProject_Add() function in the " "ExternalProject module." ) define_property(DIRECTORY PROPERTY "EP_PREFIX" INHERITED BRIEF_DOCS "Top prefix for External Project storage." FULL_DOCS "See documentation of the ExternalProject_Add() function in the " "ExternalProject module." ) define_property(DIRECTORY PROPERTY "EP_STEP_TARGETS" INHERITED BRIEF_DOCS "List of ExternalProject steps that automatically get corresponding targets" FULL_DOCS "See documentation of the ExternalProject_Add_StepTargets() function in the " "ExternalProject module." ) function(_ep_write_gitclone_script script_filename source_dir git_EXECUTABLE git_repository git_tag src_name work_dir gitclone_infofile gitclone_stampfile) file(WRITE ${script_filename} "if(\"${git_tag}\" STREQUAL \"\") message(FATAL_ERROR \"Tag for git checkout should not be empty.\") endif() set(run 0) if(\"${gitclone_infofile}\" IS_NEWER_THAN \"${gitclone_stampfile}\") set(run 1) endif() if(NOT run) message(STATUS \"Avoiding repeated git clone, stamp file is up to date: '${gitclone_stampfile}'\") return() endif() execute_process( COMMAND \${CMAKE_COMMAND} -E remove_directory \"${source_dir}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to remove directory: '${source_dir}'\") endif() # try the clone 3 times incase there is an odd git clone issue set(error_code 1) set(number_of_tries 0) while(error_code AND number_of_tries LESS 3) execute_process( COMMAND \"${git_EXECUTABLE}\" clone \"${git_repository}\" \"${src_name}\" WORKING_DIRECTORY \"${work_dir}\" RESULT_VARIABLE error_code ) math(EXPR number_of_tries \"\${number_of_tries} + 1\") endwhile() if(number_of_tries GREATER 1) message(STATUS \"Had to git clone more than once: \${number_of_tries} times.\") endif() if(error_code) message(FATAL_ERROR \"Failed to clone repository: '${git_repository}'\") endif() execute_process( COMMAND \"${git_EXECUTABLE}\" checkout ${git_tag} WORKING_DIRECTORY \"${work_dir}/${src_name}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to checkout tag: '${git_tag}'\") endif() execute_process( COMMAND \"${git_EXECUTABLE}\" submodule init WORKING_DIRECTORY \"${work_dir}/${src_name}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to init submodules in: '${work_dir}/${src_name}'\") endif() execute_process( COMMAND \"${git_EXECUTABLE}\" submodule update --recursive WORKING_DIRECTORY \"${work_dir}/${src_name}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to update submodules in: '${work_dir}/${src_name}'\") endif() # Complete success, update the script-last-run stamp file: # execute_process( COMMAND \${CMAKE_COMMAND} -E copy \"${gitclone_infofile}\" \"${gitclone_stampfile}\" WORKING_DIRECTORY \"${work_dir}/${src_name}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to copy script-last-run stamp file: '${gitclone_stampfile}'\") endif() " ) endfunction() function(_ep_write_hgclone_script script_filename source_dir hg_EXECUTABLE hg_repository hg_tag src_name work_dir hgclone_infofile hgclone_stampfile) file(WRITE ${script_filename} "if(\"${hg_tag}\" STREQUAL \"\") message(FATAL_ERROR \"Tag for hg checkout should not be empty.\") endif() set(run 0) if(\"${hgclone_infofile}\" IS_NEWER_THAN \"${hgclone_stampfile}\") set(run 1) endif() if(NOT run) message(STATUS \"Avoiding repeated hg clone, stamp file is up to date: '${hgclone_stampfile}'\") return() endif() execute_process( COMMAND \${CMAKE_COMMAND} -E remove_directory \"${source_dir}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to remove directory: '${source_dir}'\") endif() execute_process( COMMAND \"${hg_EXECUTABLE}\" clone \"${hg_repository}\" \"${src_name}\" WORKING_DIRECTORY \"${work_dir}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to clone repository: '${hg_repository}'\") endif() execute_process( COMMAND \"${hg_EXECUTABLE}\" update ${hg_tag} WORKING_DIRECTORY \"${work_dir}/${src_name}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to checkout tag: '${hg_tag}'\") endif() # Complete success, update the script-last-run stamp file: # execute_process( COMMAND \${CMAKE_COMMAND} -E copy \"${hgclone_infofile}\" \"${hgclone_stampfile}\" WORKING_DIRECTORY \"${work_dir}/${src_name}\" RESULT_VARIABLE error_code ) if(error_code) message(FATAL_ERROR \"Failed to copy script-last-run stamp file: '${hgclone_stampfile}'\") endif() " ) endfunction() function(_ep_write_gitupdate_script script_filename git_EXECUTABLE git_tag git_repository work_dir) file(WRITE ${script_filename} "if(\"${git_tag}\" STREQUAL \"\") message(WARNING \"Tag for git checkout should not be empty.\") endif() execute_process( COMMAND \"${git_EXECUTABLE}\" rev-list --max-count=1 HEAD WORKING_DIRECTORY \"${work_dir}\" RESULT_VARIABLE error_code OUTPUT_VARIABLE head_sha ) if(error_code) message(WARNING \"Failed to get the hash for HEAD\") endif() execute_process( COMMAND \"${git_EXECUTABLE}\" show-ref ${git_tag} WORKING_DIRECTORY \"${work_dir}\" OUTPUT_VARIABLE show_ref_output ) # If a remote ref is asked for, which can possibly move around, # we must always do a fetch and checkout. if(\"\${show_ref_output}\" MATCHES \"remotes\") set(is_remote_ref 1) else() set(is_remote_ref 0) endif() # This will fail if the tag does not exist (it probably has not been fetched # yet). execute_process( COMMAND \"${git_EXECUTABLE}\" rev-list --max-count=1 ${git_tag} WORKING_DIRECTORY \"${work_dir}\" RESULT_VARIABLE error_code OUTPUT_VARIABLE tag_sha ) # Is the hash checkout out that we want? if(error_code OR is_remote_ref OR NOT (\"\${tag_sha}\" STREQUAL \"\${head_sha}\")) execute_process( COMMAND \"${git_EXECUTABLE}\" fetch WORKING_DIRECTORY \"${work_dir}\" RESULT_VARIABLE error_code ) if(error_code) message(WARNING \"Failed to fetch repository '${git_repository}'\") endif() execute_process( COMMAND \"${git_EXECUTABLE}\" checkout ${git_tag} WORKING_DIRECTORY \"${work_dir}\" RESULT_VARIABLE error_code ) if(error_code) message(WARNING \"Failed to checkout tag: '${git_tag}'\") endif() execute_process( COMMAND \"${git_EXECUTABLE}\" submodule update --recursive WORKING_DIRECTORY \"${work_dir}/${src_name}\" RESULT_VARIABLE error_code ) if(error_code) message(WARNING \"Failed to update submodules in: '${work_dir}/${src_name}'\") endif() endif() " ) endfunction(_ep_write_gitupdate_script) function(_ep_write_downloadfile_script script_filename remote local timeout hash tls_verify tls_cainfo) if(timeout) set(timeout_args TIMEOUT ${timeout}) set(timeout_msg "${timeout} seconds") else() set(timeout_args "# no TIMEOUT") set(timeout_msg "none") endif() if("${hash}" MATCHES "${_ep_hash_regex}") set(hash_args EXPECTED_HASH ${CMAKE_MATCH_1}=${CMAKE_MATCH_2}) else() set(hash_args "# no EXPECTED_HASH") endif() # check for curl globals in the project if(DEFINED CMAKE_TLS_VERIFY) set(tls_verify "set(CMAKE_TLS_VERIFY ${CMAKE_TLS_VERIFY})") endif() if(DEFINED CMAKE_TLS_CAINFO) set(tls_cainfo "set(CMAKE_TLS_CAINFO \"${CMAKE_TLS_CAINFO}\")") endif() # now check for curl locals so that the local values # will override the globals # check for tls_verify argument string(LENGTH "${tls_verify}" tls_verify_len) if(tls_verify_len GREATER 0) set(tls_verify "set(CMAKE_TLS_VERIFY ${tls_verify})") endif() # check for tls_cainfo argument string(LENGTH "${tls_cainfo}" tls_cainfo_len) if(tls_cainfo_len GREATER 0) set(tls_cainfo "set(CMAKE_TLS_CAINFO \"${tls_cainfo}\")") endif() file(WRITE ${script_filename} "message(STATUS \"downloading... src='${remote}' dst='${local}' timeout='${timeout_msg}'\") ${tls_verify} ${tls_cainfo} file(DOWNLOAD \"${remote}\" \"${local}\" SHOW_PROGRESS ${hash_args} ${timeout_args} STATUS status LOG log) list(GET status 0 status_code) list(GET status 1 status_string) if(NOT status_code EQUAL 0) message(FATAL_ERROR \"error: downloading '${remote}' failed status_code: \${status_code} status_string: \${status_string} log: \${log} \") endif() message(STATUS \"downloading... done\") " ) endfunction() function(_ep_write_verifyfile_script script_filename local hash) if("${hash}" MATCHES "${_ep_hash_regex}") set(algo "${CMAKE_MATCH_1}") string(TOLOWER "${CMAKE_MATCH_2}" expect_value) set(script_content "set(expect_value \"${expect_value}\") file(${algo} \"\${file}\" actual_value) if(\"\${actual_value}\" STREQUAL \"\${expect_value}\") message(STATUS \"verifying file... done\") else() message(FATAL_ERROR \"error: ${algo} hash of \${file} does not match expected value expected: \${expect_value} actual: \${actual_value} \") endif()") else() set(script_content "message(STATUS \"verifying file... warning: did not verify file - no URL_HASH specified?\")") endif() file(WRITE ${script_filename} "set(file \"${local}\") message(STATUS \"verifying file... file='\${file}'\") ${script_content} ") endfunction() function(_ep_write_extractfile_script script_filename name filename directory) set(args "") if(filename MATCHES "(\\.|=)(bz2|tar\\.gz|tgz|zip)$") set(args xfz) endif() if(filename MATCHES "(\\.|=)tar$") set(args xf) endif() if(args STREQUAL "") message(SEND_ERROR "error: do not know how to extract '${filename}' -- known types are .bz2, .tar, .tar.gz, .tgz and .zip") return() endif() file(WRITE ${script_filename} "# Make file names absolute: # get_filename_component(filename \"${filename}\" ABSOLUTE) get_filename_component(directory \"${directory}\" ABSOLUTE) message(STATUS \"extracting... src='\${filename}' dst='\${directory}'\") if(NOT EXISTS \"\${filename}\") message(FATAL_ERROR \"error: file to extract does not exist: '\${filename}'\") endif() # Prepare a space for extracting: # set(i 1234) while(EXISTS \"\${directory}/../ex-${name}\${i}\") math(EXPR i \"\${i} + 1\") endwhile() set(ut_dir \"\${directory}/../ex-${name}\${i}\") file(MAKE_DIRECTORY \"\${ut_dir}\") # Extract it: # message(STATUS \"extracting... [tar ${args}]\") execute_process(COMMAND \${CMAKE_COMMAND} -E tar ${args} \${filename} WORKING_DIRECTORY \${ut_dir} RESULT_VARIABLE rv) if(NOT rv EQUAL 0) message(STATUS \"extracting... [error clean up]\") file(REMOVE_RECURSE \"\${ut_dir}\") message(FATAL_ERROR \"error: extract of '\${filename}' failed\") endif() # Analyze what came out of the tar file: # message(STATUS \"extracting... [analysis]\") file(GLOB contents \"\${ut_dir}/*\") list(LENGTH contents n) if(NOT n EQUAL 1 OR NOT IS_DIRECTORY \"\${contents}\") set(contents \"\${ut_dir}\") endif() # Move \"the one\" directory to the final directory: # message(STATUS \"extracting... [rename]\") file(REMOVE_RECURSE \${directory}) get_filename_component(contents \${contents} ABSOLUTE) file(RENAME \${contents} \${directory}) # Clean up: # message(STATUS \"extracting... [clean up]\") file(REMOVE_RECURSE \"\${ut_dir}\") message(STATUS \"extracting... done\") " ) endfunction() function(_ep_set_directories name) get_property(prefix TARGET ${name} PROPERTY _EP_PREFIX) if(NOT prefix) get_property(prefix DIRECTORY PROPERTY EP_PREFIX) if(NOT prefix) get_property(base DIRECTORY PROPERTY EP_BASE) if(NOT base) set(prefix "${name}-prefix") endif() endif() endif() if(prefix) set(tmp_default "${prefix}/tmp") set(download_default "${prefix}/src") set(source_default "${prefix}/src/${name}") set(binary_default "${prefix}/src/${name}-build") set(stamp_default "${prefix}/src/${name}-stamp") set(install_default "${prefix}") else() set(tmp_default "${base}/tmp/${name}") set(download_default "${base}/Download/${name}") set(source_default "${base}/Source/${name}") set(binary_default "${base}/Build/${name}") set(stamp_default "${base}/Stamp/${name}") set(install_default "${base}/Install/${name}") endif() get_property(build_in_source TARGET ${name} PROPERTY _EP_BUILD_IN_SOURCE) if(build_in_source) get_property(have_binary_dir TARGET ${name} PROPERTY _EP_BINARY_DIR SET) if(have_binary_dir) message(FATAL_ERROR "External project ${name} has both BINARY_DIR and BUILD_IN_SOURCE!") endif() endif() set(top "${CMAKE_CURRENT_BINARY_DIR}") set(places stamp download source binary install tmp) foreach(var ${places}) string(TOUPPER "${var}" VAR) get_property(${var}_dir TARGET ${name} PROPERTY _EP_${VAR}_DIR) if(NOT ${var}_dir) set(${var}_dir "${${var}_default}") endif() if(NOT IS_ABSOLUTE "${${var}_dir}") get_filename_component(${var}_dir "${top}/${${var}_dir}" ABSOLUTE) endif() set_property(TARGET ${name} PROPERTY _EP_${VAR}_DIR "${${var}_dir}") endforeach() if(build_in_source) get_property(source_dir TARGET ${name} PROPERTY _EP_SOURCE_DIR) set_property(TARGET ${name} PROPERTY _EP_BINARY_DIR "${source_dir}") endif() # Make the directories at CMake configure time *and* add a custom command # to make them at build time. They need to exist at makefile generation # time for Borland make and wmake so that CMake may generate makefiles # with "cd C:\short\paths\with\no\spaces" commands in them. # # Additionally, the add_custom_command is still used in case somebody # removes one of the necessary directories and tries to rebuild without # re-running cmake. foreach(var ${places}) string(TOUPPER "${var}" VAR) get_property(dir TARGET ${name} PROPERTY _EP_${VAR}_DIR) file(MAKE_DIRECTORY "${dir}") if(NOT EXISTS "${dir}") message(FATAL_ERROR "dir '${dir}' does not exist after file(MAKE_DIRECTORY)") endif() endforeach() endfunction() # IMPORTANT: this MUST be a macro and not a function because of the # in-place replacements that occur in each ${var} # macro(_ep_replace_location_tags target_name) set(vars ${ARGN}) foreach(var ${vars}) if(${var}) foreach(dir SOURCE_DIR BINARY_DIR INSTALL_DIR TMP_DIR) get_property(val TARGET ${target_name} PROPERTY _EP_${dir}) string(REPLACE "<${dir}>" "${val}" ${var} "${${var}}") endforeach() endif() endforeach() endmacro() function(_ep_write_initial_cache target_name script_filename args) # Write out values into an initial cache, that will be passed to CMake with -C set(script_initial_cache "") set(regex "^([^:]+):([^=]+)=(.*)$") set(setArg "") foreach(line ${args}) if("${line}" MATCHES "^-D") if(setArg) # This is required to build up lists in variables, or complete an entry set(setArg "${setArg}${accumulator}\" CACHE ${type} \"Initial cache\" FORCE)") set(script_initial_cache "${script_initial_cache}\n${setArg}") set(accumulator "") set(setArg "") endif() string(REGEX REPLACE "^-D" "" line ${line}) if("${line}" MATCHES "${regex}") string(REGEX MATCH "${regex}" match "${line}") set(name "${CMAKE_MATCH_1}") set(type "${CMAKE_MATCH_2}") set(value "${CMAKE_MATCH_3}") set(setArg "set(${name} \"${value}") else() message(WARNING "Line '${line}' does not match regex. Ignoring.") endif() else() # Assume this is a list to append to the last var set(accumulator "${accumulator};${line}") endif() endforeach() # Catch the final line of the args if(setArg) set(setArg "${setArg}${accumulator}\" CACHE ${type} \"Initial cache\" FORCE)") set(script_initial_cache "${script_initial_cache}\n${setArg}") endif() # Replace location tags. _ep_replace_location_tags(${target_name} script_initial_cache) # Write out the initial cache file to the location specified. if(NOT EXISTS "${script_filename}.in") file(WRITE "${script_filename}.in" "\@script_initial_cache\@\n") endif() configure_file("${script_filename}.in" "${script_filename}") endfunction() function(ExternalProject_Get_Property name) foreach(var ${ARGN}) string(TOUPPER "${var}" VAR) get_property(${var} TARGET ${name} PROPERTY _EP_${VAR}) if(NOT ${var}) message(FATAL_ERROR "External project \"${name}\" has no ${var}") endif() set(${var} "${${var}}" PARENT_SCOPE) endforeach() endfunction() function(_ep_get_configure_command_id name cfg_cmd_id_var) get_target_property(cmd ${name} _EP_CONFIGURE_COMMAND) if(cmd STREQUAL "") # Explicit empty string means no configure step for this project set(${cfg_cmd_id_var} "none" PARENT_SCOPE) else() if(NOT cmd) # Default is "use cmake": set(${cfg_cmd_id_var} "cmake" PARENT_SCOPE) else() # Otherwise we have to analyze the value: if(cmd MATCHES "^[^;]*/configure") set(${cfg_cmd_id_var} "configure" PARENT_SCOPE) elseif(cmd MATCHES "^[^;]*/cmake" AND NOT cmd MATCHES ";-[PE];") set(${cfg_cmd_id_var} "cmake" PARENT_SCOPE) elseif(cmd MATCHES "config") set(${cfg_cmd_id_var} "configure" PARENT_SCOPE) else() set(${cfg_cmd_id_var} "unknown:${cmd}" PARENT_SCOPE) endif() endif() endif() endfunction() function(_ep_get_build_command name step cmd_var) set(cmd "${${cmd_var}}") if(NOT cmd) set(args) _ep_get_configure_command_id(${name} cfg_cmd_id) if(cfg_cmd_id STREQUAL "cmake") # CMake project. Select build command based on generator. get_target_property(cmake_generator ${name} _EP_CMAKE_GENERATOR) if("${CMAKE_GENERATOR}" MATCHES "Make" AND ("${cmake_generator}" MATCHES "Make" OR NOT cmake_generator)) # The project uses the same Makefile generator. Use recursive make. set(cmd "$(MAKE)") if(step STREQUAL "INSTALL") set(args install) endif() if(step STREQUAL "TEST") set(args test) endif() else() # Drive the project with "cmake --build". get_target_property(cmake_command ${name} _EP_CMAKE_COMMAND) if(cmake_command) set(cmd "${cmake_command}") else() set(cmd "${CMAKE_COMMAND}") endif() set(args --build ${binary_dir} --config ${CMAKE_CFG_INTDIR}) if(step STREQUAL "INSTALL") list(APPEND args --target install) endif() # But for "TEST" drive the project with corresponding "ctest". if(step STREQUAL "TEST") string(REGEX REPLACE "^(.*/)cmake([^/]*)$" "\\1ctest\\2" cmd "${cmd}") set(args "") endif() endif() else() # Non-CMake project. Guess "make" and "make install" and "make test". if("${CMAKE_GENERATOR}" MATCHES "Makefiles") # Try to get the parallel arguments set(cmd "$(MAKE)") else() set(cmd "make") endif() if(step STREQUAL "INSTALL") set(args install) endif() if(step STREQUAL "TEST") set(args test) endif() endif() # Use user-specified arguments instead of default arguments, if any. get_property(have_args TARGET ${name} PROPERTY _EP_${step}_ARGS SET) if(have_args) get_target_property(args ${name} _EP_${step}_ARGS) endif() list(APPEND cmd ${args}) endif() set(${cmd_var} "${cmd}" PARENT_SCOPE) endfunction() function(_ep_write_log_script name step cmd_var) ExternalProject_Get_Property(${name} stamp_dir) set(command "${${cmd_var}}") set(make "") set(code_cygpath_make "") if("${command}" MATCHES "^\\$\\(MAKE\\)") # GNU make recognizes the string "$(MAKE)" as recursive make, so # ensure that it appears directly in the makefile. string(REGEX REPLACE "^\\$\\(MAKE\\)" "\${make}" command "${command}") set(make "-Dmake=$(MAKE)") if(WIN32 AND NOT CYGWIN) set(code_cygpath_make " if(\${make} MATCHES \"^/\") execute_process( COMMAND cygpath -w \${make} OUTPUT_VARIABLE cygpath_make ERROR_VARIABLE cygpath_make RESULT_VARIABLE cygpath_error OUTPUT_STRIP_TRAILING_WHITESPACE ) if(NOT cygpath_error) set(make \${cygpath_make}) endif() endif() ") endif() endif() set(config "") if("${CMAKE_CFG_INTDIR}" MATCHES "^\\$") string(REPLACE "${CMAKE_CFG_INTDIR}" "\${config}" command "${command}") set(config "-Dconfig=${CMAKE_CFG_INTDIR}") endif() # Wrap multiple 'COMMAND' lines up into a second-level wrapper # script so all output can be sent to one log file. if("${command}" MATCHES ";COMMAND;") set(code_execute_process " ${code_cygpath_make} execute_process(COMMAND \${command} RESULT_VARIABLE result) if(result) set(msg \"Command failed (\${result}):\\n\") foreach(arg IN LISTS command) set(msg \"\${msg} '\${arg}'\") endforeach() message(FATAL_ERROR \"\${msg}\") endif() ") set(code "") set(cmd "") set(sep "") foreach(arg IN LISTS command) if("x${arg}" STREQUAL "xCOMMAND") set(code "${code}set(command \"${cmd}\")${code_execute_process}") set(cmd "") set(sep "") else() set(cmd "${cmd}${sep}${arg}") set(sep ";") endif() endforeach() set(code "${code}set(command \"${cmd}\")${code_execute_process}") file(WRITE ${stamp_dir}/${name}-${step}-impl.cmake "${code}") set(command ${CMAKE_COMMAND} "-Dmake=\${make}" "-Dconfig=\${config}" -P ${stamp_dir}/${name}-${step}-impl.cmake) endif() # Wrap the command in a script to log output to files. set(script ${stamp_dir}/${name}-${step}.cmake) set(logbase ${stamp_dir}/${name}-${step}) file(WRITE ${script} " ${code_cygpath_make} set(command \"${command}\") execute_process( COMMAND \${command} RESULT_VARIABLE result OUTPUT_FILE \"${logbase}-out.log\" ERROR_FILE \"${logbase}-err.log\" ) if(result) set(msg \"Command failed: \${result}\\n\") foreach(arg IN LISTS command) set(msg \"\${msg} '\${arg}'\") endforeach() set(msg \"\${msg}\\nSee also\\n ${logbase}-*.log\\n\") message(FATAL_ERROR \"\${msg}\") else() set(msg \"${name} ${step} command succeeded. See also ${logbase}-*.log\\n\") message(STATUS \"\${msg}\") endif() ") set(command ${CMAKE_COMMAND} ${make} ${config} -P ${script}) set(${cmd_var} "${command}" PARENT_SCOPE) endfunction() # This module used to use "/${CMAKE_CFG_INTDIR}" directly and produced # makefiles with "/./" in paths for custom command dependencies. Which # resulted in problems with parallel make -j invocations. # # This function was added so that the suffix (search below for ${cfgdir}) is # only set to "/${CMAKE_CFG_INTDIR}" when ${CMAKE_CFG_INTDIR} is not going to # be "." (multi-configuration build systems like Visual Studio and Xcode...) # function(_ep_get_configuration_subdir_suffix suffix_var) set(suffix "") if(CMAKE_CONFIGURATION_TYPES) set(suffix "/${CMAKE_CFG_INTDIR}") endif() set(${suffix_var} "${suffix}" PARENT_SCOPE) endfunction() function(_ep_get_step_stampfile name step stampfile_var) ExternalProject_Get_Property(${name} stamp_dir) _ep_get_configuration_subdir_suffix(cfgdir) set(stampfile "${stamp_dir}${cfgdir}/${name}-${step}") set(${stampfile_var} "${stampfile}" PARENT_SCOPE) endfunction() function(ExternalProject_Add_StepTargets name) set(steps ${ARGN}) foreach(step ${steps}) _ep_get_step_stampfile(${name} ${step} stamp_file) add_custom_target(${name}-${step} DEPENDS ${stamp_file}) # Depend on other external projects (target-level). get_property(deps TARGET ${name} PROPERTY _EP_DEPENDS) foreach(arg IN LISTS deps) add_dependencies(${name}-${step} ${arg}) endforeach() endforeach() endfunction() function(ExternalProject_Add_Step name step) set(cmf_dir ${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles) _ep_get_configuration_subdir_suffix(cfgdir) set(complete_stamp_file "${cmf_dir}${cfgdir}/${name}-complete") _ep_get_step_stampfile(${name} ${step} stamp_file) add_custom_command(APPEND OUTPUT ${complete_stamp_file} DEPENDS ${stamp_file} ) _ep_parse_arguments(ExternalProject_Add_Step ${name} _EP_${step}_ "${ARGN}") # Steps depending on this step. get_property(dependers TARGET ${name} PROPERTY _EP_${step}_DEPENDERS) foreach(depender IN LISTS dependers) _ep_get_step_stampfile(${name} ${depender} depender_stamp_file) add_custom_command(APPEND OUTPUT ${depender_stamp_file} DEPENDS ${stamp_file} ) endforeach() # Dependencies on files. get_property(depends TARGET ${name} PROPERTY _EP_${step}_DEPENDS) # Dependencies on steps. get_property(dependees TARGET ${name} PROPERTY _EP_${step}_DEPENDEES) foreach(dependee IN LISTS dependees) _ep_get_step_stampfile(${name} ${dependee} dependee_stamp_file) list(APPEND depends ${dependee_stamp_file}) endforeach() # The command to run. get_property(command TARGET ${name} PROPERTY _EP_${step}_COMMAND) if(command) set(comment "Performing ${step} step for '${name}'") else() set(comment "No ${step} step for '${name}'") endif() get_property(work_dir TARGET ${name} PROPERTY _EP_${step}_WORKING_DIRECTORY) # Replace list separators. get_property(sep TARGET ${name} PROPERTY _EP_LIST_SEPARATOR) if(sep AND command) string(REPLACE "${sep}" "\\;" command "${command}") endif() # Replace location tags. _ep_replace_location_tags(${name} comment command work_dir) # Custom comment? get_property(comment_set TARGET ${name} PROPERTY _EP_${step}_COMMENT SET) if(comment_set) get_property(comment TARGET ${name} PROPERTY _EP_${step}_COMMENT) endif() # Run every time? get_property(always TARGET ${name} PROPERTY _EP_${step}_ALWAYS) if(always) set_property(SOURCE ${stamp_file} PROPERTY SYMBOLIC 1) set(touch) else() set(touch ${CMAKE_COMMAND} -E touch ${stamp_file}) endif() # Wrap with log script? get_property(log TARGET ${name} PROPERTY _EP_${step}_LOG) if(command AND log) _ep_write_log_script(${name} ${step} command) endif() add_custom_command( OUTPUT ${stamp_file} COMMENT ${comment} COMMAND ${command} COMMAND ${touch} DEPENDS ${depends} WORKING_DIRECTORY ${work_dir} VERBATIM ) # Add custom "step target"? get_property(step_targets TARGET ${name} PROPERTY _EP_STEP_TARGETS) if(NOT step_targets) get_property(step_targets DIRECTORY PROPERTY EP_STEP_TARGETS) endif() foreach(st ${step_targets}) if("${st}" STREQUAL "${step}") ExternalProject_Add_StepTargets(${name} ${step}) break() endif() endforeach() endfunction() function(_ep_add_mkdir_command name) ExternalProject_Get_Property(${name} source_dir binary_dir install_dir stamp_dir download_dir tmp_dir) _ep_get_configuration_subdir_suffix(cfgdir) ExternalProject_Add_Step(${name} mkdir COMMENT "Creating directories for '${name}'" COMMAND ${CMAKE_COMMAND} -E make_directory ${source_dir} COMMAND ${CMAKE_COMMAND} -E make_directory ${binary_dir} COMMAND ${CMAKE_COMMAND} -E make_directory ${install_dir} COMMAND ${CMAKE_COMMAND} -E make_directory ${tmp_dir} COMMAND ${CMAKE_COMMAND} -E make_directory ${stamp_dir}${cfgdir} COMMAND ${CMAKE_COMMAND} -E make_directory ${download_dir} ) endfunction() function(_ep_get_git_version git_EXECUTABLE git_version_var) if(git_EXECUTABLE) execute_process( COMMAND "${git_EXECUTABLE}" --version OUTPUT_VARIABLE ov ERROR_VARIABLE ev OUTPUT_STRIP_TRAILING_WHITESPACE ) string(REGEX REPLACE "^git version (.+)$" "\\1" version "${ov}") set(${git_version_var} "${version}" PARENT_SCOPE) endif() endfunction() function(_ep_is_dir_empty dir empty_var) file(GLOB gr "${dir}/*") if("${gr}" STREQUAL "") set(${empty_var} 1 PARENT_SCOPE) else() set(${empty_var} 0 PARENT_SCOPE) endif() endfunction() function(_ep_add_download_command name) ExternalProject_Get_Property(${name} source_dir stamp_dir download_dir tmp_dir) get_property(cmd_set TARGET ${name} PROPERTY _EP_DOWNLOAD_COMMAND SET) get_property(cmd TARGET ${name} PROPERTY _EP_DOWNLOAD_COMMAND) get_property(cvs_repository TARGET ${name} PROPERTY _EP_CVS_REPOSITORY) get_property(svn_repository TARGET ${name} PROPERTY _EP_SVN_REPOSITORY) get_property(git_repository TARGET ${name} PROPERTY _EP_GIT_REPOSITORY) get_property(hg_repository TARGET ${name} PROPERTY _EP_HG_REPOSITORY ) get_property(url TARGET ${name} PROPERTY _EP_URL) get_property(fname TARGET ${name} PROPERTY _EP_DOWNLOAD_NAME) # TODO: Perhaps file:// should be copied to download dir before extraction. string(REGEX REPLACE "^file://" "" url "${url}") set(depends) set(comment) set(work_dir) if(cmd_set) set(work_dir ${download_dir}) elseif(cvs_repository) find_package(CVS) if(NOT CVS_EXECUTABLE) message(FATAL_ERROR "error: could not find cvs for checkout of ${name}") endif() get_target_property(cvs_module ${name} _EP_CVS_MODULE) if(NOT cvs_module) message(FATAL_ERROR "error: no CVS_MODULE") endif() get_property(cvs_tag TARGET ${name} PROPERTY _EP_CVS_TAG) set(repository ${cvs_repository}) set(module ${cvs_module}) set(tag ${cvs_tag}) configure_file( "${CMAKE_ROOT}/Modules/RepositoryInfo.txt.in" "${stamp_dir}/${name}-cvsinfo.txt" @ONLY ) get_filename_component(src_name "${source_dir}" NAME) get_filename_component(work_dir "${source_dir}" PATH) set(comment "Performing download step (CVS checkout) for '${name}'") set(cmd ${CVS_EXECUTABLE} -d ${cvs_repository} -q co ${cvs_tag} -d ${src_name} ${cvs_module}) list(APPEND depends ${stamp_dir}/${name}-cvsinfo.txt) elseif(svn_repository) find_package(Subversion) if(NOT Subversion_SVN_EXECUTABLE) message(FATAL_ERROR "error: could not find svn for checkout of ${name}") endif() get_property(svn_revision TARGET ${name} PROPERTY _EP_SVN_REVISION) get_property(svn_username TARGET ${name} PROPERTY _EP_SVN_USERNAME) get_property(svn_password TARGET ${name} PROPERTY _EP_SVN_PASSWORD) get_property(svn_trust_cert TARGET ${name} PROPERTY _EP_SVN_TRUST_CERT) set(repository "${svn_repository} user=${svn_username} password=${svn_password}") set(module) set(tag ${svn_revision}) configure_file( "${CMAKE_ROOT}/Modules/RepositoryInfo.txt.in" "${stamp_dir}/${name}-svninfo.txt" @ONLY ) get_filename_component(src_name "${source_dir}" NAME) get_filename_component(work_dir "${source_dir}" PATH) set(comment "Performing download step (SVN checkout) for '${name}'") set(svn_user_pw_args "") if(DEFINED svn_username) set(svn_user_pw_args ${svn_user_pw_args} "--username=${svn_username}") endif() if(DEFINED svn_password) set(svn_user_pw_args ${svn_user_pw_args} "--password=${svn_password}") endif() if(svn_trust_cert) set(svn_trust_cert_args --trust-server-cert) endif() set(cmd ${Subversion_SVN_EXECUTABLE} co ${svn_repository} ${svn_revision} --non-interactive ${svn_trust_cert_args} ${svn_user_pw_args} ${src_name}) list(APPEND depends ${stamp_dir}/${name}-svninfo.txt) elseif(git_repository) find_package(Git) if(NOT GIT_EXECUTABLE) message(FATAL_ERROR "error: could not find git for clone of ${name}") endif() # The git submodule update '--recursive' flag requires git >= v1.6.5 # _ep_get_git_version("${GIT_EXECUTABLE}" git_version) if(git_version VERSION_LESS 1.6.5) message(FATAL_ERROR "error: git version 1.6.5 or later required for 'git submodule update --recursive': git_version='${git_version}'") endif() get_property(git_tag TARGET ${name} PROPERTY _EP_GIT_TAG) if(NOT git_tag) set(git_tag "master") endif() # For the download step, and the git clone operation, only the repository # should be recorded in a configured RepositoryInfo file. If the repo # changes, the clone script should be run again. But if only the tag # changes, avoid running the clone script again. Let the 'always' running # update step checkout the new tag. # set(repository ${git_repository}) set(module) set(tag) configure_file( "${CMAKE_ROOT}/Modules/RepositoryInfo.txt.in" "${stamp_dir}/${name}-gitinfo.txt" @ONLY ) get_filename_component(src_name "${source_dir}" NAME) get_filename_component(work_dir "${source_dir}" PATH) # Since git clone doesn't succeed if the non-empty source_dir exists, # create a cmake script to invoke as download command. # The script will delete the source directory and then call git clone. # _ep_write_gitclone_script(${tmp_dir}/${name}-gitclone.cmake ${source_dir} ${GIT_EXECUTABLE} ${git_repository} ${git_tag} ${src_name} ${work_dir} ${stamp_dir}/${name}-gitinfo.txt ${stamp_dir}/${name}-gitclone-lastrun.txt ) set(comment "Performing download step (git clone) for '${name}'") set(cmd ${CMAKE_COMMAND} -P ${tmp_dir}/${name}-gitclone.cmake) list(APPEND depends ${stamp_dir}/${name}-gitinfo.txt) elseif(hg_repository) find_package(Hg) if(NOT HG_EXECUTABLE) message(FATAL_ERROR "error: could not find hg for clone of ${name}") endif() get_property(hg_tag TARGET ${name} PROPERTY _EP_HG_TAG) if(NOT hg_tag) set(hg_tag "tip") endif() # For the download step, and the hg clone operation, only the repository # should be recorded in a configured RepositoryInfo file. If the repo # changes, the clone script should be run again. But if only the tag # changes, avoid running the clone script again. Let the 'always' running # update step checkout the new tag. # set(repository ${hg_repository}) set(module) set(tag) configure_file( "${CMAKE_ROOT}/Modules/RepositoryInfo.txt.in" "${stamp_dir}/${name}-hginfo.txt" @ONLY ) get_filename_component(src_name "${source_dir}" NAME) get_filename_component(work_dir "${source_dir}" PATH) # Since hg clone doesn't succeed if the non-empty source_dir exists, # create a cmake script to invoke as download command. # The script will delete the source directory and then call hg clone. # _ep_write_hgclone_script(${tmp_dir}/${name}-hgclone.cmake ${source_dir} ${HG_EXECUTABLE} ${hg_repository} ${hg_tag} ${src_name} ${work_dir} ${stamp_dir}/${name}-hginfo.txt ${stamp_dir}/${name}-hgclone-lastrun.txt ) set(comment "Performing download step (hg clone) for '${name}'") set(cmd ${CMAKE_COMMAND} -P ${tmp_dir}/${name}-hgclone.cmake) list(APPEND depends ${stamp_dir}/${name}-hginfo.txt) elseif(url) get_filename_component(work_dir "${source_dir}" PATH) get_property(hash TARGET ${name} PROPERTY _EP_URL_HASH) if(hash AND NOT "${hash}" MATCHES "${_ep_hash_regex}") message(FATAL_ERROR "URL_HASH is set to\n ${hash}\n" "but must be ALGO=value where ALGO is\n ${_ep_hash_algos}\n" "and value is a hex string.") endif() get_property(md5 TARGET ${name} PROPERTY _EP_URL_MD5) if(md5 AND NOT "MD5=${md5}" MATCHES "${_ep_hash_regex}") message(FATAL_ERROR "URL_MD5 is set to\n ${md5}\nbut must be a hex string.") endif() if(md5 AND NOT hash) set(hash "MD5=${md5}") endif() set(repository "external project URL") set(module "${url}") set(tag "${hash}") configure_file( "${CMAKE_ROOT}/Modules/RepositoryInfo.txt.in" "${stamp_dir}/${name}-urlinfo.txt" @ONLY ) list(APPEND depends ${stamp_dir}/${name}-urlinfo.txt) if(IS_DIRECTORY "${url}") get_filename_component(abs_dir "${url}" ABSOLUTE) set(comment "Performing download step (DIR copy) for '${name}'") set(cmd ${CMAKE_COMMAND} -E remove_directory ${source_dir} COMMAND ${CMAKE_COMMAND} -E copy_directory ${abs_dir} ${source_dir}) else() if("${url}" MATCHES "^[a-z]+://") # TODO: Should download and extraction be different steps? if("x${fname}" STREQUAL "x") string(REGEX MATCH "[^/\\?]*$" fname "${url}") endif() if(NOT "${fname}" MATCHES "(\\.|=)(bz2|tar|tgz|tar\\.gz|zip)$") string(REGEX MATCH "([^/\\?]+(\\.|=)(bz2|tar|tgz|tar\\.gz|zip))/.*$" match_result "${url}") set(fname "${CMAKE_MATCH_1}") endif() if(NOT "${fname}" MATCHES "(\\.|=)(bz2|tar|tgz|tar\\.gz|zip)$") message(FATAL_ERROR "Could not extract tarball filename from url:\n ${url}") endif() string(REPLACE ";" "-" fname "${fname}") set(file ${download_dir}/${fname}) get_property(timeout TARGET ${name} PROPERTY _EP_TIMEOUT) get_property(tls_verify TARGET ${name} PROPERTY _EP_TLS_VERIFY) get_property(tls_cainfo TARGET ${name} PROPERTY _EP_TLS_CAINFO) _ep_write_downloadfile_script("${stamp_dir}/download-${name}.cmake" "${url}" "${file}" "${timeout}" "${hash}" "${tls_verify}" "${tls_cainfo}") set(cmd ${CMAKE_COMMAND} -P ${stamp_dir}/download-${name}.cmake COMMAND) set(comment "Performing download step (download, verify and extract) for '${name}'") else() set(file "${url}") set(comment "Performing download step (verify and extract) for '${name}'") endif() _ep_write_verifyfile_script("${stamp_dir}/verify-${name}.cmake" "${file}" "${hash}") list(APPEND cmd ${CMAKE_COMMAND} -P ${stamp_dir}/verify-${name}.cmake COMMAND) _ep_write_extractfile_script("${stamp_dir}/extract-${name}.cmake" "${name}" "${file}" "${source_dir}") list(APPEND cmd ${CMAKE_COMMAND} -P ${stamp_dir}/extract-${name}.cmake) endif() else() _ep_is_dir_empty("${source_dir}" empty) if(${empty}) message(SEND_ERROR "error: no download info for '${name}' -- please specify existing/non-empty SOURCE_DIR or one of URL, CVS_REPOSITORY and CVS_MODULE, SVN_REPOSITORY, GIT_REPOSITORY, HG_REPOSITORY or DOWNLOAD_COMMAND") endif() endif() get_property(log TARGET ${name} PROPERTY _EP_LOG_DOWNLOAD) if(log) set(log LOG 1) else() set(log "") endif() ExternalProject_Add_Step(${name} download COMMENT ${comment} COMMAND ${cmd} WORKING_DIRECTORY ${work_dir} DEPENDS ${depends} DEPENDEES mkdir ${log} ) endfunction() function(_ep_add_update_command name) ExternalProject_Get_Property(${name} source_dir tmp_dir) get_property(cmd_set TARGET ${name} PROPERTY _EP_UPDATE_COMMAND SET) get_property(cmd TARGET ${name} PROPERTY _EP_UPDATE_COMMAND) get_property(cvs_repository TARGET ${name} PROPERTY _EP_CVS_REPOSITORY) get_property(svn_repository TARGET ${name} PROPERTY _EP_SVN_REPOSITORY) get_property(git_repository TARGET ${name} PROPERTY _EP_GIT_REPOSITORY) get_property(hg_repository TARGET ${name} PROPERTY _EP_HG_REPOSITORY ) set(work_dir) set(comment) set(always) if(cmd_set) set(work_dir ${source_dir}) elseif(cvs_repository) if(NOT CVS_EXECUTABLE) message(FATAL_ERROR "error: could not find cvs for update of ${name}") endif() set(work_dir ${source_dir}) set(comment "Performing update step (CVS update) for '${name}'") get_property(cvs_tag TARGET ${name} PROPERTY _EP_CVS_TAG) set(cmd ${CVS_EXECUTABLE} -d ${cvs_repository} -q up -dP ${cvs_tag}) set(always 1) elseif(svn_repository) if(NOT Subversion_SVN_EXECUTABLE) message(FATAL_ERROR "error: could not find svn for update of ${name}") endif() set(work_dir ${source_dir}) set(comment "Performing update step (SVN update) for '${name}'") get_property(svn_revision TARGET ${name} PROPERTY _EP_SVN_REVISION) get_property(svn_username TARGET ${name} PROPERTY _EP_SVN_USERNAME) get_property(svn_password TARGET ${name} PROPERTY _EP_SVN_PASSWORD) get_property(svn_trust_cert TARGET ${name} PROPERTY _EP_SVN_TRUST_CERT) set(svn_user_pw_args "") if(DEFINED svn_username) set(svn_user_pw_args ${svn_user_pw_args} "--username=${svn_username}") endif() if(DEFINED svn_password) set(svn_user_pw_args ${svn_user_pw_args} "--password=${svn_password}") endif() if(svn_trust_cert) set(svn_trust_cert_args --trust-server-cert) endif() set(cmd ${Subversion_SVN_EXECUTABLE} up ${svn_revision} --non-interactive ${svn_trust_cert_args} ${svn_user_pw_args}) set(always 1) elseif(git_repository) if(NOT GIT_EXECUTABLE) message(FATAL_ERROR "error: could not find git for fetch of ${name}") endif() set(work_dir ${source_dir}) set(comment "Performing update step for '${name}'") get_property(git_tag TARGET ${name} PROPERTY _EP_GIT_TAG) if(NOT git_tag) set(git_tag "master") endif() _ep_write_gitupdate_script(${tmp_dir}/${name}-gitupdate.cmake ${GIT_EXECUTABLE} ${git_tag} ${git_repository} ${work_dir} ) set(cmd ${CMAKE_COMMAND} -P ${tmp_dir}/${name}-gitupdate.cmake) set(always 1) elseif(hg_repository) if(NOT HG_EXECUTABLE) message(FATAL_ERROR "error: could not find hg for pull of ${name}") endif() set(work_dir ${source_dir}) set(comment "Performing update step (hg pull) for '${name}'") get_property(hg_tag TARGET ${name} PROPERTY _EP_HG_TAG) if(NOT hg_tag) set(hg_tag "tip") endif() if("${HG_VERSION_STRING}" STREQUAL "2.1") message(WARNING "Mercurial 2.1 does not distinguish an empty pull from a failed pull: http://mercurial.selenic.com/wiki/UpgradeNotes#A2.1.1:_revert_pull_return_code_change.2C_compile_issue_on_OS_X http://thread.gmane.org/gmane.comp.version-control.mercurial.devel/47656 Update to Mercurial >= 2.1.1. ") endif() set(cmd ${HG_EXECUTABLE} pull COMMAND ${HG_EXECUTABLE} update ${hg_tag} ) set(always 1) endif() get_property(log TARGET ${name} PROPERTY _EP_LOG_UPDATE) if(log) set(log LOG 1) else() set(log "") endif() ExternalProject_Add_Step(${name} update COMMENT ${comment} COMMAND ${cmd} ALWAYS ${always} WORKING_DIRECTORY ${work_dir} DEPENDEES download ${log} ) endfunction() function(_ep_add_patch_command name) ExternalProject_Get_Property(${name} source_dir) get_property(cmd_set TARGET ${name} PROPERTY _EP_PATCH_COMMAND SET) get_property(cmd TARGET ${name} PROPERTY _EP_PATCH_COMMAND) set(work_dir) if(cmd_set) set(work_dir ${source_dir}) endif() ExternalProject_Add_Step(${name} patch COMMAND ${cmd} WORKING_DIRECTORY ${work_dir} DEPENDEES download ) endfunction() # TODO: Make sure external projects use the proper compiler function(_ep_add_configure_command name) ExternalProject_Get_Property(${name} source_dir binary_dir tmp_dir) # Depend on other external projects (file-level). set(file_deps) get_property(deps TARGET ${name} PROPERTY _EP_DEPENDS) foreach(dep IN LISTS deps) get_property(is_ep TARGET ${dep} PROPERTY _EP_IS_EXTERNAL_PROJECT) if(is_ep) _ep_get_step_stampfile(${dep} "done" done_stamp_file) list(APPEND file_deps ${done_stamp_file}) endif() endforeach() get_property(cmd_set TARGET ${name} PROPERTY _EP_CONFIGURE_COMMAND SET) if(cmd_set) get_property(cmd TARGET ${name} PROPERTY _EP_CONFIGURE_COMMAND) else() get_target_property(cmake_command ${name} _EP_CMAKE_COMMAND) if(cmake_command) set(cmd "${cmake_command}") else() set(cmd "${CMAKE_COMMAND}") endif() get_property(cmake_args TARGET ${name} PROPERTY _EP_CMAKE_ARGS) list(APPEND cmd ${cmake_args}) # If there are any CMAKE_CACHE_ARGS, write an initial cache and use it get_property(cmake_cache_args TARGET ${name} PROPERTY _EP_CMAKE_CACHE_ARGS) if(cmake_cache_args) set(_ep_cache_args_script "${tmp_dir}/${name}-cache.cmake") _ep_write_initial_cache(${name} "${_ep_cache_args_script}" "${cmake_cache_args}") list(APPEND cmd "-C${_ep_cache_args_script}") endif() get_target_property(cmake_generator ${name} _EP_CMAKE_GENERATOR) get_target_property(cmake_generator_toolset ${name} _EP_CMAKE_GENERATOR_TOOLSET) if(cmake_generator) list(APPEND cmd "-G${cmake_generator}") if(cmake_generator_toolset) list(APPEND cmd "-T${cmake_generator_toolset}") endif() else() if(CMAKE_EXTRA_GENERATOR) list(APPEND cmd "-G${CMAKE_EXTRA_GENERATOR} - ${CMAKE_GENERATOR}") else() list(APPEND cmd "-G${CMAKE_GENERATOR}") endif() if(cmake_generator_toolset) message(FATAL_ERROR "Option CMAKE_GENERATOR_TOOLSET not allowed without CMAKE_GENERATOR.") endif() if(CMAKE_GENERATOR_TOOLSET) list(APPEND cmd "-T${CMAKE_GENERATOR_TOOLSET}") endif() endif() list(APPEND cmd "${source_dir}") endif() # If anything about the configure command changes, (command itself, cmake # used, cmake args or cmake generator) then re-run the configure step. # Fixes issue http://public.kitware.com/Bug/view.php?id=10258 # if(NOT EXISTS ${tmp_dir}/${name}-cfgcmd.txt.in) file(WRITE ${tmp_dir}/${name}-cfgcmd.txt.in "cmd='\@cmd\@'\n") endif() configure_file(${tmp_dir}/${name}-cfgcmd.txt.in ${tmp_dir}/${name}-cfgcmd.txt) list(APPEND file_deps ${tmp_dir}/${name}-cfgcmd.txt) list(APPEND file_deps ${_ep_cache_args_script}) get_property(log TARGET ${name} PROPERTY _EP_LOG_CONFIGURE) if(log) set(log LOG 1) else() set(log "") endif() ExternalProject_Add_Step(${name} configure COMMAND ${cmd} WORKING_DIRECTORY ${binary_dir} DEPENDEES update patch DEPENDS ${file_deps} ${log} ) endfunction() function(_ep_add_build_command name) ExternalProject_Get_Property(${name} binary_dir) get_property(cmd_set TARGET ${name} PROPERTY _EP_BUILD_COMMAND SET) if(cmd_set) get_property(cmd TARGET ${name} PROPERTY _EP_BUILD_COMMAND) else() _ep_get_build_command(${name} BUILD cmd) endif() get_property(log TARGET ${name} PROPERTY _EP_LOG_BUILD) if(log) set(log LOG 1) else() set(log "") endif() ExternalProject_Add_Step(${name} build COMMAND ${cmd} WORKING_DIRECTORY ${binary_dir} DEPENDEES configure ${log} ) endfunction() function(_ep_add_install_command name) ExternalProject_Get_Property(${name} binary_dir) get_property(cmd_set TARGET ${name} PROPERTY _EP_INSTALL_COMMAND SET) if(cmd_set) get_property(cmd TARGET ${name} PROPERTY _EP_INSTALL_COMMAND) else() _ep_get_build_command(${name} INSTALL cmd) endif() get_property(log TARGET ${name} PROPERTY _EP_LOG_INSTALL) if(log) set(log LOG 1) else() set(log "") endif() ExternalProject_Add_Step(${name} install COMMAND ${cmd} WORKING_DIRECTORY ${binary_dir} DEPENDEES build ${log} ) endfunction() function(_ep_add_test_command name) ExternalProject_Get_Property(${name} binary_dir) get_property(before TARGET ${name} PROPERTY _EP_TEST_BEFORE_INSTALL) get_property(after TARGET ${name} PROPERTY _EP_TEST_AFTER_INSTALL) get_property(cmd_set TARGET ${name} PROPERTY _EP_TEST_COMMAND SET) # Only actually add the test step if one of the test related properties is # explicitly set. (i.e. the test step is omitted unless requested...) # if(cmd_set OR before OR after) if(cmd_set) get_property(cmd TARGET ${name} PROPERTY _EP_TEST_COMMAND) else() _ep_get_build_command(${name} TEST cmd) endif() if(before) set(dep_args DEPENDEES build DEPENDERS install) else() set(dep_args DEPENDEES install) endif() get_property(log TARGET ${name} PROPERTY _EP_LOG_TEST) if(log) set(log LOG 1) else() set(log "") endif() ExternalProject_Add_Step(${name} test COMMAND ${cmd} WORKING_DIRECTORY ${binary_dir} ${dep_args} ${log} ) endif() endfunction() function(ExternalProject_Add name) _ep_get_configuration_subdir_suffix(cfgdir) # Add a custom target for the external project. set(cmf_dir ${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles) set(complete_stamp_file "${cmf_dir}${cfgdir}/${name}-complete") add_custom_target(${name} ALL DEPENDS ${complete_stamp_file}) set_property(TARGET ${name} PROPERTY _EP_IS_EXTERNAL_PROJECT 1) _ep_parse_arguments(ExternalProject_Add ${name} _EP_ "${ARGN}") _ep_set_directories(${name}) _ep_get_step_stampfile(${name} "done" done_stamp_file) _ep_get_step_stampfile(${name} "install" install_stamp_file) # The 'complete' step depends on all other steps and creates a # 'done' mark. A dependent external project's 'configure' step # depends on the 'done' mark so that it rebuilds when this project # rebuilds. It is important that 'done' is not the output of any # custom command so that CMake does not propagate build rules to # other external project targets, which may cause problems during # parallel builds. However, the Ninja generator needs to see the entire # dependency graph, and can cope with custom commands belonging to # multiple targets, so we add the 'done' mark as an output for Ninja only. set(complete_outputs ${complete_stamp_file}) if(${CMAKE_GENERATOR} MATCHES "Ninja") set(complete_outputs ${complete_outputs} ${done_stamp_file}) endif() add_custom_command( OUTPUT ${complete_outputs} COMMENT "Completed '${name}'" COMMAND ${CMAKE_COMMAND} -E make_directory ${cmf_dir}${cfgdir} COMMAND ${CMAKE_COMMAND} -E touch ${complete_stamp_file} COMMAND ${CMAKE_COMMAND} -E touch ${done_stamp_file} DEPENDS ${install_stamp_file} VERBATIM ) # Depend on other external projects (target-level). get_property(deps TARGET ${name} PROPERTY _EP_DEPENDS) foreach(arg IN LISTS deps) add_dependencies(${name} ${arg}) endforeach() # Set up custom build steps based on the target properties. # Each step depends on the previous one. # # The target depends on the output of the final step. # (Already set up above in the DEPENDS of the add_custom_target command.) # _ep_add_mkdir_command(${name}) _ep_add_download_command(${name}) _ep_add_update_command(${name}) _ep_add_patch_command(${name}) _ep_add_configure_command(${name}) _ep_add_build_command(${name}) _ep_add_install_command(${name}) # Test is special in that it might depend on build, or it might depend # on install. # _ep_add_test_command(${name}) endfunction() mips-1.0/script/build_system.cmake0000755002402200254040000001402212520407731016650 0ustar thomasfdtaigget_filename_component(TF_CURRENT_DIR_NAME ${CMAKE_CURRENT_SOURCE_DIR} NAME) if(DEFINED TF_CURRENT_DIR) set(TF_CURRENT_DIR ${TF_CURRENT_DIR}_${TF_CURRENT_DIR_NAME}) else(DEFINED TF_CURRENT_DIR) set(TF_CURRENT_DIR ${TF_CURRENT_DIR_NAME}) endif(DEFINED TF_CURRENT_DIR) set(HEADER_FILE ${CMAKE_BINARY_DIR}/data.hpp) set (SOURCES ) set (HEADERS ) set (SUBDIRECTORIES ) macro(INSTALL_HEADERS_WITH_DIRECTORY LOCATION HEADER_LIST) foreach(HEADER ${${HEADER_LIST}}) string(REGEX MATCH "(.*)[/\\]" DIR ${HEADER}) install(FILES ${HEADER} DESTINATION include/${LOCATION}/${DIR}) endforeach(HEADER) endmacro(INSTALL_HEADERS_WITH_DIRECTORY) macro(INCLUDE_SUBDIRECTORY_HEADER_SOURCES DIR) add_subdirectory(${DIR}) set(cur_cpp ) set(cur_hpp ) get_directory_property(cur_cpp DIRECTORY ${DIR} DEFINITION SOURCES) get_directory_property(cur_hpp DIRECTORY ${DIR} DEFINITION HEADERS) foreach(item ${cur_cpp}) list(APPEND SOURCES ${DIR}/${item}) endforeach(item) foreach(item ${cur_hpp}) list(APPEND HEADERS ${DIR}/${item}) endforeach(item) endmacro(INCLUDE_SUBDIRECTORY_HEADER_SOURCES) macro(INCLUDE_ALL_SUBDIRECTORIES_HEADER_SOURCES DIRS) foreach(dir ${${DIRS}}) INCLUDE_SUBDIRECTORY_HEADER_SOURCES(${dir}) endforeach(dir) endmacro(INCLUDE_ALL_SUBDIRECTORIES_HEADER_SOURCES) function(CREATE_EXAMPLE) set(optional) set(oneValueArgs NAME) set(multiValueArgs SOURCES HEADERS REQUIRED_FLAGS INCLUDE_DIRS LIBRARIES DATA_FILES) cmake_parse_arguments(CREATE_EXAMPLE "${optional}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) set(ALL_FLAGS_OKAY True) foreach(flag ${CREATE_EXAMPLE_REQUIRED_FLAGS}) if(NOT FLAG) set(ALL_FLAGS_OKAY False) endif(NOT FLAG) endforeach(flag ${CREATE_EXAMPLE_REQUIRED_FLAGS}) if(ALL_FLAGS_OKAY) set(INCLUDE_DIRS ${PROJECT_SOURCE_DIR} ${CMAKE_BINARY_DIR}/data "${CREATE_EXAMPLE_INCLUDE_DIRS}") include_directories(${INCLUDE_DIRS}) add_executable(${CREATE_EXAMPLE_NAME} EXCLUDE_FROM_ALL ${CREATE_EXAMPLE_SOURCES} ${CREATE_EXAMPLE_HEADERS}) target_link_libraries(${CREATE_EXAMPLE_NAME} ${PROJECT_NAME} ${Boost_LIBRARIES} ${CREATE_EXAMPLE_LIBRARIES}) add_dependencies(all_examples ${CREATE_EXAMPLE_NAME}) foreach(DATA_FILE ${CREATE_EXAMPLE_DATA_FILES}) set(DATA_TARGET export-${DATA_FILE}) add_dependencies(${CREATE_EXAMPLE_NAME} ${DATA_TARGET}) endforeach() endif() endfunction(CREATE_EXAMPLE) function(CREATE_TEST) set(optional) set(oneValueArgs NAME) set(multiValueArgs SOURCES HEADERS REQUIRED_FLAGS INCLUDE_DIRS LIBRARIES DATA_FILES) cmake_parse_arguments(CREATE_EXAMPLE "${optional}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) set(ALL_FLAGS_OKAY True) foreach(flag ${CREATE_EXAMPLE_REQUIRED_FLAGS}) if(NOT ${FLAG}) set(ALL_FLAGS_OKAY False) endif(NOT ${FLAG}) endforeach(flag ${CREATE_EXAMPLE_REQUIRED_FLAGS}) if(${ALL_FLAGS_OKAY}) set(CURRENT_TEST_NAME ${TF_CURRENT_DIR}_${CREATE_EXAMPLE_NAME}) set(INCLUDE_DIRS ${PROJECT_SOURCE_DIR} ${CMAKE_BINARY_DIR}/data "${CREATE_EXAMPLE_INCLUDE_DIRS}") include_directories(${INCLUDE_DIRS}) add_executable(${CREATE_EXAMPLE_NAME} EXCLUDE_FROM_ALL ${CREATE_EXAMPLE_SOURCES} ${CREATE_EXAMPLE_HEADERS}) target_link_libraries(${CREATE_EXAMPLE_NAME} ${PROJECT_NAME} ${Boost_LIBRARIES} ${CREATE_EXAMPLE_LIBRARIES}) add_test(${CURRENT_TEST_NAME} ${CREATE_EXAMPLE_NAME}) add_dependencies(all_tests ${CREATE_EXAMPLE_NAME}) foreach(DATA_FILE ${CREATE_EXAMPLE_DATA_FILES}) set(DATA_TARGET export-${DATA_FILE}) add_dependencies(${CREATE_EXAMPLE_NAME} ${DATA_TARGET}) endforeach() endif(${ALL_FLAGS_OKAY}) endfunction(CREATE_TEST) macro(CREATE_MATLAB_LIB NAME FILES) foreach(MLFILE ${${FILES}}) LIST(APPEND MLTOT ${CMAKE_CURRENT_SOURCE_DIR}/${MLFILE}) endforeach(MLFILE) add_custom_command(OUTPUT ${MATLAB_BUILD}/lib${NAME}.so DEPENDS ${MLTOT} COMMAND ${MATLAB_BIN}/bin/mcc -g -f ${PROJECT_SOURCE_DIR}/script/conf/matlab_mcc_conf -v -B cpplib:lib${NAME} ${MLTOT} WORKING_DIRECTORY ${MATLAB_BUILD}) add_custom_target(${NAME} ALL DEPENDS ${MATLAB_BUILD}/lib${NAME}.so) endmacro(CREATE_MATLAB_LIB) macro(ADD_MATLAB_DEPENDENCY TARGET MATLABTARGET) add_dependencies(${TARGET} ${MATLABTARGET}) set_target_properties(${TARGET} PROPERTIES COMPILE_FLAGS "-I${MATLAB_INCLUDE} -I${MATLAB_BUILD}") set_target_properties(${TARGET} PROPERTIES LINK_FLAGS "-L${MATLAB_LIB} -lmwmclmcrrt ${MATLAB_BUILD}/lib${MATLABTARGET}.so") endmacro(ADD_MATLAB_DEPENDENCY) macro(CREATE_LIBRARY LIBNAME) add_library(${LIBNAME} SHARED ${SOURCES} ${HEADERS} ) target_link_libraries( ${LIBNAME} ${LIBRARIES} ) install (TARGETS ${LIBNAME} DESTINATION lib ) INSTALL_HEADERS_WITH_DIRECTORY(${LIBNAME} HEADERS ) endmacro(CREATE_LIBRARY) macro(ADD_DATA_FILE UID FILENAME) set(DATA_TARGET export-${UID}) set(DATA_DST_DIR ${CMAKE_BINARY_DIR}/data/${UID}) set(DATA_DST_FILE ${DATA_DST_DIR}/${FILENAME}) file(APPEND ${DATA_DEFINED_HEADER_FILE} "#define DATA_FILE_${UID} \"${DATA_DST_FILE}\" \n") #if(NOT TARGET ${DATA_TARGET}) #endif(NOT TARGET ${DATA_TARGET}) get_filename_component(FILENAME "${SRC_FILE}" NAME) add_custom_target(${DATA_TARGET} COMMENT "Exporting files into build tree") add_custom_command(TARGET ${DATA_TARGET} COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/${FILENAME} ${DATA_DST_FILE}) endmacro(ADD_DATA_FILE) macro(ADD_DATA_DIR UID DIRNAME) set(DATA_TARGET export-${UID}) set(DATA_DST_DIR ${CMAKE_BINARY_DIR}/data/${UID}) file(APPEND ${DATA_DEFINED_HEADER_FILE} "#define DATA_DIR_${UID} \"${DATA_DST_DIR}/\" \n") if(NOT TARGET ${DATA_TARGET}) add_custom_target(${DATA_TARGET} ALL COMMENT "Exporting files into build tree") endif(NOT TARGET ${DATA_TARGET}) add_custom_command(TARGET ${DATA_TARGET} COMMAND ${CMAKE_COMMAND} -E copy_directory "${CMAKE_CURRENT_SOURCE_DIR}/${DIRNAME}" "${DATA_DST_DIR}") endmacro(ADD_DATA_DIR) mips-1.0/CMakeLists.txt0000644002402200254040000000213412520407731014375 0ustar thomasfdtaigcmake_minimum_required(VERSION 2.8.3) include(CMakeParseArguments) project(mips) set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} "-Wextra -Wall -Wno-unused-local-typedefs") add_custom_target(all_examples COMMENT "Building all examples") add_custom_target(all_tests COMMENT "Building all tests") add_custom_target(complete DEPENDS all_examples all_tests COMMENT "Building everything") set (DATA_DEFINED_HEADER_FILE ${CMAKE_BINARY_DIR}/data/data.hpp) file (WRITE ${DATA_DEFINED_HEADER_FILE} "#ifndef MIPS_DATA_HEADER\n#define MIPS_DATA_HEADER\n") ENABLE_TESTING() include(CTest) set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/script/modules/") set (Boost_USE_MULTITHREADED OFF) find_package(Boost 1.53 COMPONENTS filesystem system program_options serialization unit_test_framework REQUIRED) INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIRS} ${PROJECT_SOURCE_DIR}) install(DIRECTORY script/cmake/ DESTINATION cmake_modules) add_subdirectory(data) add_subdirectory(mips) add_subdirectory(examples) add_subdirectory(doc) add_dependencies(complete all) file (APPEND ${DATA_DEFINED_HEADER_FILE} "#endif\n") mips-1.0/mips/0000755002402200254040000000000012520407731012605 5ustar thomasfdtaigmips-1.0/mips/patternMatching/0000755002402200254040000000000012520407731015735 5ustar thomasfdtaigmips-1.0/mips/patternMatching/treeInNetwork.hxx0000644002402200254040000017644012520407731021302 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include "treeInNetwork.hpp" #include "mips/core/utils.hpp" namespace mips { namespace pattern_matching { template TreeInNetwork<_Graph, _Pattern>::TreeInNetwork() { m_debug_level=0; P_SIZE=-1, G_SIZE=-1; m_successprob=0.2; P=NULL; G=G_OLD=NULL; } template TreeInNetwork<_Graph, _Pattern>::~TreeInNetwork() { //No need to destroy these. Either the calling class/method or the //compiler would take care of it (once P & G go out of scope in the //calling method). //if(P!=NULL or G!=NULL) { // delete P; delete G; //} // Leaving the member variables P and G in place to reduce the size // of call stack of some of the internal methods (which would be // called an exponential number of times). // No need to empty/delete internal maps and vectors, these should be // done by the compiler itself. } /* Properties */ template void TreeInNetwork<_Graph, _Pattern>::setDebugLevel(int d) { m_debug_level = d; } template int TreeInNetwork<_Graph, _Pattern>::getDebugLevel() { return m_debug_level; } template void TreeInNetwork<_Graph, _Pattern>::setSuccessProb(float p) { m_successprob = p; } template float TreeInNetwork<_Graph, _Pattern>::getSuccessProb() { return m_successprob; } /* End of properties */ /** * Pattern p must be a tree. The first node must be the root of the * tree. */ template bool TreeInNetwork<_Graph, _Pattern>::isIsomorphic(_Pattern *p, _Graph *g, vertex_descriptor root_vertex) { isIsomorphic(p, g, root_vertex, m_successprob); } template bool TreeInNetwork<_Graph, _Pattern>::isIsomorphic(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, float success_prob) { //, float success_prob=0.2) { using namespace boost; //TODO: Need to throw exception here if p or g is null P = p; G = g; G_SIZE = num_vertices(*G); P_SIZE = num_vertices(*P); m_field = mips::groupalgebra::Field( (int) round_float((log(P_SIZE)/log(2)) + 3, 0) ); //request field of length log_2(P_SIZE)+3 //Field(P_SIZE); //(int) round_float((log(psize)/log(2)) + 3, 0) m_circuit_array.resize(P_SIZE*G_SIZE); //numpy.empty((len(H_mat), len(G_mat)), dtype=numpy.int32) m_input_gates.resize(G_SIZE); // = vector(G_SIZE); m_refresh_vals = std::vector(P_SIZE*G_SIZE, 0); m_refresh_inputs = true; g_vlabel_map = get(vertex_label, *G); p_vlabel_map = get(vertex_label, *P); g_elabel_map = get(edge_label, *G); p_elabel_map = get(edge_label, *P); g_index_map = get(vertex_index, *G); p_index_map = get(vertex_index, *P); //for(int i=0; i1) std::cout << "** Doing "<< 0 << ',' << *vstart << " **" << std::endl; if( evaluate_circuit_with_vector(idx+1, root_vertex, *vstart, success_prob) > 0 ) return true; } return false; } template bool TreeInNetwork<_Graph, _Pattern>::isHomomorphic(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, int thresh) { //return isHomomorphic_normal(p, g, root_vertex); return isHomomorphic_small(p, g, root_vertex, thresh); } template bool TreeInNetwork<_Graph, _Pattern>::isHomomorphic_normal(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, int cutoff) { using namespace boost; P = p; G = g; G_SIZE = num_vertices(*G); P_SIZE = num_vertices(*P); //m_field = Field(P_SIZE); m_circuit_array.resize(P_SIZE*G_SIZE); //new Field_val[P_SIZE*G_SIZE]; //new Field_val*[P_SIZE]; //numpy.empty((len(H_mat), m_input_gates.resize(G_SIZE); // = vector(G_SIZE); m_refresh_vals = std::vector(P_SIZE*G_SIZE, 0); m_refresh_inputs = true; //m_refresh_circuit = 0; g_vlabel_map = get(vertex_label, *G); p_vlabel_map = get(vertex_label, *P); g_elabel_map = get(edge_label, *G); p_elabel_map = get(edge_label, *P); g_index_map = get(vertex_index, *G); p_index_map = get(vertex_index, *P); int frequency=0; typename _Graph::vertex_iterator vstart, vend; for(tie(vstart, vend) = vertices(*g); vstart!=vend; vstart++) { //(int idx=0; idx5) std::cout << "** In homomorphism doing " << 0 << "," << *vstart << " **" << std::endl; if( compute_homomorphism(1, root_vertex, *vstart, _Graph::null_vertex()) > 0 ) frequency++; if(frequency>=cutoff) return true; } return false; } template bool TreeInNetwork<_Graph, _Pattern>::isHomomorphic_small(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, int cutoff) { using namespace boost; P = p; G = g; G_SIZE = num_vertices(*G); P_SIZE = num_vertices(*P); //m_field = Field(P_SIZE); g_vlabel_map = get(vertex_label, *G); g_elabel_map = get(edge_label, *G); g_index_map = get(vertex_index, *G); p_vlabel_map = get(vertex_label, *P); p_elabel_map = get(edge_label, *P); p_index_map = get(vertex_index, *P); typename _Graph::vertex_iterator gstart, gend, pstart, pend; m_circuit_array.clear(); //we don't use it. m_refresh_vals.clear(); // = vector(P_SIZE*G_SIZE, -2); m_use_homomorph.clear(); // = vector(P_SIZE*G_SIZE, 0); m_input_gates.resize(G_SIZE); // = vector(G_SIZE); if(G != G_OLD) { vertex_labelset_t &gset = get_property(*G, graph_vertex_labelset); //glabel_type max = *max_element(gset.begin(), gset.end()); std::map label_index; for(int i=0; i(gset.size(), 0); //.resize(((unsigned)max)+1); //gset.size()); //FIXME create a labelset index here m_v_perlabel_idx.resize(G_SIZE); m_randset.resize(G_SIZE); unsigned gidx=0, i; for(tie(gstart,gend)=vertices(*G); gstart != gend; gstart++) { //i = (unsigned) get(g_vlabel_map, *gstart); i = label_index[ get(g_vlabel_map, *gstart) ]; gidx = get(g_index_map, *gstart); m_v_perlabel_idx[ gidx ] = m_vertices_per_label[i]; //.size(); m_vertices_per_label[i]++; //.push_back( *gstart ); m_randset[gidx] = gidx; } m_label_index = label_index; G_OLD = G; } m_small_circuit.resize(P_SIZE); m_refresh_small_circuit.resize(P_SIZE); m_use_small_homomorph.resize(P_SIZE); unsigned pidx, tmp_size=0, SMALL_SIZE=0; for(tie(pstart,pend)=vertices(*P); pstart != pend; pstart++) { pidx = get(p_index_map, *pstart); tmp_size = m_vertices_per_label[ m_label_index[get(p_vlabel_map,*pstart)] ]; //m_vertices_per_label[(int) get(p_vlabel_map,*pstart)].size(); m_small_circuit[pidx].clear(); m_small_circuit[pidx].resize(tmp_size, 0); // we no longer use this value as a marker --> m_field.fieldOutOfBounds ); m_refresh_small_circuit[pidx] = std::vector( tmp_size, -2 ); //vector( m_vertices_per_label[(int) get(p_vlabel_map,*pstart)].size(), -2 ); m_use_small_homomorph[pidx] = std::vector(tmp_size, 0); SMALL_SIZE += tmp_size; } //m_circuit_size += SMALL_SIZE; //perf. stats //m_no_candidates++; //comment out this if memory is becoming an issue //m_scalars.reserve(SMALL_SIZE*P_SIZE); m_refresh_inputs = true; int frequency=0; typename _Graph::vertex_iterator vstart, vend; for(tie(vstart, vend) = vertices(*g); vstart!=vend; vstart++) { //(int idx=0; idx5) std::cout << "** In homomorphism doing " << 0 << "," << *vstart << " **" << std::endl; if( compute_small_circuit_homomorphism(1, root_vertex, *vstart, _Graph::null_vertex()) ) frequency++; if(frequency>=cutoff) return true; } return false; } /** Helper function(s) */ inline void printDepth(int depth) { while(depth-- > 0) std::cout << " "; } /* * Evaluates the circuit 2^P_SIZE (2^k) times on a given tree root and gnode, * without explicitly constructing it. The method expects m_circuit_array to * be a vector of Field_val of size P_SIZE*G_SIZE. * * Developer's NOTE: * 1. Sub-classes should be aware of what they're doing, if they ever * call this method * 2. Please ensure the random number generator (method) * being used generates ints upto 2^k-1, otherwise the method breaks down. * 3. Currently, due to the size of m_input_gates base type, the method only * supports patterns of size 2**32. * * TO FIX: * The current STL random number generator is only generating upto 2^31-1. */ template typename TreeInNetwork<_Graph, _Pattern>::Field_val TreeInNetwork<_Graph, _Pattern>::evaluate_circuit_with_vector(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const float success_prob) { // success_prob=0.2) { int rounds = (int) ceil( round_float(log(1-success_prob)/log(0.8), 4) ); //int(math.ceil(math.log(1 - success_prob, 0.75))); unsigned long _2k = pow(2, P_SIZE); return evaluate_circuit_with_vector(cid, root_vertex, g_vertex, rounds, _2k); } template typename TreeInNetwork<_Graph, _Pattern>::Field_val TreeInNetwork<_Graph, _Pattern>::evaluate_circuit_with_vector(const int circuit_id, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const int rounds, const unsigned long _2k) { // success_prob=0.2) { //global m_input_gates, m_scalars, m_circuit_array //rand.seed(0) -- should have some replicability of results /* prob of success of the method is 0.2 by repeating the method above number of times we make sure that the probably of failure is reduced to as minimum as possible ... rounding calculation to 4 decimal places to avoid floating point inaccuracies. */ unsigned long sid, eigen_val; //k); Field_val val, tmp; int cid = circuit_id+1; //m_refresh_circuit = 0; if(m_debug_level>3) std::cout << "rounds: " << rounds << " 2k: " << _2k << std::endl; m_refresh_inputs = true; cid *= -1; //m_refresh_circuit = !m_refresh_circuit; //if(m_refresh_vals[root*G_SIZE+gnode]==cid) { // std::cout << "CAN'T HAVE AN ALREADY COMPUTED VALUE SO SOON!!" << ' ' << (root*G_SIZE+gnode) << ',' << m_refresh_vals[root*G_SIZE+gnode] << ',' << cid << std::endl; //m_refresh_circuit << std::endl; //assert(m_refresh_vals[root*G_SIZE+gnode]!=m_refresh_circuit); //ok i know this is retarded, but it should be removed after debugging //} if(not compute_homomorphism(cid, root_vertex, g_vertex, _Graph::null_vertex())) { if(m_debug_level>3) std::cout << "No homomorph, returning val: " << 0 << std::endl; return 0; } if(m_debug_level>5) { std::cout << " homomorph input gates were: "; for(int i=0; i3) std::cout << "Homomorph found." << std::endl; m_refresh_inputs = false; //if refresh_scalars: m_scalars = [] -- always refresh for(int i=0; i5) std::cout << "** Doing round " << i << " **" << std::endl; //m_input_gates.clear(); // = map(); //no need to clear m_scalars.clear(); // = vector(); m_refresh_inputs = true; //start computing val = 0; for(eigen_val = 0; eigen_val < _2k; eigen_val++) { //eigen_val in range(2**k) { //TODO: if(eigen_val>0) refresh_circuit = false //can probably also add refresh_inputs=false //m_circuit_array.assign(P_SIZE*G_SIZE, m_field.fieldOutOfBounds); //no difference if allocate new memory //fill(m_circuit_array, m_circuit_array+(P_SIZE*G_SIZE), m_field.fieldOutOfBounds); //memset(m_circuit_array, m_field.fieldOutOfBounds, sizeof(m_circuit_array[0])*P_SIZE*G_SIZE); sid=0; cid = cid*-1; tmp = compute_circuit_with_vector(cid, root_vertex, g_vertex, _Graph::null_vertex(), eigen_val, sid); //TODO: add parameters for refresh_circuit and refresh_inputs //tmp = compute_circuit_wo_sharing(root, gnode, -1, eigen_val, sid); if(m_refresh_inputs) m_refresh_inputs = false; if(m_debug_level > 5) std::cout << "eigen " << eigen_val << ": " << tmp << std::endl; val = m_field.add(val, tmp); } //if(m_refresh_vals[root*G_SIZE+gnode]!=cid) { //m_refresh_circuit) { // std::cout << "CAN'T HAVE DIFFERENT REFRESH VAL!!" << std::endl; // assert(m_refresh_vals[root*G_SIZE+gnode]==cid); //m_refresh_circuit); //ok i know this is retarded, but it should be removed after debugging //} //std::cout << "leaving refresh vals at: " << m_refresh_vals[root*G_SIZE+gnode] << ',' << cid << std::endl; //m_refresh_circuit << std::endl; if(m_debug_level>5) { std::cout << " input gates were: "; for(int i=0; i4) { std::cout << " Got val: " << val << std::endl; } //if(gnode==24408 or gnode==81210 or gnode==472840) // std::cout << " Scalars: " << m_scalars.size() << " InputGates: " << m_input_gates.size() << std::endl; if(val > 0) break; } //end rounds //this is just to release memory //m_input_gates = map(); m_scalars = vector(); if(m_debug_level>3) std::cout << "returning val: " << val << std::endl; return val; } /** *.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.* * * Methods for full circuit without tree sharing, but with circuit sharing for the whole network * ** *.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.* */ template typename TreeInNetwork<_Graph, _Pattern>::Field_val TreeInNetwork<_Graph, _Pattern>::compute_circuit_with_vector(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent, const unsigned long eigen_val, unsigned long &sid, int depth) { //global m_circuit_array, m_input_gates, m_scalars int root = get(p_index_map, root_vertex), gnode = get(g_index_map, g_vertex); if(m_debug_level>6) { printDepth(depth); std::cout << "**doing nodes " << root << ',' << gnode << std::endl;} if(m_use_homomorph[root*G_SIZE+gnode]==1) return 0; if(m_refresh_vals[root*G_SIZE+gnode]==cid) { //m_circuit_array[root*G_SIZE+gnode] < m_field.fieldOutOfBounds) { //std::cout << "returning old result " << "(" << root << ',' << G_SIZE << ',' << gnode << ':' << (root*G_SIZE+gnode) << "), " << m_circuit_array[root*G_SIZE+gnode] << ", (" << cid << ',' << m_refresh_vals[root*G_SIZE+gnode] << ")" << std::endl; if(m_debug_level>6) { printDepth(depth); std::cout << "returning old result " << m_circuit_array[root*G_SIZE+gnode] << std::endl; } return m_circuit_array[root*G_SIZE+gnode]; //m_circuit_array[root*G_SIZE+gnode]; } if(get(g_vlabel_map, g_vertex) != get(p_vlabel_map, root_vertex)) { m_refresh_vals[root*G_SIZE+gnode] = cid; m_circuit_array[root*G_SIZE+gnode] = 0; if(m_debug_level>6) { printDepth(depth); std::cout << "returning unmatched labels" << std::endl;} return 0; } else { //r_children = [c for c in H_mat[root] if c not in consumed] //std::cout << "num_nodes: " << P->adj_list.size() << std::endl; //for(int i=0; iadj_list.size(); i++) // std::cout << "node " << i << ":" << P->adj_list[i].size() << std::endl; //std::cout << " size of rchildren of " << root << " is: " << (*P).adj_list[root].size() << std::endl; //std::cout << " pattern is: " << *P; //std::cout << " adding rchilds "; std::cout.flush(); //vector r_children; typename _Graph::edge_descriptor r_children[P_SIZE]; int num_rchildren=0; typename _Graph::out_edge_iterator rc, end; for(tie(rc, end) = out_edges(root, *P); rc != end; rc++) if( target(*rc, *P) != root_parent) { //(*rc).destNode != root_parent) { //std::cout << *rc << ' '; //r_children.push_back(*rc); r_children[num_rchildren++] = *rc; } //std::cout << std::endl; //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(m_refresh_inputs) { //following condition should be redundant: or m_input_gates[gnode]==0) { //std::cout << "getting gate for " << gnode << std::endl; m_input_gates[gnode] = randint(1, pow(2, P_SIZE)-1); if(m_debug_level>1 and m_input_gates[gnode]>0) ; //std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; m_scalars.push_back(randint(2, m_field.getFieldMax())); //Field[1])); } Field_val val; val = 1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //this is the fourier style transform //std::cout << " sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; val = m_field.multiply(m_scalars[sid], val); sid += 1; if(num_rchildren==0) { //r_children.size()==0) { m_refresh_vals[root*G_SIZE+gnode] = cid; m_circuit_array[root*G_SIZE+gnode] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning1 " << val << std::endl; } return val; } //TODO: Incorporate this further down where we check for edge labels //else if(G->adj_list[gnode].size()==0) { //and r_children.size()>0 // m_circuit_array[root*G_SIZE+gnode] = 0; // //std::cout << "zero g_children" << std::endl; // return 0; //} else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) // << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { typename _Graph::edge_descriptor r_new; typename _Graph::out_edge_iterator gc; for(int r=0; r6) { printDepth(depth); std::cout << " [doing gchildren" << std::endl; } for(tie(gc,end) = out_edges(gnode, *G); gc != end; gc++) { if(m_debug_level>6) { printDepth(depth); std::cout << " *" << get(g_index_map, target(*gc, *G)) << "* "; } if(get(p_elabel_map, r_new) == get(g_elabel_map, *gc)) //(r_new->lbl == gc->lbl) sum_val = m_field.add(sum_val, compute_circuit_with_vector(cid, target(r_new, *P), target(*gc, *G), root, eigen_val, sid, depth+1)); } if(m_debug_level>6) { printDepth(depth); std::cout << "]" << std::endl;} if(m_debug_level>6) { printDepth(depth); std::cout << "(" << root << ',' << gnode << "): val, r, sum_val: " << val << ',' << r << ',' << sum_val << std::endl; } val = m_field.multiply(val,sum_val); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; m_refresh_vals[root*G_SIZE+gnode] = cid; m_circuit_array[root*G_SIZE+gnode] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning2 " << val << std::endl; } return val; } } } template int TreeInNetwork<_Graph, _Pattern>::compute_homomorphism(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent) { //global m_circuit_array, m_input_gates, m_scalars int root = get(p_index_map, root_vertex), gnode = get(g_index_map, g_vertex); //std::cout << "**doing nodes " << root << ',' << gnode << std::endl; if(m_refresh_vals[root*G_SIZE+gnode]==cid) {//m_circuit_array[root*G_SIZE+gnode] < m_field.fieldOutOfBounds) { //std::cout << "returning old result " << m_circuit_array[root*G_SIZE+gnode] << std::endl; return m_circuit_array[root*G_SIZE+gnode]; //m_circuit_array[root*G_SIZE+gnode]; } if(get(g_vlabel_map, gnode) != get(p_vlabel_map, root)) { m_refresh_vals[root*G_SIZE+gnode] = cid; m_use_homomorph[root*G_SIZE+gnode] = 1; m_circuit_array[root*G_SIZE+gnode] = 0; //std::cout << "returning unmatched labels" << std::endl; return 0; } else { //r_children = [c for c in H_mat[root] if c not in consumed] //std::cout << "num_nodes: " << P->adj_list.size() << std::endl; //for(int i=0; iadj_list.size(); i++) // std::cout << "node " << i << ":" << P->adj_list[i].size() << std::endl; //std::cout << " size of rchildren of " << root << " is: " << (*P).adj_list[root].size() << std::endl; //std::cout << " pattern is: " << *P; //std::cout << " adding rchilds "; std::cout.flush(); //vector r_children; typename _Graph::edge_descriptor r_children[P_SIZE]; int num_rchildren=0; typename _Graph::out_edge_iterator rc, end; for(tie(rc,end) = out_edges(root, *P); rc != end; rc++) if( target(*rc, *P) != root_parent) { //(*rc).destNode != root_parent) { //std::cout << *rc << ' '; //r_children.push_back(*rc); r_children[num_rchildren++] = *rc; } //std::cout << std::endl; //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(m_refresh_inputs) { //following condition should be redundant: or m_input_gates[gnode]==0) { //std::cout << "getting gate for " << gnode << std::endl; m_input_gates[gnode] = 1; //random(1, pow(2, P_SIZE)-1); //if(m_debug_level>1 and m_input_gates[gnode]>0) // std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } //if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; // m_scalars.push_back(random(2, m_field.fieldMax)); //Field[1])); //} Field_val val; val = m_input_gates[gnode]; //1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //std::cout << " sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; //val = field_prod(m_scalars[sid], val, m_field); //sid += 1; if(num_rchildren==0) { //r_children.size()==0) { m_refresh_vals[root*G_SIZE+gnode] = cid; m_circuit_array[root*G_SIZE+gnode] = val; //std::cout << "returning1 " << val << std::endl; return val; } //TODO: Incorporate this further down where we check for edge labels //else if(G->adj_list[gnode].size()==0) { //and r_children.size()>0 // m_circuit_array[root*G_SIZE+gnode] = 0; // //std::cout << "zero g_children" << std::endl; // return 0; //} else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) // << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { typename _Graph::edge_descriptor r_new; typename _Graph::out_edge_iterator gc; for(int r=0; radj_list[gnode][idx]; if(get(p_elabel_map, r_new) == get(g_elabel_map, *gc)) //(r_new->lbl == gc->lbl) sum_val = sum_val + compute_homomorphism(cid, target(r_new, *P), target(*gc, *G), root_vertex); //field_sum(sum_val, // compute_circuit_with_vector(r_new->destNode, G->adj_list[gnode][idx].destNode, root, eigen_val, sid)); } //std::cout << ']'; //std::cout << "\tval, sum_val: " << val << ',' << sum_val << std::endl; val = val*sum_val; //field_prod(val,sum_val, m_field); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; m_refresh_vals[root*G_SIZE+gnode] = cid; m_circuit_array[root*G_SIZE+gnode] = val; if(val==0) m_use_homomorph[root*G_SIZE+gnode]=1; //std::cout << "returning2 " << val << std::endl; return val; } } } /** *.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.* * * Methods for small circuit without tree sharing (circuit shared for whole network) - ALSO calculates performance stats * ** *.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.* */ template typename TreeInNetwork<_Graph, _Pattern>::Field_val TreeInNetwork<_Graph, _Pattern>::compute_small_circuit(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent, const unsigned long eigen_val, unsigned long &sid, int depth) { //global m_circuit_array, m_input_gates, m_scalars const int root = get(p_index_map, root_vertex), gnode = get(g_index_map, g_vertex); const int g_per_v_idx = m_v_perlabel_idx[gnode]; if(m_debug_level>6) { printDepth(depth); std::cout << "**doing nodes " << root << ',' << gnode << std::endl;} //TODO: ADD COUNTS HERE FOR REAL_CIRCUIT_SIZE, SKIPPED_HOMO, SKIPPED_ISO (CIRCUIT_SIZE (of m_small_circuit) TO BE CALCULATED AT CREATION TIME) //std::cout << "checking " << root << ", " << gnode << " (" << g_per_v_idx << ")" << std::endl; //std::cout << "labels " << get(p_vlabel_map, root) << ", " << get(g_vlabel_map, gnode) << std::endl; //std::cout << "respective dimensions are " << m_small_circuit.size() << ", " << m_small_circuit[root].size() << std::endl; std::cout.flush(); if(get(g_vlabel_map, g_vertex) != get(p_vlabel_map, root_vertex)) { // just in case we don't check this in the calling procedure. //we dont' store these gnodes, so no need to store/cache results //m_small_circuit[root][g_per_v_idx] = m_field.fieldOutOfBounds; if(m_debug_level>6) { printDepth(depth); std::cout << "returning unmatched labels" << std::endl;} return 0; } else if(m_use_small_homomorph[root][g_per_v_idx]==1) { //THIS APPEARS TO BE MAKING THE CODE RUN SLOWER return 0; } else if(m_refresh_small_circuit[root][g_per_v_idx]==cid) { //std::cout << "returning old result " << "(" << root << ',' << G_SIZE << ',' << gnode << ':' << (root*G_SIZE+gnode) << "), " << m_circuit_array[root*G_SIZE+gnode] << ", (" << cid << ',' << m_refresh_vals[root*G_SIZE+gnode] << ")" << std::endl; if(m_debug_level>6) { printDepth(depth); std::cout << "returning old result " << m_small_circuit[root][g_per_v_idx] << std::endl; } return m_small_circuit[root][g_per_v_idx]; } else { if(eigen_val==0) { if(!m_do_homomorph_test) m_real_circuit_size++; //shows the true size of the circuit m_nodes_visited++; } //r_children = [c for c in H_mat[root] if c not in consumed] //std::cout << "num_nodes: " << P->adj_list.size() << std::endl; //for(int i=0; iadj_list.size(); i++) // std::cout << "node " << i << ":" << P->adj_list[i].size() << std::endl; //std::cout << " size of rchildren of " << root << " is: " << (*P).adj_list[root].size() << std::endl; //std::cout << " pattern is: " << *P; //std::cout << " adding rchilds "; std::cout.flush(); //vector r_children; std::vector r_children(P_SIZE); int num_rchildren=0; typename _Graph::out_edge_iterator rc, end; for(tie(rc, end) = out_edges(root, *P); rc != end; rc++) if( target(*rc, *P) != root_parent) { //(*rc).destNode != root_parent) { //std::cout << *rc << ' '; //r_children.push_back(*rc); r_children[num_rchildren++] = *rc; } //std::cout << std::endl; //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(m_refresh_inputs) { //following condition should be redundant: or m_input_gates[gnode]==0) { //DEBUG: Remove later on //if(m_refresh_inputs and m_input_gates[gnode]==0) { //following condition should be redundant: or m_input_gates[gnode]==0) { //std::cout << "getting gate for " << gnode << std::endl; m_input_gates[gnode] = randint(1, pow(2, P_SIZE)-1); //std::cout << "getting gate for " << gnode << ": " << m_input_gates[gnode] << std::endl; if(m_debug_level>1 and m_input_gates[gnode]>0) ; //std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; m_scalars.push_back(randint(2, m_field.getFieldMax())); //Field[1])); //std::cout << "getting for scalar for " << gnode << ": " << m_scalars.at(m_scalars.size()-1) << std::endl; } Field_val val; val = 1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //this is the fourier style transform //std::cout << " sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; val = m_field.multiply(m_scalars[sid], val); sid += 1; if(num_rchildren==0) { //r_children.size()==0) { //m_refresh_vals[root*G_SIZE+gnode] = cid; m_refresh_small_circuit[root][g_per_v_idx] = cid; m_small_circuit[root][g_per_v_idx] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning1 " << val << std::endl; } return val; } //TODO: Incorporate this further down where we check for edge labels //else if(G->adj_list[gnode].size()==0) { //and r_children.size()>0 // m_circuit_array[root*G_SIZE+gnode] = 0; // //std::cout << "zero g_children" << std::endl; // return 0; //} else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) // << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { typename _Graph::edge_descriptor r_new; typename _Graph::out_edge_iterator gc; for(int r=0; r6) { printDepth(depth); std::cout << " [doing gchildren" << std::endl; } for(tie(gc,end) = out_edges(gnode, *G); gc != end; gc++) { if(m_debug_level>6) { printDepth(depth); std::cout << " *" << get(g_index_map, target(*gc, *G)) << "* "; } if(get(p_elabel_map, r_new) == get(g_elabel_map, *gc) and get(g_vlabel_map, target(*gc, *G)) == get(p_vlabel_map, target(r_new, *P))) sum_val = m_field.add(sum_val, compute_small_circuit(cid, target(r_new, *P), target(*gc, *G), root, eigen_val, sid, depth+1)); } if(m_debug_level>6) { printDepth(depth); std::cout << "]" << std::endl;} if(m_debug_level>6) { printDepth(depth); std::cout << "(" << root << ',' << gnode << "): val, r, sum_val: " << val << ',' << r << ',' << sum_val << std::endl; } val = m_field.multiply(val,sum_val); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; m_refresh_small_circuit[root][g_per_v_idx] = cid; //m_refresh_vals[root*G_SIZE+gnode] = cid; m_small_circuit[root][g_per_v_idx] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning2 " << val << std::endl; } return val; } } //end else } //end compute_small_circuit template int TreeInNetwork<_Graph, _Pattern>::compute_small_circuit_homomorphism(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent) { //global m_circuit_array, m_input_gates, m_scalars const int root = get(p_index_map, root_vertex), gnode = get(g_index_map, g_vertex); const int g_per_v_idx = m_v_perlabel_idx[gnode]; //std::cout << "**doing nodes " << root << ',' << gnode << std::endl; if(get(g_vlabel_map, gnode) != get(p_vlabel_map, root)) { //we don't store the following nodes in the circuit //m_refresh_small_circuit[root][g_per_v_idx] = cid; //m_use_small_homomorph[root][g_per_v_idx] = 1; //m_small_circuit[root][g_per_v_idx] = 0; //std::cout << "returning unmatched labels" << std::endl; return 0; } else if(m_refresh_small_circuit[root][g_per_v_idx]==cid) {//m_small_circuit[root][g_per_v_idx] < m_field.fieldOutOfBounds) { //std::cout << "returning old result " << m_small_circuit[root][g_per_v_idx] << std::endl; return m_small_circuit[root][g_per_v_idx]; //m_small_circuit[root][g_per_v_idx]; } else { m_real_circuit_size++; //shows the true size of the circuit //r_children = [c for c in H_mat[root] if c not in consumed] //std::cout << "num_nodes: " << P->adj_list.size() << std::endl; //for(int i=0; iadj_list.size(); i++) // std::cout << "node " << i << ":" << P->adj_list[i].size() << std::endl; //std::cout << " size of rchildren of " << root << " is: " << (*P).adj_list[root].size() << std::endl; //std::cout << " pattern is: " << *P; //std::cout << " adding rchilds "; std::cout.flush(); //vector r_children; std::vector r_children(P_SIZE); int num_rchildren=0; typename _Graph::out_edge_iterator rc, end; for(tie(rc,end) = out_edges(root, *P); rc != end; rc++) if( target(*rc, *P) != root_parent) { //(*rc).destNode != root_parent) { //std::cout << *rc << ' '; //r_children.push_back(*rc); r_children[num_rchildren++] = *rc; } //std::cout << std::endl; //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(m_refresh_inputs) { //following condition should be redundant: or m_input_gates[gnode]==0) { //std::cout << "getting gate for " << gnode << std::endl; m_input_gates[gnode] = 1; //random(1, pow(2, P_SIZE)-1); //if(m_debug_level>1 and m_input_gates[gnode]>0) // std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } //if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; // m_scalars.push_back(random(2, m_field.fieldMax)); //Field[1])); //} Field_val val; val = m_input_gates[gnode]; //1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //std::cout << " sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; //val = field_prod(m_scalars[sid], val, m_field); //sid += 1; if(num_rchildren==0) { //r_children.size()==0) { m_refresh_small_circuit[root][g_per_v_idx] = cid; m_small_circuit[root][g_per_v_idx] = val; //std::cout << "returning1 " << val << std::endl; return val; } //TODO: Incorporate this further down where we check for edge labels //else if(G->adj_list[gnode].size()==0) { //and r_children.size()>0 // m_small_circuit[root][g_per_v_idx] = 0; // //std::cout << "zero g_children" << std::endl; // return 0; //} else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) //std::cout << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { typename _Graph::edge_descriptor r_new; typename _Graph::out_edge_iterator gc; for(int r=0; radj_list[gnode][idx]; if(get(p_elabel_map, r_new) == get(g_elabel_map, *gc)) //(r_new->lbl == gc->lbl) sum_val = sum_val + compute_small_circuit_homomorphism(cid, target(r_new, *P), target(*gc, *G), root_vertex); //field_sum(sum_val, // compute_circuit_with_vector(r_new->destNode, G->adj_list[gnode][idx].destNode, root, eigen_val, sid)); } //std::cout << ']'; //std::cout << "\tval, sum_val: " << val << ',' << sum_val << std::endl; val = val*sum_val; //field_prod(val,sum_val, m_field); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; m_refresh_small_circuit[root][g_per_v_idx] = cid; m_small_circuit[root][g_per_v_idx] = val; if(val==0) m_use_small_homomorph[root][g_per_v_idx]=1; //std::cout << "returning2 " << val << std::endl; return val; } } } // end compute_small_circuit_homomorphism /** *.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.* * * Methods for small circuit using shared trees * ** *.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.*:*.* */ template typename TreeInNetwork<_Graph, _Pattern>::Field_val TreeInNetwork<_Graph, _Pattern>::compute_small_circuit_using_shared_trees(const int cid, const int root, const vertex_descriptor g_vertex, const int root_parent, const unsigned long eigen_val, SharedTreeDAG& common_subtrees, unsigned long &sid, int depth) { //global m_circuit_array, m_input_gates, m_scalars const int gnode = get(g_index_map, g_vertex); //, root = get(p_index_map, root_vertex) const int g_per_v_idx = m_v_perlabel_idx[gnode]; if(m_debug_level>6) { printDepth(depth);std::cout << "**doing nodes " << root << ',' << gnode << std::endl;} SharedTreeDAGNode *C = &common_subtrees[root]; //std::cout << "checking " << root << ", " << gnode << " (" << g_per_v_idx << ")" << std::endl; //std::cout << "labels " << C->getLabel() << ", " << get(g_vlabel_map, gnode) << std::endl; //std::cout << "respective dimensions are " << m_small_circuit.size() << ", " << m_small_circuit[root].size() << std::endl; std::cout.flush(); if(get(g_vlabel_map, gnode) != C->getLabel()) { //m_refresh_small_circuit[root][g_per_v_idx] = cid; //m_small_circuit[root][g_per_v_idx] = 0; if(m_debug_level>6) { printDepth(depth); std::cout << "returning unmatched labels" << std::endl;} return 0; } else if(m_use_small_homomorph[root][g_per_v_idx]==1) { //THIS DOES NOT APPEAR TO BE MAKING THE CODE RUN SLOWER if(m_debug_level>6) { printDepth(depth); std::cout << "returning homomorph result " << 0 << std::endl; } return 0; } else if(m_refresh_small_circuit[root][g_per_v_idx]==cid) { //m_small_circuit[root][g_per_v_idx] < m_field.fieldOutOfBounds) { //std::cout << "returning old result " << "(" << root << ',' << G_SIZE << ',' << gnode << ':' << (root*G_SIZE+gnode) << "), " << m_small_circuit[root][g_per_v_idx] << ", (" << cid << ',' << m_refresh_vals[root][g_per_v_idx] << ")" << std::endl; if(m_debug_level>6) { printDepth(depth); std::cout << "returning old result " << (unsigned) m_small_circuit[root][g_per_v_idx] << std::endl; } return m_small_circuit[root][g_per_v_idx]; } else { if(eigen_val==0) { if(!m_do_homomorph_test) m_real_circuit_size++; //shows the true size of the circuit m_nodes_visited++; } //r_children = [c for c in H_mat[root] if c not in consumed] //std::cout << "num_nodes: " << P->adj_list.size() << std::endl; //for(int i=0; iadj_list.size(); i++) // std::cout << "node " << i << ":" << P->adj_list[i].size() << std::endl; //std::cout << " size of rchildren of " << root << " is: " << (*P).adj_list[0].size() << std::endl; //std::cout << " pattern is: " << *P; //std::cout << " root-parent is: " << root_parent; //std::cout << " adding rchilds "; std::cout.flush(); //vector r_children; SharedTreeDAGEdge *r_children[P_SIZE]; int num_rchildren=0; for(SharedTreeDAGNode::outedge_iterator rc = C->edges.begin(); rc < C->edges.end(); rc++) if( (*rc).destNode != root_parent) { //std::cout << *rc << ' '; //r_children.push_back(*rc); r_children[num_rchildren++] = &(*rc); } //std::cout << std::endl; //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(m_refresh_inputs) { //following condition should be redundant: or m_input_gates[gnode]==0) { m_input_gates[gnode] = randint(1, pow(2, P_SIZE)-1); //std::cout << "Got gate for " << gnode << ": " << m_input_gates[gnode] << " P_size: " << P_SIZE << " round: " << random(1, pow(2, P_SIZE)-1) << std::endl; if(m_debug_level>1 and m_input_gates[gnode]>0) ; //std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; m_scalars.push_back(randint(2, m_field.getFieldMax())); //Field[1])); } Field_val val; val = 1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //this is the fourier style transform //printDepth(depth); std::cout << "(" << root << ',' << gnode << "):" << "sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; val = m_field.multiply(m_scalars[sid], val); sid += 1; if(num_rchildren==0) { //r_children.size()==0) { m_refresh_small_circuit[root][g_per_v_idx] = cid; m_small_circuit[root][g_per_v_idx] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning1 " << (unsigned) val << std::endl; } return val; } //TODO: Incorporate this further down where we check for edge labels //else if(G->adj_list[gnode].size()==0) { //and r_children.size()>0 // m_small_circuit[root][g_per_v_idx] = 0; // //std::cout << "zero g_children" << std::endl; // return 0; //} else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) // << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { SharedTreeDAGEdge *r_new; typename _Graph::out_edge_iterator gc, end; for(int r=0; r6) { printDepth(depth); std::cout << " [doing gchildren" << std::endl; } for(tie(gc,end)=out_edges(gnode, *G); gc != end; gc++) { if(m_debug_level>6) { printDepth(depth); std::cout << " *" << get(g_index_map, target(*gc, *G)) << "* "; } if(r_new->getLabel() == get(g_elabel_map, *gc)) sum_val = m_field.add(sum_val, compute_small_circuit_using_shared_trees(cid, r_new->destNode, target(*gc, *G), root, eigen_val, common_subtrees, sid, depth+1)); } if(m_debug_level>6) { printDepth(depth); std::cout << "]" << std::endl;} if(m_debug_level>6) { printDepth(depth); std::cout << "(" << root << ',' << gnode << "): val, r, sum_val: " << (unsigned) val << ',' << r << ',' << (unsigned) sum_val << std::endl; } val = m_field.multiply(val,sum_val); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; m_refresh_small_circuit[root][g_per_v_idx] = cid; m_small_circuit[root][g_per_v_idx] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning2 " << (unsigned) val << std::endl; } return val; } } } //end compute_small_circuit_using_shared_trees template int TreeInNetwork<_Graph, _Pattern>::compute_small_circuit_homomorphism_using_shared_trees(const int cid, const int root, const vertex_descriptor g_vertex, const int root_parent, SharedTreeDAG& common_subtrees, int depth) { const int gnode = get(g_index_map, g_vertex); //, root = get(p_index_map, root_vertex) const int g_per_v_idx = m_v_perlabel_idx[gnode]; if(m_debug_level>7) { printDepth(depth);std::cout << "**doing nodes " << root << ',' << gnode << std::endl;} SharedTreeDAGNode *C = &common_subtrees[root]; //std::cout << "checking " << root << ", " << gnode << " (" << g_per_v_idx << ")" << std::endl; //std::cout << "labels " << C->getLabel() << ", " << get(g_vlabel_map, gnode) << std::endl; //std::cout << "respective dimensions are " << m_small_circuit.size() << ", " << m_small_circuit[root].size() << std::endl; std::cout.flush(); if(get(g_vlabel_map, gnode) != C->getLabel()) { //we dont' store this node in the circuit //m_refresh_small_circuit[root][g_per_v_idx] = cid; //m_use_small_homomorph[root][g_per_v_idx] = 1; //m_small_circuit[root][g_per_v_idx] = 0; if(m_debug_level>7) { printDepth(depth); std::cout << "returning unmatched labels" << std::endl; } return 0; } else if(m_refresh_small_circuit[root][g_per_v_idx]==cid) { //m_small_circuit[root][g_per_v_idx] < m_field.fieldOutOfBounds) { if(m_debug_level>7) { printDepth(depth); std::cout << "returning old result " << (unsigned) m_small_circuit[root][g_per_v_idx] << std::endl; } return m_small_circuit[root][g_per_v_idx]; //m_small_circuit[root][g_per_v_idx]; } else { m_real_circuit_size++; //shows the true size of the circuit //r_children = [c for c in H_mat[root] if c not in consumed] //std::cout << "num_nodes: " << P->adj_list.size() << std::endl; //for(int i=0; iadj_list.size(); i++) // std::cout << "node " << i << ":" << P->adj_list[i].size() << std::endl; //std::cout << " size of rchildren of " << root << " is: " << (*P).adj_list[root].size() << std::endl; //std::cout << " pattern is: " << *P; //std::cout << " adding rchilds "; std::cout.flush(); //vector r_children; SharedTreeDAGEdge *r_children[P_SIZE]; int num_rchildren=0; for(SharedTreeDAGNode::outedge_iterator rc = C->edges.begin(); rc < C->edges.end(); rc++) if( (*rc).destNode != root_parent) { //std::cout << *rc << ' '; //r_children.push_back(*rc); r_children[num_rchildren++] = &(*rc); } //std::cout << std::endl; //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(m_refresh_inputs) { //following condition should be redundant: or m_input_gates[gnode]==0) { //std::cout << "getting gate for " << gnode << std::endl; m_input_gates[gnode] = 1; //random(1, pow(2, P_SIZE)-1); //if(m_debug_level>1 and m_input_gates[gnode]>0) // std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } //if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; // m_scalars.push_back(random(2, m_field.fieldMax)); //Field[1])); //} Field_val val; val = m_input_gates[gnode]; //1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //std::cout << " sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; //val = field_prod(m_scalars[sid], val, m_field); //sid += 1; if(num_rchildren==0) { //r_children.size()==0) { m_refresh_small_circuit[root][g_per_v_idx] = cid; m_small_circuit[root][g_per_v_idx] = val; if(m_debug_level>7) { printDepth(depth); std::cout << "returning1 " << (unsigned) val << std::endl; } return val; } //TODO: Incorporate this further down where we check for edge labels //else if(G->adj_list[gnode].size()==0) { //and r_children.size()>0 // m_small_circuit[root][g_per_v_idx] = 0; // //std::cout << "zero g_children" << std::endl; // return 0; //} else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) // << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { SharedTreeDAGEdge *r_new; typename _Graph::out_edge_iterator gc, end; for(int r=0; r7) { printDepth(depth); std::cout << " [doing gchildren" << std::endl; } for(tie(gc, end)=out_edges(gnode, *G); gc != end; gc++) { if(m_debug_level>7) { printDepth(depth); std::cout << " *" << get(g_index_map, target(*gc, *G)) << "* "; } if(r_new->getLabel() == get(g_elabel_map, *gc)) sum_val = sum_val + compute_small_circuit_homomorphism_using_shared_trees(cid, r_new->destNode, target(*gc, *G), root, common_subtrees, depth+1); //field_sum(sum_val, // compute_circuit_with_vector(r_new->destNode, G->adj_list[gnode][idx].destNode, root, eigen_val, sid)); } if(m_debug_level>7) { printDepth(depth); std::cout << ']' << std::endl; } if(m_debug_level>7) { printDepth(depth); std::cout << "(" << root << ',' << gnode << "): val, r, sum_val: " << val << ',' << r << ',' << sum_val << std::endl; } val = val*sum_val; //field_prod(val,sum_val, m_field); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; m_refresh_small_circuit[root][g_per_v_idx] = cid; m_small_circuit[root][g_per_v_idx] = val; if(val==0) m_use_small_homomorph[root][g_per_v_idx]=1; if(m_debug_level>7) { printDepth(depth); std::cout << "returning2 " << (unsigned) val << std::endl; } return val; } } } //end compute_small_circuit_homomorphism_using_shared_trees //Developer's comment: circuit_gates[0] is always W0 template void TreeInNetwork<_Graph, _Pattern>::compute_wo_fourier(const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent, const GF2l_Z2k_Dynamic &GF2, std::vector< std::vector > &occurArray, std::vector &circuit_gates, std::vector &input_gates, GF2l_Z2k_Dynamic::value &result, int depth) { const int root = get(p_index_map, root_vertex), gnode = get(g_index_map, g_vertex); if(m_debug_level>6) { printDepth(depth); std::cout << "**doing nodes " << root << ',' << gnode << std::endl;} if(get(g_vlabel_map, g_vertex) != get(p_vlabel_map, root_vertex)) { // just in case we don't check this in the calling procedure. if(m_debug_level>6) { printDepth(depth); std::cout << "returning unmatched labels" << std::endl;} result = circuit_gates[0]; //W0; return; } // else if(m_refresh_small_circuit[root][g_per_v_idx]==cid) { // //std::cout << "returning old result " << "(" << root << ',' << G_SIZE << ',' << gnode << ':' << (root*G_SIZE+gnode) << "), " << m_circuit_array[root*G_SIZE+gnode] << ", (" << cid << ',' << m_refresh_vals[root*G_SIZE+gnode] << ")" << std::endl; // if(m_debug_level>6) { printDepth(depth); std::cout << "returning old result " << m_small_circuit[root][g_per_v_idx] << std::endl; } // return m_small_circuit[root][g_per_v_idx]; // } else if(occurArray[root][gnode]>0) { result = circuit_gates[occurArray[root][gnode]]; if(m_debug_level>6) { printDepth(depth); std::cout << "returning old result " << GF2.toStrPolyBinary(result) << std::endl; } return; } else { std::vector r_children(P_SIZE); int num_rchildren=0; typename _Graph::out_edge_iterator rc, end; for(tie(rc, end) = out_edges(root, *P); rc != end; rc++) if( target(*rc, *P) != root_parent) { //(*rc).destNode != root_parent) { r_children[num_rchildren++] = *rc; } //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(input_gates[gnode]==circuit_gates[0]) { //W0) { // m_refresh_inputs) { input_gates[gnode] = GF2.random(); //randint(1, pow(2, P_SIZE)-1); //std::cout << "getting gate for " << gnode << ": " << GF2.toStrInt(input_gates[gnode]) << std::endl; if(m_debug_level>1) ; //std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } //NO need to re-use scalars //if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; // m_scalars.push_back(randint(2, m_field.fieldMax)); //Field[1])); //} //Field_val val; //val = 1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //this is the fourier style transform //std::cout << " sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; //val = m_field.multiply(m_scalars[sid], val); //sid += 1; result = input_gates[gnode]; GF2.multiplyScalar(result, GF2.randomScalar()); //std::cout << "getting for scalar for " << gnode << ": " << GF2.toStrInt(result) << std::endl; occurArray[root][gnode] = circuit_gates.size(); circuit_gates.push_back(result); if(num_rchildren==0) { //r_children.size()==0) { //m_refresh_vals[root*G_SIZE+gnode] = cid; //m_refresh_small_circuit[root][g_per_v_idx] = cid; //m_small_circuit[root][g_per_v_idx] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning1 " << GF2.toStrPolyBinary(result) << std::endl; } return; } else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) // << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { GF2l_Z2k_Dynamic::value new_result, sum_result; typename _Graph::edge_descriptor r_new; typename _Graph::out_edge_iterator gc; for(int r=0; r6) { printDepth(depth); std::cout << " [doing gchildren" << std::endl; } for(tie(gc,end) = out_edges(gnode, *G); gc != end; gc++) { if(m_debug_level>6) { printDepth(depth); std::cout << " *" << get(g_index_map, target(*gc, *G)) << "* "; } if(get(p_elabel_map, r_new) == get(g_elabel_map, *gc) and get(g_vlabel_map, target(*gc, *G)) == get(p_vlabel_map, target(r_new, *P))) //sum_val = m_field.add(sum_val, // compute_small_circuit(cid, target(r_new, *P), target(*gc, *G), root, eigen_val, sid, depth+1)); GF2.zero(new_result); //this adds an extra 2^k loop in our inner loop :( compute_wo_fourier(target(r_new, *P), target(*gc, *G), root_vertex, GF2, occurArray, circuit_gates, input_gates, new_result, depth+1); GF2.add(sum_result, new_result); } if(m_debug_level>6) { printDepth(depth); std::cout << "]" << std::endl;} if(m_debug_level>6) { printDepth(depth); std::cout << "(" << root << ',' << gnode << "): val, r, sum_val: " << GF2.toStrPolyBinary(result) << ',' << r << ',' << GF2.toStrPolyBinary(sum_result) << std::endl; } GF2.multiply(result, sum_result); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; //GF2.multiplyScalar(result, GF2.randomScalar()); //THIS IS NOT WORKING. DON'T KNOW WHY? circuit_gates[occurArray[root][gnode]] = result; if(m_debug_level>6) { printDepth(depth); std::cout << "returning2 " << GF2.toStrPolyBinary(result) << std::endl; } return; // val; } } //end else return; } template void TreeInNetwork<_Graph, _Pattern>::compute_wo_fourier_using_shared_trees(const int root, const vertex_descriptor g_vertex, const int root_parent, SharedTreeDAG& common_subtrees, std::vector< std::vector > &occurArray, const GF2l_Z2k_Dynamic &GF2, std::vector &circuit_gates, std::vector &input_gates, GF2l_Z2k_Dynamic::value &result, int depth) { const int gnode = get(g_index_map, g_vertex); SharedTreeDAGNode *C = &common_subtrees[root]; if(m_debug_level>6) { printDepth(depth); std::cout << "**doing nodes " << root << ',' << gnode << std::endl;} if(get(g_vlabel_map, g_vertex) != C->getLabel()) { // just in case we don't check this in the calling procedure. if(m_debug_level>6) { printDepth(depth); std::cout << "returning unmatched labels" << std::endl;} result = circuit_gates[0]; //W0; return; } // else if(m_refresh_small_circuit[root][g_per_v_idx]==cid) { // //std::cout << "returning old result " << "(" << root << ',' << G_SIZE << ',' << gnode << ':' << (root*G_SIZE+gnode) << "), " << m_circuit_array[root*G_SIZE+gnode] << ", (" << cid << ',' << m_refresh_vals[root*G_SIZE+gnode] << ")" << std::endl; // if(m_debug_level>6) { printDepth(depth); std::cout << "returning old result " << m_small_circuit[root][g_per_v_idx] << std::endl; } // return m_small_circuit[root][g_per_v_idx]; // } else if(occurArray[root][gnode]>0) { result = circuit_gates[occurArray[root][gnode]]; if(m_debug_level>6) { printDepth(depth); std::cout << "returning old result " << GF2.toStrPolyBinary(result) << std::endl; } return; } else { SharedTreeDAGEdge *r_children[P_SIZE]; int num_rchildren=0; GraphAdjList::out_edge_iterator rc, end; for(SharedTreeDAGNode::outedge_iterator rc = C->edges.begin(); rc < C->edges.end(); rc++) if( (*rc).destNode != root_parent) { //(*rc).destNode != root_parent) { r_children[num_rchildren++] = &(*rc); } //in_g1 = m_input_gates.get(gnode); //if in_g1==None: m_input_gates[gnode] = in_g1 = rand.randint(1, 2**k-1) if(input_gates[gnode]==circuit_gates[0]) { //W0) { // m_refresh_inputs) { input_gates[gnode] = GF2.random(); //randint(1, pow(2, P_SIZE)-1); //std::cout << "getting gate for " << gnode << ": " << GF2.toStrInt(input_gates[gnode]) << std::endl; if(m_debug_level>1) ; //std::cout << "refreshing an already used gate " << gnode << ": " << m_input_gates[gnode] << std::endl; } //NO need to re-use scalars //if(sid >= m_scalars.size()) { //std::cout << "getting scalar for " << root << ',' << gnode << std::endl; // m_scalars.push_back(randint(2, m_field.fieldMax)); //Field[1])); //} //Field_val val; //val = 1 - num_bits_set(m_input_gates[gnode] & eigen_val) % 2; // gives 0/1 //this is the fourier style transform //std::cout << " sid, input_gate, eigen_val, val: " << sid << ',' << m_input_gates[gnode] << ',' << eigen_val << ',' << val << std::endl; //val = m_field.multiply(m_scalars[sid], val); //sid += 1; result = input_gates[gnode]; GF2.multiplyScalar(result, GF2.randomScalar()); //std::cout << "getting for scalar for " << gnode << ": " << GF2.toStrInt(result) << std::endl; occurArray[root][gnode] = circuit_gates.size(); circuit_gates.push_back(result); if(num_rchildren==0) { //r_children.size()==0) { //m_refresh_vals[root*G_SIZE+gnode] = cid; //m_refresh_small_circuit[root][g_per_v_idx] = cid; //m_small_circuit[root][g_per_v_idx] = val; if(m_debug_level>6) { printDepth(depth); std::cout << "returning1 " << GF2.toStrPolyBinary(result) << std::endl; } return; } else { //vector * g_children = &G->adj_list[gnode]; //G_mat[gnode] //val = field_prod(getScalar(sid), val) // << " doing rchildren"; //for(vector::iterator r_new = r_children.begin(); r_new < r_children.end(); r_new++) { GF2l_Z2k_Dynamic::value new_result, sum_result; SharedTreeDAGEdge *r_new; GraphAdjList::out_edge_iterator gc; for(int r=0; r6) { printDepth(depth); std::cout << " [doing gchildren" << std::endl; } for(tie(gc,end) = out_edges(gnode, *G); gc != end; gc++) { if(m_debug_level>6) { printDepth(depth); std::cout << " *" << get(g_index_map, target(*gc, *G)) << "* "; } if(r_new->getLabel() == get(g_elabel_map, *gc) and get(g_vlabel_map, target(*gc, *G)) == common_subtrees[r_new->destNode].getLabel()) GF2.zero(new_result); //this adds an extra 2^k loop in our inner loop :( compute_wo_fourier_using_shared_trees(r_new->destNode, target(*gc, *G), root, common_subtrees, occurArray, GF2, circuit_gates, input_gates, new_result, depth+1); GF2.add(sum_result, new_result); } if(m_debug_level>6) { printDepth(depth); std::cout << "]" << std::endl;} if(m_debug_level>6) { printDepth(depth); std::cout << "(" << root << ',' << gnode << "): val, r, sum_val: " << GF2.toStrPolyBinary(result) << ',' << r << ',' << GF2.toStrPolyBinary(sum_result) << std::endl; } GF2.multiply(result, sum_result); //this should give 0 if |rchildren|>0 and |gchildren|==0 } //end for r_new //std::cout << std::endl; //GF2.multiplyScalar(result, GF2.randomScalar()); //THIS IS NOT WORKING. DON'T KNOW WHY? circuit_gates[occurArray[root][gnode]] = result; if(m_debug_level>6) { printDepth(depth); std::cout << "returning2 " << GF2.toStrPolyBinary(result) << std::endl; } return; // val; } } //end else return; } } //namespace pattern_matching } //namespace mips mips-1.0/mips/patternMatching/graphInNetwork.hxx0000644002402200254040000023501212520407731021433 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include "graphInNetwork.hpp" #include //using namespace std; //using namespace boost; namespace mips { namespace pattern_matching { template < typename LabeledPatternType,typename LabeledGraphType > GraphInNetwork::GraphInNetwork() : m_countFreq(false), m_freqCount(0), m_cutoff(0), m_debug_level(0) { m_corep = m_coreg = m_outp = m_outg = std::vector(1, 0); outfname = ""; } template < typename LabeledPatternType,typename LabeledGraphType > GraphInNetwork::~GraphInNetwork() {} /* Properties */ template < typename LabeledPatternType,typename LabeledGraphType > void GraphInNetwork::setMapOut(std::string fname) { outfile.open(fname.c_str()); } template < typename LabeledPatternType,typename LabeledGraphType > std::string GraphInNetwork::getMapOut() { return outfname; } template < typename LabeledPatternType,typename LabeledGraphType > void GraphInNetwork::setDebugLevel(int d) { m_debug_level = d; } template < typename LabeledPatternType,typename LabeledGraphType > int GraphInNetwork::getDebugLevel() { return m_debug_level; } template < typename LabeledPatternType,typename LabeledGraphType > void GraphInNetwork::setCountFreq(bool count) { m_countFreq = count; } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::getCountFreq() { return m_countFreq; } template < typename LabeledPatternType,typename LabeledGraphType > void GraphInNetwork::setCutoff(int c) { m_cutoff = c; } template < typename LabeledPatternType,typename LabeledGraphType > int GraphInNetwork::getCutoff() { return m_cutoff; } /* Other methods */ template < typename LabeledPatternType,typename LabeledGraphType > int GraphInNetwork::getFreqCount() { return m_freqCount; } template void printVector(std::vector v, std::ostream& out, int c=0) { for(int i=0; i bool ullmanFeasible(const LabeledPatternType * p, const LabeledGraphType * g, const typename boost::graph_traits< typename LabeledPatternType::Graph >::vertex_descriptor pnode, const typename boost::graph_traits< typename LabeledGraphType::Graph >::vertex_descriptor gnode, std::vector map) { using namespace boost; //Commented out by SR // // typename property_map::const_type // g_vlabel_map = get(vertex_label, *g), // p_vlabel_map = get(vertex_label, p->graph()); // typename property_map::const_type // g_elabel_map = get(edge_label, *g), // p_elabel_map = get(edge_label, p->graph()); // typename property_map::const_type // g_index_map = get(vertex_index, *g), // p_index_map = get(vertex_index, p->graph()); typename property_map< typename LabeledGraphType::Graph, vertex_index_t>::const_type g_index_map = get(vertex_index, g->graph()); typename property_map< typename LabeledPatternType::Graph, vertex_index_t>::const_type p_index_map = get(vertex_index, p->graph()); typename LabeledGraphType::ConstVertexLabelMap g_vlabel_map = g->vertexLabelMap(); typename LabeledGraphType::ConstEdgeLabelMap g_elabel_map = g->edgeLabelMap(); typename LabeledPatternType::ConstVertexLabelMap p_vlabel_map = p->vertexLabelMap(); typename LabeledPatternType::ConstEdgeLabelMap p_elabel_map = p->edgeLabelMap(); if(get(p_vlabel_map, pnode) != get(g_vlabel_map, gnode)) return false; //vector::iterator it; //GraphAdjList::out_edge_iterator pe, peend, ge, geend; typename boost::graph_traits::out_edge_iterator ge, geend; typename boost::graph_traits::out_edge_iterator pe, peend; //GraphAdjList::adjacency_iterator gv, gvend; for(tie(pe,peend)=out_edges(pnode, p->graph()); pe != peend; pe++) { int e = get(p_index_map, target(*pe, p->graph())); //pn->destNode; //p->adj_list[pnode][j].destNode; if(map[e] != -1) { //only look at pnodes that have already been mapped tie(ge,geend) = boost::out_edges(gnode, g->graph()); //tie(gv,gvend) = adjacent_vertices(gnode, *g); while( ge != geend && !(target(*ge,g->graph()) == vertex(map[e], g->graph())) ) ++ge; if( ge == geend ) //(!igraph_vector_binsearch2(g_outneis, node2)) { return false; else if( get(g_elabel_map, *ge) != get(p_elabel_map, *pe) ) //gn->lbl != pn->lbl) return false; } } return true; } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::ullmanIsomorphInternal(LabeledPatternType *p, LabeledGraphType *g, const typename boost::graph_traits< Pattern >::vertex_descriptor pn, const int pnidx, const typename boost::graph_traits< Graph >::vertex_descriptor gn, std::vector map) { //Commented out by SR //Changing the property map types from just GraphAdjList to Graph and Pattern. // // typename property_map::type // g_index_map = get(vertex_index, *g), // p_index_map = get(vertex_index, p->graph()); using namespace std; using namespace boost; typename property_map::type g_index_map = get(vertex_index, g->graph()); typename property_map::type p_index_map = get(vertex_index, p->graph()); if(m_debug_level>3) cout << " Checking (pnode,gnode): (" << pn << ',' << gn << ")"; if(ullmanFeasible(p, g, pn, gn, map)) { if(m_debug_level>3) cout << " .... " << "are feasible.Recursing." << endl; //select this (pn,gn) map map[pnidx] = get(g_index_map, gn); if( find(map.begin(), map.end(), -1)==map.end() ) { m_freqCount++; if(m_debug_level>4) {cout << " found occurence: ("; printVector(map, cout); cout << ")" << endl;} if(outfile) { outfile << "Occurrence " << m_freqCount << ": ["; printVector(map, outfile); outfile << "]" << endl; } //if(outfile) { for(int i=0; i::vertex_iterator newgn, gend; typename boost::graph_traits::vertex_iterator newpn, p0, pend; tie(p0,pend)=vertices(p->graph()); //select next pnode unsigned int newidx = pnidx; newpn=p0+pnidx; //this may save a few iterations do { newpn++; //newpn = (newpn + 1) % p->no_nodes; if(newpn==pend) {newpn = p0; newidx=0;} else newidx = get(p_index_map, *newpn); } while(map[newidx]!=-1); //select new gnode and test tie(newgn, gend)=vertices(g->graph()); for(newgn; newgn != gend; newgn++) { if(*newgn==gn) continue; // we've already mapped this if( find(map.begin(), map.end(), get(g_index_map, *newgn)) != map.end() ) continue; // we've already mapped this ullmanIsomorphInternal(p, g, *newpn, newidx, *newgn, map); if(m_cutoff>0) if(m_freqCount>=m_cutoff) return true; } //end select new gnode //unselect this (pn,gn) map map[pnidx] = -1; } //end if feasible if(m_debug_level>3) cout << "...are not feasible." << endl; return false; } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::ullmanIsomorph(LabeledPatternType *p, LabeledGraphType *g) { //Commented out by SR //GraphAdjList::vertex_iterator gn, gend, p0; typename boost::graph_traits< Graph >::vertex_iterator gn, gend; typename boost::graph_traits< Pattern>::vertex_iterator p0; m_freqCount=0; int tmp=m_cutoff; if(!m_countFreq) m_cutoff = 1; p0 = boost::vertices(p->graph()).first; for(boost::tie(gn,gend)=vertices(g->graph()); gn != gend; gn++) { std::vector map(num_vertices(p->graph()), -1); ullmanIsomorphInternal(p, g, *p0, 0, *gn, map); if(m_cutoff>0 and m_freqCount>=m_cutoff) break; } m_cutoff=tmp; return (m_freqCount!=0); } /** * Returns true if there is at least one root image at root->gnode. */ template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::ullmanRootImage(LabeledPatternType *p, LabeledGraphType *g, typename boost::graph_traits::vertex_descriptor root, typename boost::graph_traits::vertex_descriptor gnode) { /* int freq=0; for(int gnode=0; gnodeno_nodes; gnode++) { if( (m_useUllman ? counter.ullmanRootImage(p,g,root,gnode) : counter.VF2RootImage(p,g,root,gnode)) ) freq++; if(cutoff>0) if(freq>=cutoff) return freq; } return freq;*/ typename boost::property_map::type p_index_map = get(boost::vertex_index, p->graph()); std::vector map(num_vertices(p->graph()), -1); m_freqCount=0; int tmp = m_cutoff; //because this function is public and can be used from outside this class //if(!m_countFreq) m_cutoff = 1; ullmanIsomorphInternal(p, g, root, get(p_index_map, root), gnode, map); m_cutoff = tmp; return (m_freqCount!=0); } /** * Returns true if there are at least >= m_thresh root images of root (counts all if no threshold is provided). */ template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::ullmanRootImage(LabeledPatternType *p, LabeledGraphType *g, typename boost::graph_traits< Pattern >::vertex_descriptor root) { m_freqCount=0; int tmp,cutoff; tmp = cutoff = m_cutoff; m_cutoff = 1; if(!m_countFreq) cutoff = 1; typename boost::property_map::type p_index_map = get(boost::vertex_index, p->graph()); //GraphAdjList::vertex_iterator gnode, gend; typename boost::graph_traits::vertex_iterator gnode, gend; for(boost::tie(gnode, gend) = vertices(g->graph()); gnode != gend; gnode++) { std::vector map(num_vertices(p->graph()), -1); if(ullmanIsomorphInternal(p, g, root, get(p_index_map, root), *gnode, map)) m_cutoff++; if(cutoff>0 and m_freqCount>=cutoff) { m_cutoff = tmp; return (m_freqCount!=0); } } m_cutoff = tmp; return (m_freqCount!=0); } /**** VF2 subgraph isomorphism method, adapted (pretty much copy/paste) from igraph library (do we need to include igraph license/copyright??) */ template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::DEFUNCT_VF2IsomorphInternal(const LabeledPatternType *P, const LabeledGraphType *G, std::vector& core_p, std::vector& core_g, bool useRoot /*=false*/, typename boost::graph_traits::vertex_descriptor proot_vertex /*=-1*/, typename boost::graph_traits::vertex_descriptor groot_vertex /*=-1*/) { using namespace std; using namespace boost; /*** we don't distinguish much between directed and undirected graphs if (igraph_is_directed(graph1) != igraph_is_directed(graph2)) { IGRAPH_ERROR("Cannot compare directed and undirected graphs", IGRAPH_EINVAL); } */ const int PSIZE = num_vertices(P->graph()), GSIZE = num_vertices(G->graph()), PEDGES = num_edges(P->graph()), GEDGES = num_edges(G->graph()); if (GSIZE < PSIZE || GEDGES < PEDGES) { //igraph_ecount(graph1) < igraph_ecount(graph2)) { //need edge count return false; } int branch=0; //For book keeping int last_pnode, last_gnode, cand_pnode, cand_gnode; int in_g_size, in_p_size, out_g_size, out_p_size; int matched_nodes, depth; // long int matched_nodes=0; -- no need for long declaration /* The last two store mapping from g->p and from p->g (don't know why they're called core) */ vector in_g, out_g, in_p, out_p, indeg_g, outdeg_g, indeg_p, outdeg_p; //, core_g, core_p; vector path; //GraphAdjList::out_edge_iterator inneis_g, inneis_p, outneis_g, outneis_g_end, outneis_p; typename boost::graph_traits::out_edge_iterator inneis_g, outneis_g, outneis_g_end; typename boost::graph_traits::out_edge_iterator inneis_p, outneis_p; int outneis_size_g, outneis_size_p; //GraphAdjList::out_edge_iterator tmp_it; typename boost::graph_traits::out_edge_iterator tmp_it; // property_map::const_type // g_vlabel_map = get(vertex_label, G->graph()), // p_vlabel_map = get(vertex_label, P->graph()); // property_map::const_type // g_elabel_map = get(edge_label, G->graph()), // p_elabel_map = get(edge_label, P->graph()); // property_map::const_type // g_index_map = get(vertex_index, G->graph()), // p_index_map = get(vertex_index, P->graph()); typename property_map::const_type g_vlabel_map = get(vertex_label, G->graph()); typename property_map::const_type p_vlabel_map = get(vertex_label, P->graph()); typename property_map::const_type g_elabel_map = get(edge_label, G->graph()); typename property_map::const_type p_elabel_map = get(edge_label, P->graph()); typename property_map::const_type g_index_map = get(vertex_index, G->graph()); typename property_map::const_type p_index_map = get(vertex_index, P->graph()); const int proot = get(p_index_map, proot_vertex), groot = get(g_index_map, groot_vertex); //igraph_lazy_adjlist_t inadj1, inadj2, outadj1, outadj2; //igraph_stack_t path; //initialise variables in_g = out_g = indeg_g = outdeg_g = /*core_g =*/ vector(GSIZE, 0); in_p = out_p = indeg_p = outdeg_p = /*core_p =*/ vector(PSIZE, 0); path = vector(); path.reserve(2*PSIZE); in_g_size = in_p_size = out_g_size = out_p_size = matched_nodes = depth = 0; last_pnode = last_gnode = cand_pnode = cand_gnode = -1; for(int i=0; igraph()); //G->adj_list[i].size(); for(int i=0; igraph()); //P->adj_list[i].size(); bool skipOnce=false; if(useRoot) { //root>=0) { cand_pnode=proot; cand_gnode=groot; skipOnce = true; } depth=0; last_gnode=-1; last_pnode=-1; while (depth >= 0) { /* long */ int i; //IGRAPH_ALLOW_INTERRUPTION(); // don't need this /***************************************************************/ /* Find candidate mapping (v_p \in P, v_g \in G) */ /***************************************************************/ if(!skipOnce) { cand_gnode=-1; cand_pnode=-1; /* Search for the next pair to try */ if(useRoot and proot==last_pnode) { /* stepped back to the root, if counting root images then we don't need to select another root mapping. It needs to be done outside this method. */ return false; } else if((in_g_size < in_p_size) || (out_g_size < out_p_size)) { //Ashraf: Strange, this condition should never really be true! /* step back, nothing to do */ } else if(out_g_size > 0 && out_p_size > 0) { /**************************************************************/ /* cand_pnode, search not always needed */ if(last_pnode >= 0) { cand_pnode=last_pnode; } else { i=0; while(cand_pnode<0 && i0 && core_p[i]==0) { cand_pnode=i; } i++; } } /* search for cand_gnode now, it should be bigger than last_gnode */ i=last_gnode+1; while(cand_gnode<0 && i0 && core_g[i]==0) { cand_gnode=i; } i++; } } else if(in_g_size > 0 && in_p_size > 0) { /**************************************************************/ /* cand_pnode, search not always needed */ if(last_pnode >= 0) { cand_pnode=last_pnode; } else { i=0; while(cand_pnode<0 && i0 && core_p[i]==0) { cand_pnode=i; } i++; } } /* search for cand_gnode now, should be bigger than last_gnode */ i=last_gnode+1; while(cand_gnode<0 && i0 && core_g[i]==0) { cand_gnode=i; } i++; } } else { /**************************************************************/ /* cand_pnode, search not always needed */ if(last_pnode >= 0) { cand_pnode=last_pnode; } else { i=0; while(cand_pnode<0 && i3) { cout << " Checking (pnode,gnode)[branch]: (" << cand_pnode << ',' << cand_gnode << ")[" << branch++ << ']'; } /***************************************************************/ /* Step back if couldn't find a suitable mapping */ /***************************************************************/ if (cand_gnode<0 || cand_pnode<0) { /**************************************************************/ /* dead end, step back, if possible. Otherwise we'll terminate */ if (depth >= 1) { last_pnode= path.back(); path.pop_back(); //igraph_stack_pop(&path); last_gnode= path.back(); path.pop_back(); //igraph_stack_pop(&path); matched_nodes -= 1; core_g[last_gnode]=0; core_p[last_pnode]=0; if(m_debug_level>3) cout << " Stepping backward." << endl; //". Step backword to (pnode,gnode): (" << cand_pnode << ',' << cand_gnode << ")" << endl; if(in_g[last_gnode] != 0) { in_g_size += 1; } if(out_g[last_gnode] != 0) { out_g_size += 1; } if(in_p[last_pnode] != 0) { in_p_size += 1; } if(out_p[last_pnode] != 0) { out_p_size += 1; } /*inneis_g = &G->adj_list[last_gnode]; //igraph_lazy_adjlist_get(&inadj1, last_gnode); for (i=0; igraph()).first; //&G->adj_list[last_gnode]; //igraph_lazy_adjlist_get(&outadj1, last_gnode); outneis_size_g = outdeg_g[cand_gnode]; for (i=0; igraph())); //outneis_g[i].destNode; if(out_g[node] == depth) { out_g[node]=0; out_g_size -= 1; } } /*inneis_p=igraph_lazy_adjlist_get(&inadj2, last_pnode); for (i=0; igraph()).first; //&P->adj_list[last_pnode]; //igraph_lazy_adjlist_get(&outadj2, last_pnode); outneis_size_p = outdeg_p[cand_pnode]; for (i=0; igraph())); //outneis_p[i].destNode; if(out_p[node] == depth) { out_p[node]=0; out_p_size -= 1; } } } /* end of stepping back */ depth -= 1; } else { /***************************************************************/ /* Otherwise check if candidate mapping is suitable */ /***************************************************************/ /* step forward if worth, check if worth first */ /*long*/ int xin1=0, xin2=0, xout1=0, xout2=0; bool end=0; /*** We only have out_edges, so lets just not worry about in_edges for now (in principal it should work with just out_edges) */ //inneis_g=igraph_lazy_adjlist_get(&inadj1, cand_gnode); tie(outneis_g, outneis_g_end) = boost::out_edges(cand_gnode, G->graph()); //igraph_lazy_adjlist_get(&outadj1, cand_gnode); outneis_size_g = outdeg_g[cand_gnode]; //inneis_p=igraph_lazy_adjlist_get(&inadj2, cand_pnode); outneis_p = boost::out_edges(cand_pnode, P->graph()).first; //igraph_lazy_adjlist_get(&outadj2, cand_pnode); outneis_size_p = outdeg_p[cand_pnode]; //if(VECTOR(indeg1)[cand_gnode] < VECTOR(indeg2)[cand_pnode] || // VECTOR(outdeg1)[cand_gnode] < VECTOR(outdeg2)[cand_pnode]) { if( outdeg_g[cand_gnode] < outdeg_p[cand_pnode] || get(g_vlabel_map, vertex(cand_gnode, G->graph())) != get(p_vlabel_map, boost::vertex(cand_pnode, P->graph())) ) { end=1; } /*for (i=0; !end && igraph())); //.destNode; if(core_g[node]==0) { if (in_g[node] != 0) { xin1++; } if(out_g[node] != 0) { xout1++; } } } /*for (i=0; !end && icand_gnode edge if (!igraph_vector_binsearch2(inneis_g, node2)) { end=1; } } else { if (VECTOR(in_p)[node] != 0) { xin2++; } if (VECTOR(out_p)[node] != 0) { xout2++; } } }*/ for (i=0; !end && igraph())); //.destNode; if(core_p[node] != 0) { ///*long*/ typename LabeledGraphType::vertex_descriptor gnode2=vertex(core_p[node]-1, G->graph()); typename boost::graph_traits::vertex_descriptor gnode2=vertex(core_p[node]-1, G->graph()); /* check if there is a cand_gnode->node2 edge -- similar to Ullmans backward check*/ /* add edge label check here */ if( (tmp_it=find(G->graph(), outneis_g, outneis_g_end, gnode2))==outneis_g_end ) { //(!igraph_vector_binsearch2(outneis_g, node2)) { end=1; } else if( get(g_elabel_map, *tmp_it) != get(p_elabel_map, outneis_p[i]) ) //(tmp_it->lbl != outneis_p[i].lbl) end=1; } else { if(in_p[node] != 0) { xin2++; } if(out_p[node] != 0) { xout2++; } } } if (!end && (xin1>=xin2 && xout1>=xout2)) { if(m_debug_level>3) cout << " .... " << "are feasible.Step forward." << endl; /* Ok, we add the (cand_gnode, cand_pnode) pair to the mapping */ depth += 1; path.push_back(cand_gnode); //IGRAPH_CHECK(igraph_stack_push(&path, cand_gnode)); path.push_back(cand_pnode); //IGRAPH_CHECK(igraph_stack_push(&path, cand_pnode)); matched_nodes += 1; core_g[cand_gnode]=cand_pnode+1; core_p[cand_pnode]=cand_gnode+1; /* update in_*, out_* */ if(in_g[cand_gnode] != 0) { in_g_size -= 1; } if(out_g[cand_gnode] != 0) { out_g_size -= 1; } if(in_p[cand_pnode] != 0) { in_p_size -= 1; } if(out_p[cand_pnode] != 0) { out_p_size -= 1; } /*inneis_g=igraph_lazy_adjlist_get(&inadj1, cand_gnode); //in neighbours? for (i=0; igraph()).first; //igraph_lazy_adjlist_get(&outadj1, cand_gnode); //out neighbours? outneis_size_g = outdeg_g[cand_gnode]; for (i=0; igraph())); //.destNode; if (out_g[node]==0 && core_g[node]==0) { out_g[node]=depth; out_g_size += 1; } } /*inneis_p=igraph_lazy_adjlist_get(&inadj2, cand_pnode); //in neighbours? for (i=0; igraph()).first; //igraph_lazy_adjlist_get(&outadj2, cand_pnode); //out neighbours? outneis_size_p = outdeg_p[cand_pnode]; for (i=0; igraph())); //.destNode; if (out_p[node]==0 && core_p[node]==0) { out_p[node]=depth; out_p_size += 1; } } last_gnode=-1; last_pnode=-1; /* this the first time here */ if(skipOnce) skipOnce=false; } else { // don't add, checks have failed if(m_debug_level>3) cout << "...are not feasible." << endl; if(skipOnce) return false; last_gnode=cand_gnode; last_pnode=cand_pnode; } } //end else CHECK & ADD candidates if (matched_nodes==PSIZE) { // && function) { //this might not count all isomorphs, as matched_nodes may become > no_of_nodes2 //if (!function(core_g, core_p, arg)) { m_freqCount++; if(m_debug_level>4) {cout << " matched_nodes: " << matched_nodes << ',' << PSIZE << endl; cout << " found occurence: ("; printVector(core_p, cout, -1); cout << ")" << endl;} if(m_cutoff>0) if(m_freqCount>=m_cutoff) return true; //break; //} } } //end while(depth>=0) //no suitable mapping found return (m_freqCount!=0); } /**** VF2 method, adapted from igraph library (do we need to include igraph license/copyright??), avoids expensive *full* intialization of datastructures. Finds monomorphism from P to G, i.e. non-induced subgraph isomorphism from P to G. NOTE: induced subgraph isomorphism from P to G, allows more optimizations (pruning criteria) which are not possible for non-induced subgraph isomorphism. */ //SR - The following code duplicates a more general method futher down. Consider refactoring the code. template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::VF2IsomorphInternalFast(const LabeledPatternType *P, const LabeledGraphType *G, bool useRoot /*=false*/, typename boost::graph_traits::vertex_descriptor proot_vertex /*=-1*/, typename boost::graph_traits::vertex_descriptor groot_vertex /*=-1*/) { using namespace std; using namespace boost; typename boost::graph_traits::out_edge_iterator g_outneis, g_outneis_end, itr_tmp; typename boost::graph_traits::out_edge_iterator p_outneis; int g_outneis_size, p_outneis_size; //SR - Code segment replaced by code for LabeledGraphs. // typename property_map::const_type // g_vlabel_map = get(vertex_label, G->graph()); // typename property_map::const_type // g_elabel_map = get(edge_label, G->graph()); // typename property_map::const_type // p_vlabel_map = get(vertex_label, P->graph()); // typename property_map::const_type // p_elabel_map = get(edge_label, P->graph()); typename LabeledGraphType::ConstVertexLabelMap g_vlabel_map = G->vertexLabelMap(); typename LabeledGraphType::ConstEdgeLabelMap g_elabel_map = G->edgeLabelMap(); typename LabeledPatternType::ConstVertexLabelMap p_vlabel_map = P->vertexLabelMap(); typename LabeledPatternType::ConstEdgeLabelMap p_elabel_map = P->edgeLabelMap(); typename property_map::const_type g_index_map = get(vertex_index, G->graph()); typename property_map::const_type p_index_map = get(vertex_index, P->graph()); typename boost::graph_traits::vertex_iterator gstart, gend; typename boost::graph_traits::vertex_iterator pstart, pend; tie(pstart, pend) = vertices(P->graph()); tie(gstart, gend) = vertices(G->graph()); // const int PSIZE = get_property(P->graph(), graph_numv); // assert(PSIZE == num_vertices(P->graph())); const int PSIZE = num_vertices(P->graph()); // const int GSIZE = get_property(G->graph(), graph_numv); // assert(GSIZE == num_vertices(G->graph())); const int GSIZE = num_vertices(G->graph()); //const int PEDGES = get_property(P->graph(), graph_nume); //assert(PEDGES == num_edges(P->graph())); const int PEDGES = num_edges(P->graph()); // const int GEDGES = get_property(G->graph(), graph_nume); // assert(GEDGES == num_edges(G->graph())); const int GEDGES = num_edges(G->graph()); const int proot = get(p_index_map, proot_vertex); const int groot = get(g_index_map, groot_vertex); vector path; typename boost::graph_traits::vertex_descriptor candg, lastg; typename boost::graph_traits::vertex_descriptor candp, lastp; int depth, p_out_len, g_out_len, matched_nodes, branch=0; int i, node; typename Graph::vertex_descriptor gnode2; // tmp variables, MUST be initialized before use if(m_outp.size() < PSIZE) { m_outp.resize(PSIZE, 0); m_corep.resize(PSIZE, 0); } if(m_outg.size() < GSIZE) { m_outg.resize(GSIZE, 0); m_coreg.resize(GSIZE, 0); } //cout << "m_corep,p.no_nodes: " << m_corep.size() << ',' << PSIZE << " m_coreg,g.no_nodes:" << m_coreg.size() << ',' << GSIZE << endl; /* Wipe off results from last run */ if(m_corep[0]>0) { int j; for(i=0; i < PSIZE; i++) { //if(m_corep[i] <= 0) continue; candg = m_corep[i]-1; //wipe g first g_outneis = boost::out_edges(candg, G->graph()).first; g_outneis_size = boost::out_degree(candg, G->graph()); for(j=0; j < g_outneis_size; j++) m_outg[get(g_index_map, target(*(g_outneis+j), G->graph()))] = 0; m_coreg[candg] = 0; //now wipe p p_outneis = out_edges(i, P->graph()).first; p_outneis_size = out_degree(i, P->graph()); for(j=0; j < p_outneis_size; j++) m_outp[get(p_index_map, target(*(p_outneis+j), P->graph()))] = 0; m_corep[i] = 0; } } //Initialization //m_freqCount = 0; // no need for this initialization depth = matched_nodes = p_out_len = g_out_len = 0; lastp = lastg = candp = candg = -1; if (GSIZE < PSIZE || GEDGES < PEDGES) { return false; } //main search loop while(depth >= 0) { candp = candg = -1; if(depth==0 and useRoot) { if(lastp==proot) //we've checked the root->groot mapping, its not a root image return false; else { candp=proot; candg=groot; } } else if( //(in_g_size < in_p_size) || // don't have inward edges at the moment. (g_out_len < p_out_len) ) { //Ashraf: Strange, this condition should never really be true! /* step back, nothing to do */ } //else if(p_out_len > 0 && g_out_len > 0) { /* incorporate this further down */ //} //else if(in_g_size > 0 && in_p_size > 0) { /* we don't have inward edges at the moment */ //} else { // select candp //if(lastp>=0) if( lastp != std::numeric_limits::vertex_descriptor>::max()) candp = lastp; else { i=0; if(p_out_len > 0 && g_out_len > 0) { //while( candp<0 && i::vertex_descriptor>::max() && i 0 && m_corep[i] == 0) candp = i; i++; } } else { while(candp == std::numeric_limits::vertex_descriptor>::max() && i 0 && g_out_len > 0) { while(candg == std::numeric_limits::vertex_descriptor>::max() && i 0 and m_coreg[i] == 0) candg = i; i++; } } else { while(candg == std::numeric_limits::vertex_descriptor>::max() && i3) { cout << " Checking (pnode,gnode)[branch]: (" << candp << ',' << candg << ")[" << branch++ << ']'; } if(candg == std::numeric_limits::vertex_descriptor>::max() || candp == std::numeric_limits::vertex_descriptor>::max() ) { /**************************************************************/ /* dead end, step back, if possible. Otherwise we'll terminate */ if(depth >= 1) { lastp= path.back(); path.pop_back(); //igraph_stack_pop(&path); lastg= path.back(); path.pop_back(); //igraph_stack_pop(&path); matched_nodes -= 1; m_coreg[lastg]=0; m_corep[lastp]=0; if(m_debug_level>3) cout << " Stepping backward." << endl; //". Step backword to (pnode,gnode): (" << candp << ',' << candg << ")" << endl; //if(in_g[lastg] != 0) // in_g_size += 1; if(m_outg[lastg] != 0) g_out_len += 1; //if(in_p[lastp] != 0) // in_p_size += 1; if(m_outp[lastp] != 0) p_out_len += 1; g_outneis = boost::out_edges(lastg, G->graph()).first; //&G->adj_list[lastg]; g_outneis_size = boost::out_degree(lastg, G->graph()); for (i=0; igraph())); if(m_outg[node] == depth) { m_outg[node]=0; g_out_len -= 1; } } p_outneis = out_edges(lastp, P->graph()).first; //&P->adj_list[lastp]; p_outneis_size = out_degree(lastp, P->graph()); for (i=0; igraph())); if(m_outp[node] == depth) { m_outp[node]=0; p_out_len -= 1; } } } //end if depth > 0 depth -= 1; } //end stepping back else { /***************************************************************/ /* Otherwise check if candidate mapping is suitable */ /* step forward if OK */ /***************************************************************/ int xoutg=0, xoutp=0; //, xnewg=0, xnewp=0; bool end=0; tie(g_outneis, g_outneis_end) = boost::out_edges(candg, G->graph()); //igraph_lazy_adjlist_get(&outadj1, candg); g_outneis_size = boost::out_degree(candg, G->graph()); p_outneis = boost::out_edges(candp, P->graph()).first; //igraph_lazy_adjlist_get(&outadj2, candp); p_outneis_size = boost::out_degree(candp, P->graph()); if( g_outneis_size < p_outneis_size //outdeg_g[candg] < outdeg_p[candp] || get(g_vlabel_map, vertex(candg,G->graph())) != get(p_vlabel_map, vertex(candp,P->graph())) ) { if(m_debug_level >3 && g_outneis_size > p_outneis_size ) std::cout<<"..labels ("<graph()))<<") -> ("<graph()))<<") do not match..."; else if( m_debug_level > 3) std::cout<<".. out degree of "<::iterator it; for (i=0; !end && igraph())); if(m_coreg[node]==0) { if(m_outg[node] != 0) xoutg++; } } for (i=0; !end && igraph())); if(m_corep[node] != 0) { gnode2=boost::vertex(m_corep[node]-1,G->graph()); //FIXME if needed, this should NOT be P->graph(), if its a bug //node2 is vertex_descriptor /* check if there is a candg->node2 edge -- similar to Ullmans backward check*/ /* add edge label check here */ itr_tmp = g_outneis; while( itr_tmp != g_outneis_end && !(boost::target(*itr_tmp,G->graph()) == gnode2) ) ++itr_tmp; if( itr_tmp == g_outneis_end ) { //(!igraph_vector_binsearch2(g_outneis, node2)) { if(m_debug_level >3) std::cout<<"..no "<"<lbl != p_outneis[i].lbl) { if(m_debug_level >3) std::cout<<".. edges labels ("< ("<=xoutp) { //(/*xin1>=xin2 &&*/ xoutg>=xoutp && xnewg>=xnewp)) { /* Ok, we add the (candg, candp) pair to the mapping */ if(m_debug_level>3) cout << " .... " << "are feasible.Step forward." << endl; depth += 1; path.push_back(candg); //IGRAPH_CHECK(igraph_stack_push(&path, candg)); path.push_back(candp); //IGRAPH_CHECK(igraph_stack_push(&path, candp)); matched_nodes += 1; m_coreg[candg]=candp+1; m_corep[candp]=candg+1; //cout<<"m_corep:"; //for (int ii=0; ii< m_corep.size(); ++ii) cout<adj_list[candg]; //no need for this, its still the same g_outneis for (i=0; igraph())); if (m_outg[node]==0 && m_coreg[node]==0) { m_outg[node]=depth; g_out_len += 1; } } //p_outneis= &P->adj_list[candp]; //no need for this, its still the same p_outneis for (i=0; igraph())); if (m_outp[node]==0 && m_corep[node]==0) { m_outp[node]=depth; p_out_len += 1; } } lastg=-1; lastp=-1; /* this the first time here */ //if(skipOnce) skipOnce=false; } else { // don't add, checks have failed if(m_debug_level>3) cout << "...are not feasible." << endl; //if(skipOnce) return false; lastg=candg; lastp=candp; } } //end else CHECK & ADD candidates if (matched_nodes == PSIZE) { m_freqCount++; if(m_debug_level>4) {cout << " matched_nodes: " << matched_nodes << ',' << PSIZE << endl; cout << " found occurence: ("; printVector(m_corep, cout, -1); cout << ")" << endl;} if(outfile) { outfile << "Occurrence " << m_freqCount << ": ["; printVector(m_corep, outfile, -1); outfile << "]" << endl; } //if(outfile) { for(int i=0; i cvtTmp(m_corep.begin(), m_corep.end()); vector cvtTmp(m_corep.begin(), m_corep.end()); if(m_cutoff>0) if(m_freqCount>=m_cutoff) return true; } }//end main search loop return (m_freqCount!=0); } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::VF2IsomorphInternalFast(const LabeledPatternType *P, const LabeledGraphType *G, std::vector >& M, bool useRoot /*=false*/, typename boost::graph_traits::vertex_descriptor proot_vertex /*=-1*/, typename boost::graph_traits::vertex_descriptor groot_vertex /*=-1*/) { using namespace std; using namespace boost; typename boost::graph_traits::out_edge_iterator g_outneis, g_outneis_end, itr_tmp; typename boost::graph_traits::out_edge_iterator p_outneis; int g_outneis_size, p_outneis_size; //SR - Code segment replaced by code for LabeledGraphs. // typename property_map::const_type // g_vlabel_map = get(vertex_label, G->graph()); // typename property_map::const_type // g_elabel_map = get(edge_label, G->graph()); // typename property_map::const_type // p_vlabel_map = get(vertex_label, P->graph()); // typename property_map::const_type // p_elabel_map = get(edge_label, P->graph()); typename LabeledGraphType::ConstVertexLabelMap g_vlabel_map = G->vertexLabelMap(); typename LabeledGraphType::ConstEdgeLabelMap g_elabel_map = G->edgeLabelMap(); typename LabeledPatternType::ConstVertexLabelMap p_vlabel_map = P->vertexLabelMap(); typename LabeledPatternType::ConstEdgeLabelMap p_elabel_map = P->edgeLabelMap(); typename property_map::const_type g_index_map = get(vertex_index, G->graph()); typename property_map::const_type p_index_map = get(vertex_index, P->graph()); typename boost::graph_traits::vertex_iterator gstart, gend; typename boost::graph_traits::vertex_iterator pstart, pend; tie(pstart, pend) = vertices(P->graph()); tie(gstart, gend) = vertices(G->graph()); // const int PSIZE = get_property(P->graph(), graph_numv); // assert(PSIZE == num_vertices(P->graph())); const int PSIZE = num_vertices(P->graph()); // const int GSIZE = get_property(G->graph(), graph_numv); // assert(GSIZE == num_vertices(G->graph())); const int GSIZE = num_vertices(G->graph()); //const int PEDGES = get_property(P->graph(), graph_nume); //assert(PEDGES == num_edges(P->graph())); const int PEDGES = num_edges(P->graph()); // const int GEDGES = get_property(G->graph(), graph_nume); // assert(GEDGES == num_edges(G->graph())); const int GEDGES = num_edges(G->graph()); const int proot = get(p_index_map, proot_vertex); const int groot = get(g_index_map, groot_vertex); vector path; typename boost::graph_traits::vertex_descriptor candg, lastg; typename boost::graph_traits::vertex_descriptor candp, lastp; int depth, p_out_len, g_out_len, matched_nodes, branch=0; int i, node; typename Graph::vertex_descriptor gnode2; // tmp variables, MUST be initialized before use if(m_outp.size() < PSIZE) { m_outp.resize(PSIZE, 0); m_corep.resize(PSIZE, 0); } if(m_outg.size() < GSIZE) { m_outg.resize(GSIZE, 0); m_coreg.resize(GSIZE, 0); } //cout << "m_corep,p.no_nodes: " << m_corep.size() << ',' << PSIZE << " m_coreg,g.no_nodes:" << m_coreg.size() << ',' << GSIZE << endl; /* Wipe off results from last run */ if(m_corep[0]>0) { int j; for(i=0; i < PSIZE; i++) { //if(m_corep[i] <= 0) continue; candg = m_corep[i]-1; //wipe g first g_outneis = boost::out_edges(candg, G->graph()).first; g_outneis_size = boost::out_degree(candg, G->graph()); for(j=0; j < g_outneis_size; j++) m_outg[get(g_index_map, target(*(g_outneis+j), G->graph()))] = 0; m_coreg[candg] = 0; //now wipe p p_outneis = out_edges(i, P->graph()).first; p_outneis_size = out_degree(i, P->graph()); for(j=0; j < p_outneis_size; j++) m_outp[get(p_index_map, target(*(p_outneis+j), P->graph()))] = 0; m_corep[i] = 0; } } //Initialization //m_freqCount = 0; // no need for this initialization depth = matched_nodes = p_out_len = g_out_len = 0; lastp = lastg = candp = candg = -1; if (GSIZE < PSIZE || GEDGES < PEDGES) { return false; } //main search loop while(depth >= 0) { candp = candg = -1; if(depth==0 and useRoot) { if(lastp==proot) //we've checked the root->groot mapping, its not a root image return false; else { candp=proot; candg=groot; } } else if( //(in_g_size < in_p_size) || // don't have inward edges at the moment. (g_out_len < p_out_len) ) { //Ashraf: Strange, this condition should never really be true! /* step back, nothing to do */ } //else if(p_out_len > 0 && g_out_len > 0) { /* incorporate this further down */ //} //else if(in_g_size > 0 && in_p_size > 0) { /* we don't have inward edges at the moment */ //} else { // select candp //if(lastp>=0) if( lastp != std::numeric_limits::vertex_descriptor>::max()) candp = lastp; else { i=0; if(p_out_len > 0 && g_out_len > 0) { //while( candp<0 && i::vertex_descriptor>::max() && i 0 && m_corep[i] == 0) candp = i; i++; } } else { while(candp == std::numeric_limits::vertex_descriptor>::max() && i 0 && g_out_len > 0) { while(candg == std::numeric_limits::vertex_descriptor>::max() && i 0 and m_coreg[i] == 0) candg = i; i++; } } else { while(candg == std::numeric_limits::vertex_descriptor>::max() && i3) { cout << " Checking (pnode,gnode)[branch]: (" << candp << ',' << candg << ")[" << branch++ << ']'; } if(candg == std::numeric_limits::vertex_descriptor>::max() || candp == std::numeric_limits::vertex_descriptor>::max() ) { /**************************************************************/ /* dead end, step back, if possible. Otherwise we'll terminate */ if(depth >= 1) { lastp= path.back(); path.pop_back(); //igraph_stack_pop(&path); lastg= path.back(); path.pop_back(); //igraph_stack_pop(&path); matched_nodes -= 1; m_coreg[lastg]=0; m_corep[lastp]=0; if(m_debug_level>3) cout << " Stepping backward." << endl; //". Step backword to (pnode,gnode): (" << candp << ',' << candg << ")" << endl; //if(in_g[lastg] != 0) // in_g_size += 1; if(m_outg[lastg] != 0) g_out_len += 1; //if(in_p[lastp] != 0) // in_p_size += 1; if(m_outp[lastp] != 0) p_out_len += 1; g_outneis = boost::out_edges(lastg, G->graph()).first; //&G->adj_list[lastg]; g_outneis_size = boost::out_degree(lastg, G->graph()); for (i=0; igraph())); if(m_outg[node] == depth) { m_outg[node]=0; g_out_len -= 1; } } p_outneis = out_edges(lastp, P->graph()).first; //&P->adj_list[lastp]; p_outneis_size = out_degree(lastp, P->graph()); for (i=0; igraph())); if(m_outp[node] == depth) { m_outp[node]=0; p_out_len -= 1; } } } //end if depth > 0 depth -= 1; } //end stepping back else { /***************************************************************/ /* Otherwise check if candidate mapping is suitable */ /* step forward if OK */ /***************************************************************/ int xoutg=0, xoutp=0; //, xnewg=0, xnewp=0; bool end=0; tie(g_outneis, g_outneis_end) = boost::out_edges(candg, G->graph()); //igraph_lazy_adjlist_get(&outadj1, candg); g_outneis_size = boost::out_degree(candg, G->graph()); p_outneis = boost::out_edges(candp, P->graph()).first; //igraph_lazy_adjlist_get(&outadj2, candp); p_outneis_size = boost::out_degree(candp, P->graph()); if( g_outneis_size < p_outneis_size //outdeg_g[candg] < outdeg_p[candp] || get(g_vlabel_map, vertex(candg,G->graph())) != get(p_vlabel_map, vertex(candp,P->graph())) ) { if(m_debug_level >3 && g_outneis_size > p_outneis_size ) std::cout<<"..labels ("<graph()))<<") -> ("<graph()))<<") do not match..."; else if( m_debug_level > 3) std::cout<<".. out degree of "<::iterator it; for (i=0; !end && igraph())); if(m_coreg[node]==0) { if(m_outg[node] != 0) xoutg++; } } for (i=0; !end && igraph())); if(m_corep[node] != 0) { gnode2=boost::vertex(m_corep[node]-1,G->graph()); //FIXME if needed, this should NOT be P->graph(), if its a bug //node2 is vertex_descriptor /* check if there is a candg->node2 edge -- similar to Ullmans backward check*/ /* add edge label check here */ itr_tmp = g_outneis; while( itr_tmp != g_outneis_end && !(boost::target(*itr_tmp,G->graph()) == gnode2) ) ++itr_tmp; if( itr_tmp == g_outneis_end ) { //(!igraph_vector_binsearch2(g_outneis, node2)) { if(m_debug_level >3) std::cout<<"..no "<"<lbl != p_outneis[i].lbl) { if(m_debug_level >3) std::cout<<".. edges labels ("< ("<=xoutp) { //(/*xin1>=xin2 &&*/ xoutg>=xoutp && xnewg>=xnewp)) { /* Ok, we add the (candg, candp) pair to the mapping */ if(m_debug_level>3) cout << " .... " << "are feasible.Step forward." << endl; depth += 1; path.push_back(candg); //IGRAPH_CHECK(igraph_stack_push(&path, candg)); path.push_back(candp); //IGRAPH_CHECK(igraph_stack_push(&path, candp)); matched_nodes += 1; m_coreg[candg]=candp+1; m_corep[candp]=candg+1; //cout<<"m_corep:"; //for (int ii=0; ii< m_corep.size(); ++ii) cout<adj_list[candg]; //no need for this, its still the same g_outneis for (i=0; igraph())); if (m_outg[node]==0 && m_coreg[node]==0) { m_outg[node]=depth; g_out_len += 1; } } //p_outneis= &P->adj_list[candp]; //no need for this, its still the same p_outneis for (i=0; igraph())); if (m_outp[node]==0 && m_corep[node]==0) { m_outp[node]=depth; p_out_len += 1; } } lastg=-1; lastp=-1; /* this the first time here */ //if(skipOnce) skipOnce=false; } else { // don't add, checks have failed if(m_debug_level>3) cout << "...are not feasible." << endl; //if(skipOnce) return false; lastg=candg; lastp=candp; } } //end else CHECK & ADD candidates if (matched_nodes == PSIZE) { m_freqCount++; if(m_debug_level>4) {cout << " matched_nodes: " << matched_nodes << ',' << PSIZE << endl; cout << " found occurence: ("; printVector(m_corep, cout, -1); cout << ")" << endl;} //if(outfile) { outfile << "Occurrence " << m_freqCount << ": ["; printVector(m_corep, outfile, -1); outfile << "]" << endl; } //if(outfile) { for(int i=0; i cvtTmp(m_corep.begin(), m_corep.end()); vector cvtTmp(m_corep.begin(), m_corep.end()); M.push_back(cvtTmp); if(m_cutoff>0) if(m_freqCount>=m_cutoff) return true; } }//end main search loop return (m_freqCount!=0); } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::VF2Isomorph(const LabeledPatternType *P, const LabeledGraphType *G, std::vector& map_p, std::vector& map_g) { int tmp=m_cutoff; if(!m_countFreq) m_cutoff = 1; m_freqCount=0; //VF2IsomorphInternalFast(P, G, -1, -1); VF2IsomorphInternalFast(P, G); m_cutoff = tmp; return (m_freqCount!=0); } //yuyi template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork< LabeledPatternType,LabeledGraphType >::VF2Isomorph(const LabeledPatternType *P, const LabeledGraphType *G, std::vector& map_p, std::vector& map_g, std::vector< std::vector >& M) { int tmp=m_cutoff; if(!m_countFreq) m_cutoff = 1; m_freqCount=0; //VF2IsomorphInternalFast(P, G, -1, -1); VF2IsomorphInternalFast(P, G, M); m_cutoff = tmp; return (m_freqCount!=0); } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::VF2Isomorph(const LabeledPatternType *P, const LabeledGraphType *G) { //vector map_p = vector(P->no_nodes, 0); //vector map_g = vector(G->no_nodes, 0); return VF2Isomorph(P, G, m_corep, m_coreg); //map_p, map_g); } //yuyi template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::VF2Isomorph(const LabeledPatternType * P, const LabeledGraphType * G, std::vector >& M) { //vector map_p = vector(P->no_nodes, 0); //vector map_g = vector(G->no_nodes, 0); return VF2Isomorph(P, G, m_corep, m_coreg, M); //map_p, map_g); } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::VF2RootImage(const LabeledPatternType *P, const LabeledGraphType *G, typename boost::graph_traits::vertex_descriptor root, typename boost::graph_traits< Graph >::vertex_descriptor gnode) { //vector map_p = vector(P->no_nodes, 0); //vector map_g = vector(G->no_nodes, 0); m_freqCount=0; int tmp = m_cutoff; //needed as this method can be used outside this class //if(!m_countFreq) m_cutoff = 1; //VF2IsomorphInternal(P, G, map_p, map_g, root, gnode); VF2IsomorphInternalFast(P, G, true, root, gnode); m_cutoff = tmp; return (m_freqCount!=0); } template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork< LabeledPatternType, LabeledGraphType >::VF2RootImage(const LabeledPatternType *P, const LabeledGraphType *G, typename boost::graph_traits< Pattern >::vertex_descriptor root) { //vector map_p = vector(P->no_nodes, 0); //vector map_g = vector(G->no_nodes, 0); m_freqCount=0; int tmp, cutoff; tmp = cutoff = m_cutoff; m_cutoff = 1; //cout << "m_cutoff, cutoff, tmp " << m_cutoff << ',' << cutoff << ',' << tmp << endl; if(!m_countFreq) { cutoff = 1; } typename boost::graph_traits< Graph >::vertex_iterator gnode, gend; for(boost::tie(gnode,gend)=vertices(G->graph()); gnode!=gend; gnode++) { VF2IsomorphInternalFast(P, G, true, root, *gnode); if(cutoff>0 and m_freqCount>=cutoff) { m_cutoff = tmp; return (m_freqCount!=0); } } m_cutoff = tmp; //cout << "returning " << m_freqCount << endl; return (m_freqCount!=0); } /// ---------------------------------------------------------------------------------------------------------------------------------- /// mostafa: template < typename LabeledPatternType,typename LabeledGraphType > bool GraphInNetwork::firstBestParentSubgraphIsomorphismMapping(LabeledGraphType& network, LabeledPatternType& pattern, std::vector ¤tMapping) { using namespace std; using namespace boost; /// types: typedef typename boost::property_map::type NetworkVertexLabelType; typedef typename boost::property_map::type NetworkEdgeLabelType; typedef typename boost::graph_traits::vertex_descriptor NetworkVertexDescriptor; typedef typename boost::graph_traits::edge_descriptor NetworkEdgeDescriptor; typedef typename boost::graph_traits::edge_iterator NetworkEdgeIter; typedef typename boost::graph_traits::vertex_iterator NetworkVertexIter; typedef typename boost::graph_traits::out_edge_iterator NetworkOutEdgeIter; typedef typename boost::property_map::type PatternVertexLabelType; typedef typename boost::property_map::type PatternEdgeLabelType; typedef typename boost::graph_traits::vertex_descriptor PatternVertexDescriptor; typedef typename boost::graph_traits::edge_descriptor PatternEdgeDescriptor; typedef typename boost::graph_traits::edge_iterator PatternEdgeIter; typedef typename boost::graph_traits::vertex_iterator PatternVertexIter; typedef typename boost::graph_traits::out_edge_iterator PatternOutEdgeIter; /// variables: unsigned unmappedElement=std::numeric_limits::max(); NetworkVertexLabelType networkVertexLabelMap=get(vertex_label, network ); NetworkEdgeLabelType networkEdgeLabelMap=get(edge_label, network ); PatternVertexLabelType patternVertexLabelMap=get(vertex_label, pattern); PatternEdgeLabelType patternEdgeLabelMap=get(edge_label, pattern); typename boost::property_map::type patternIndex=boost::get( boost::vertex_index,pattern); unsigned start=0; currentMapping.clear(); for(unsigned i=0;i pp=boost::out_edges(boost::vertex(p,pattern),pattern); for(PatternOutEdgeIter iter=pp.first;iter!=pp.second;iter++) { PatternVertexDescriptor v2=boost::target(*iter,pattern); if ( currentMapping[patternIndex[v2]]!=unmappedElement ) { /// check the edge labels pair en=boost::edge(boost::vertex(n,network),boost::vertex(currentMapping[patternIndex[v2]],network),network); if (en.second==false) { consistent=false; break; } if ( get(networkEdgeLabelMap, en.first)!= get(patternEdgeLabelMap, *iter) ) { consistent=false; break; } } } found=true; currentMapping[p]=n; break; } } } if (!found) { if (p==0) return false; else { start=currentMapping[p-1]+1; p=p-2; } } else start=0; } for(unsigned i=0;i bool GraphInNetwork::nextBestParentSubgraphIsomorphismMapping(LabeledGraphType& network, LabeledPatternType& pattern, std::vector ¤tMapping) { using namespace std; using namespace boost; /// types: typedef typename boost::property_map::type NetworkVertexLabelType; typedef typename boost::property_map::type NetworkEdgeLabelType; typedef typename boost::graph_traits::vertex_descriptor NetworkVertexDescriptor; typedef typename boost::graph_traits::edge_descriptor NetworkEdgeDescriptor; typedef typename boost::graph_traits::edge_iterator NetworkEdgeIter; typedef typename boost::graph_traits::vertex_iterator NetworkVertexIter; typedef typename boost::graph_traits::out_edge_iterator NetworkOutEdgeIter; typedef typename boost::property_map::type PatternVertexLabelType; typedef typename boost::property_map::type PatternEdgeLabelType; typedef typename boost::graph_traits::vertex_descriptor PatternVertexDescriptor; typedef typename boost::graph_traits::edge_descriptor PatternEdgeDescriptor; typedef typename boost::graph_traits::edge_iterator PatternEdgeIter; typedef typename boost::graph_traits::vertex_iterator PatternVertexIter; typedef typename boost::graph_traits::out_edge_iterator PatternOutEdgeIter; /// variables: unsigned unmappedElement=std::numeric_limits::max(); NetworkVertexLabelType networkVertexLabelMap =get(vertex_label, network); NetworkEdgeLabelType networkEdgeLabelMap =get(edge_label, network); PatternVertexLabelType patternVertexLabelMap =get(vertex_label, pattern); PatternEdgeLabelType patternEdgeLabelMap =get(edge_label, pattern); typename boost::property_map::type patternIndex=boost::get( boost::vertex_index,pattern); std::vector tempMapping; for(unsigned i=0;i=0;p=p-1) { for(unsigned n=tempMapping[p]+1;n pp=boost::out_edges(boost::vertex(p,pattern),pattern); for(PatternOutEdgeIter iter=pp.first;iter!=pp.second;iter++) { PatternVertexDescriptor v2=boost::target(*iter,pattern); if ( currentMapping[patternIndex[v2]]!=unmappedElement ) { /// check the edge labels pair en=boost::edge(boost::vertex(n,network), boost::vertex(currentMapping[patternIndex[v2]],network), network); if (en.second==false) { consistent=false; break; } if ( get(networkEdgeLabelMap, en.first)!= get(patternEdgeLabelMap, *iter) ) { consistent=false; break; } } } currentMapping[p]=n; bool validMapping=true; for (int p2=p+1;p2 pp=boost::out_edges(boost::vertex(p2,pattern),pattern); for(PatternOutEdgeIter iter=pp.first;iter!=pp.second;iter++) { PatternVertexDescriptor v2=boost::target(*iter,pattern); if ( currentMapping[patternIndex[v2]]!=unmappedElement ) { /// check the edge labels pair en=boost::edge(boost::vertex(n2,network),boost::vertex(currentMapping[patternIndex[v2]],network),network); if (en.second==false) { consistent=false; break; } if ( get(networkEdgeLabelMap, en.first)!= get(patternEdgeLabelMap, *iter) ) { consistent=false; break; } } } found=true; currentMapping[p2]=n2; break; } } } if (!found) { if (p2<=p+1||p2==0) { validMapping=false; goto L; } else { start=currentMapping[p2-1]+1; p2=p2-2; } } else start=0; } L: if (validMapping) return true; } } } for(unsigned i=p;i::null_vertex() after switch to boost template class TreeInNetwork { public: typedef mips::groupalgebra::Field::value Field_val; typedef mips::groupalgebra::GF2l_Z2k_Dynamic GF2l_Z2k_Dynamic; typedef typename _Graph::vertex_descriptor vertex_descriptor; //remove this once we switch completely to boost typedef typename boost::property_map::value_type VertexLabel; typedef typename boost::property_map::value_type EdgeLabel; typedef mips::core::SharedTreeDAG SharedTreeDAG; typedef mips::core::SharedTreeDAGNode SharedTreeDAGNode; typedef mips::core::SharedTreeDAGEdge SharedTreeDAGEdge; /* Properties */ float getSuccessProb(); void setSuccessProb(float); int getDebugLevel(); void setDebugLevel(int); TreeInNetwork(); ~TreeInNetwork(); bool isIsomorphic(_Pattern *p, _Graph *g, vertex_descriptor root_vertex); bool isIsomorphic(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, float success_prob); bool isHomomorphic(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, int thresh=0); int evaluate_circuit_with_vector(const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const float success_prob); protected: bool m_do_homomorph_test; //default true long long m_real_circuit_size; long long m_nodes_visited; int m_debug_level; int P_SIZE, G_SIZE; //we should be able to store networks upto 2 billion nodes, using ints for vertex ids (32 bit signed int: -2,147,483,648 to +2,147,483,647) float m_successprob; //the above should be sufficient for now. _Graph *G, *G_OLD; _Pattern *P; typename boost::property_map<_Graph, boost::vertex_label_t>::type g_vlabel_map, p_vlabel_map; typename boost::property_map<_Graph, boost::edge_label_t>::type g_elabel_map, p_elabel_map; typename boost::property_map<_Graph, boost::vertex_index_t>::type g_index_map, p_index_map; std::map m_label_index; std::vector< unsigned int > m_vertices_per_label; std::vector< unsigned int > m_v_perlabel_idx; std::vector< unsigned int > m_randset; mips::groupalgebra::Field m_field; bool m_refresh_inputs; std::vector m_scalars; //this stores coefficients of our Field -- currently the field can supports patterns of size upto 2**28 public: std::vector m_circuit_array; //this stores values (coefficients after + and *) of our Field protected: std::vector< std::vector > m_small_circuit; //smaller variant of the above std::vector m_input_gates; //this stores random k-bit vectors, where k==P_SIZE. Thus, unlike our Field, //the algorithm can only support patterns of size upto 32 (=sizeof(int)). std::vector m_refresh_vals; //whether to refresh the circuit or not. The circuit needs to be refreshed for each eigen_val. //multiple iterations over one particular eigen_val can share the circuit and need not refreshing. //For the time being leave it as int, as our cids (circuit ids) are ints. std::vector< std::vector > m_refresh_small_circuit; //smaller variant of the above std::vector m_use_homomorph; std::vector< std::vector > m_use_small_homomorph; //smaller variant of the above //private: bool isHomomorphic_normal(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, int cutoff); bool isHomomorphic_small(_Pattern *p, _Graph *g, vertex_descriptor root_vertex, int cutoff); //LEAVE the cids to int at the moment, as we might use -ve numbers for circuit ids (we may want to convert these to unsigned ints at some stage) int compute_homomorphism(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent); Field_val compute_small_circuit(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent, const unsigned long eigen_val, unsigned long &sid, int depth=0); int compute_small_circuit_homomorphism(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent); //with common subtrees Field_val compute_small_circuit_using_shared_trees(const int cid, const int root, const vertex_descriptor g_vertex, const int root_parent, const unsigned long eigen_val, SharedTreeDAG& common_subtrees, unsigned long &sid, int depth=0); int compute_small_circuit_homomorphism_using_shared_trees(const int cid, const int root, const vertex_descriptor g_vertex, const int root_parent, SharedTreeDAG& common_subtrees, int depth=0); //Computing w/o fourier, i.e. using actual polynomials rather than their information-theoretic representation void compute_wo_fourier(const vertex_descriptor root, const vertex_descriptor g_vertex, const vertex_descriptor root_parent, const GF2l_Z2k_Dynamic &gf2, std::vector< std::vector > &occurarray, std::vector &circuitgates, std::vector &inputgates, GF2l_Z2k_Dynamic::value &result, int depth=0); void compute_wo_fourier_using_shared_trees(const int root, const vertex_descriptor g_vertex, const int root_parent, SharedTreeDAG& common_subtrees, std::vector< std::vector > &occurArray, const GF2l_Z2k_Dynamic &gf2, std::vector &circuitgates, std::vector &inputgates, GF2l_Z2k_Dynamic::value &result, int depth=0); //Marked for future culling Field_val evaluate_circuit_with_vector(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const float success_prob); Field_val evaluate_circuit_with_vector(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const int rounds, const unsigned long _2k); Field_val compute_circuit_with_vector(const int cid, const vertex_descriptor root_vertex, const vertex_descriptor g_vertex, const vertex_descriptor root_parent, const unsigned long eigen_val, unsigned long &sid, int depth=0); }; } //namepace_pattern_matching } //namespace_mips #include "treeInNetwork.hxx" #endif /* __PATTERNMATCHING_TREEINNETWORK_H__ */ mips-1.0/mips/patternMatching/CMakeLists.txt0000644002402200254040000000041312520407731020473 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) set (SOURCES treeInNetwork.hxx graphInNetwork.hxx ) set (HEADERS treeInNetwork.hpp graphInNetwork.hpp ) set (SUBDIRECTORIES ) INCLUDE_ALL_SUBDIRECTORIES_HEADER_SOURCES(SUBDIRECTORIES) mips-1.0/mips/patternMatching/graphInNetwork.hpp0000644002402200254040000001466712520407731021426 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PATTERNMATCHING_GRAPHINNETWORK_HPP #define MIPS_PATTERNMATCHING_GRAPHINNETWORK_HPP //#include "mips/core/LabeledGraphType/LabeledGraphTypeAdjList.hpp" #include //#include "mips/core/tree/enum/extenderRootedTree.hpp" //#include "mips/core/group/binIrredPolyField.hpp" //#include //#include //typedef LabeledGraphTypeAdjList::vertex_descriptor vertex_descriptor; //remove this once we switch completely to boost //inline vertex_descriptor null_vertex() { return -1; } //replace this with boost::LabeledGraphType_traits::null_vertex() after switch to boost //contains definition of vertex_label_t ... #include #include #include namespace mips { namespace pattern_matching { template class GraphInNetwork { public: typedef typename LabeledGraphType::Graph Graph; typedef typename LabeledPatternType::Graph Pattern; private: bool m_countFreq; int m_freqCount, m_cutoff, m_debug_level; std::vector m_corep, m_coreg, m_outp, m_outg; std::string outfname; std::ofstream outfile; public: GraphInNetwork(); ~GraphInNetwork(); void setMapOut(std::string fname); std::string getMapOut(); void setCountFreq(bool count); bool getCountFreq(); void setCutoff(int c); int getCutoff(); void setDebugLevel(int c); int getDebugLevel(); int getFreqCount(); bool ullmanIsomorph ( LabeledPatternType *p, LabeledGraphType *g ); bool ullmanRootImage( LabeledPatternType *p, LabeledGraphType *g, typename boost::graph_traits< Pattern >::vertex_descriptor root, typename boost::graph_traits< Graph >::vertex_descriptor gnode); bool ullmanRootImage( LabeledPatternType *p, LabeledGraphType *g, typename boost::graph_traits< Pattern >::vertex_descriptor root); bool VF2Isomorph(const LabeledPatternType *p, const LabeledGraphType *g); bool VF2Isomorph(const LabeledPatternType *p, const LabeledGraphType *g, std::vector >& M); //yuyi add this function bool VF2Isomorph(const LabeledPatternType *p, const LabeledGraphType *g, std::vector& map_p, std::vector& map_g); bool VF2Isomorph(const LabeledPatternType *p, const LabeledGraphType *g, std::vector& map_p, std::vector& map_g, std::vector >& M); //yuyi add this function bool VF2RootImage(const LabeledPatternType *p, const LabeledGraphType *g, typename boost::graph_traits< Pattern >::vertex_descriptor root, typename boost::graph_traits< Graph >::vertex_descriptor gnode); bool VF2RootImage(const LabeledPatternType *p, const LabeledGraphType *g, typename boost::graph_traits< Pattern >::vertex_descriptor root); /// mostafa /// this method finds the first subLabeledGraphType isomorphism mapping from 'LabeledPatternType' to 'network' /// such that it first matchs LabeledPatternTypes's first vertex, then second ... and at the end it backtracks. /// it return 'true' if the mapping exists and 'false' otherwise static bool firstBestParentSubgraphIsomorphismMapping(LabeledGraphType& network, LabeledPatternType& pattern, std::vector &mapping); /// mostafa /// this method finds the next subLabeledGraphType isomorphism mapping from 'LabeledPatternType' to 'network' /// such that it first matchs LabeledPatternTypes's first vertex, then second ... and at the end it backtracks. /// it return 'true' if the mapping exists and 'false' otherwise static bool nextBestParentSubgraphIsomorphismMapping(LabeledGraphType& network, LabeledPatternType & pattern, std::vector &mapping); protected: bool ullmanIsomorphInternal(LabeledPatternType *p, LabeledGraphType *g, const typename boost::graph_traits::vertex_descriptor pn, const int pnidx, const typename boost::graph_traits::vertex_descriptor gn, std::vector map); bool DEFUNCT_VF2IsomorphInternal(const LabeledPatternType *P, const LabeledGraphType *G, std::vector& core_p, std::vector& core_g, bool useRoot=false, typename boost::graph_traits::vertex_descriptor root=-1, typename boost::graph_traits::vertex_descriptor gnode=-1); bool VF2IsomorphInternalFast(const LabeledPatternType *P, const LabeledGraphType *G, bool useRoot=false, typename boost::graph_traits::vertex_descriptor root=-1, typename boost::graph_traits::vertex_descriptor gnode=-1); bool VF2IsomorphInternalFast(const LabeledPatternType *P, const LabeledGraphType *G, std::vector >& M, bool useRoot=false, typename boost::graph_traits::vertex_descriptor root=-1, typename boost::graph_traits::vertex_descriptor gnode=-1); ////bool isIsomorphic(GraphAdjList *p, GraphAdjList *g, int root); }; } //namespace pattern_matching } //namespace mips #include "graphInNetwork.hxx" #endif /* MIPS_PATTERNMATCHING_GRAPHINNETWORK_HPP */ mips-1.0/mips/prediction/0000755002402200254040000000000012520407731014745 5ustar thomasfdtaigmips-1.0/mips/prediction/attributeType.hpp0000644002402200254040000000352112520407731020324 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_ATTRIBUTETYPE_HPP #define MIPS_PREDICTION_ATTRIBUTETYPE_HPP #include namespace mips { namespace prediction { /*! * \brief The NominalAttributeType struct defines an attribute of a nominal type, it takes values from an unordered set of values. * * e.g.: red, blue, green * * \sa OrdinalAttributeType, NumericalAttributeType */ struct NominalAttributeType {}; /*! * \brief The OrdinalAttributeType struct defines an attribute of an ordinal type, it takes values from a totally ordered set. * * e.g: small, medium, large, extra large * * \sa NominalAttributeType, NumericalAttributeType */ struct OrdinalAttributeType {}; /*! * \brief The NumericalAttributeType struct defines an attribute of a numerical type, it takes values from (a subset of) the real numbers * * \sa NominalAttributeType, OrdinalAttributeType */ struct NumericalAttributeType {}; template struct IsNominalAttributeType : public boost::false_type {}; template struct IsOrdinalAttributeType : public boost::false_type {}; template struct IsNumericalAttributeType : public boost::false_type {}; template <> struct IsNominalAttributeType : public boost::true_type {}; template <> struct IsOrdinalAttributeType : public boost::true_type {}; template <> struct IsNumericalAttributeType : public boost::true_type {}; } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_ATTRIBUTETYPE_HPP mips-1.0/mips/prediction/namespace.hpp0000644002402200254040000000125412520407731017414 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_NAMESPACE_HPP #define MIPS_PREDICTION_NAMESPACE_HPP namespace mips { /*! * \brief The prediction namespace contains the basic elements to predict the target value for an example using a model, * print a model to the user and different ways how to learn a model. */ namespace prediction { } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_NAMESPACE_HPP mips-1.0/mips/prediction/decisionTree/0000755002402200254040000000000012520407731017362 5ustar thomasfdtaigmips-1.0/mips/prediction/decisionTree/fTestPruner.hpp0000644002402200254040000000602012520407731022352 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_FTESTPRUNER_HPP #define MIPS_PREDICTION_DECISIONTREE_FTESTPRUNER_HPP #include "decisionTree.hpp" #include #include #include namespace mips { namespace prediction { namespace decision_tree { struct FTestPruner { FTestPruner(double alpha = 0.05) : _alpha(alpha) {} template Tree operator()(const Tree& tree) { using namespace mips::core::tree; typedef typename traits::NodeDescriptor NodeDescriptor; typedef typename traits::NodeIterators NodeIterators; if (_alpha == 0) return tree; Tree newTree(tree); bool didChange = true; while (didChange) { didChange = false; // run over all the leafs BOOST_FOREACH(NodeDescriptor node, leaf_nodes(newTree, mips::core::tree::rootNode(newTree))) { // only left nodes if (!isYesNode(newTree, node)) continue; // check whether sybling is also a leaf NodeDescriptor syblNode = sibling_node(newTree, node); if (!isLeaf(newTree, syblNode)) continue; // if we ware able to prune, lets start over again if ((didChange = pruneNode(newTree, node, syblNode))) break; } } return newTree; } private: template bool pruneNode( Tree& tree, typename mips::core::tree::traits::NodeDescriptor lNode, typename mips::core::tree::traits::NodeDescriptor rNode) { using namespace boost::math; using namespace mips::core::tree; typedef typename traits::NodeData NodeData; typedef typename NodeData::Statistic Statistic; typedef typename Statistic::ValueType ValueType; // get the variance for the child nodes const Statistic& yStat = tree[lNode].globalStatistic; const Statistic& nStat = tree[rNode].globalStatistic; ValueType yVar = yStat.value(); ValueType nVar = nStat.value(); if (yVar == 0 || nVar == 0) return false; ValueType fVal = yVar / nVar; fisher_f dist(yStat.count() - 1, nStat.count() - 1); double uv = quantile(complement(dist, _alpha / 2)); double lv = quantile(dist, _alpha / 2); if (fVal <= lv || fVal >= uv) return false; // okay, let's prune the node // update the statistics of the parent node typename traits::NodeDescriptor parent = parentNode(tree, lNode); tree[parent].currentTestStatistic = Statistic(); tree[parent].test = typename NodeData::Test(); // and remove the child nodes destroy_node(tree, lNode); destroy_node(tree, rNode); return true; } double _alpha; }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_FTESTPRUNER_HPP mips-1.0/mips/prediction/decisionTree/depthFirstDecisionTreeLearner.hpp0000644002402200254040000002154212520407731026022 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DEPTHFIRSTDECISIONTREELEARNER_HPP #define MIPS_PREDICTION_DECISIONTREE_DEPTHFIRSTDECISIONTREELEARNER_HPP #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * \brief DepthFirstDecisionTreeLearner is the base class to create a decision *tree learner and can be completely adapted by changing the template *parameters. * \extends concepts::mips::prediction::decision_tree::TestGenerator * \extends concepts::mips::prediction::decision_tree::ExampleGenerator * \extends concepts::mips::prediction::decision_tree::SplittingCriterium * \ingroup DecisionTree * * Learner is the base class to create a decision tree learner and can be *completely adapted by changing the template parameters. * The class has 5 template parameters: * \tparam TestGenerator This class should follow the \link *concepts::mips::prediction::decision_tree::TestGenerator *TestGenerator\endlink-concept. It is used to generate the test collections for *each node. * * \tparam ExampleGenerator This class should follow the \link *concepts::mips::prediction::decision_tree::ExampleGenerator *ExampleGenerator\endlink-concept. * It is used to generate the example collection for each node. * * \tparam SplittingCriterium This class should follow the \link *concepts::mips::prediction::decision_tree::SplittingCriterium *SplittingCriterium\endlink-concept. * It is used to find the best test for a node. * * \tparam AttributeExtractor This class should follow the \link *concepts::mips::prediction::AttributeExtractor *AttributeExtractor\endlink-concept. * It is used to extract the target attribute from an example. * * \tparam Tree This is the type of tree that will be learned. * * The policy classes orthogonal, so that changing, e.g., the testGenerator, *should not influence the other policies. * The learner inherits from the TestGenerator, ExampleGenerator, and the *SplittingCriterium, so that methods exposed in the policies are also available * in the learner class. This way it is possible to set the necessary parameters *before running the decision tree learner. The targetAttributeExtractor * is considered a functor and can be passed as a parameter to the *operator()(const AttributeExtractor & targetAttributeExtractor) function. * As a last template parameter, the configuration policy also contains a Tree *type, the output type of the learner. * Calling the operator () causes the decision tree learner to learn a tree, and *return that tree as a result. * * Take a look at the default traits class for the decision tree: * \li mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits */ template < class _TestGenerator, class _ExampleGenerator, class _SplittingCriterium, class _AttributeExtractor, class _Tree > struct DepthFirstDecisionTreeLearner : public _TestGenerator, public _ExampleGenerator, public _SplittingCriterium { public: /*! * \brief Exposes the type of the TestGenerator. * * \details The TestGenerator implements the \link concepts::mips::prediction::decision_tree::TestGenerator * TestGenerator concept\endlink. */ typedef _TestGenerator TestGenerator; /*! * \brief Exposes the type of the ExampleGenerator. * * The ExampleGenerator implements the \link concepts::mips::prediction::decision_tree::ExampleGenerator * ExampleGenerator concept\endlink. */ typedef _ExampleGenerator ExampleGenerator; /*! * \brief Exposes the type of the SplittingCriterium. * * The SplittingCriterium implements the \link concepts::mips::prediction::decision_tree::SplittingCriterium * SplittingCriterium concept\endlink. */ typedef _SplittingCriterium SplittingCriterium; /*! * \brief Exposes the type of the AttributeExtractor. The AttributeExtractor * implements the * \link concepts::mips::prediction::AttributeExtractor AttributeExtractor * concept\endlink. */ typedef _AttributeExtractor AttributeExtractor; typedef AttributeExtractor TargetValueExtractor; /*! * \brief Exposes the type of the Tree generated. */ typedef _Tree Tree; typedef Tree Model; /*! * \brief Exposing the Tree type as a result_type makes it possible to use the * Learner as a standard functor to use with standard STL-algorithms. */ typedef Tree result_type; private: typedef typename ExampleGenerator::ExampleDB ExampleDB; typedef typename ExampleGenerator::Example Example; typedef typename TestGenerator::TestCollection TestCollection; typedef typename TestGenerator::TestCollectionManager TestCollectionManager; typedef typename TestCollection::iterator TestIterator; typedef typename TestGenerator::Test Test; typedef typename SplittingCriterium::HeuristicResultType HeuristicResultType; typedef typename SplittingCriterium::Statistic Statistic; typedef mips::core::RootedTreeTraits TreeTraits; typedef typename TreeTraits::NodeDescriptor NodeDescriptor; typedef decision_tree::detail::NodeInfo< Tree, TestCollection, TestCollectionManager, ExampleDB> NodeInfo; typedef decision_tree::detail::NodeData NodeData; typedef decision_tree::detail::TestInfo TestInfo; private: typedef std::list NodeStack; public: /*! * \brief This constructor creates a default tree learner. */ DepthFirstDecisionTreeLearner(); /*! * \brief operator() learns a tree * \param targetAttributeExtractor is a variable to set the target attribute *extractor if necessary (Otherwise a default constructed * extractor will be used. * \return The learned decision tree * * The tree is learned using the examples and test as returned by the *TestGenerator and the ExampleGenerator. For each node, the * SplittingCriterium defines wether it is a leaf node, or which test is *chosen. */ Tree operator()(const TargetValueExtractor & targetValueExtractor = TargetValueExtractor()); /*! * \brief testGenerator Allows access to the underlying test generator object * \return a const reference to the test generator object */ const TestGenerator &testGenerator() const { return *this; } /*! * \brief exampleGenerator Allows access to the underlying example generator * object * \return a const reference to the example generator object */ const ExampleGenerator &exampleGenerator() const { return *this; } /*! * \brief splittingCriterium Allows access to the underlying splitting * criterium object * \return a const reference to the splitting criterium object */ const SplittingCriterium &splittingCriterium() const { return *this; } /*! * \brief testGenerator Allows access to the underlying test generator object * \return a reference to the test generator object */ TestGenerator &testGenerator() { return *this; } /*! * \brief exampleGenerator Allows access to the underlying example generator * object * \return a reference to the example generator object */ ExampleGenerator &exampleGenerator() { return *this; } /*! * \brief splittingCriterium Allows access to the underlying splitting * criterium object * \return a reference to the splitting criterium object */ SplittingCriterium &splittingCriterium() { return *this; } // deprecated functions TestGenerator &rTestGenerator() { return *this; } ExampleGenerator &rExampleGenerator() { return *this; } SplittingCriterium &rSplittingCriterium() { return *this; } const SplittingCriterium &decisionStrategy() const { return *this; } SplittingCriterium &rDecisionStrategy() { return *this; } private: void expandNode(NodeInfo &curNodeInfo, NodeStack &nodes, const AttributeExtractor &AttributeExtractor); void createRootNode(Tree &tree, NodeStack &nodes); void createChildNodes(NodeInfo &parentnodeInfo, NodeStack &nodes); void fillInLeafNode(const NodeInfo &nodeInfo, const Statistic &alwaysTrueStatistic); }; } // decision_tree namespace } // prediction namespace } // mips namespace #include "depthFirstDecisionTreeLearner.hxx" #endif // MIPS_PREDICTION_DECISIONTREE_DEPTHFIRSTDECISIONTREELEARNER_HPP mips-1.0/mips/prediction/decisionTree/splitExampleGenerator.hxx0000644002402200254040000000407512520407731024437 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_SPLITEXAMPLEGENERATOR_HXX #define MIPS_PREDICTION_DECISIONTREE_SPLITEXAMPLEGENERATOR_HXX #include #include "splitExampleGenerator.hpp" namespace mips { namespace prediction { namespace decision_tree { template typename SplitExampleGenerator<_Example, _Container, _ContainerAccessPolicy, _ExampleDBContainerAccessPolicy>::ExampleDB SplitExampleGenerator<_Example, _Container, _ContainerAccessPolicy, _ExampleDBContainerAccessPolicy>::generateRootNodeExampleDB() { return ExampleDB(ContainerAccessPolicy::get()); } template template std::pair::ExampleDB, typename SplitExampleGenerator<_Example, _Container, _ContainerAccessPolicy, _ExampleDBContainerAccessPolicy>::ExampleDB> SplitExampleGenerator<_Example, _Container, _ContainerAccessPolicy,_ExampleDBContainerAccessPolicy>::generateChildNodeExampleDB(const NodeInfo & parentNode, const Test & parentSplitTest) { std::pair dbs; // get the test from the node for(typename Container::const_iterator i = parentNode.exampleDB.get().begin(); i != parentNode.exampleDB.get().end(); ++i) ( utilities::wrapPointer(parentSplitTest) (*i) ? dbs.first : dbs.second).get().push_back(*i); // reset the database dbs.first.reset(); dbs.second.reset(); return dbs; } } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_SPLITEXAMPLEGENERATOR_HXX mips-1.0/mips/prediction/decisionTree/defaultTree.hpp0000644002402200254040000000400612520407731022337 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DEFAULTTREE_HPP #define MIPS_PREDICTION_DECISIONTREE_DEFAULTTREE_HPP #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * DefaultDecisionTreeTraits is traits class that defines the Model, ModelPrinter and the Classifier used. * * This class is similar to mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits, but it only considers the tree, not the way how the * three is learned. */ template < class Test, class Statistic, class Tree = mips::core::RootedPointerTree, bool> > struct DefaultDecisionTreeTraits { /*! * \brief Model is a general type to define the result from learning a decision tree. */ typedef Tree Model; /*! * \brief ModelPrinter is generic way to print the tree. The information displayed is dependent on the statistic used in the tree. */ typedef prediction::ModelPrinter ModelPrinter; /*! * \brief Classifier classifies an example. The actual value is selected by the type of statistic used. */ typedef prediction::Classifier Classifier; }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DEFAULTTREE_HPP mips-1.0/mips/prediction/decisionTree/decisionTree.hpp0000644002402200254040000002171512520407731022516 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DECISIONTREE_HPP #define MIPS_PREDICTION_DECISIONTREE_DECISIONTREE_HPP #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * Type RootedTreeTraits class to check whether an object is aa decision tree or not */ template struct IsDecisionTree : public core::IsTree {}; /** * @brief yesBranch This function follows the yes-branch for a given node in a given tree * @param tree The Binary rooted tree * @param descriptor The current node * @return The nodeDescriptor when following the yes-branch, or a default constructed nodeDescriptor if the yes-branch can not be followed. */ template typename mips::core::RootedTreeTraits::NodeDescriptor yesBranch(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor descriptor) { using namespace mips::core; typedef typename RootedTreeTraits::BranchDescriptor Branch; typedef typename RootedTreeTraits::NodeDescriptor NodeDescriptor; BOOST_FOREACH(Branch branch, childBranches(tree, descriptor)) if(tree[branch] == true) return childNode(tree, branch); return NodeDescriptor(); } /** * @brief noBranch This function follows the no-branch for a given node in a given tree * @param tree The Binary rooted tree * @param descriptor The current node * @return The nodeDescriptor when following the no-branch, or a default constructed nodeDescriptor if the no-branch can not be followed. */ template typename mips::core::RootedTreeTraits::NodeDescriptor noBranch(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor descriptor) { using namespace mips::core; typedef typename RootedTreeTraits::BranchDescriptor Branch; typedef typename RootedTreeTraits::NodeDescriptor NodeDescriptor; BOOST_FOREACH(Branch branch, childBranches(tree, descriptor)) if(tree[branch] == false) return childNode(tree, branch); return NodeDescriptor(); } /** * @brief sibling_node Finds the other nodeDescriptor having the same parent in this binary tree * @param tree The binary rooted tree * @param node The node descriptor * @return The sibling nodeDescriptor or a default constructed nodeDescriptor if the sibling can not be found (e.g. the sibling of the root node) */ template typename mips::core::RootedTreeTraits::NodeDescriptor sibling_node(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node) { using namespace mips::core; typedef typename RootedTreeTraits::BranchDescriptor Branch; typedef typename RootedTreeTraits::NodeDescriptor NodeDescriptor; NodeDescriptor parent = parentNode(tree, node); if(parent == NodeDescriptor()) return NodeDescriptor(); BOOST_FOREACH(NodeDescriptor syblNode, child_nodes(tree, parent)) { if(node != syblNode) return syblNode; } return NodeDescriptor(); } /** * @brief isYesNode Finds out whether the current node is a yes-node, i.e., is obtained by following the yes-branch from the parent * @param tree The binary rooted tree * @param node The node descriptor * @return true if this node has a parent and this node is found by following the yes-branch from the parent, otherwise false. */ template bool isYesNode(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node) { using namespace mips::core; typedef typename RootedTreeTraits::NodeDescriptor NodeDescriptor; NodeDescriptor parent = parentNode(tree, node); if(parent == NodeDescriptor()) return false; return (yesBranch(tree, parent) == node); } /** * @brief isNoNode Finds out whether the current node is a no-node, i.e., is obtained by following the no-branch from the parent * @param tree The binary rooted tree * @param node The node descriptor * @return true if this node has a parent and this node is found by following the no-branch from the parent, otherwise false. */ template bool isNoNode(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node) { using namespace mips::core; typename RootedTreeTraits::NodeDescriptor parent = parentNode(tree, node); if(!parent) return false; return (noBranch(tree, parent) == node); } /** * @brief getPathDescription Gets a textual description of the path from the rootnode to this node. * @param tree The binary rooted tree * @param node The node from which we want to extract the path. * @return A textual representation of the path where 'R' is the root node, 'y' means following the yes-branch and 'n' following the no-branch. */ template std::string getPathDescription(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node) { using namespace mips::core; typename RootedTreeTraits::NodeDescriptor parent = parentNode(tree, node); if(!parent) return "R"; return getPathDescription(tree, parent) + (isYesNode(tree, node) ? "y" : "n"); } template struct IsLeafFunctor : public std::unary_function::NodeDescriptor, bool> { IsLeafFunctor(const Tree * tree) : _tree(tree) {} typedef typename mips::core::RootedTreeTraits::NodeDescriptor NodeDescriptor; bool operator()(NodeDescriptor node) const { return mips::core::isLeaf(*_tree, node); } private: const Tree * _tree; }; template /** * @brief leaf_nodes finds a pair of iterators over all the leaf nodes of the subtree of the current node * @param tree The rooted binary tree * @param node The node for which the subtree should be considerd * @return A pair of iterators over all the leaf nodes. This will run in lineair time over all the elements in the subtree. */ std::pair< boost::filter_iterator, typename mips::core::RootedTreeTraits::NodeIterator>, boost::filter_iterator, typename mips::core::RootedTreeTraits::NodeIterator> > leaf_nodes(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node) { typedef boost::filter_iterator, typename mips::core::RootedTreeTraits::NodeIterator> _iterator; typedef typename mips::core::RootedTreeTraits::NodeIterators NodeIterators; NodeIterators n = mips::core::allNodes(tree, node); return std::make_pair(_iterator(IsLeafFunctor(&tree), n.first, n.second), _iterator(IsLeafFunctor(&tree), n.second, n.second)); } /** * @brief findClassifyingNode Find the leaf node wherein that describes the example when following the path decided by the tests * @param tree The binary rooted tree * @param example The example (the tests embedded in the binary rooted tree should work on this example) * @param rootNode The root node of the subtree. If no node is supplied, the rootnode of the complete tree is used. * @return The leaf node in which the example is classified by following the path indicated by tests on the supplied example. */ template typename mips::core::RootedTreeTraits::NodeDescriptor findClassifyingNode(const Tree & tree, const Example & example, typename mips::core::RootedTreeTraits::NodeDescriptor rootNode = typename mips::core::RootedTreeTraits::NodeDescriptor() ) { using namespace mips::core; typedef typename mips::core::RootedTreeTraits::NodeDescriptor NodeDescriptor; NodeDescriptor node = (rootNode == NodeDescriptor() ? mips::core::rootNode(tree) : rootNode); if(node == NodeDescriptor()) return NodeDescriptor(); while(node != NodeDescriptor() && !isLeaf(tree, node)) node = utilities::wrapPointer(tree[node].test) (example) ? yesBranch(tree, node) : noBranch(tree, node); return node; } /** * @brief classify Extracts the nodelabel of the classifying node for a given example * @param tree The binary rooted tree * @param example The example (the tests embedded in the binary rooted tree should work on this example) * @return The nodelabel of the leaf node in which the example is classified by following the path indicated by tests on the supplied example. */ template const typename mips::core::RootedTreeTraits::NodeLabel & classify(const Tree & tree, const Example & example) { return tree[findClassifyingNode(tree, example)]; } } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DECISIONTREE_HPP mips-1.0/mips/prediction/decisionTree/defaultSplittingCriterium.hxx0000644002402200254040000001340012520407731025317 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DEFAULTSPLITTINGCRITERIUM_HXX #define MIPS_PREDICTION_DECISIONTREE_DEFAULTSPLITTINGCRITERIUM_HXX #include "defaultSplittingCriterium.hpp" #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { template template std::pair< detail::TestInfo< typename DefaultSplittingCriterium<_Heuristic, _PrePruner, _ExampleDBWrapper, _TestCollectionWrapper>::HeuristicResultType, typename DefaultSplittingCriterium<_Heuristic, _PrePruner, _ExampleDBWrapper, _TestCollectionWrapper>::Statistic, typename NodeInfo::TestCollection::value_type>, typename DefaultSplittingCriterium<_Heuristic, _PrePruner, _ExampleDBWrapper, _TestCollectionWrapper>::Statistic> DefaultSplittingCriterium<_Heuristic, _PrePruner, _ExampleDBWrapper, _TestCollectionWrapper>::findBestTest(NodeInfo & curNodeInfo, const TargetAttributeExtractor & extractor) { typedef typename NodeInfo::TestCollection TestCollection; typedef typename TestCollection::value_type Test; typedef typename NodeInfo::ExampleDB ExampleDB; typedef typename NodeInfo::Tree Tree; typedef typename TestCollectionWrapper::template result_type::type MyTestCollection; typedef typename ExampleDBWrapper::template result_type::type MyExampleDB; typedef typename TargetAttributeExtractor::result_type TargetAttribute; typedef mips::prediction::decision_tree::detail::QueryEngine< typename TestCollection::value_type, Statistic, TargetAttributeExtractor, detail::AlwaysTrueExtraTest > QueryEngine; typedef decision_tree::detail::TestInfo< HeuristicResultType, Statistic, typename NodeInfo::TestCollection::value_type> TestInfo; const Tree & tree = *curNodeInfo.tree; std::list testToDelete; // wrap the testcollection and the example MyTestCollection tests = testCollectionWrapper().operator() (curNodeInfo.testCollection); MyExampleDB db = exampleDBWrapper().operator() (curNodeInfo.exampleDB); // gather all the statistics MIPS_LOG(Learner, "Querying for node " << getPathDescription(tree, curNodeInfo.node)); QueryEngine queryEngine; queryEngine(tests, extractor, db.current(), db.end(), maxNumberOfExamples()); TestInfo bestTest; const Statistic & alwaysTrueStatistic = queryEngine.alwaysTrueStatistic(); MIPS_LOG(Learner, "Searching statistics for node " << getPathDescription(tree, curNodeInfo.node)); // loop over all the statistics and test tests.reset(); // loop over all the tests and statistics for(typename TestCollection::iterator testIt = tests.current(); testIt != tests.end(); ++testIt) { Heuristic getHeuristicValue = generateHeuristic(); PrePruner prePruner = generatePrePruner(); // gather all the details TestInfo curTest; curTest.test = *testIt; curTest.statistic = queryEngine.testStatistic(curTest.test); // check with the prepruner PrunerResult prePruneValue = prePruner(curNodeInfo.tree, curNodeInfo.node, curTest.test, curTest.statistic, alwaysTrueStatistic); // discarded for this node if(prePruneValue == Pruner_RemoveFromNode) { MIPS_LOG(Learner, "Discared by prepruner \'" << utilities::wrapPointer(curTest.test) << "\'"); } // discarded for this node and for children else if(prePruneValue == Pruner_RemoveFromNodeAndChildren) { MIPS_LOG(Learner, "Removing test \'" << utilities::wrapPointer(curTest.test) << "\' for child nodes of " << getPathDescription(tree, curNodeInfo.node)); testToDelete.push_back(curTest.test); } // okay, the prepruner accepts this value else { curTest.value = getHeuristicValue(curTest.statistic, alwaysTrueStatistic); //MIPS_LOG(Learner, "Test (heuristic=" << curTest.value << " ): " << utilities::wrapPointer(curTest.test)); // better than the previous test? if(bestTest.value < curTest.value) { //MIPS_LOG_CONTINUE(Learner, " <-- new best test "); bestTest = curTest; } } } // actually delete all the tests for(typename std::list::const_iterator it = testToDelete.begin(); it != testToDelete.end(); ++it) curNodeInfo.testCollectionManager.removeTest(curNodeInfo.testCollection, *it); MIPS_LOG(Learner, "Done"); return std::make_pair(bestTest, alwaysTrueStatistic); } } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DEFAULTSPLITTINGCRITERIUM_HXX mips-1.0/mips/prediction/decisionTree/pruneResult.hpp0000644002402200254040000000204612520407731022425 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_PRUNERESULT_HPP #define MIPS_PREDICTION_DECISIONTREE_PRUNERESULT_HPP namespace mips { namespace prediction { namespace decision_tree { enum PrunerResult { Pruner_DoNothing = 0, /*!< tells the the splitting criterium that a test is fine */ Pruner_RemoveFromNode = -1, /*!< tells the splitting criterium that a test is not valid for a node */ Pruner_RemoveFromNodeAndChildren = -2 /*!< tells the splitting criterium that a test is not valid for a node, and also not for all the child nodes. */ }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_PRUNERESULT_HPP mips-1.0/mips/prediction/decisionTree/defaultLearner.hpp0000644002402200254040000002223212520407731023031 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DEFAULTLEARNER_HPP #define MIPS_PREDICTION_DECISIONTREE_DEFAULTLEARNER_HPP #include #include #include #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * \brief DefaultDecisionTreeLearnerTraits creates a traits class for a standard decision tree. * \ingroup DecisionTree * * DefaultDecisionTreeLearnerTraits creates a traits class for a standard decision tree. * This traits class has 10 template parameters of which 3 are obligatory, the *other 7 have a default value. * \tparam Test Should follow the \link *concepts::mips::prediction::decision_tree::Test Test\endlink concept. This *type defines the base test type. * Note that polymorphic tests are allowed, it is possible to use pointers *to objects as a test. In this case the test type should be the * pointer type. * * \tparam Example The example type. * * \tparam TargetAttributeExtractor Extract the target value from an example. *This class should follow the * concepts::mips::prediction::AttributeExtractor-concept. Especially the *AttributeType is important, it defines the default heuristic to be used. * * \tparam PrePruner The prepruner is used to prune tests while constructing *the tree. This class should follow the * concepts::mips::prediction::decision_tree::PrePruner-concept. By *default the * mips::prediction::decision_tree::MinimumLeafSizePruner is used and this *is set to a minimum leaf size of 10. * * \tparam TestCollectionWrapper This wrapper is used in the *mips::prediction::decision_tree::DefaultSplittingStrategy to alter the test *collection. By * default no wrapping (mips::prediction::decision_tree::NoChangeWrapper) *is used, but e.g. with mips::prediction::decision_forest::RandomFraction * it is possible to select a fraction of the test. The concept for a *wrapper is defined in concepts::mips::prediction::decision_tree::DBWrapper. * The wrapper is used in the FindBestTest function, only the test that *remain after the wrapping are used to find a best test. * * \tparam ExampleDBWrapper This wrapper is used in the *mips::prediction::decision_tree::DefaultSplittingStrategy to alter the example *collection. By * default no wrapping (mips::prediction::decision_tree::NoChangeWrapper) *is used. The concept for a wrapper is defined in * concepts::mips::prediction::decision_tree::DBWrapper. The wrapper is *used in the FindBestTest function, only the example that remain after * the wrapping are used to find a best test. * * \tparam TestGenerator This class should follow the *concepts::mips::prediction::decision_tree::TestGenerator concept and is * used to generate tests for each node. By default the *mips::prediction::decision_tree::RemoveUsedTestGenerator is used. This test *generator stores * the test internally in a container and makes sure that a test used for *an ancestor-node, is not used anymore for child-nodes. * * \tparam ExampleGenerator This class should follow the *concepts::mips::prediction::decision_tree::ExampleGenerator-concept and is *used to generate * an example database for each node. By default the *mips::prediction::decision_tree::SplitExampleGenerator is used. This *example-generator stores * the examples in a container, and splits for each node the examples in *to two groups, a yes-part and a no-part. These databases of examples are * used resp. in the yes-branch and the no-branch. * * \tparam Heuristic This class should follow the *concepts::mips::prediction::decision_tree::Heuristic-concept and is used to *calculate a score for * each test based on its statistic. By default, the heuristic is chosen *based on the target attribute extractor, more specifically on the * attributeType: * \li mips::core::prediction::OrdinalAttributeType selects the *mips::core::prediction::decision_tree::InformationGain heuristic. * \li mips::core::prediction::NominalAttributeType selects the *mips::core::prediction::decision_tree::InformationGain heuristic. * \li mips::core::prediction::NumericalAttributeType selects the *mips::core::prediction::decision_tree::VarianceReduction heuristic. * * * \tparam Tree The type of tree that is generated. By default a *mips::core::tree::PointerTree is created. Other trees are possible, but the *required * functionality for these trees is still badly documented. * * * If the heuristic uses statistic that accepts weighted values, the default *decision tree can learn a weighted tree. The target attribute extractor * should have as a ResultType a mips::core::WeightedValue-type. The \link *mips::core::prediction::decision_tree::InformationGain InformationGain\endlink * and the \link mips::core::prediction::decision_tree::VarianceReduction *VarianceReduction\endlink heuristic use statistics that accept weighted *values. * * \sa Learner, mips::prediction::decision_tree::RemoveUsedTestGenerator, *mips::prediction::decision_tree::SplitExampleGenerator, * mips::prediction::decision_tree::DefaultSplittingCriterium. */ template , class _ExampleGenerator = SplitExampleGenerator<_Example>, class _Heuristic = typename detail::HeuristicDecider<_TargetAttributeExtractor>::type, class _Tree = mips::core::RootedPointerTree, bool> > struct DefaultDecisionTreeLearnerTraits { private: typedef _TestGenerator TestGenerator; typedef typename TestGenerator::Test Test; typedef typename TestGenerator::TestCollection TestCollection; typedef typename TestGenerator::TestCollectionManager TestCollectionManager; typedef _ExampleGenerator ExampleGenerator; typedef typename ExampleGenerator::Example Example; typedef typename ExampleGenerator::ExampleDB ExampleDB; typedef _TargetAttributeExtractor TargetAttributeExtractor; typedef typename TargetAttributeExtractor::AttributeType TargetAttributeType; typedef typename TargetAttributeExtractor::result_type TargetAttributeResultType; typedef _Tree Tree; typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; typedef DefaultSplittingCriterium<_Heuristic, _PrePruner, _TestCollectionWrapper, _ExampleDBWrapper> SplittingCriterium; typedef typename SplittingCriterium::Statistic Statistic; typedef typename SplittingCriterium::Heuristic Heuristic; typedef typename SplittingCriterium::PrePruner PrePruner; typedef typename SplittingCriterium::TestCollectionWrapper TestCollectionWrapper; typedef typename SplittingCriterium::ExampleDBWrapper ExampleDBWrapper; public: /*! * \brief this type defines the learner that can be used by using the *operator() method. * * As the decision tree learner inherits from the test generator policy, the *example generator policy and the splitting criterium policy, initializing * values for these components can be done directly through the learner class. * * In the default implementation of the decision tree the following methods are * available: * \copydetails mips::prediction::decision_tree::DepthFirstDecisionTreeLearner */ typedef decision_tree::DepthFirstDecisionTreeLearner< TestGenerator, ExampleGenerator, SplittingCriterium, TargetAttributeExtractor, Tree> Learner; /*! * \brief Model is a general type to define the result from learning a * decision tree. */ typedef Tree Model; /*! * \brief ModelPrinter is generic way to print the tree. The information * displayed is dependent on the statistic used in the tree. */ typedef prediction::ModelPrinter ModelPrinter; /*! * \brief Classifier classifies an example. The actual value is selected by * the type of statistic used. */ typedef prediction::Classifier Classifier; }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DEFAULTLEARNER_HPP mips-1.0/mips/prediction/decisionTree/depthPruner.hpp0000644002402200254040000000276312520407731022403 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DEPTHPRUNER_HPP #define MIPS_PREDICTION_DECISIONTREE_DEPTHPRUNER_HPP #include "pruneResult.hpp" namespace mips { namespace prediction { namespace decision_tree { template struct DepthPruner { template /*! * \brief Checks if a give node should be pruned based on height of tree. * \param curNode A node of the tree * \return PrunerResult::Pruner_DoNothing if node level is not greater than * threshold height; PrunerResult::Pruner_RemoveFromNodeAndChildren otherwise. */ PrunerResult operator()(const Node& curNode, const Test& /*test*/, const Statistic& /*statistics*/, const Statistic& /*alwaysTrueStatistic*/) { unsigned int count = 1; typename Node::ParentPtr parent = curNode.parent(); while (parent != typename Node::ParentPtr()) { count++; parent = parent->parent(); } return (count < _MaxDepth ? Pruner_DoNothing : Pruner_RemoveFromNodeAndChildren); } }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DEPTHPRUNER_HPP mips-1.0/mips/prediction/decisionTree/namespace.hpp0000644002402200254040000000136612520407731022035 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_NAMESPACE_HPP #define MIPS_PREDICTION_DECISIONTREE_NAMESPACE_HPP namespace mips { namespace prediction { /*! * \brief The decision tree namespace contains the basic elements to learn a decision tree, and uses learned decision trees to predict the target value * for an example. */ namespace decision_tree { } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_NAMESPACE_HPP mips-1.0/mips/prediction/decisionTree/noChangeDBWrapper.hpp0000644002402200254040000000224612520407731023370 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_NOCHANGEDBWRAPPER_HPP #define MIPS_PREDICTION_DECISIONTREE_NOCHANGEDBWRAPPER_HPP namespace mips { namespace prediction { namespace decision_tree { /*! * \brief The NoChangeWrapper struct implements the *concepts::mips::prediction::decision_tree::DBWrapper concept. * * It does not change the database, but returns the same database. The compiler *will remove this in the optimization step. */ struct NoChangeDBWrapper { /*! * The result type is the same as supplied DB */ template struct result_type { typedef _DB type; }; /*! * the operator return the same database as passed, but not by reference. */ template DB operator()(const DB& db) { return db; } }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_NOCHANGEDATABASEWRAPPER_HPP mips-1.0/mips/prediction/decisionTree/varianceReduction.hpp0000644002402200254040000000564512520407731023552 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_VARIANCEREDUCTION_HPP #define MIPS_PREDICTION_DECISIONTREE_VARIANCEREDUCTION_HPP #include #include namespace mips { namespace prediction { namespace decision_tree { template struct VarianceReductionTraits; /*! \brief VarianceReduction based prediction. This class is implements a heuristic for a decision tree based on variance reduction. \tparam _ValueType The return value type */ template struct VarianceReduction { typedef mips::core::Variance<_EntityType, _CountType, _ValueType> Statistic; typedef _ValueType ResultType; ResultType operator()(const Statistic& curStatistic, const Statistic& alwaysTrueStatistic) { typedef ResultType VT; unsigned int Y_count = curStatistic.count(); unsigned int N_count = alwaysTrueStatistic.count() - Y_count; if (Y_count == 0 || N_count == 0) return -1; VT Y_squared = curStatistic.sumOfSquares(); VT N_squared = alwaysTrueStatistic.sumOfSquares() - Y_squared; VT Y_sum = curStatistic.sum(); VT N_sum = alwaysTrueStatistic.sum() - Y_sum; VT Y_mean = Y_sum / Y_count; VT N_mean = N_sum / N_count; VT Y_squared_mean = Y_squared / Y_count; VT N_squared_mean = N_squared / N_count; VT Y_Var = Y_squared_mean - Y_mean * Y_mean; VT N_Var = N_squared_mean - N_mean * N_mean; // MIPS_LOG(OUT, "Real var:" << alwaysTrueStatistic.count() << "; " << // Y_count << "; " << N_count << "; " << alwaysTrueStatistic.value() << "; " // << Y_Var << "; " << N_Var << ";"); // MIPS_LOG(VarianceReduction, "(squaredSum, sum, weightedVariance, count): // Y(" << Y_squared << "," << Y_sum << "," << Y_Var << "," << Y_count << "); // N(" << N_squared << "," << N_sum << "," << N_Var << "," << N_count << // ");"); return alwaysTrueStatistic.value() * alwaysTrueStatistic.count() - (Y_Var * Y_count + N_Var * N_count); } }; template struct VarianceReductionTraits< mips::core::WeightedVariable<_EntityType, _CountType>, _ValueType> { typedef VarianceReduction<_EntityType, _ValueType, _CountType> result_type; }; template struct VarianceReductionTraits { typedef VarianceReduction<_EntityType, _ValueType, unsigned int> result_type; }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_VARIANCEREDUCTION_HPP mips-1.0/mips/prediction/decisionTree/test/0000755002402200254040000000000012520407731020341 5ustar thomasfdtaigmips-1.0/mips/prediction/decisionTree/test/informationGainTest.cpp0000644002402200254040000000055112520407731025032 0ustar thomasfdtaig#include #define BOOST_TEST_MODULE informationGainTest #define BOOST_TEST_DYN_LINK #include BOOST_AUTO_TEST_CASE(informationGainConstructionTest) { typedef mips::prediction::decision_tree::InformationGain IG; IG heuristic; BOOST_CHECK(sizeof(heuristic) > 0); } mips-1.0/mips/prediction/decisionTree/test/CMakeLists.txt0000755002402200254040000000031212520407731023100 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) CREATE_TEST(NAME informationGainTest SOURCES informationGainTest.cpp) CREATE_TEST(NAME varianceReductionTest SOURCES varianceReductionTest.cpp) mips-1.0/mips/prediction/decisionTree/test/varianceReductionTest.cpp0000644002402200254040000000055212520407731025354 0ustar thomasfdtaig#include #define BOOST_TEST_MODULE varianceReductionTest #define BOOST_TEST_DYN_LINK #include BOOST_AUTO_TEST_CASE(varianceReductionConstructionTest) { typedef mips::prediction::decision_tree::VarianceReduction VR; VR heuristic; BOOST_CHECK(sizeof(heuristic) > 0); } mips-1.0/mips/prediction/decisionTree/minimumLeafSizePruner.hpp0000644002402200254040000000440312520407731024366 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_MINIMUMLEAFSIZEPRUNER_HPP #define MIPS_PREDICTION_DECISIONTREE_MINIMUMLEAFSIZEPRUNER_HPP #include "pruneResult.hpp" #include #include namespace mips { namespace prediction { namespace decision_tree { /** * @brief The MinimumLeafSizePruner struct is a prepruner that does not allow leafs of a size smaller than a threshold. * * By default this value is set to 10, but it can be changed by using the setMinimumSize() function */ struct MinimumLeafSizePruner { MinimumLeafSizePruner() : _minSize(10) { } /*! * \brief Checks if a give node should be pruned based on node size. * \param statistics The statistics after applying a test. * \param alwaysTrueStatistic The base statistics value to compare against. * \return PrunerResult::Pruner_DoNothing if node size is greater than * threshold size; PrunerResult::Pruner_RemoveFromNodeAndChildren otherwise. */ template PrunerResult operator()( const Tree & /*tree*/, const Node & /*curNode*/, const Test & /*test*/, const Statistic & statistics, const Statistic & alwaysTrueStatistic) { bool retVal = statistics.count() >= minimumSize() && (alwaysTrueStatistic.count() - statistics.count()) >= minimumSize(); return (retVal ? Pruner_DoNothing : Pruner_RemoveFromNodeAndChildren); } /*! * \brief Sets the minimum size of a node. * \param minSize The size of a node. */ void setMinimumSize(unsigned int minSize) { _minSize = minSize; } /*! * \brief Returns the mininum size of a node. * \return The minimum size of a node. */ unsigned int minimumSize() const { return _minSize; } private: unsigned int _minSize; }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_MINIMUMLEAFSIZEPRUNER_HPP mips-1.0/mips/prediction/decisionTree/removeUsedTestGenerator.hpp0000644002402200254040000001344712520407731024731 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_REMOVEUSEDTESTGENERATOR_HPP #define MIPS_PREDICTION_DECISIONTREE_REMOVEUSEDTESTGENERATOR_HPP #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * \brief RemoveUsedTestGenerator is a \link concepts::mips::prediction::decision_tree::TestGenerator test generator\endlink which stores the tests * internally in a container and removes the used tests for child node test collections. * \extends concepts::mips::utilities::wrappers::ContainerAccess * \extends concepts::mips::prediction::decision_tree::TestGenerator * * This class has 4 template parameters of which the first is obligatory: * \tparam Test The type of test used. This class should follow the concepts::mips::prediction::decision_tree::Test concept but can be a pointer type to * allow polymorphic behaviour. * * \tparam Container The container in which the tests are internally stored. By default this is std::list * * \tparam ContainerAccessPolicy The way how the container is stored internally in this test generator, by default this is as member variable, but * possible solutions are utilities::wrappers::ContainerAccessThroughMemberVariable, * utilities::wrappers::ContainerAccessThroughInheritance, utilities::wrappers::ContainerAccessThroughPointerStorage or * utilities::wrappers::ContainerAccessThroughSmartPointer. This class should implement the * \link concepts::mips::utilities::wrappers::ContainerAccess ContainerAccess\endlink-concept. * * \tparam TestCollectionAccessPolicy: This class defines the way how the test container is stored in TestCollection. By default this by a smart pointer, * such that copying of the test collection does not mean copying the internal container. * possible solutions are utilities::wrappers::ContainerAccessThroughMemberVariable, * utilities::wrappers::ContainerAccessThroughInheritance, utilities::wrappers::ContainerAccessThroughPointerStorage or * utilities::wrappers::ContainerAccessThroughSmartPointer. This class should implement the * \link concepts::mips::utilities::wrappers::ContainerAccess ContainerAccess\endlink-concept. * * RemoveUsedTestGenerator inherits from the ContainerAccessPolicy, and therefore the container is directly accessable through the container() function. * * \ingroup DecisionTree */ template < class _Test, class _Container = std::list<_Test>, class _ContainerAccessPolicy = utilities::StoreByValue<_Container>, class _TestCollectionAccessPolicy = utilities::StoreBySmartPointer<_Container> > struct RemoveUsedTestGenerator : public _ContainerAccessPolicy { typedef _Container Container; typedef _ContainerAccessPolicy ContainerAccessPolicy; typedef _TestCollectionAccessPolicy TestCollectionAccessPolicy; /*! * \brief TestCollection is a sequential database of tests, which are internally stored in a container as defined by the template parameter Container * and the container is stored as defined by the template parameter TestCollectionAccessPolicy. */ typedef utilities::SequentialDatabaseWrapper TestCollection; typedef typename TestCollection::value_type Test; /*! * \brief TestCollectionManager is a specialised class to manage the fact of removing tests */ struct TestCollectionManager; /*! * \brief generateRootNodeTestCollection returns the same collection as stored internally. * \return A TestCollection * * The internal test container can be accessed using the container() function. */ TestCollection generateRootNodeTestCollection(); /*! * \brief generateChildNodeTestCollection generates two identical collection, by taking the test collection of the parent node and removing the * the test selected for this node. * * \return A pair of test collections */ template std::pair generateChildNodeTestCollection(NodeInfo & parentNode); /*! * \brief testContainer allows acces to the container containing the tests * \return A reference to the test container * * This method is equal to container() */ Container & testContainer() { return ContainerAccessPolicy::get(); } /*! * \brief testContainer allows acces to the container containing the tests * \return A const reference to the test container * * This method is equal to container() */ const Container & testContainer() const { return ContainerAccessPolicy::get(); } /*! * \brief manager returns a TestCollectionManager object * \return a TestCollectionManager * * this object can be used to remove tests from a test collection */ TestCollectionManager manager() { return TestCollectionManager(); } }; } // decision_tree namespace } // prediction namespace } // mips namespace #include "removeUsedTestGenerator.hxx" #endif // MIPS_PREDICTION_DECISIONTREE_REMOVEUSEDTESTGENERATOR_HPP mips-1.0/mips/prediction/decisionTree/whitneymann.hpp0000644002402200254040000001454012520407731022440 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_WHITNEYMAN_HPP #define MIPS_PREDICTION_DECISIONTREE_WHITNEYMAN_HPP #include "../../core/statistics/ranking.hpp" #include "boost/intrusive/avltree.hpp" #include "list" #include namespace mips { namespace prediction { namespace decision_tree { /*! * \brief The heuristic based on the Whitney Mann test. * It follows the concepts::mips::classfier::decision_tree::heuristic concept. */ template struct WhitneyMann { typedef _CountType CountType; typedef core::Ranking<_EntityType, _CountType,_StatistiekKeuze> Statistic; typedef std::pair<_EntityType,_CountType> Pair; typedef std::map<_EntityType,_CountType> Map; typedef std::list List; typedef _ValueType ResultType; /** * \brief calculates the whitney mann U-value */ ResultType operator() (const Statistic & curStatistic, const Statistic & alwaysTrueStatistic) { /** * Algoritme: * 1) Iterate over all the examples and mark them with their respective rank * and whether they passed the test. This divides the examples in two populations. * 2) Calculate the sum of the ranks for both populations. * 3) From this, and the size of the respective populations calculate the U-value. */ //DO THE RANKING TRANSFORM // std::map keyToRank; // long int currRank = 1; // for(typename Map::const_iterator it = alwaysTrueStatistic.value().begin(); it != alwaysTrueStatistic.value().end() ; it++) // { // keyToRank[it->first] = currRank; // currRank += it->second; // } // std::map keyToAdjustedRank; // for(typename Map::const_iterator it = alwaysTrueStatistic.value().begin(); it != alwaysTrueStatistic.value().end() ; it++) // { // long int firstTiedRank = (*keyToRank.find((*it).first)).second; // long int endRank = firstTiedRank + (*it).second - 1; // long double adjustedRank = (firstTiedRank + endRank)/2.0d; // keyToAdjustedRank[it->first] = adjustedRank; // } // Map curMap = curStatistic.value(); // Map otherMap; // for(typename Statistic::ValueType::const_iterator it = alwaysTrueStatistic.value().begin(); it != alwaysTrueStatistic.value().end(); it ++) // { // if(curMap.find((*it).first)== curMap.end()) // otherMap[(*it).first] = (*it).second; // else if (it->second - curMap[it->first] != 0) // otherMap[it->first] = it->second - curMap[it->first]; // } // //Step 2 // List curList = createAdjustedRanks(curMap,keyToAdjustedRank); // List otherList = createAdjustedRanks(otherMap,keyToAdjustedRank); //DONT DO THE RANKING TRANSFORM AGAIN Map curMap = curStatistic.value(); Map otherMap; for(typename Statistic::ValueType::const_iterator it = alwaysTrueStatistic.value().begin(); it != alwaysTrueStatistic.value().end(); it ++) { if(curMap.find((*it).first)== curMap.end()) otherMap[(*it).first] = (*it).second; else if (it->second - curMap[it->first] != 0) otherMap[it->first] = it->second - curMap[it->first]; } List curList; for(typename Map::const_iterator it = curMap.begin(); it != curMap.end() ; it++) { curList.push_back((*it).first); } List otherList; for(typename Map::const_iterator it = otherMap.begin(); it != otherMap.end() ; it++) { otherList.push_back((*it).first); } long int n1 = curList.size(); long int n2 = otherList.size(); if(n1 == 0 || n2 == 0) return -1; long double R1 = 0; long double R2 = 0; for(typename List::const_iterator it = curList.begin(); it != curList.end(); it ++) R1 += (*it);// * (*it).second; for(typename List::const_iterator it = otherList.begin(); it != otherList.end(); it ++) R2 += (*it);// * (*it).second; //Step 3 long double U1 = R1 - n1 *(n1+1)/2; long double U2 = R2 - n2 *(n2+1)/2; /*long double difference = U1 + U2 - n1 * n2; if(difference != 0) { long double U1Gain = std::abs(difference) * 0.5; long double U2Gain = std::abs(difference) * 0.5; U1 += U1Gain; U2 += U2Gain; }*/ //We return the max U-value because we are interessed in how sure we are //that the median of the populations isn't equal. //This property of the U-value must hold. //That is the sum of the U-values is the product of the population sizes. //assert(U1+U2 == n1*n2); return max(U1,U2); } private: inline ResultType max (ResultType one,ResultType two) { if (one > two) return one; else return two; } //Calculates the adjusted ranks based. The ranking value of an example needs //to be averaged when multiple examples are equal. Each of these examples gets as rank the average of the ranks. std::list createAdjustedRanks(const Map & map, const std::map keyToRank){ std::list AdjustedRankedAllItems; typedef typename Statistic::ValueType Map; for(typename Map::const_iterator it = map.begin(); it != map.end() ; ++it) for(int i = it->second ; i != 0 ; i--) { long double rank = keyToRank.find(it->first)->second; AdjustedRankedAllItems.push_back(rank); } return AdjustedRankedAllItems; } }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // WHITNEYMAN_HPP mips-1.0/mips/prediction/decisionTree/removeUsedTestGenerator.hxx0000644002402200254040000000564412520407731024751 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_REMOVEUSEDTESTGENERATOR_HXX #define MIPS_PREDICTION_DECISIONTREE_REMOVEUSEDTESTGENERATOR_HXX #include "removeUsedTestGenerator.hpp" #include namespace mips { namespace prediction { namespace decision_tree { template typename RemoveUsedTestGenerator<_Test, _Container, _ContainerAccessPolicy, _TestCollectionAccessPolicy>::TestCollection RemoveUsedTestGenerator<_Test, _Container, _ContainerAccessPolicy, _TestCollectionAccessPolicy>::generateRootNodeTestCollection() { return TestCollection(ContainerAccessPolicy::get()); } template template std::pair::TestCollection, typename RemoveUsedTestGenerator<_Test, _Container, _ContainerAccessPolicy, _TestCollectionAccessPolicy>::TestCollection > RemoveUsedTestGenerator<_Test, _Container, _ContainerAccessPolicy, _TestCollectionAccessPolicy>::generateChildNodeTestCollection(NodeInfo & parentNode) { // create shared managed containers TestCollection cL, cR; // get the parent test and the parent test collection const Test & parentTest = parentNode.nodeData().test; const TestCollection & parentTestCollection = parentNode.testCollection; // copy all the tests cL.get().assign(parentTestCollection.get().begin(), parentTestCollection.get().end()); cL.get().erase(std::find(cL.get().begin(), cL.get().end(), parentTest)); cR.get().assign(cL.get().begin(), cL.get().end()); // and reset the databases cL.reset(); cR.reset(); return std::make_pair(cL, cR); } /*! * \brief TestCollectionManager is a helper class to easily delete a test from a test collection. */ template struct RemoveUsedTestGenerator<_Test, _Container, _ContainerAccessPolicy, _TestCollectionAccessPolicy>::TestCollectionManager { /*! * \brief removeTest removes a test from a test collection * \param collection The original collection from which the test should be deleted * \param test The test */ void removeTest(TestCollection & collection, const Test & test) { collection.get().erase(std::find(collection.get().begin(), collection.get().end(), test)); } }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_REMOVEUSEDTESTGENERATOR_HXX mips-1.0/mips/prediction/decisionTree/detail/0000755002402200254040000000000012520407731020624 5ustar thomasfdtaigmips-1.0/mips/prediction/decisionTree/detail/heuristicDecider.hpp0000644002402200254040000000570212520407731024620 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_HEURISTICDECIDER_HPP #define MIPS_PREDICTION_DECISIONTREE_HEURISTICDECIDER_HPP #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { template struct HeuristicDecider; /*********************************************************************** * * ExtractHeuristicTypes: Used to get the right types based on the *attributeValueType * (difference between weighted value and normal value) * */ template struct ExtractHeuristicTypes; template struct ExtractHeuristicTypes { typedef unsigned int CountType; typedef _AttributeValueType EntityType; typedef long double ValueType; }; template struct ExtractHeuristicTypes< mips::core::WeightedVariable<_AttributeValueType, _CountType> > { typedef _CountType CountType; typedef _AttributeValueType EntityType; typedef long double ValueType; }; /************************************************************************ * * HeuristicDecider: Decides the "best" heuristic based on the *targetAttributeType * currently: Numerical type: Variance Reduction * other: Information Gain * */ template struct HeuristicDecider< TargetAttributeExtractor, typename boost::enable_if >::type> { typedef ExtractHeuristicTypes HT; typedef VarianceReduction type; }; template struct HeuristicDecider< TargetAttributeExtractor, typename boost::enable_if >::type> { typedef ExtractHeuristicTypes HT; typedef InformationGain type; }; } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_HEURISTICDECIDER_HPP mips-1.0/mips/prediction/decisionTree/detail/queryEngine.hpp0000644002402200254040000000647612520407731023645 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DETAIL_QUERYENGINE_HPP #define MIPS_PREDICTION_DECISIONTREE_DETAIL_QUERYENGINE_HPP #include #include #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { /** * \brief This class implements the concepts::mips::prediction::decision_tree::ExtraTest concept. * * Each call to operator() returns false, and the statistics returned is a default-constructed statistic. **/ template struct NoExtraTest; /** * \brief The QueryEngine gathers for all the tests the statistics based on the supplied samples. * * This engine runs one time over all the examples and, for each example, runs over all the tests and gathers the statistics. As the query-engine inherits from * the StaticticsTestContainerPolicy, accessing the statistics can be achieved through this base class. The StatisticsTestContainerPolicy should implement the * concepts::mips::prediction::decision_tree::StatisticsTestContainer concept. By default, the statistics are stored using StoreStatisticsInVector. * * ExampleIterator should implement the forward iterator concept * TestCollection should implement the stream transactional database concept **/ template < class _Test, class _Statistic, class _TargetAttributeExtractor, class _ExtraTest = NoExtraTest<_Test> > struct QueryEngine : public _ExtraTest { public: typedef _Statistic Statistic; typedef _Test Test; typedef _TargetAttributeExtractor TargetAttributeExtractor; typedef _ExtraTest ExtraTest; typedef std::map StatisticsContainer; /** * For a query, the example iterators are used, and are run from exampleBegin to exampleEnd, or till maxNrOfExamples has been reached. * For each example, all tests are run. TestCollection should be a stream transaction database concept. **/ template void operator() (TestCollection & tests, const TargetAttributeExtractor & extractor, ExampleIterator exampleBegin, ExampleIterator exampleEnd, std::size_t maxNrOfExamples = -1); void clear() { _statistics.clear(); ExtraTest::clear(); } Statistic & rTestStatistic(const Test & test) { return _statistics[test]; } const Statistic & testStatistic(const Test & test) const { typename StatisticsContainer::const_iterator i = _statistics.find(test); if(i == _statistics.end()) return _defaultStatistic; else return i->second; } private: StatisticsContainer _statistics; Statistic _defaultStatistic; }; } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #include "queryEngine.hxx" #endif // MIPS_PREDICTION_DECISIONTREE_DETAIL_QUERYENGINE_HPP mips-1.0/mips/prediction/decisionTree/detail/treePrinter.cpp0000644002402200254040000000207612520407731023640 0ustar thomasfdtaig #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { void indentStream(std::istream & inputStream, std::ostream & outputStream, const std::string & indent) { while(inputStream.good()) { std::string curLine; std::getline(inputStream, curLine); if(curLine.empty()) continue; outputStream << indent << curLine << std::endl; } } void indentStream(std::istream & inputStream, std::ostream & outputStream, const std::string & firstIndent, const std::string & nextIndent) { bool onFirst = true; while(inputStream.good()) { std::string curLine; std::getline(inputStream, curLine); if(curLine.empty()) continue; if(onFirst) { outputStream << firstIndent << curLine << std::endl; onFirst = false; } else outputStream << nextIndent << curLine << std::endl; } } } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace mips-1.0/mips/prediction/decisionTree/detail/alwaysTrueExtraTest.hpp0000644002402200254040000000230612520407731025342 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_ALWAYSTRUEEXTRATEST_HPP #define MIPS_PREDICTION_DECISIONTREE_ALWAYSTRUEEXTRATEST_HPP #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { template struct AlwaysTrueExtraTest { typedef _Statistic Statistic; typedef _ValueType ValueType; AlwaysTrueExtraTest() {} void clear() { _statistic = Statistic(); } bool operator()(const ValueType & value) { _statistic += value; return true; } const Statistic & alwaysTrueStatistic() const { return _statistic; } private: Statistic _statistic; }; } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_ALWAYSTRUEEXTRATEST_HPP mips-1.0/mips/prediction/decisionTree/detail/nodeInfo.hpp0000644002402200254040000000404112520407731023075 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DETAIL_NODEINFO_HPP #define MIPS_PREDICTION_DECISIONTREE_DETAIL_NODEINFO_HPP #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { template struct NodeInfo { typedef _Tree Tree; typedef _TestCollection TestCollection; typedef _TestCollectionManager TestCollectionManager; typedef _ExampleDB ExampleDB; typedef typename mips::core::RootedTreeTraits::NodeDescriptor NodeDescriptor; typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; NodeInfo(Tree * nTree = 0, NodeDescriptor nNode = NodeDescriptor(), const TestCollection & nTestCollection = TestCollection(), const ExampleDB & nExampleDB = ExampleDB(), const TestCollectionManager & nTestCollectionManager = TestCollectionManager()) : tree(nTree), node(nNode), testCollection(nTestCollection), exampleDB(nExampleDB), testCollectionManager(nTestCollectionManager) { } Tree * tree; NodeDescriptor node; TestCollection testCollection; ExampleDB exampleDB; TestCollectionManager testCollectionManager; NodeData & nodeData() { return (*tree)[node]; } const NodeData & nodeData() const { return (*tree)[node]; } }; } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DETAIL_NODEINFO_HPP mips-1.0/mips/prediction/decisionTree/detail/queryEngine.hxx0000644002402200254040000000524212520407731023653 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_QUERYENGINE_HXX #define MIPS_PREDICTION_DECISIONTREE_QUERYENGINE_HXX #include #include #include "queryEngine.hpp" #include #include #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { template template void QueryEngine<_Test, _Statistic, _TargetAttributeExtractor, _ExtraTest>::operator() (TestCollection & tests, const _TargetAttributeExtractor & extractor, ExampleIterator exampleBegin, ExampleIterator exampleEnd, std::size_t maxNrOfExamples) { typedef typename std::iterator_traits::reference ExampleType; typedef typename TestCollection::iterator TestIterator; typedef typename std::iterator_traits::reference TestRef; typedef typename TargetAttributeExtractor::result_type TargetAttributeType; // loop over all examples for(std::size_t exampleNumber = 0; exampleBegin != exampleEnd && (maxNrOfExamples <= 0 || exampleNumber < maxNrOfExamples); ++exampleBegin, ++exampleNumber) { // loop initialization tests.reset(); // get the current example & target attribute ExampleType example = *exampleBegin; TargetAttributeType targetAttribute = extractor(example); //MIPS_LOG(QueryEngine, "Example: " << utilities::toStream(example)); // loop over all the tests for(TestIterator tIter = tests.current(); tIter != tests.end(); ++tIter) { TestRef test = *tIter; // update the statistics, this test is accepted if(utilities::wrapPointer(test)(example)) rTestStatistic(test) += targetAttribute; } // do the optional extra test ExtraTest::operator() (targetAttribute); } } template struct NoExtraTest { typedef _ValueType ValueType; void clear() {} bool operator()(const ValueType & /*example*/) const { return false; } }; } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_QUERYENGINE_HXX mips-1.0/mips/prediction/decisionTree/detail/treePrinter.hpp0000644002402200254040000000547212520407731023650 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ /******************************************************************* * Copyright (c) 2010 MiGraNT - DTAI - kuleuven.be * * Permission is hereby granted, free of charge, to any person * obtaining a copy of this software and associated documentation * files (the "Software"), to deal in the Software without * restriction, including without limitation the rights to use, * copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the * Software is furnished to do so, subject to the following * conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR * OTHER DEALINGS IN THE SOFTWARE. *******************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DETAIL_TREEPRINTER_HPP #define MIPS_PREDICTION_DECISIONTREE_DETAIL_TREEPRINTER_HPP #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { struct EmptyNodePrinter; struct EmptyTreeStatisticsPrinter; template < class NodePrinter = EmptyNodePrinter, class TreeStatisticsPrinter = EmptyTreeStatisticsPrinter> struct TreePrinter { template void operator()(const Node & node, std::ostream & stream) const; private: template void printSubTree(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node, std::ostream & stream, const std::string & firstIndent = std::string(), const std::string & nextIndent = std::string()) const; }; void indentStream(std::istream & inputStream, std::ostream & outputStream, const std::string & firstIndent, const std::string & nextIndent); void indentStream(std::istream & inputStream, std::ostream & outputStream, const std::string & indent); } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #include "treePrinter.hxx" #endif // MIPS_PREDICTION_DECISIONTREE_DETAIL_TREEPRINTER_HPP mips-1.0/mips/prediction/decisionTree/detail/treePrinter.hxx0000644002402200254040000000541512520407731023665 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DETAIL_TREEPRINTER_HXX #define MIPS_PREDICTION_DECISIONTREE_DETAIL_TREEPRINTER_HXX #include "treePrinter.hpp" #include //#include "../classify.hpp" namespace mips { namespace prediction { namespace decision_tree { namespace detail { struct EmptyNodePrinter { template void operator()(const Tree & /*tree*/, typename mips::core::RootedTreeTraits::NodeDescriptor /*node*/, std::ostream & /*str*/) const {} }; struct EmptyTreeStatisticsPrinter { template void operator()(const Tree & /*tree*/, std::ostream & /*str*/) const {} }; template template void TreePrinter::operator()(const Tree & tree, std::ostream & stream) const { printSubTree(tree, mips::core::rootNode(tree), stream); stream << std::endl; TreeStatisticsPrinter printTreeStatistics; printTreeStatistics(tree, stream); } template template void TreePrinter::printSubTree(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node, std::ostream & stream, const std::string & firstIndent, const std::string & nextIndent) const { using namespace mips::core; typedef typename RootedTreeTraits::NodeDescriptor Node; std::stringstream str; NodePrinter printNode; printNode(tree, node, str); bool leafNode = isLeaf(tree, node); std::string nextExt = nextIndent + (leafNode ? " " : "| "); std::string firstExt = firstIndent; if(isRoot(tree, node)) firstExt += "R: "; else if(isYesNode(tree, node)) firstExt += "Y: "; else firstExt += "N: "; indentStream(str, stream, firstExt, nextExt); stream << nextExt << std::endl; // go to the children if(!leafNode) { Node ln = yesBranch(tree, node); Node rn = noBranch(tree, node); std::string newNodeIndent = nextIndent + "+---> "; std::string nextIndentL = nextIndent + "| "; std::string nextIndentR = nextIndent + " "; printSubTree(tree, ln, stream, newNodeIndent, nextIndentL); printSubTree(tree, rn, stream, newNodeIndent, nextIndentR); } } } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DETAIL_TREEPRINTER_HXX mips-1.0/mips/prediction/decisionTree/detail/nodeData.hpp0000644002402200254040000000221112520407731023050 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DETAIL_NODEDATA_HPP #define MIPS_PREDICTION_DECISIONTREE_DETAIL_NODEDATA_HPP #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { template struct NodeData { friend class boost::serialization::access; typedef _Test Test; typedef _Statistic Statistic; Test test; Statistic currentTestStatistic; Statistic globalStatistic; private: template void serialize(Archive & ar, const unsigned int /*version*/) { ar & test; ar & currentTestStatistic; ar & globalStatistic; } }; } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DETAIL_NODEDATA_HPP mips-1.0/mips/prediction/decisionTree/detail/testInfo.hpp0000644002402200254040000000207412520407731023133 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DETAIL_TESTINFO_HPP #define MIPS_PREDICTION_DECISIONTREE_DETAIL_TESTINFO_HPP namespace mips { namespace prediction { namespace decision_tree { namespace detail { template struct TestInfo { typedef _HeuristicResultType HeuristicResultType; typedef _Statistic Statistic; typedef _Test Test; TestInfo() : value(HeuristicResultType()), statistic(Statistic()), test(Test()) { } HeuristicResultType value; Statistic statistic; Test test; }; } // detail namespace } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DETAIL_TESTINFO_HPP mips-1.0/mips/prediction/decisionTree/splitExampleGenerator.hpp0000644002402200254040000001215312520407731024413 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_SPLITEXAMPLEGENERATOR_HPP #define MIPS_PREDICTION_DECISIONTREE_SPLITEXAMPLEGENERATOR_HPP #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * \ingroup ExampleGenerator * \brief SplitExampleGenerator implements the *concepts::mips::prediction::decision_tree::ExampleGenerator concept, by *storing internally the examples * and by splitting the parents examples in a yes and in a no fraction, based on *the chosen test. * * \extends concepts::mips::utilities::wrappers::ContainerAccess * \extends concepts::mips::prediction::decision_tree::ExampleGenerator * * * This class has 4 template parameters of which the first is obligatory: * \tparam Example The type of example to be stored, as it is stored in a *container, ideally this object takes little place and is easily copyable * (e.g. a pointer) * * \tparam Container The container in which the examples are internally stored. *By default this is std::list * * \tparam ContainerAccessPolicy The way how the container is stored internally *in this example generator. By default this is as member variable, but * possible solutions are *utilities::wrappers::ContainerAccessThroughMemberVariable, * utilities::wrappers::ContainerAccessThroughInheritance, *utilities::wrappers::ContainerAccessThroughPointerStorage or * utilities::wrappers::ContainerAccessThroughSmartPointer. This class *should implement the * \link concepts::mips::utilities::wrappers::ContainerAccess *ContainerAccess\endlink-concept. * * \tparam TestCollectionAccessPolicy This class defines the way how the *example container is stored in ExampleDB. By default this by a smart pointer, * such that copying of the example database does not mean copying the *internal container. * possible solutions are *utilities::wrappers::ContainerAccessThroughMemberVariable, * utilities::wrappers::ContainerAccessThroughInheritance, *utilities::wrappers::ContainerAccessThroughPointerStorage or * utilities::wrappers::ContainerAccessThroughSmartPointer. This class *should implement the * \link concepts::mips::utilities::wrappers::ContainerAccess *ContainerAccess\endlink-concept. * */ template , class _ContainerAccessPolicy = utilities::StoreByValue<_Container>, class _ExampleDBContainerAccessPolicy = utilities::StoreBySmartPointer<_Container> > struct SplitExampleGenerator : public _ContainerAccessPolicy { /*! * \brief Example exposes the type of the example */ typedef _Example Example; typedef _Container Container; typedef _ContainerAccessPolicy ContainerAccessPolicy; typedef _ExampleDBContainerAccessPolicy ExampleDBContainerAccessPolicy; /*! * \brief ExampleDB is a sequential database of examples, which are internally * stored in a container as defined by the template parameter Container * and the container is stored as defined by the template parameter * TestCollectionAccessPolicy. */ typedef utilities::SequentialDatabaseWrapper ExampleDB; /*! * \brief generateRootNodeExampleDB returns the same collection as stored *internally. * \return A ExampleDB * * The internal example container can be accessed using the container() *function. */ ExampleDB generateRootNodeExampleDB(); /*! * \brief generateChildNodeExampleDB splits the example set from the parent *into two parts, a yes and no part. * \return A pair of exampleDB's, one for the yes-part, one for the no part * * Actual copy of the example type are made here. Storing pointers in the *container can increase the speed, especially if Example takes a while to * copy. */ template std::pair generateChildNodeExampleDB(const NodeInfo& parentNode, const Test& parentSplitTest); /*! * \brief exampleContainer allows acces to the container containing the *examples * \return A reference to the example container * * This method is equal to container() */ Container & exampleContainer() { return ContainerAccessPolicy::get(); } /*! * \brief exampleContainer allows acces to the container containing the *examples * \return A const reference to the example container * * This method is equal to container() */ const Container & exampleContainer() const { return ContainerAccessPolicy::get(); } }; } // decision_tree namespace } // prediction namespace } // mips namespace #include "splitExampleGenerator.hxx" #endif // MIPS_PREDICTION_DECISIONTREE_SPLITEXAMPLEGENERATOR_HPP mips-1.0/mips/prediction/decisionTree/serialization.hpp0000644002402200254040000000124412520407731022751 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_SERIALIZATION_HPP #define MIPS_PREDICTION_DECISIONTREE_SERIALIZATION_HPP #include #include #include #include #endif // MIPS_PREDICTION_DECISIONTREE_SERIALIZATION_HPP mips-1.0/mips/prediction/decisionTree/classifier.hpp0000644002402200254040000001216612520407731022225 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_PREDICTION_HPP #define MIPS_PREDICTION_DECISIONTREE_PREDICTION_HPP #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { template struct DefaultStatisticsToClassConvertor; /*************************************************************** * * Classifier for a histogram, takes the entity with the maximum frequency * ***************************************************************/ template struct DefaultStatisticsToClassConvertor< mips::core::Histogram<_EntityType, _CountType> > : public std::unary_function, _EntityType> { typedef mips::core::Histogram<_EntityType, _CountType> argument_type; typedef _EntityType result_type; result_type operator()(const argument_type &histogram) const { typedef typename argument_type::ValueType histogramType; histogramType h = histogram.value(); std::pair<_EntityType, _CountType> maxElement = std::make_pair(_EntityType(), _CountType()); for (typename histogramType::iterator it = h.begin(); it != h.end(); ++it) if (it->second >= maxElement.second) maxElement = (*it); return maxElement.first; } }; /*************************************************************** * * Classifier for a variance statistic, takes the mean * ***************************************************************/ /*! * */ template struct DefaultStatisticsToClassConvertor< mips::core::Variance<_EntityType, _CountType, _ValueType> > : public std::unary_function< mips::core::Variance<_EntityType, _CountType, _ValueType>, _ValueType> { typedef mips::core::Variance<_EntityType, _CountType, _ValueType> argument_type; typedef _ValueType result_type; result_type operator()(const argument_type &varianceStatistic) const { return varianceStatistic.mean(); } }; ///*************************************************************** // * // * Classifier for a ranking statistic, returns mean // * // ***************************************************************/ // template // struct // DefaultStatisticsToClassConvertor > // : public // std::unary_function, _EntityType> //{ // typedef mips::core::statistics::Ranking<_EntityType, _CountType, // RankingType> argument_type; // typedef _EntityType result_type; // result_type operator()(const argument_type & rankingStatistic) const // { // return rankingStatistic.result(); // } //}; } // detail namespace template struct DecisionTreeClassifier { BOOST_MPL_ASSERT((mips::core::IsTree)); typedef detail::DefaultStatisticsToClassConvertor< typename core::RootedTreeTraits::NodeLabel::Statistic> SConvertor; typedef typename SConvertor::result_type result_type; DecisionTreeClassifier(const Tree &) {} DecisionTreeClassifier() {} template result_type operator()(const Tree &tree, const Example &example) const { SConvertor convert; return convert(classify(tree, example).globalStatistic); } }; } // decision_tree namespace /*************************************************************** * * Specialization of the DefaultModelToOutputClassifier to work with decision *trees * ***************************************************************/ /*! * \brief DefaultModelClassifier is the specialization of *mips::prediction::DefaultModelClassifier for decision trees. * * This class uses the decision tree to the leaf node of an example. Then the *decision_tree::DefaultStatisticsToClassConvertor is used to convert the * the statistic to the actual predicted value. The default behaviour depends on *the statistic and is defined as follows: * * \li mips::core::statistic::Variance: the mean value * \li mips::core::statistic::Histogram: (one of) the element(s) with the *highest frequence * */ template < class _Tree > struct DefaultModelClassifier<_Tree, typename boost::enable_if >::type> : public decision_tree::DecisionTreeClassifier<_Tree> { DefaultModelClassifier(const _Tree &) {} DefaultModelClassifier() {} }; } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_PREDICTION_HPP mips-1.0/mips/prediction/decisionTree/modelPrinter.hpp0000644002402200254040000001746012520407731022547 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_MODELPRINTER_HPP #define MIPS_PREDICTION_DECISIONTREE_MODELPRINTER_HPP #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { namespace detail { template struct DecisionTreeNodePrinter; template struct DecisionTreeStatisticsPrinter; } // detail namespace template struct DecisionTreePrinter : public detail::TreePrinter< detail::DecisionTreeNodePrinter::NodeLabel::Statistic>, detail::DecisionTreeStatisticsPrinter::NodeLabel::Statistic> > { BOOST_MPL_ASSERT( (mips::core::IsTree) ); DecisionTreePrinter() {} }; namespace detail { /**************************************************************** * * NodePrinter and TreeStatisticsPrinter * Implemented for Variance statistic and Histogram statistic * */ template struct DecisionTreeNodePrinter { template void operator()(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node, std::ostream & str) const { if(!isLeaf(tree, node)) str << " test: " << utilities::wrapPointer(tree[node].test) << std::endl; else str << " " << std::endl; } }; template struct DecisionTreeNodePrinter > { template void operator()(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node, std::ostream & str) const { typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; const NodeData & d = tree[node]; if(!isLeaf(tree, node)) { str << " variance: " << d.globalStatistic.value() << std::endl; str << " mean: " << d.globalStatistic.mean() << std::endl; str << "count(Y/A): " << d.currentTestStatistic.count() << "/" << d.globalStatistic.count() << std::endl; str << " test: " << utilities::wrapPointer(d.test) << std::endl; } else { str << " variance: " << d.globalStatistic.value() << std::endl; str << " mean: " << d.globalStatistic.mean() << std::endl; str << " count: " << d.globalStatistic.count() << std::endl; } } }; //template //struct DefaultNodePrinter > //{ // template // void operator()(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node, std::ostream & str) const // { // typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; // const NodeData & d = tree[node]; // if(!isLeaf(tree, node)) // { // str << " mean: " << d.globalStatistic.mean() << std::endl; // str << "count(Y/A): " << d.currentTestStatistic.count() << "/" << d.globalStatistic.count() << std::endl; // str << " test: " << utilities::toStream(d.test) << std::endl; // } // else // { // str << " mean: " << d.globalStatistic.mean() << std::endl; // str << " count: " << d.globalStatistic.count() << std::endl; // } // } //}; template struct DecisionTreeNodePrinter > { template void operator()(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node, std::ostream & str) const { typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; typedef typename mips::core::Histogram::ValueType HistogramType; const NodeData & d = tree[node]; typename mips::core::Histogram::ValueType histogram = d.globalStatistic.value(); if(!isLeaf(tree, node)) str << " test: " << utilities::wrapPointer(d.test) << std::endl; str << " count: " << d.globalStatistic.count() << std::endl; str << " histogram category -> count (percentage)" << std::endl; for(typename HistogramType::const_iterator i = histogram.begin(); i != histogram.end(); ++i) str << " +->: " << i->first << " -> " << i->second << " (" << 100.0*(i->second)/d.globalStatistic.count() << "%)" << std::endl; } }; template struct DecisionTreeStatisticsPrinter : public EmptyTreeStatisticsPrinter {}; template struct DecisionTreeStatisticsPrinter > { template void operator()(const Tree & tree, std::ostream & stream) const { typedef typename mips::core::RootedTreeTraits::NodeDescriptor NodeDescriptor; typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; NodeDescriptor rootNode = mips::core::rootNode(tree); const NodeData & d = tree[rootNode]; ValueType rootNodeVariance = d.globalStatistic.count()*d.globalStatistic.value(); ValueType leafNodesVariance = findWeightedNodeStatistic(tree, rootNode); stream << "Weighted root node variance: " << rootNodeVariance << std::endl; stream << "Weighted leaf nodes variance: " << leafNodesVariance << std::endl; if(rootNodeVariance == ValueType()) stream << "Variance reduction: Uncomputable" << std::endl; else if(leafNodesVariance == ValueType()) stream << "Variance reduction: Infinite" << std::endl; else stream << "Variance reduction: " << rootNodeVariance / leafNodesVariance << std::endl; } template ValueType findWeightedNodeStatistic(const Tree & tree, typename mips::core::RootedTreeTraits::NodeDescriptor node) const { typedef typename mips::core::RootedTreeTraits::NodeDescriptor NodeDescriptor; typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; if(mips::core::isLeaf(tree, node)) { const NodeData & d = tree[node]; return d.globalStatistic.count()*d.globalStatistic.value(); } else { ValueType val = ValueType(); BOOST_FOREACH(NodeDescriptor node, mips::core::childNodes(tree, node)) val += findWeightedNodeStatistic(tree, node); return val; } } }; } // detail namespace } // decision_tree namespace /*! * \brief This class is specialisation of DefaultModelPrinter for trees. It prints each node in the tree with the information stored in the statistics. */ template struct DefaultModelPrinter<_Tree, typename boost::enable_if >::type > : public decision_tree::DecisionTreePrinter<_Tree> { }; } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_MODELPRINTER_HPP mips-1.0/mips/prediction/decisionTree/depthFirstDecisionTreeLearner.hxx0000644002402200254040000001200312520407731026032 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DEPTHFIRSTDECISIONTREELEARNER_HXX #define MIPS_PREDICTION_DECISIONTREE_DEPTHFIRSTDECISIONTREELEARNER_HXX #include #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_tree { template DepthFirstDecisionTreeLearner::DepthFirstDecisionTreeLearner() {} template T DepthFirstDecisionTreeLearner::operator()( const AttributeExtractor& targetAttributeExtractor) { Tree tree; NodeStack nodesToExpand; createRootNode(tree, nodesToExpand); while (!nodesToExpand.empty()) { typename NodeStack::iterator it = --nodesToExpand.end(); NodeInfo& curNodeInfo = *it; expandNode(curNodeInfo, nodesToExpand, targetAttributeExtractor); nodesToExpand.erase(it); } return tree; } template void DepthFirstDecisionTreeLearner::expandNode( NodeInfo& curNodeInfo, NodeStack& nodes, const AttributeExtractor& targetAttributeExtractor) { // get the test collection std::pair results = rDecisionStrategy().findBestTest(curNodeInfo, targetAttributeExtractor); const TestInfo& bestTest = results.first; const Statistic& alwaysTrueStatistic = results.second; // the best test is not good anyway, so a leaf node if (bestTest.value <= HeuristicResultType()) { MIPS_LOG(Learner, "no best test found"); fillInLeafNode(curNodeInfo, alwaysTrueStatistic); } else { MIPS_LOG(Learner, "best test: " << utilities::wrapPointer(bestTest.test) << "[" << bestTest.value << "]"); NodeData& d = (*curNodeInfo.tree)[curNodeInfo.node]; // fill in the parent node d.test = bestTest.test; d.currentTestStatistic = bestTest.statistic; d.globalStatistic = alwaysTrueStatistic; // construct the two child nodes with the right information createChildNodes(curNodeInfo, nodes); } MIPS_LOG_CONTINUE(Learner, std::endl); } template void DepthFirstDecisionTreeLearner::createRootNode( Tree& tree, NodeStack& nodes) { nodes.push_back(NodeInfo()); NodeInfo& nInfo = nodes.back(); nInfo.tree = &tree; nInfo.node = mips::core::addChildNode(tree); nInfo.testCollection = TestGenerator::generateRootNodeTestCollection(); nInfo.exampleDB = ExampleGenerator::generateRootNodeExampleDB(); nInfo.testCollectionManager = TestGenerator::manager(); } template void DepthFirstDecisionTreeLearner::fillInLeafNode( const NodeInfo& nodeInfo, const Statistic& alwaysTrueStatistic) { MIPS_LOG(Learner, "Creating a leaf node " << getPathDescription(*nodeInfo.tree, nodeInfo.node)); // fill in the parent node NodeData& d = (*nodeInfo.tree)[nodeInfo.node]; d.globalStatistic = alwaysTrueStatistic; } template void DepthFirstDecisionTreeLearner::createChildNodes( NodeInfo& parentNodeInfo, NodeStack& nodes) { nodes.push_back(NodeInfo()); NodeInfo& lnInfo = nodes.back(); nodes.push_back(NodeInfo()); NodeInfo& rnInfo = nodes.back(); // generate the nodes lnInfo.node = mips::core::addChildNode(*parentNodeInfo.tree, parentNodeInfo.node, NodeData(), true); rnInfo.node = mips::core::addChildNode( *parentNodeInfo.tree, parentNodeInfo.node, NodeData(), false); lnInfo.tree = parentNodeInfo.tree; rnInfo.tree = parentNodeInfo.tree; // fill in the details for the node boost::tuples::tie(lnInfo.exampleDB, rnInfo.exampleDB) = ExampleGenerator::generateChildNodeExampleDB( parentNodeInfo, parentNodeInfo.nodeData().test); boost::tuples::tie(lnInfo.testCollection, rnInfo.testCollection) = TestGenerator::generateChildNodeTestCollection(parentNodeInfo); MIPS_LOG(Learner, "Creating two child nodes for " << getPathDescription(*parentNodeInfo.tree, parentNodeInfo.node)); } } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_DEPTHFIRSTDECISIONTREELEARNER_HXX mips-1.0/mips/prediction/decisionTree/defaultSplittingCriterium.hpp0000644002402200254040000001452012520407731025303 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_DEFAULTSPLITTINGCRITERIUM_HPP #define MIPS_PREDICTION_DECISIONTREE_DEFAULTSPLITTINGCRITERIUM_HPP #include #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * \brief The default splitting criterium calculates the statistics for all the test, and uses this statistics in combination with a prepruner to find the * best test. * * This class can have up to 4 template parameters of which the first 2 are obligatory: * \tparam Heuristic This class should follow the \link concepts::mips::prediction::decision_tree::Heuristic Heuristic\endlink-concept and is used to * calculate a score for each test based on its statistic, e.g. mips::prediction::decision_tree::InformationGain, * mips::prediction::decision_tree::VarianceReduction * * \tparam PrePruner The prepruner is used to prune tests while constructing the tree. This class should follow the \link * concepts::mips::prediction::decision_tree::PrePruner PrePruner\endlink concept. Possible prepruners are * mips::prediction::decision_tree::MinimumLeafSizePruner or mips::prediction::decision_tree::NoPrePruner. * * \tparam TestCollectionWrapper The test collection wrapper is used in the beginning of the findBestTest-function. The wrapper defines which tests are * available during the test selection, and also how they are stored. E.g.: Test generated by a "slow" refinement operator can better be stored in * a temporary container, than refining them time after time. This class should follow the * \link concepts::mips::prediction::decision_tree::DBWrapper\endlink-concept. Possible wrappers are mips::prediction::decision_tree::NoChangeDBWrapper * and mips::prediction::decision_fores::RandomFraction. * * \tparam ExampleDBWrapper The example database wrapper is used in the beginning of the findBestTest-function. The wrapper defines which examples are * available during the test selection, and also how they are stored. This class should follow the * \link concepts::mips::prediction::decision_tree::DBWrapper\endlink-concept. Possible wrappers are mips::prediction::decision_tree::NoChangeDBWrapper * and mips::prediction::decision_forest::RandomFraction. * * This class implements the concepts::mips::prediction::decision_tree::SplittingCriterium concept. */ template struct DefaultSplittingCriterium { public: typedef _Heuristic Heuristic; typedef typename Heuristic::Statistic Statistic; typedef typename Heuristic::ResultType HeuristicResultType; typedef _PrePruner PrePruner; typedef _TestCollectionWrapper TestCollectionWrapper; typedef _ExampleDBWrapper ExampleDBWrapper; DefaultSplittingCriterium() : _maxNumberOfExamples(-1) {} /*! * \brief Builds a Heuristic to be used. * \return A heuristic object. */ Heuristic generateHeuristic() const { return heuristic(); } /*! * \brief Builds the PrePruner to be used. * \return A pre prunner object. */ PrePruner generatePrePruner() const { return prePruner(); } /*! * \brief Returns the Heuristic to be used. * \return A const heuristic object. */ const Heuristic& heuristic() const { return _heuristic; } /*! * \brief Returns the PrePruner to be used. * \return A const pre prunner object. */ const PrePruner& prePruner() const { return _prePruner; } /*! * \brief Returns the Heuristic to be used. * \return A heuristic object. */ Heuristic& heuristic() { return _heuristic; } /*! * \brief Returns the PrePruner to be used. * \return A const pre prunner object. */ PrePruner& prePruner() { return _prePruner; } /*! * \brief Returns the maximum number of examples. * \return The maximum number of examples. */ int maxNumberOfExamples() const { return _maxNumberOfExamples; } /*! * \brief Sets the maximum number of examples. * \param newMax the maximum number of exmaples. */ void setMaxNumberOfExamples(int newMax) { _maxNumberOfExamples = newMax; } /*! * \brief Returns a Test collection. * \return A reference to the test collection. */ TestCollectionWrapper& testCollectionWrapper() { return _testCollectionWrapper; } /*! * \brief Returns a reference to a database * \return Reference to a database. */ ExampleDBWrapper& exampleDBWrapper() { return _exampleDBWrapper; } /*! * \brief findBestTest find the test with the highest heuristic value and returns this test (together with the necessary information). * * This function loops over all examples, and for each example updates the statistic for this test. Afterwards it loops over all the tests and checks * whether this test (together with its statistic etc.) is approved by the PrePruner. If so, the heuristic value is calculated. The test with the * highest heuristic value is returned in the end. If no such test is found, an empty TestInfo object is returned. */ template std::pair, Statistic> findBestTest(NodeInfo & curNodeInfo, const TargetAttributeExtractor & extractor); private: Heuristic _heuristic; TestCollectionWrapper _testCollectionWrapper; ExampleDBWrapper _exampleDBWrapper; PrePruner _prePruner; int _maxNumberOfExamples; }; } // decision_tree namespace } // prediction namespace } // mips namespace #include "defaultSplittingCriterium.hxx" #endif // MIPS_PREDICTION_DECISIONTREE_DEFAULTSPLITTINGCRITERIUM_HPP mips-1.0/mips/prediction/decisionTree/concepts/0000755002402200254040000000000012520407731021200 5ustar thomasfdtaigmips-1.0/mips/prediction/decisionTree/concepts/heuristic.hpp0000644002402200254040000000374212520407731023716 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_CONCEPTS_HEURISTIC_HPP #define MIPS_PREDICTION_DECISIONTREE_CONCEPTS_HEURISTIC_HPP #include "statistic.hpp" #include namespace concepts { namespace mips { namespace prediction { namespace decision_tree { typedef void ResultType; /** * @brief The Heuristic concept defines the structure for a heuristic. * * A heuristic is essentially a binary functor, calculating an heuristic value *for a test. As input the statistics calculated for the test * and the statistics calculated for all the examples (an always true test) are *given. */ struct Heuristic : public std::binary_function { public: typedef concepts::mips::core::statistics::Statistic first_argument_type; typedef concepts::mips::core::statistics::Statistic second_argument_type; typedef ResultType result_type; typedef concepts::mips::core::statistics::Statistic Statistic; public: /** * The value for a valid test should always be larger than the default *constructed element (zero essentially for numerical types). A value smaller * than the default constructed element is considered as an unacceptable *statistic and thus an in acceptable test. **/ result_type operator()( const concepts::mips::core::statistics::Statistic& curStatistic, const concepts::mips::core::statistics::Statistic& alwaysTrueStatistic) const; }; } // decision_tree namespace } // prediction namespace } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_DECISIONTREE_CONCEPTS_HEURISTIC_HPP mips-1.0/mips/prediction/decisionTree/concepts/testGenerator.hpp0000644002402200254040000001001012520407731024527 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef PREDICTION_DECISIONTREE_CONCEPTS_TESTGENERATORPOLICY_HPP #define PREDICTION_DECISIONTREE_CONCEPTS_TESTGENERATORPOLICY_HPP #include "test.hpp" #include namespace concepts { namespace mips { namespace prediction { namespace decision_tree { /*! * \ingroup TestGenerator * \brief The TestGenerator is a concept class, is shows the structure a test generator class should have to be used with a * \link mips::prediction::decision_tree::DepthFirstDecisionTreeLearner decision tree learner\endlink. * * The test generator generates the tests for each node in the decision tree. Furthermore it also generates a TestCollectionManager, an object * that is responsable for removing tests from test collections. */ struct TestGenerator { private: private: typedef void NodeInfo; public: /*! * \brief TestCollectionManager should follow the concepts::mips::prediction::decision_tree::TestCollectionManager concept. * * It is used to remove tests from a test collection when the splitting criterium has decided the test is not a good test. */ typedef TestCollectionManager TestCollectionManager; /*! * \brief TestCollection should follow the concepts::mips::database::SequentialTransactionalDatabase concept. * * TestCollection represents a database of tests. */ typedef mips::database::SequentialTransactionalDatabase TestCollection; /*! * \brief Test should follow the concepts::mips::prediction::decision_tree::Test concept. * * Test defines the basic test type. This can also be a pointer to allow polymorphic behaviour of tests. */ typedef concepts::mips::prediction::decision_tree::Test Test; public: /*! * \brief generateRootNodeTestCollection generates the tests to be used for the root node * \return A Testcollection */ TestCollection generateRootNodeTestCollection(); /*! * \brief generateChildNodeTestCollection generates the new tests for a parent node. * \param parentNode A \link mips::prediction::decision_tree::NodeInfo NodeInfo\endlink object specifying all details for the parent node * \return A pair of test collections <yes, no> for the yes- and the no-branch. */ std::pair generateChildNodeTestCollection(NodeInfo & parentNode); /*! * \brief manager returns an object that follows the \link concepts::mips::prediction::decision_tree::TestCollectionManager * TestCollectionManager\endlink policy. It is used to remove discarded tests from a test collection. * \return */ TestCollectionManager manager(); }; /*! * \brief The TestCollectionManager concept defines the structure for a TestCollectionManager. */ struct TestCollectionManager { private: typedef void TestCollection; typedef void Test; public: /*! * \brief removeTest Removes a test from a test collection * \param collection concepts::The collection wherein the test can be found * \param test The test to remove from this collection * * This function is e.g. called in the \link mips::prediction::decision_tree::DefaultSplittingCriterium default splitting criterium\endlink * if the prepruner has decided that a test is not usable on this level and also not on child levels. */ void removeTest(TestCollection & collection, const Test & test); }; } // decision_tree namespace } // prediction namespace } // mips namespace } // concepts namespace #endif // PREDICTION_DECISIONTREE_CONCEPTS_TESTGENERATORPOLICY_HPP mips-1.0/mips/prediction/decisionTree/concepts/splittingCriterium.hpp0000644002402200254040000000531112520407731025612 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_CONCEPTS_SPLITTINGCRITERIUM_HPP #define MIPS_PREDICTION_DECISIONTREE_CONCEPTS_SPLITTINGCRITERIUM_HPP #include "heuristic.hpp" #include "prePruner.hpp" namespace concepts { namespace mips { namespace prediction { namespace decision_tree { /*! * \brief The SplittingCriterium concept defines the structure a splitting criterium should follow. * * The splitting criterium is used to find the best test for a given node. */ struct SplittingCriterium { private: typedef void TargetAttributeExtractor; public: /*! * \brief Statistic The statistic calculated in this criterium. This information is stored in the tree. */ typedef concepts::mips::core::statistics::Statistic Statistic; /*! * \brief findBestTest find the best test for a node * \param curNodeInfo All the necessary information for the current node * \param extractor The target attribute extractor to be used * \return A pair <currentTestInfo, globalStatistic> where currentTestInfo gives the information for the best test (the test and the statistic). * GlobalStatistic returns statistic on the complete node. When no best test is found, currentTestInfo should be a default construct testInfo-object. * * The splitting criterium should calculate at least statistics for all the examples (can be considered as calculating the statistics for a test that * is always positive), select a new best test and also calculate statistics for this test. * * The database of examples and the database of tests are available in the curNodeInfo object. A small remainder however: make sure when implementing * this class that the example database in NodeInfo is a stream transactional database, so the reset() functionality is not necessary available. The * testCollection however is a sequential transactional database, so it is resettable. */ std::pair findBestTest(mips::prediction::decision_tree::NodeInfo & curNodeInfo, const concepts::mips::prediction::decision_tree::TargetAttributeExtractor & extractor); }; } // decision_tree namespace } // prediction namespace } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_DECISIONTREE_CONCEPTS_SPLITTINGCRITERIUM_HPP mips-1.0/mips/prediction/decisionTree/concepts/prePruner.hpp0000644002402200254040000000410412520407731023672 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_CONCEPTS_PREPRUNER_HPP #define MIPS_PREDICTION_DECISIONTREE_CONCEPTS_PREPRUNER_HPP #include "statistic.hpp" #include "../pruneResult.hpp" namespace concepts { namespace mips { namespace prediction { namespace decision_tree { /*! * \brief The PrePruner concept defines the structure a prepruner should follow. * * The prepruner is for example used (but not exclusive) in mips::prediction::decision_tree::DefaultSplittingCriterium * * \sa mips::prediction::decision_tree::DefaultSplittingCriterium */ struct PrePruner { private: typedef void Tree; typedef void Test; typedef void Statistic; typedef void Node; public: /*! * \brief operator () Decides whether the test should be pruned or not. * \param tree The decision tree as build thus far * \param curNode The node which the learner is trying to expand * \param test The test * \param statistics The statistics for the test * \param alwaysTrueStatistic The statistics for the complete node, for all the examples * \return Whether the test should be pruned or not * * The action done is dependent on \link mips::prediction::decision_tree::PrunerResult the result of this function\endlink. */ mips::prediction::decision_tree::PrunerResult operator() (const Tree & tree, const Node & curNode, const concepts::mips::prediction::decision_tree::Test & test, const concepts::mips::core::statistics::Statistic & statistics, const concepts::mips::core::statistics::Statistic & alwaysTrueStatistic); }; } // decision_tree namespace } // prediction namespace } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_DECISIONTREE_CONCEPTS_PREPRUNER_HPP mips-1.0/mips/prediction/decisionTree/concepts/test.hpp0000644002402200254040000000511112520407731022666 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECSIONTREE_CONCEPTS_TESTCONCEPT_HPP #define MIPS_PREDICTION_DECSIONTREE_CONCEPTS_TESTCONCEPT_HPP namespace concepts { namespace mips { namespace prediction { namespace decision_tree { /*! * \brief The Test struct defines the concepts a test should follow. * * Note that if tests are passed by pointer (e.g. polymorphic behaviour of tests), that these conditions are automatically met. For all comparison * functions an integral comparison is based on the pointer address. For the operator(), the test is automatically dereferenced if it is a pointer. */ struct Test { private: typedef void Example; public: /*! * \brief Test should be default constructable. * * The default constructed test should be considered as "no test". When Test is a pointer, this already done as the default constructed value is 0. * E.g., internally the decision tree asks the splitting criterium to find the best test. This is compared to a default constructed test to check * whether a best test was found or not. * * The operator() is not called on default constructed test, so this function should not be valid in that case. */ Test(); /*! * \brief operator () is the function to test on a certain example. * \param example The example on which to test * \return a boolean value telling if the test has succeeded or not. * * Note that both the test as the example should remain constant. */ bool operator()(const Example & example) const; /*! * \brief operator == Two test should be comparable * \param rhs Another test * \return True when they are equal, false otherwise */ bool operator==(const Test & rhs) const; /*! * \brief operator != Two test should be comparable * \param rhs Another test * \return True when they are not equal, false when they are */ bool operator!=(const Test & rhs) const; /*! * \brief operator < Two tests should be comparable * \param rhs Another test * \return True when this test compares less than rhs, false otherwise */ bool operator<(const Test & rhs) const; }; } // decision_tree namespace } // prediction namespace } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_DECSIONTREE_CONCEPTS_TESTCONCEPT_HPP mips-1.0/mips/prediction/decisionTree/concepts/exampleGenerator.hpp0000644002402200254040000000546212520407731025222 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_CONCEPTS_EXAMPLEGENERATOR_HPP #define MIPS_PREDICTION_DECISIONTREE_CONCEPTS_EXAMPLEGENERATOR_HPP #include namespace concepts { namespace mips { namespace prediction { namespace decision_tree { /*! * \ingroup ExampleGenerator * \brief The ExampleGenerator Concept * * This class is a concept, it describes how a valid example generator for the decision tree should look like, which function should be exposed. This * does not mean that an example generator can not have extra functionality. * * This class is used to supply a database of examples for each node. * * \sa mips::prediction::decision_tree::Learner */ struct ExampleGenerator { private: typedef void Tree; typedef void NodeInfo; typedef void Test; public: /*! * \brief ExampleDB should follow a \link concepts::mips::database::StreamTransactionDatabase stream transaction database concept\endlink. This * is the type of database returned for each node. */ typedef StreamTransactionDatabaseConcept ExampleDB; /*! * \brief Example exposes the type of an example. */ typedef ExampleDB::value_type Example; /*! * \brief generateRootNodeExampleDB * \return A database of type ExampleDB, specifying the examples for the root node. */ ExampleDB generateRootNodeExampleDB(); /*! * \brief generateChildNodeExampleDB * \param parentNode An object of type \link mips::prediction::decision_tree::NodeInfo NodeInfo \endlink specifying the details for the parent * \param parentSplitTest The test chosen as best test by the SplittingCriterium. * \return A pair of example databases <yes-examples, no-examples > giving the examples for the yes-branch of this nodeand for the * no-branch of this node. * * This function should return two databases of examples for the child node of the current node. Note that parentNode is fully constructed and * attached to its parents, so it is possible to access tests of ancestors, etc. */ std::pair generateChildNodeExampleDB(const NodeInfo & parentNode, const Test & parentSplitTest); }; } // decision_tree namespace } // prediction namespace } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_DECISIONTREE_CONCEPTS_EXAMPLEGENERATOR_HPP mips-1.0/mips/prediction/decisionTree/noActionPruner.hpp0000644002402200254040000000166112520407731023045 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_NOACTIONPRUNER_HPP #define MIPS_PREDICTION_DECISIONTREE_NOACTIONPRUNER_HPP #include "pruneResult.hpp" namespace mips { namespace prediction { namespace decision_tree { struct NoActionPruner { template PrunerResult operator() (const Tree & /*tree*/, const Node & /*curNode*/, const Test & /*test*/, const Statistic & /*statistics*/, const Statistic & /*alwaysTrueStatistic*/) { return Pruner_DoNothing; } }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_NOACTIONPRUNER_HPP mips-1.0/mips/prediction/decisionTree/informationgain.hpp0000644002402200254040000000673412520407731023271 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONTREE_INFORMATIONGAIN_HPP #define MIPS_PREDICTION_DECISIONTREE_INFORMATIONGAIN_HPP #include #include namespace mips { namespace prediction { namespace decision_tree { /*! * \brief The InformationGain heuristic */ template struct InformationGain { typedef _CountType CountType; typedef core::Histogram<_EntityType, _CountType> Statistic; typedef _ValueType ResultType; /*! * \brief Computes the information gain heuristic. * \param curStatistic The statitics after applying a test. * \param alwaysTrueStatistic The base statistics value to compare against. * \return The information gain value. */ ResultType operator()(const Statistic& curStatistic, const Statistic& alwaysTrueStatistic) { typename Statistic::ValueType totalFrequencyTable = alwaysTrueStatistic.value(); typename Statistic::ValueType curStatsFrequencyTable = curStatistic.value(); typename Statistic::ValueType complimentFrequencyTable; for (typename Statistic::ValueType::iterator iter = totalFrequencyTable.begin(); iter != totalFrequencyTable.end(); ++iter) complimentFrequencyTable[(*iter).first] = totalFrequencyTable[(*iter).first] - curStatsFrequencyTable[(*iter).first]; ResultType totalSize = alwaysTrueStatistic.count(); ResultType curStatsSize = curStatistic.count(); ResultType compStatsSize = totalSize - curStatsSize; ResultType totalEntropy = entropy(totalFrequencyTable, totalSize); ResultType entropyCurStatistic = entropy(curStatsFrequencyTable, curStatsSize); ResultType entropyComplimentStatistic = entropy(complimentFrequencyTable, compStatsSize); ResultType NormEntCurStat = curStatsSize / totalSize * entropyCurStatistic; ResultType NormEntCompStat = compStatsSize / totalSize * entropyComplimentStatistic; ResultType IG = totalEntropy - (NormEntCurStat + NormEntCompStat); // Information Gain can never be less then zero, but precision errors may // drive it slightly less then 0. assert(IG > -1); return IG; } private: ResultType entropy(const typename Statistic::ValueType& table, CountType totalCount) const { if (totalCount <= CountType()) return ResultType(); ResultType entropy = ResultType(); for (typename Statistic::ValueType::const_iterator iter = table.begin(); iter != table.end(); ++iter) { // If one of the entries is 0, skip over it. if ((*iter).second > CountType()) { ResultType prob = (static_cast((*iter).second)) / (static_cast(totalCount)); entropy += prob * log2(prob); } } // Entropy can never be less then zero. assert(-entropy >= ResultType()); return -entropy; } ResultType log2(const ResultType value) const { return log(value) / log(2); } }; } // decision_tree namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONTREE_INFORMATIONGAIN_HPP mips-1.0/mips/prediction/decisionTree/CMakeLists.txt0000644002402200254040000000244012520407731022122 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) set (SOURCES detail/treePrinter.cpp ) set (HEADERS namespace.hpp # basic concepts concepts/test.hpp modelPrinter.hpp classifier.hpp defaultLearner.hpp defaultTree.hpp serialization.hpp # splitting criterium concepts/splittingCriterium.hpp defaultSplittingCriterium.hpp defaultSplittingCriterium.hxx noChangeDBWrapper.hpp # heuristics concepts/heuristic.hpp varianceReduction.hpp informationgain.hpp whitneymann.hpp # tree traversal functions decisionTree.hpp # internals detail/nodeInfo.hpp detail/nodeData.hpp detail/testInfo.hpp detail/alwaysTrueExtraTest.hpp detail/queryEngine.hpp detail/queryEngine.hxx detail/treePrinter.hpp detail/treePrinter.hxx detail/heuristicDecider.hpp # example generators concepts/exampleGenerator.hpp splitExampleGenerator.hpp splitExampleGenerator.hxx # test generators concepts/testGenerator.hpp removeUsedTestGenerator.hpp removeUsedTestGenerator.hxx # depthfirst learner depthFirstDecisionTreeLearner.hpp depthFirstDecisionTreeLearner.hxx # pruners concepts/prePruner.hpp pruneResult.hpp noActionPruner.hpp depthPruner.hpp fTestPruner.hpp minimumLeafSizePruner.hpp ) set (SUBDIRECTORIES test) INCLUDE_ALL_SUBDIRECTORIES_HEADER_SOURCES(SUBDIRECTORIES) mips-1.0/mips/prediction/classifier.hpp0000644002402200254040000000556312520407731017613 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_PREDICTION_HPP #define MIPS_PREDICTION_PREDICTION_HPP namespace mips { namespace prediction { /*! * \brief DefaultModelClassifier should be specialized to allow standard *behaviour to classify a model. * * \tparam Model The model that is used to classify * \tparam Enable A second parameter to use with boosts enable_if construction * * A specialisation of this class should follow the \link *concepts::mips::prediction::ModelClassifier ModelClassifier\endlink-concept. * Possible specialisations can be found for the * \li decision tree * \li decision forest */ template struct DefaultModelClassifier; /*! * \brief Classifier is the default way to classify an example using a model, it *behaves as a unary-functor. * * It has two template parameters, but normally only the first should be used: * \tparam Model the model type * \tparam ModelClassifier the prediction for a certain model. By default the *DefaultModelToOutputClassifier is used, which should * specialized for each type of Model. By using another class, this *behaviour can be changed. This class should follow the * \link concepts::mips::prediction::ModelClassifier\endlink-concept. * * This class classifies an example based on a model. It should be optimized for *classifying many examples on the same model. */ template < class _Model, class _ModelClassifier = DefaultModelClassifier<_Model> > struct Classifier : public _ModelClassifier { typedef _Model Model; /*! * \brief result_type As a typical unary function, this class exposes its * result_type. As the operator() can accept different types * of examples, the argument_type is not exposed. */ typedef typename _ModelClassifier::result_type result_type; /*! * \brief the Classifier construct requires the model to work on. * \param model A const pointer to the model should be passed. This class * takes no ownership of the model, nor copies it. */ Classifier(const Model* model) : _ModelClassifier(*model), _model(model) {} /*! * \tparam The operator() function classifies a given example * \param Example the example to classify * \return the class to which the example belongs * * Both the model as the example remain constant during this operation. */ template result_type operator()(const Example & example) const { return _ModelClassifier::operator()(*_model, example); } private: const Model* _model; }; } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_PREDICTION_HPP mips-1.0/mips/prediction/modelPrinter.hpp0000644002402200254040000000402712520407731020125 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_MODELPRINTER_HPP #define MIPS_PREDICTION_MODELPRINTER_HPP namespace mips { namespace prediction { /*! * \brief DefaultModelPrinter should be specialized to allow standard behaviour to print a model. * * \tparam Model The model that is used to classify * \tparam Enable A second parameter to use with boosts enable_if construction * * A specialisation of this class should follow the \link concepts::mips::prediction::ModelPrinter ModelPrinter\endlink-concept. * Possible specialisations can be found for the * \li decision tree * \li decision forest */ template struct DefaultModelPrinter; /*! * \brief ModelPrinter is the default class to print a model, it behaves as a binary-functor with parameters a model and std::ostream. * * It has two template parameters, but normally only the first should be used * \tparam Model the model type * \tparam ModelPrinter the printer for a certain model. By default the DefaultModelPrinter is used, which should * specialized for each type of Model. By using another class, this behaviour can be changed. * * This class prints a model. */ template > struct ModelPrinter : public _ModelPrinter { typedef _Model Model; /*! * \brief operator () prints out a model to a stream * \param model The model * \param stream The stream * * The model is assured to remain unchanged. */ void operator()(const Model & model, std::ostream & stream) const { return _ModelPrinter::operator()(model, stream); } }; } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_MODELPRINTER_HPP mips-1.0/mips/prediction/decisionForest/0000755002402200254040000000000012520407731017725 5ustar thomasfdtaigmips-1.0/mips/prediction/decisionForest/probabilisticSplittingCriterium.hxx0000644002402200254040000001627012520407731027074 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICSPLITTINGCRITERIUM_HXX #define MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICSPLITTINGCRITERIUM_HXX #include "probabilisticSplittingCriterium.hpp" #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_forest { template template std::pair< decision_tree::detail::TestInfo< typename ProbabilisticSplittingCriterium<_Heuristic, _PrePruner, _TestChoserCriterium, _ExampleDBWrapper, _TestCollectionWrapper>::HeuristicResultType, typename ProbabilisticSplittingCriterium<_Heuristic, _PrePruner, _TestChoserCriterium, _ExampleDBWrapper, _TestCollectionWrapper>::Statistic, typename NodeInfo::TestCollection::value_type>, typename ProbabilisticSplittingCriterium<_Heuristic, _PrePruner, _TestChoserCriterium, _ExampleDBWrapper, _TestCollectionWrapper>::Statistic> ProbabilisticSplittingCriterium<_Heuristic, _PrePruner, _TestChoserCriterium, _ExampleDBWrapper, _TestCollectionWrapper>::findBestTest(NodeInfo & curNodeInfo, const TargetAttributeExtractor & extractor) { using namespace mips::prediction::decision_tree; typedef typename NodeInfo::TestCollection TestCollection; typedef typename TestCollection::value_type Test; typedef typename NodeInfo::ExampleDB ExampleDB; typedef typename NodeInfo::Tree Tree; typedef typename TestCollectionWrapper::template result_type::type MyTestCollection; typedef typename ExampleDBWrapper::template result_type::type MyExampleDB; typedef typename TargetAttributeExtractor::result_type TargetAttribute; typedef mips::prediction::decision_tree::detail::QueryEngine< typename TestCollection::value_type, Statistic, TargetAttributeExtractor, decision_tree::detail::AlwaysTrueExtraTest > QueryEngine; typedef decision_tree::detail::TestInfo< HeuristicResultType, Statistic, typename NodeInfo::TestCollection::value_type> TestInfo; const Tree & tree = *curNodeInfo.tree; std::list testToDelete; // wrap the testcollection and the example MyTestCollection tests = testCollectionWrapper().operator() (curNodeInfo.testCollection); MyExampleDB db = exampleDBWrapper().operator() (curNodeInfo.exampleDB); // gather all the statistics MIPS_LOG(Learner, "Querying for node " << decision_tree::getPathDescription(tree, curNodeInfo.node)); QueryEngine queryEngine; queryEngine(tests, extractor, db.current(), db.end(), maxNumberOfExamples()); std::list testInfos; const Statistic & alwaysTrueStatistic = queryEngine.alwaysTrueStatistic(); MIPS_LOG(Learner, "Searching statistics for node " << decision_tree::getPathDescription(tree, curNodeInfo.node)); // loop over all the statistics and test tests.reset(); HeuristicResultType totalScore = HeuristicResultType(); Heuristic getHeuristicValue = generateHeuristic(); // loop over all the tests and statistics for(typename TestCollection::iterator testIt = tests.current(); testIt != tests.end(); ++testIt) { PrePruner prePruner = generatePrePruner(); // gather all the details TestInfo curTest; curTest.test = *testIt; curTest.statistic = queryEngine.testStatistic(curTest.test); // check with the prepruner decision_tree::PrunerResult prePruneValue = prePruner(curNodeInfo.tree, curNodeInfo.node, curTest.test, curTest.statistic, alwaysTrueStatistic); // discarded for this node if(prePruneValue == Pruner_RemoveFromNode) { MIPS_LOG(Learner, "Discarded by Prepruner \'" << utilities::wrapPointer(curTest.test) << "\'"); } // discarded for this node and for children else if(prePruneValue == Pruner_RemoveFromNodeAndChildren) { MIPS_LOG(Learner, "Removing test \'" << utilities::wrapPointer(curTest.test) << "\' for child nodes of " << decision_tree::getPathDescription(tree, curNodeInfo.node)); testToDelete.push_back(curTest.test); } // okay, the prepruner accepts this value else { curTest.value = heuristicAdaptor()(getHeuristicValue(curTest.statistic, alwaysTrueStatistic)); //MIPS_LOG(Learner, "Test (heuristic=" << curTest.value << " ): " << utilities::wrapPointer(curTest.test)); if(curTest.value >= HeuristicResultType()) { totalScore += curTest.value; testInfos.push_back(curTest); } } } // actually delete all the no-longer necessary tests for(typename std::list::const_iterator it = testToDelete.begin(); it != testToDelete.end(); ++it) curNodeInfo.testCollectionManager.removeTest(curNodeInfo.testCollection, *it); // no good tests found? if(totalScore == HeuristicResultType() || testInfos.empty()) return std::make_pair(TestInfo(), alwaysTrueStatistic); // take random number between 0 en totalScore boost::random::uniform_real_distribution distribution(HeuristicResultType(), totalScore); HeuristicResultType rnd = distribution(_generator); typename std::list::const_iterator it = testInfos.begin(); while(it != testInfos.end()) { rnd -= it->value; // okay, this is the value if(rnd <= HeuristicResultType()) { // set the heuristic value to the right value TestInfo chosenTest = *it; chosenTest.value = getHeuristicValue(chosenTest.statistic, alwaysTrueStatistic); // and this is the test we've chosen return std::make_pair(chosenTest, alwaysTrueStatistic); } ++it; } // if we get over here, the list should have been empty return std::make_pair(TestInfo(), alwaysTrueStatistic); } } // decision_forest namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICSPLITTINGCRITERIUM_HXX mips-1.0/mips/prediction/decisionForest/randomFraction.hpp0000644002402200254040000000610412520407731023405 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_RANDOMFRACTION_HPP #define MIPS_PREDICTION_DECISIONFOREST_RANDOMFRACTION_HPP #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_forest { /*! * \brief The RandomFraction struct implements the concepts::mips::prediction::decision_tree::DBWrapper concept * * This class stores the element in a sequential database based on a list. It will use the elements in the original container with a * predefined probability. Calling this class two times on the same container will highly likely give rise to two completely different * databases. */ struct RandomFraction { private: struct SelectByFraction { SelectByFraction(double fraction) : _fraction(fraction*RAND_MAX) {} template bool operator()(const T & /*element*/) { return rand() < _fraction; } int _fraction; }; public: /*! * \brief Wrapper class to define the new type of a database after using this wrapper. */ template struct result_type { typedef utilities::SequentialDatabaseWrapper, utilities::StoreBySmartPointer > > type; }; /*! * \brief RandomFraction Constructs a random fraction database wrapper with default fraction value 0.1 (10% of all tests accepted) * \param fraction */ RandomFraction(double fraction = 0.1) : _fraction(fraction) {} /*! * \brief perator() copies element from a database DB to a new database with probability fraction. * \param db The database to be used. * \return A sequential database based on a list containing the selected tests. */ template typename result_type::type operator() (DB & db) { typename result_type::type t; t.get().assign( boost::make_filter_iterator(SelectByFraction(_fraction), db.current(), db.end()), boost::make_filter_iterator(SelectByFraction(_fraction), db.end())); return t; } /*! * \brief fraction Get the current fraction * \return the current fraction of test chosen */ double fraction() const { return _fraction; } /*! * \brief setFraction Set the current fraction * \param fraction The new fraction. Should be a number between 0 (no tests chosen) and 1 (all tests chosen). */ void setFraction(double fraction) { _fraction = fraction; } double _fraction; }; } // decision_forest namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_RANDOMFRACTION_HPP mips-1.0/mips/prediction/decisionForest/probabilisticForest.hpp0000644002402200254040000002503312520407731024452 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICFOREST_HPP #define MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICFOREST_HPP #include #include #include #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_forest { /*! * \brief ProbabilisticForestTraits creates a traits class for a decision forest where a test is chosen with a probability proportional to the * heuristic value. * * This traits class has 11 template parameters of which 3 are obligatory, the other 8 can be chosen by default: * \tparam Test should follow the \link concepts::mips::prediction::decision_tree::Test Test\endlink concept. This type defines the base test type. * Note that polymorphic tests are allowed, it is possible to use pointers to objects as a test. In this case the test type should be the * pointer type. * * \tparam Example The example type. * * \tparam TargetAttributeExtractor Extract the target value from an example. This class should follow the * concepts::mips::prediction::AttributeExtractor-concept. Especially the AttributeType is important, it defines the default heuristic to be used. * * \tparam PrePruner The prepruner is used to prune tests while constructing each tree in the forest. This class should follow the * concepts::mips::prediction::decision_tree::PrePruner-concept. By default the * mips::prediction::decision_tree::MinimumLeafSizePruner is used and this is set to a minimum leaf size of 25. * * \tparam HeuristicValueAdapator This binary functor is used to change the heuristic value, as a test is chosen with a probability proportional to * the heuristic score. By default the actual heuristic value is chosen. This class should be a unary_functor with argument and result_type * castable to Heuristic::ResultType. * * \tparam TestCollectionWrapper This wrapper is used in the mips::prediction::decision_tree::DefaultSplittingStrategy to alter the test collection. By * default no wrapping (mips::prediction::decision_tree::NoChangeWrapper) is used, but e.g. with mips::prediction::decision_forest::RandomFraction * it is possible to select a fraction of the test. The concept for a wrapper is defined in concepts::mips::prediction::decision_tree::DBWrapper. * The wrapper is used in the FindBestTest function, only the test that remain after the wrapping are used to find a best test. * \tparam ExampleDBWrapper This wrapper is used in the mips::prediction::decision_tree::DefaultSplittingStrategy to alter the example collection. By * default no wrapping (mips::prediction::decision_tree::NoChangeWrapper) is used. The concept for a wrapper is defined in * concepts::mips::prediction::decision_tree::DBWrapper. The wrapper is used in the FindBestTest function, only the example that remain after * the wrapping are used to find a best test. * * \tparam TestGenerator This class should follow the concepts::mips::prediction::decision_tree::TestGenerator-concept and is * used to generate tests for each node. By default the mips::prediction::decision_tree::RemoveUsedTestGenerator is used. This test generator stores * the test internally in a container and makes sure that a test used for an ancestor-node, is not used anymore for child-nodes. * * \tparam ExampleGenerator This class should follow the concepts::mips::prediction::decision_tree::ExampleGenerator-concept and is used to generate * an example database for each node. By default the mips::prediction::decision_tree::SplitExampleGenerator is used. This example-generator stores * the examples in a container, and splits for each node the examples in to two groups, a yes-part and a no-part. These databases of examples are * used resp. in the yes-branch and the no-branch. * * \tparam Heuristic This class should follow the concepts::mips::prediction::decision_tree::Heuristic-concept and is used to calculate a score for * each test based on its statistic. * * \tparam Tree The type of tree that is generated in the forest. By default a mips::core::tree::PointerTree is created. Other trees are possible, but * the required functionality for these trees is still badly documented. * * This version of a decision forest learner learns a set of trees, where for each node in each tree, the probability that a test is selected is * proportional to the heuristic value (or the adapted heuristic value). * * \sa mips::prediction::decision_forest::Learner, mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits */ template < class _Test, class _Example, class _TargetAttributeExtractor, class _PrePruner = decision_tree::MinimumLeafSizePruner, class _HeuristicAdaptor = detail::Identity_T, class _TestCollectionWrapper = decision_tree::NoChangeDBWrapper, class _ExampleDBWrapper = decision_tree::NoChangeDBWrapper, class _TestGenerator = decision_tree::RemoveUsedTestGenerator<_Test>, class _ExampleGenerator = decision_tree::SplitExampleGenerator<_Example>, class _Heuristic = typename decision_tree::detail::HeuristicDecider<_TargetAttributeExtractor>::type, class _Tree = mips::core::RootedPointerTree, bool> > struct ProbabilisticForestTraits { typedef _TestGenerator TestGenerator; typedef typename _TestGenerator::Test Test; typedef typename _TestGenerator::TestCollection TestCollection; typedef typename _TestGenerator::TestCollectionManager TestCollectionManager; typedef _ExampleGenerator ExampleGenerator; typedef typename ExampleGenerator::Example Example; typedef typename ExampleGenerator::ExampleDB ExampleDB; typedef _TargetAttributeExtractor TargetAttributeExtractor; typedef typename TargetAttributeExtractor::AttributeType TargetAttributeType; typedef typename TargetAttributeExtractor::result_type TargetAttributeResultType; typedef _Tree Tree; typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; typedef ProbabilisticSplittingCriterium<_Heuristic, _PrePruner, _HeuristicAdaptor, _TestCollectionWrapper, _ExampleDBWrapper> SplittingCriterium; typedef typename SplittingCriterium::Statistic Statistic; typedef typename SplittingCriterium::Heuristic Heuristic; typedef typename SplittingCriterium::PrePruner PrePruner; typedef typename SplittingCriterium::HeuristicAdaptor HeuristicAdaptor; typedef typename SplittingCriterium::TestCollectionWrapper TestCollectionWrapper; typedef typename SplittingCriterium::ExampleDBWrapper ExampleDBWrapper; /*! * \brief this type defines the learner that can be used by using the operator() method. * * As the decision forest learner inherits from a decision tree learner, initializing settings for the example generator, test generator and the * splitting criterium can be done directlty through the lesrner. */ typedef decision_forest::DepthFirstForestLearner Learner; /*! * \brief Model is a general type to define the result from learning a decision tree. */ typedef typename Learner::result_type Model; /*! * \brief ModelPrinter is generic way to print the tree. The information displayed is dependent on the statistic used in the tree. */ typedef prediction::ModelPrinter ModelPrinter; /*! * \brief Classifier classifies an example. The actual value is selected by the type of statistic used. */ typedef prediction::Classifier Classifier; }; } // decision_forest namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICFOREST_HPP mips-1.0/mips/prediction/decisionForest/defaultForest.hpp0000644002402200254040000000355612520407731023256 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_DEFAULTFOREST_HPP #define MIPS_PREDICTION_DECISIONFOREST_DEFAULTFOREST_HPP #include #include "modelPrinter.hpp" #include "classifier.hpp" namespace mips { namespace prediction { namespace decision_forest { /*! * DefaultDecisionForestTraits is traits class that defines the Model, ModelPrinter and the Classifier used. * * This class defines the base elements for a forest: The model, the printer and the clasifier. */ template < class Test, class Statistic, class Tree = mips::core::RootedPointerTree, bool> > struct DefaultDecisionForestTraits { /*! * \brief Model is a general type to define the result from learning a decision forest. */ typedef std::vector Model; /*! * \brief ModelPrinter is generic way to print the forest. The information displayed is dependent on the statistic used in the tree. */ typedef prediction::ModelPrinter ModelPrinter; /*! * \brief Classifier classifies an example. The actual value is selected by the type of statistic used. */ typedef prediction::Classifier Classifier; }; } // decision_forest namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_DEFAULTFOREST_HPP mips-1.0/mips/prediction/decisionForest/serialization.hpp0000644002402200254040000000105312520407731023312 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_SERIALIZATION_HPP #define MIPS_PREDICTION_DECISIONFOREST_SERIALIZATION_HPP #include #include #endif // MIPS_PREDICTION_DECISIONFOREST_SERIALIZATION_HPP mips-1.0/mips/prediction/decisionForest/classifier.hpp0000644002402200254040000000653112520407731022567 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_PREDICTION_HPP #define MIPS_PREDICTION_DECISIONFOREST_PREDICTION_HPP #include #include #include "decisionForest.hpp" namespace mips { namespace prediction { namespace decision_forest { template struct StatisticReductor { typedef _Statistic result_type; void operator()(const _Statistic & globalStatistic, result_type & reductor) { reductor += globalStatistic; } }; template < class _EntityType, class _CountType, class _ValueType> struct StatisticReductor > { typedef mips::core::Variance<_EntityType, _CountType, _ValueType> result_type; void operator()(const result_type & globalStatistic, result_type & reductor) { reductor += globalStatistic.mean(); } }; } // decision_forest namespace /*! * \brief DefaultModelClassifier is the specialization of mips::prediction::DefaultModelClassifier for a decision forest. * * This classes adds all the different statistics from the different forests together and then uses the decision_tree::DefaultStatisticsToClassConvertor * to reduce this statistic to one value. This value is then returned. * * \sa decision_tree::DefaultStatisticsToClassConvertor * */ template struct DefaultModelClassifier<_Forest, typename boost::enable_if >::type > { typedef typename core::RootedTreeTraits::NodeLabel NodeData; typedef typename NodeData::Statistic Statistic; typedef decision_forest::StatisticReductor Reductor; typedef typename Reductor::result_type _IntermediateStatisticType; typedef typename decision_tree::detail::DefaultStatisticsToClassConvertor<_IntermediateStatisticType>::result_type result_type; DefaultModelClassifier(const _Forest & /*forest*/) {} template /*! * \brief Extracts and returns the reduced statistics for an example * for a given forest. * * \param forest A const reference to a Forest * \param example An example * \return result_type */ result_type operator()(const _Forest & forest, const Example & example) const { _IntermediateStatisticType reduction; Reductor r; // perform the reduction BOOST_FOREACH(const NodeData & nodeData, decision_forest::classify_iterators(forest, example)) { r(nodeData.globalStatistic, reduction); } decision_tree::detail::DefaultStatisticsToClassConvertor convertor; return convertor(reduction); } }; } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_PREDICTION_HPP mips-1.0/mips/prediction/decisionForest/modelPrinter.hpp0000644002402200254040000000315612520407731023107 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_MODELPRINTER_HPP #define MIPS_PREDICTION_DECISIONFOREST_MODELPRINTER_HPP #include "decisionForest.hpp" #include #include namespace mips { namespace prediction { /*! * \brief This class is specialisation of DefaultModelPrinter for forests. It prints each tree in the forest to the supplied stream. */ template struct DefaultModelPrinter<_Forest, typename boost::enable_if >::type > { /*! * \brief operator () Prints each tree in the forest to the stream * \param forest The forest to print * \param stream The stream to write to */ void operator()(const _Forest & forest, std::ostream & stream) const { typedef typename _Forest::value_type Tree; ModelPrinter treePrinter; unsigned int count = 1; BOOST_FOREACH(const Tree & tree, forest) { stream << "Printing tree " << count << " in the forest:" << std::endl; treePrinter(tree, stream); stream << std::endl << std::endl; ++count; } } }; } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_MODELPRINTER_HPP mips-1.0/mips/prediction/decisionForest/decisionForest.hpp0000644002402200254040000000400712520407731023417 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_DECISIONFOREST_HPP #define MIPS_PREDICTION_DECISIONFOREST_DECISIONFOREST_HPP #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_forest { /*! * Type traits class to check whether an type is a decision forest or not */ template struct IsDecisionForest : public boost::false_type {}; template struct IsDecisionForest > : public decision_tree::IsDecisionTree {}; template struct classifiy_iterator_traits { typedef boost::transform_iterator::NodeLabel & (const typename Forest::value_type &)>, typename Forest::const_iterator> iterator; }; template std::pair::iterator, typename classifiy_iterator_traits::iterator> classify_iterators(const Forest & forest, const Example & example) { typedef typename Forest::value_type Tree; typedef typename Forest::const_iterator iterator; boost::function::NodeLabel & (const Tree &)> f = boost::bind(decision_tree::classify, _1, boost::cref(example)); typedef typename classifiy_iterator_traits::iterator it; return std::make_pair(it(forest.begin(), f), it(forest.end(), f)); } } // decision_forest namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_DECISIONFOREST_HPP mips-1.0/mips/prediction/decisionForest/randomFractionForest.hpp0000644002402200254040000002254112520407731024573 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_RANDOMFRACTIONFOREST_HPP #define MIPS_PREDICTION_DECISIONFOREST_RANDOMFRACTIONFOREST_HPP #include #include #include #include #include #include #include #include #include #include namespace mips { namespace prediction { namespace decision_forest { /*! * \brief RandomFractionForestLearnerTraits creates a traits class for a decision forest. * * This traits class has 9 template parameters of which 3 are obligatory, the other 6 can be chosen by default: * \tparam Test should follow the \link concepts::mips::prediction::decision_tree::Test Test\endlink concept. This type defines the base test type. * Note that polymorphic tests are allowed, it is possible to use pointers to objects as a test. In this case the test type should be the * pointer type. * * \tparam Example The example type. * * \tparam TargetAttributeExtractor Extract the target value from an example. This class should follow the * concepts::mips::prediction::AttributeExtractor-concept. Especially the AttributeType is important, it defines the default heuristic to be used. * * \tparam PrePruner The prepruner is used to prune tests while constructing each tree in the forest. This class should follow the * concepts::mips::prediction::decision_tree::PrePruner-concept. By default the * mips::prediction::decision_tree::MinimumLeafSizePruner is used and this is set to a minimum leaf size of 25. * * \tparam ExampleDBWrapper This wrapper is used in the mips::prediction::decision_tree::DefaultSplittingStrategy to alter the example collection. By * default no wrapping (mips::prediction::decision_tree::NoChangeWrapper) is used. The concept for a wrapper is defined in * concepts::mips::prediction::decision_tree::DBWrapper. The wrapper is used in the FindBestTest function, only the example that remain after * the wrapping are used to find a best test. * * \tparam TestGenerator This class should follow the concepts::mips::prediction::decision_tree::TestGenerator-concept and is * used to generate tests for each node. By default the mips::prediction::decision_tree::RemoveUsedTestGenerator is used. This test generator stores * the test internally in a container and makes sure that a test used for an ancestor-node, is not used anymore for child-nodes. * * \tparam ExampleGenerator This class should follow the concepts::mips::prediction::decision_tree::ExampleGenerator-concept and is used to generate * an example database for each node. By default the mips::prediction::decision_tree::SplitExampleGenerator is used. This example-generator stores * the examples in a container, and splits for each node the examples in to two groups, a yes-part and a no-part. These databases of examples are * used resp. in the yes-branch and the no-branch. * * \tparam Heuristic This class should follow the concepts::mips::prediction::decision_tree::Heuristic-concept and is used to calculate a score for * each test based on its statistic. * * \tparam Tree The type of tree that is generated in the forest. By default a mips::core::tree::PointerTree is created. Other trees are possible, but * the required functionality for these trees is still badly documented. * * This version of a decision forest learner uses for each node for each tree a random fraction of the tests. The default values are 10% tests for 30 trees. * * \sa mips::prediction::decision_forest::Learner, mips::prediction::decision_tree::DefaultDecisionTreeLearnerTraits */ template < class _Test, class _Example, class _TargetAttributeExtractor, class _PrePruner = decision_tree::MinimumLeafSizePruner, class _ExampleDBWrapper = decision_tree::NoChangeDBWrapper, class _TestGenerator = decision_tree::RemoveUsedTestGenerator<_Test>, class _ExampleGenerator = decision_tree::SplitExampleGenerator<_Example>, class _Heuristic = typename decision_tree::detail::HeuristicDecider<_TargetAttributeExtractor>::type, class _Tree = mips::core::RootedPointerTree, bool> > struct RandomFractionForestLearnerTraits { typedef _TestGenerator TestGenerator; typedef typename _TestGenerator::Test Test; typedef typename _TestGenerator::TestCollection TestCollection; typedef typename _TestGenerator::TestCollectionManager TestCollectionManager; typedef _ExampleGenerator ExampleGenerator; typedef typename ExampleGenerator::Example Example; typedef typename ExampleGenerator::ExampleDB ExampleDB; typedef _TargetAttributeExtractor TargetAttributeExtractor; typedef typename TargetAttributeExtractor::AttributeType TargetAttributeType; typedef typename TargetAttributeExtractor::result_type TargetAttributeResultType; typedef _Tree Tree; typedef typename mips::core::RootedTreeTraits::NodeLabel NodeData; typedef decision_tree::DefaultSplittingCriterium< _Heuristic, _PrePruner, RandomFraction, _ExampleDBWrapper> SplittingCriterium; typedef typename SplittingCriterium::Statistic Statistic; typedef typename SplittingCriterium::Heuristic Heuristic; typedef typename SplittingCriterium::PrePruner PrePruner; typedef typename SplittingCriterium::TestCollectionWrapper TestCollectionWrapper; typedef typename SplittingCriterium::ExampleDBWrapper ExampleDBWrapper; /*! * \brief this type defines the learner that can be used by using the operator() method. * * As the decision forest learner inherits from a decision tree learner, initializing settings for the example generator, test generator and the * splitting criterium can be done directlty through the lesrner. */ typedef decision_forest::DepthFirstForestLearner Learner; /*! * \brief Model is a general type to define the result from learning a decision tree. */ typedef typename Learner::result_type Model; /*! * \brief ModelPrinter is generic way to print the tree. The information displayed is dependent on the statistic used in the tree. */ typedef prediction::ModelPrinter ModelPrinter; /*! * \brief Classifier classifies an example. The actual value is selected by the type of statistic used. */ typedef prediction::Classifier Classifier; }; } // decision_forest namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_RANDOMFRACTIONFOREST_HPP mips-1.0/mips/prediction/decisionForest/depthFirstForestLearner.hpp0000644002402200254040000001123012520407731025243 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_DEPTHFIRSTFORESTLEARNER_HPP #define MIPS_PREDICTION_DECISIONFOREST_DEPTHFIRSTFORESTLEARNER_HPP #include namespace mips { namespace prediction { namespace decision_forest { /*! * The decision forest learner implements a decision forest of a predefined amount of trees. * * The class has the same functionality as \link mips::prediction::decision_tree::Learner decision tree learner\endlink, except that it * does not learn one tree, but a predefined number of trees. * * It uses the same template parameters as the decision_tree::Learner: * \tparam TestGenerator This class should follow the \link concepts::mips::prediction::decision_tree::TestGenerator TestGenerator\endlink-concept. It is * used to generate the test collections for each node. * * \tparam ExampleGenerator This class should follow the \link concepts::mips::prediction::decision_tree::ExampleGenerator ExampleGenerator\endlink-concept. * It is used to generate the example collection for each node. * * \tparam SplittingCriterium This class should follow the \link concepts::mips::prediction::decision_tree::SplittingCriterium SplittingCriterium\endlink-concept. * It is used to find the best test for a node. * * \tparam AttributeExtractor This class should follow the \link concepts::mips::prediction::AttributeExtractor AttributeExtractor\endlink-concept. * It is used to extract the target attribute from an example. * * \tparam Tree This is the type of tree the forest will be populated with. * * Take a look at the default traits classes for the decision forest: * \li mips::prediction::decision_forest::RandomFractionForestLearnerTraits */ template < class _TestGenerator, class _ExampleGenerator, class _SplittingCriterium, class _AttributeExtractor, class _Tree > struct DepthFirstForestLearner : public decision_tree::DepthFirstDecisionTreeLearner<_TestGenerator, _ExampleGenerator, _SplittingCriterium, _AttributeExtractor, _Tree> { typedef decision_tree::DepthFirstDecisionTreeLearner< _TestGenerator, _ExampleGenerator, _SplittingCriterium, _AttributeExtractor, _Tree > TreeLearner; typedef std::vector<_Tree> Forest; typedef Forest result_type; /*! * \brief Learner constructs a default decision forest learner * \param numberOfTrees This number is standard set to 30, */ DepthFirstForestLearner(unsigned int numberOfTrees = 30) : _numberOfTrees(numberOfTrees){} /*! * \brief operator () Learns numberOfTrees() trees * \param targetAttributeExtractor * \return */ Forest operator()(const typename TreeLearner::AttributeExtractor & targetAttributeExtractor = typename TreeLearner::AttributeExtractor()) { Forest f(_numberOfTrees); for(unsigned int i = 0; i < _numberOfTrees; i++) { f[i] = treeLearner() (targetAttributeExtractor); MIPS_LOG(Learner, "Learned tree" << i ); } return f; } /*! * \brief Returns an tree learner object * \return An object of type TreeLearner */ TreeLearner & treeLearner() { return *this; } /*! * \brief Returns a const tree learner object * \return An object of type const TreeLearner */ const TreeLearner & treeLearner() const { return *this; } /*! * \brief Returns the number of trees learned. * \return unsigned int. */ unsigned int numberOfTrees() const { return _numberOfTrees; } /*! * \brief Set the number of tress to learn. * \param numberOfTrees The number of trees to learn. */ void setNumberofTrees(unsigned int numberOfTrees) { _numberOfTrees = numberOfTrees; } private: unsigned int _numberOfTrees; }; } // decision_forest namespace } // prediction namespace } // mips namespace #endif // MIPS_PREDICTION_DECISIONFOREST_DEPTHFIRSTFORESTLEARNER_HPP mips-1.0/mips/prediction/decisionForest/CMakeLists.txt0000644002402200254040000000077112520407731022472 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) set (SOURCES ) set (HEADERS defaultForest.hpp depthFirstForestLearner.hpp classifier.hpp modelPrinter.hpp decisionForest.hpp serialization.hpp probabilisticForest.hpp probabilisticSplittingCriterium.hpp probabilisticSplittingCriterium.hxx # random fraction forest randomFractionForest.hpp randomFraction.hpp ) set (SUBDIRECTORIES) INCLUDE_ALL_SUBDIRECTORIES_HEADER_SOURCES(SUBDIRECTORIES) mips-1.0/mips/prediction/decisionForest/probabilisticSplittingCriterium.hpp0000644002402200254040000001762512520407731027061 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICSPLITTINGCRITERIUM_HPP #define MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICSPLITTINGCRITERIUM_HPP #include #include #include namespace mips { namespace prediction { namespace decision_forest { namespace detail { struct Identity_T { template _T operator()(const _T& t) const { return t; } }; } // detail namespace /*! * \brief The probabilistic splitting criterium calculates the statistics for *all the test, and uses this statistics in combination with a prepruner to * chose a test with a probability proportional to the heuristic value. * * This class can have up to 5 template parameters of which the first 2 are *obligatory: * \tparam Heuristic This class should follow the \link *concepts::mips::prediction::decision_tree::Heuristic Heuristic\endlink-concept *and is used to * calculate a score for each test based on its statistic, e.g. *mips::prediction::decision_tree::InformationGain, * mips::prediction::decision_tree::VarianceReduction * * \tparam PrePruner The prepruner is used to prune tests while constructing the *tree. This class should follow the \link * concepts::mips::prediction::decision_tree::PrePruner PrePruner\endlink *concept. Possible prepruners are * mips::prediction::decision_tree::MinimumLeafSizePruner or *mips::prediction::decision_tree::NoPrePruner. * * \tparam HeuristicAdaptor: Each heuristic value is passed through this *functor, and all heuristic values larger then zero are accepted. This * class should be a unary functor with argument and result_type *Heuristic::ResultType. By default the identity functor is used, so no changes *are * made. * *The test chose criterium is a binary functor receiving an range of iterators *on a TestInfo object, and should return an * iterator to the chosen object. This class should follow the \link *concepts::mips::prediction::decision_tree::TestChoser TestChoser\endlink *concept. * By default, this is set to *mips::prediction::decision_tree::ChoseBestTest. * * \tparam TestCollectionWrapper The test collection wrapper is used in the *beginning of the findBestTest-function. The wrapper defines which tests are * available during the test selection, and also how they are stored. *E.g.: Test generated by a "slow" refinement operator can better be stored in * a temporary container, than refining them time after time. This class *should follow the * \link *concepts::mips::prediction::decision_tree::DBWrapper\endlink-concept. Possible *wrappers are mips::prediction::decision_tree::NoChangeDBWrapper * and mips::prediction::decision_fores::RandomFraction. * * \tparam ExampleDBWrapper The example database wrapper is used in the *beginning of the findBestTest-function. The wrapper defines which examples are * available during the test selection, and also how they are stored. This *class should follow the * \link *concepts::mips::prediction::decision_tree::DBWrapper\endlink-concept. Possible *wrappers are mips::prediction::decision_tree::NoChangeDBWrapper * and mips::prediction::decision_forest::RandomFraction. * * This class implements the *concepts::mips::prediction::decision_tree::SplittingCriterium concept. * * A test is chosen with a probability proportional to the adapted heuristic *value. Note that the value returned in the test info object is the real * heuristic value, not the changed value. */ template struct ProbabilisticSplittingCriterium : public _HeuristicAdaptor { public: typedef _HeuristicAdaptor HeuristicAdaptor; typedef _Heuristic Heuristic; typedef typename Heuristic::Statistic Statistic; typedef typename Heuristic::ResultType HeuristicResultType; typedef boost::random::mt19937 RandomNumberGenerator; typedef _PrePruner PrePruner; typedef _TestCollectionWrapper TestCollectionWrapper; typedef _ExampleDBWrapper ExampleDBWrapper; ProbabilisticSplittingCriterium() : _maxNumberOfExamples(-1) {} /*! * \brief Builds a Heuristic to be used. * \return A heuristic object. */ Heuristic generateHeuristic() const { return heuristic(); } /*! * \brief Builds the PrePruner to be used. * \return A pre prunner object. */ PrePruner generatePrePruner() const { return prePruner(); } /*! * \brief Returns the Heuristic to be used. * \return A const heuristic object. */ const Heuristic& heuristic() const { return _heuristic; } /*! * \brief Returns the PrePruner to be used. * \return A const pre prunner object. */ const PrePruner& prePruner() const { return _prePruner; } /*! * \brief Returns the Heuristic to be used. * \return A heuristic object. */ Heuristic& heuristic() { return _heuristic; } /*! * \brief Returns the PrePruner to be used. * \return A const pre prunner object. */ PrePruner& prePruner() { return _prePruner; } /*! * \brief Returns a reference to heuristic adaptor object. * \return A reference to a heuristic adaptor. */ HeuristicAdaptor& heuristicAdaptor() { return *this; } /*! * \brief Returns a random number generator * \return A reference to a random number generator. */ RandomNumberGenerator& randomNumberGenerator() { return _generator; } /*! * \brief Returns the maximum number of examples. * \return The maximum number of examples. */ int maxNumberOfExamples() const { return _maxNumberOfExamples; } /*! * \brief Sets the maximum number of examples. * \param newMax the maximum number of exmaples. */ void setMaxNumberOfExamples(int newMax) { _maxNumberOfExamples = newMax; } /*! * \brief Returns a Test collection. * \return A reference to the test collection. */ TestCollectionWrapper& testCollectionWrapper() { return _testCollectionWrapper; } /*! * \brief Returns a reference to a database * \return Reference to a database. */ ExampleDBWrapper& exampleDBWrapper() { return _exampleDBWrapper; } /*! * \brief findBestTest find the test with the highest heuristic value and *returns this test (together with the necessary information). * * This function loops over all examples, and for each example updates the *statistic for this test. Afterwards it loops over all the tests and checks * whether this test (together with its statistic etc.) is approved by the *PrePruner. If so, the heuristic value is calculated. The test with the * highest heuristic value is returned in the end. If no such test is found, *an empty TestInfo object is returned. */ template std::pair, Statistic> findBestTest(NodeInfo& curNodeInfo, const TargetAttributeExtractor& extractor); private: Heuristic _heuristic; TestCollectionWrapper _testCollectionWrapper; ExampleDBWrapper _exampleDBWrapper; PrePruner _prePruner; int _maxNumberOfExamples; boost::random::mt19937 _generator; }; } // decision_forest namespace } // prediction namespace } // mips namespace #include "probabilisticSplittingCriterium.hxx" #endif // MIPS_PREDICTION_DECISIONFOREST_PROBABILISTICSPLITTINGCRITERIUM_HPP mips-1.0/mips/prediction/concepts/0000755002402200254040000000000012520407731016563 5ustar thomasfdtaigmips-1.0/mips/prediction/concepts/attributeExtractor.hpp0000644002402200254040000000406212520407731023175 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_CONCEPTS_ATTRIBUTEEXTRACTOR_HPP #define MIPS_PREDICTION_CONCEPTS_ATTRIBUTEEXTRACTOR_HPP #include #include "../attributeType.hpp" namespace concepts { namespace mips { namespace prediction { typedef void Example; typedef void ResultType; /*! * \brief The AttributeExtractor is a concept class defining the structure for an attribute extractor. * * AttributeExtractor should inherit from std::unary_function, and in such a way exposing: * \li Example as argument_type, * \li Result as result_type */ struct AttributeExtractor : public std::unary_function { /*! * \brief AttributeType should be a typedef of one of the following types: * \li mips::prediction::NominalAttributeType * \li mips::prediction::OrdinalAttributeType * \li mips::prediction::NumericalAttributeType * */ typedef void AttributeType; /*! * \brief argument_type exposes the actual type of the example */ typedef Example argument_type; /*! * \brief result_type exposes the actual type of the result. * * Some algorithms accept weighted examples, in that case result_type will be a pair . */ typedef ResultType result_type; /*! * \brief operator () extract the attribute from an example * \param example the example to extract the attribute from * \return a value of type ResultType * * This function should be constant, it should not change the AttributeExtractor nor the example. */ ResultType operator() (const Example & example) const; }; } // prediction namespace } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_CONCEPTS_ATTRIBUTEEXTRACTOR_HPP mips-1.0/mips/prediction/concepts/modelPrinter.hpp0000644002402200254040000000201112520407731021732 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_CONCEPTS_MODELPRINTER_HPP #define MIPS_PREDICTION_CONCEPTS_MODELPRINTER_HPP namespace concepts { namespace mips { namespace prediction { /*! * \brief The ModelPrinter concept defines the structure if a ModelPrinter to be used with mips::prediction::ModelPrinter * * Essentialy the model printer is a functor, passing a const model and a stream */ struct ModelPrinter { /*! * \brief operator () prints out the model * \param model The model to be printer * \param stream The stream to be printed to */ void operator()(const Model & model, std::ostream & stream) const; }; } // prediction namespacee } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_CONCEPTS_MODELPRINTER_HPP mips-1.0/mips/prediction/concepts/modelClassifier.hpp0000644002402200254040000000360612520407731022406 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2014 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_PREDICTION_CONCEPTS_MODELPREDICTION_HPP #define MIPS_PREDICTION_CONCEPTS_MODELPREDICTION_HPP namespace concepts { namespace mips { namespace prediction { typedef void Model; typedef void Example; typedef void ClassificationType; /*! * \brief The ModelClassifier concept defines the structure for a ModelClassifier, to be used with mips::prediction::Classifier. * * A model prediction is constructed for one model, but (possibly) many examples. Therefore in the constructor the ModelClassifier has the * occasion to preprocessing according to the model. The operator() function should classify a model. */ struct ModelClassifier { /*! * \brief result_type ModelClassifier exposes its result_type. The example-type is not necessary as a model may classify many example types. */ typedef ClassificationType result_type; /*! * \brief ModelClassifier Constructs a prediction for this model. * \param model This allows the model prediction to "prepare" itself for classifying with this model. */ ModelClassifier(const Model & model); /*! * \brief operator () * \param model The model to be used. This is garanteed to be the same model as in the constructor. * \param example The example to classify * \return The classification of the example according to the model * * This function should not change the model prediction. */ result_type operator()(const Model & model, const Example & example) const; }; } // prediction namespace } // mips namespace } // concepts namespace #endif // MIPS_PREDICTION_CONCEPTS_MODELPREDICTION_HPP mips-1.0/mips/prediction/CMakeLists.txt0000644002402200254040000000077412520407731017515 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) set (SOURCES ) set (HEADERS namespace.hpp # different types of target attributes concepts/attributeExtractor.hpp attributeType.hpp # generic class for classifying with a learned model concepts/modelClassifier.hpp classifier.hpp # generic class of printing a learned model concepts/modelPrinter.hpp modelPrinter.hpp ) set (SUBDIRECTORIES decisionTree decisionForest ) INCLUDE_ALL_SUBDIRECTORIES_HEADER_SOURCES(SUBDIRECTORIES) mips-1.0/mips/core/0000755002402200254040000000000012520407731013535 5ustar thomasfdtaigmips-1.0/mips/core/utils.cpp0000755002402200254040000000233412520407731015406 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #include "utils.hpp" #include #include #include using std::string; using std::vector; using std::cout; // template // void printVector(const vector v) { // cout << '['; // for(int i=0; i0) cout << ", "; // cout << v[i]; // } // cout << ']'; // } /* * Strips aways trailing whitespace characters from a string */ string& rtrim(string& str) { //string str ("erase trailing white-spaces \n"); string whitespaces (" \t\f\v\n\r"); int found=0; found=str.find_last_not_of(whitespaces); if (found!=string::npos) str.erase(found+1); else str.clear(); // str is all whitespace //cout << '"' << str << '"' << endl; return str; } /* * Rounds a floating point number to specified number of decimal places. */ float round_float(float x, int y) { float e = pow(10, y); return (x>=0 ? ((long)(x*e+0.5))/e : ((long)(x*e-0.5))/e ); } mips-1.0/mips/core/group/0000755002402200254040000000000012520407731014671 5ustar thomasfdtaigmips-1.0/mips/core/group/binIrredPolyField.hpp0000644002402200254040000001614512520407731020757 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_CORE_GROUP_BINIRREDPOLYFIELD_HPP #define MIPS_CORE_GROUP_BINIRREDPOLYFIELD_HPP #include #include #include "irredPolyList.hpp" #include #include namespace mips { namespace groupalgebra { /* * ******************************************************************** * Implements a field of irreducible polynomials (modulo some * irreducible polynomial) with characteristic 2, using binary bits. * If no size is given in the constructor then the default is: * x^17+x^3+1 as the irreducible polynomial; and * every polynomial (with no monomial repeating due to the * characteristic) of degree at most x^16 as its elements. * ******************************************************************** */ /* * We're using all 32 bits for our field. Based on the formula: log_2(k)+3, * the field can support patterns of size [2, 2**28] (2**28 = 268,435,456), * if using 32-bits. * CURRENTLY, we use 8-bits (boost::uint8_t below), meaning we can support * patterns of size [2,255-3]. */ class Field { public: /* Everything in this header needs to be checked if type 'value' ever * changes */ typedef boost::uint8_t value; value fieldOutOfBounds; // -- used in the old miner as a marker (e.g. to mark cells that no need computing) private: /** Following fields are used in the Peasant's algorithm for multiplication for this field */ value modulo; value highestBit; // -- highest bit set in the field of our chosen size. value mask; value irredPoly; // -- the irreducible polynomial default: irreducible polynomial x^17+x^3+1 value fieldMax; //maximum value of the field /** Size 'l' of the field */ unsigned m_l; public: Field(int size=17) { //Throw exception/error if psize<2 or psize>252 (NOT psize>2**28 anymore -- not 31, because of the +3 in the formula l=log_2(k)+3) assert(size>1 && size<253); //m_l = size = (int) round_float((log(psize)/log(2)) + 3, 0); m_l = size; irredPoly = irred_poly_list[size-POLY_OFFSET][0]; //default: irreducible polynomial x^17+x^3+1 fieldMax = irred_poly_list[size-POLY_OFFSET][1]; //maximum size of the field (we won't allow 0 and 1) modulo = irredPoly ^ (value) pow(2, (value)(log(irredPoly) / log(2)) ); // 0 the left-most bit highestBit = pow(2, (value) (log(fieldMax) / log(2)) ); // get the highest bit mask = highestBit-1; fieldOutOfBounds = fieldMax+1; } /* Returns the size of the field. */ int getFieldSize() const; /* Returns the maximum value representable by this field. */ value getFieldMax() const; value zero() const; void zero(value &val) const; value add(value a, value b) const; value subtract(value a, value b) const; value multiply(value val1, value val2) const; std::string toStrInt(value val) const; std::string toStrBinary(value val) const; std::string toStrPoly(value val) const; std::string toStrPoly_old(value val) const; }; inline Field::value Field::getFieldMax() const { return this->fieldMax; } inline int Field::getFieldSize() const { return this->m_l; } inline Field::value Field::zero() const { return 0; } inline void Field::zero(Field::value &val) const { val=0; } inline Field::value Field::add(Field::value a, Field::value b) const { //return a+b; return (a ^ b); } inline Field::value Field::subtract(Field::value a, Field::value b) const { //return a-b; return add(a,b); } /* * Peasants algorithm for modulo multiplication. */ inline Field::value Field::multiply(Field::value val1, Field::value val2) const { //return val1*val2 //if val1<>0 and val2<>0: // print 'multiplying', '{0:b}'.format(val1), '{0:b}'.format(val2) // std::cout << "irredPoly " << irredPoly // << " log(irredPoly) / log(2) " << log(irredPoly) / log(2) // << " (int) log(irredPoly) / log(2) " << (value) (log(irredPoly) / log(2)) // << " pow(2,log_2(irredPoly) " << (value) pow(2, (value) (log(irredPoly) / log(2)) ) // << " final " << ( irredPoly ^ (value) pow(2, (value) (log(irredPoly) / log(2))) ) // << std::endl; // std::cout << "IN OUT highestbit,mask,modulo, max,outbounds: " << this->highestBit << ',' // << this->mask << ',' // << this->modulo << ',' // << this->fieldMax << ',' // << this->fieldOutOfBounds << std::endl; // std::cout << "multiplying: " << val1 << ',' << val2 << std::endl; int p = 0, highest_set=0; //, i=0; while(val1!=0 and val2!=0) { //i++; if((val2 & 0x001)!=0) p = p ^ val1; //std::cout << "highestset,val1,and:" << highest_set << ',' << val1 << ',' << (val1 & this->highestBit) << std::endl; highest_set = val1 & this->highestBit; //std::cout << "newhighestset:" << highest_set << std::endl; //std::cout << "val1,mask,and,<<:" << val1 << ',' << this->mask << ',' << (val1 & this->mask) << ',' << ((val1 & this->mask) << 1) << std::endl; val1 = (val1 & this->mask) << 1; //std::cout << "newval1:" << val1 << std::endl; // std::cout << "IN WHILE highestbit,mask,modulo, max,outbounds: " << this->highestBit << ',' // << this->mask << ',' // << this->modulo << ',' // << this->fieldMax << ',' // << this->fieldOutOfBounds << std::endl; if(highest_set != 0) { //std::cout << "IN IF highestset,modulo,val1,^:" << highest_set << ',' << this->modulo << ',' << val1 << ',' << (val1 ^ this->modulo) << std::endl; //std::cout.flush(); val1 = val1 ^ this->modulo; } val2 = val2 >> 1; //std::cout << "it " << i << " (highestset,p,val1,val2):" << highest_set << ',' << p << ',' << val1 << ',' << val2 << std::endl; } //s = '{0:b}'.format(p) //if len(s) > 18: // print >> sys.stderr, 'product is',len(s),'bits long.\n', s //std::cout << "result: " << p << " in " << i << " iterations." << std::endl; return p; } } //namespace groupalgebra } //namespace mips /** ********************************************** This should be rationalized (renamed and/or moved somewhere else) ********************************************** */ /* * Parallel bit counting algorithm (no of bits set) * computes v^T*b_j eigen value * * http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel * http://stackoverflow.com/questions/407587/python-set-bits-count-popcount */ inline mips::groupalgebra::Field::value num_bits_set(mips::groupalgebra::Field::value i) { i = i - ((i >> 1) & 0x55555555); i = (i & 0x33333333) + ((i >> 2) & 0x33333333); return ( ((i + (i >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24; } #endif //MIPS_CORE_GROUP_BINIRREDPOLYFIELD_HPP mips-1.0/mips/core/group/test/0000755002402200254040000000000012520407731015650 5ustar thomasfdtaigmips-1.0/mips/core/group/test/testGF2Labels.cpp0000644002402200254040000004011012520407731020751 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ /******************************************************************* * Unit test for GF2l_Z2k_Dynamic_LabelOptimized ********************************************************************/ #include "mips/core/group/GF2l_Z2k.hpp" #include using namespace std; using namespace mips::groupalgebra; //Test random. // 2 -- i.e. 2^2 // 3 -- i.e. 2^3 // 5 -- i.e. 2^5 int testRandom() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing random* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfd; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1; gfd.initialize(k,l); unsigned tmp2K; // 2 -- i.e. 2^2 tmp2K = (1 << 2); val1 = gfd.random(tmp2K); for(int i=0; i=tmp2K) {cout << "Failed random1 for 2^k: " << tmp2K << ", val: "<< gfd.toStrInt(val1) << endl; return -1;} gfd.zero(val1); gfd.random(val1, tmp2K); for(int i=0; i=tmp2K) {cout << "Failed random2 for 2^k: " << tmp2K << ", val: "<< gfd.toStrInt(val1) << endl; return -1;} // 3 -- i.e. 2^3 tmp2K = (1 << 3); val1 = gfd.random(tmp2K); for(int i=0; i=tmp2K) {cout << "Failed random1 for 2^k: " << tmp2K << ", val: "<< gfd.toStrInt(val1) << endl; return -1;} gfd.zero(val1); gfd.random(val1, tmp2K); for(int i=0; i=tmp2K) {cout << "Failed random2 for 2^k: " << tmp2K << ", val: "<< gfd.toStrInt(val1) << endl; return -1;} // 5 -- i.e. 2^5 tmp2K = (1 << 3); val1 = gfd.random(tmp2K); for(int i=0; i=tmp2K) {cout << "Failed random1 for 2^k: " << tmp2K << ", val: "<< gfd.toStrInt(val1) << endl; return -1;} gfd.zero(val1); gfd.random(val1, tmp2K); for(int i=0; i=tmp2K) {cout << "Failed random2 for 2^k: " << tmp2K << ", val: "<< gfd.toStrInt(val1) << endl; return -1;} return 0; } //Test transform. Transforms a single (W_0 + v_i) value //1 << 3 //111 << 1 //11001 << 2 int testTransform() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing transform* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfd; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1, test; gfd.initialize(k,l); //void transform(value &result, unsigned shift); //1 << 3 gfd.zero(val1); gfd.zero(test); val1[0] = 1; val1[1] = 1; test[0] = test[8] = 1; gfd.transform(val1, 3); if(val1!=test) { cout << "Failed transform test1 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } //111 << 1 gfd.zero(val1); gfd.zero(test); val1[0] = 2; val1[7] = 2; test[0] = test[14] = 2; gfd.transform(val1, 1); if(val1!=test) { cout << "Failed transform test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } //11001 << 2 gfd.zero(val1); gfd.zero(test); val1[0] = 3; val1[25] = 3; test[0] = test[100] = 3; gfd.transform(val1, 2); if(val1!=test) { cout << "Failed transform test 3val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } return 0; } //test clearbits // 111 --> 011 // 111 --> 110 // 1111 --> 0011 // 11111 --> 10011 // 11111 --> 10111 --> 10101 //void clearbits(value &result, std::vector< std::pair > toclear); //void clearbits(value &result, gflong size, gflong startpos); int testClearBits() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing clearbits* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfd; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1, test; gfd.initialize(k,l); // 111 --> 011 gfd.zero(val1); gfd.zero(test); val1[0] = val1[7] = 1; test[0] = test[3] = 1; gfd.clearbits(val1, 1, 2); //if(val1!=test) { cout << "Failed clearbits test1 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 111 --> 110 gfd.zero(val1); gfd.zero(test); val1[0] = val1[7] = 1; test[0] = test[6] = 1; gfd.clearbits(val1, 1, 0); //if(val1!=test) { cout << "Failed clearbits test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 1111 --> 0011 gfd.zero(val1); gfd.zero(test); val1[0] = val1[15] = 3; test[0] = test[3] = 3; gfd.clearbits(val1, 2, 2); //if(val1!=test) { cout << "Failed clearbits test3 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 11111 --> 10011 gfd.zero(val1); gfd.zero(test); val1[0] = val1[31] = 5; //GF2l_Z2k_Dynamic_LabelOptimized::value val2; //gfd.zero(val2); //val2[0] = val2[7] = 3; //gfd.multiply(val1, val2); test[0] = test[19] = 5; //cout << "before clear " << gfd.toStrPolyBinary(val1) << endl; gfd.clearbits(val1, 2, 2); if(val1!=test) { cout << "Failed clearbits test4 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 11111 --> 10111 --> 10101 typedef std::pair ClearPos; gfd.zero(val1); gfd.zero(test); val1[0] = val1[31] = 4; vector< ClearPos > toclear; toclear.push_back(ClearPos(1,3)); //ClearPos(size, startpos); toclear.push_back(ClearPos(1,1)); gfd.clearbits(val1, toclear); test[0] = test[21] = 4; //if(val1!=test) { cout << "Failed clearbits test5 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } return 0; } //test shiftAfterClear // 110 --> 011 // 110011 --> 001111 // 10101 --> 01011 --> 00111 //void shiftAfterClear(value &result, unsigned shiftfrom, unsigned shiftto); int testShiftAfterClear() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing shiftbits* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfd; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1, test; gfd.initialize(k,l); // 110 --> 011 gfd.zero(val1); gfd.zero(test); val1[0] = val1[6] = 1; test[0] = test[3] = 1; gfd.shiftAfterClear(val1, 1, 0); if(val1!=test) { cout << "Failed shiftbits test1 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 110011 --> 1111 gfd.zero(val1); gfd.zero(test); val1[0] = val1[51] = 3; test[0] = test[15] = 3; gfd.shiftAfterClear(val1, 4, 2); if(val1!=test) { cout << "Failed shiftbits test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 10101 --> 01011 --> 00111 gfd.zero(val1); gfd.zero(test); val1[0] = val1[21] = 3; test[0] = test[7] = 3; gfd.shiftAfterClear(val1, 2, 1); gfd.shiftAfterClear(val1, 3, 2); if(val1!=test) { cout << "Failed shiftbits test3 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } return 0; } //test permute-blocks // 110 --> 011 // 00 01 11 --> 11 00 01 (shift 0 to 4 and 2 to 0) // 01 11 11 --> 11 11 01 (shift 0 to 4 and 4 to 0 -- don't touch middle) //void shiftAfterClear(value &result, unsigned shiftfrom, unsigned shiftto); int testPermuteBlocks() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing permutation-blocks* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfd; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1, val2, test; std::vector< GF2Permute > permutations; gfd.initialize(k,l); // 110 --> 011 permutations.clear(); gfd.zero(val1); gfd.zero(test); val1[0] = val1[6] = 4; test[0] = test[3] = 4; permutations.push_back( GF2Permute(2, 1, 0) ); gfd.permuteBlocks(val1, permutations); if(val1!=test) { cout << "Failed permute-blocks test1 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 00 01 11 --> 11 00 01 (shift 0 to 4 and 2 to 0) permutations.clear(); gfd.zero(val1); gfd.zero(test); val1[0] = val1[7] = 4; test[0] = test[49] = 4; permutations.push_back( GF2Permute(2, 0, 4) ); permutations.push_back( GF2Permute(2, 2, 0) ); //cout << "Before permute test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; gfd.permuteBlocks(val1, permutations); if(val1!=test) { cout << "Failed permute-blocks test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 01 11 11 --> 11 11 01 (shift 0 to 4 and 4 to 0 -- don't touch middle) permutations.clear(); gfd.zero(val1); gfd.zero(test); val1[0] = val1[31] = 2; test[0] = test[61] = 2; permutations.push_back( GF2Permute(2, 0, 4) ); permutations.push_back( GF2Permute(2, 4, 0) ); gfd.permuteBlocks(val1, permutations); if(val1!=test) { cout << "Failed permute-blocks test3 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // Test multiple shifts at once (shift 0 to 3 and 3 to 0) // 00 00 10 --> 01 00 00 // 01 00 00 --> 0 01 00 // 01 00 10 --> 01 01 00 permutations.clear(); gfd.zero(val1); gfd.zero(val2); gfd.zero(test); val1[0] = val1[2] = val1[16] = val1[18] = 31; test[0] = test[16] = test[4] = test[20] = 31; val2 = val1; permutations.push_back( GF2Permute(3, 2, 0) ); permutations.push_back( GF2Permute(2, 0, 3) ); gfd.permuteBlocks(val1, permutations); if(val1!=test) { cout << "Failed permute-blocks test4 original: " << gfd.toStrPolyBinary(val2) << ", val1: " << gfd.toStrPolyBinary(val1) << ", (intended) result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } /** Negative tests - no bits change */ // 00 11 00 --> 00 11 00 (shift 0 to 4 and 4 to 0 -- don't touch middle) permutations.clear(); gfd.zero(val1); gfd.zero(test); val1[0] = val1[12] = 3; test[0] = test[12] = 3; permutations.push_back( GF2Permute(2, 0, 4) ); permutations.push_back( GF2Permute(2, 4, 0) ); gfd.permuteBlocks(val1, permutations); if(val1!=test) { cout << "Failed permute-blocks test5 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 11 00 00 --> 11 00 00 (shift 0 to 2 and 2 to 0 -- don't touch front) permutations.clear(); gfd.zero(val1); gfd.zero(test); val1[0] = val1[48] = 3; test[0] = test[48] = 3; permutations.push_back( GF2Permute(2, 0, 2) ); permutations.push_back( GF2Permute(2, 2, 0) ); gfd.permuteBlocks(val1, permutations); if(val1!=test) { cout << "Failed permute-blocks test6 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } return 0; } //test permute-blocks // 110 --> 011 // 00 01 11 --> 11 00 01 (shift 1 to 5 and 3 to 1) // 01 11 11 --> 11 11 01 (shift 1 to 5 and 5 to 1 -- don't touch middle) //void shiftAfterClear(value &result, unsigned shiftfrom, unsigned shiftto); int testPermuteBits() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing permutation-bits* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfd; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1, test; std::vector< int > permutations; gfd.initialize(k,l); // 110 --> 011 permutations.clear(); permutations.resize(2); gfd.zero(val1); gfd.zero(test); val1[0] = val1[6] = 4; test[0] = test[3] = 4; permutations[0]=1; permutations[1]=2; gfd.permuteBits(val1, permutations); if(val1!=test) { cout << "Failed permute-bits test1 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 00 01 11 --> 11 00 01 (shift 1 to 5 and 3 to 1) permutations.clear(); permutations.resize(6); gfd.zero(val1); gfd.zero(test); val1[0] = val1[7] = 4; test[0] = test[49] = 4; permutations[0]=2; permutations[1]=3; permutations[2]=-1; permutations[3]=-1; permutations[4]=0; permutations[5]=1; //cout << "Before permute test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; gfd.permuteBits(val1, permutations); if(val1!=test) { cout << "Failed permute-bits test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 01 11 11 --> 11 11 01 (shift 1 to 5 and 5 to 1 -- don't touch middle) permutations.clear(); permutations.resize(6); gfd.zero(val1); gfd.zero(test); val1[0] = val1[31] = 2; test[0] = test[61] = 2; permutations[0]=4; permutations[1]=5; permutations[2]=2; permutations[3]=3; permutations[4]=0; permutations[5]=1; gfd.permuteBits(val1, permutations); if(val1!=test) { cout << "Failed permute-bits test3 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } return 0; } //test void resize(value &result, gflong size); // 011 --> 11 // 000111 --> 111 //// 10101 --> 01011 --> 00111 int testResize() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing resize* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfd, gfd2; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1, test; gfd.initialize(k,l); gfd.zero(test); // 011 --> 11 gfd.zero(val1); val1[0] = val1[3] = 1; gfd.resize(val1, (1<<2)); gfd2.initialize(2, l); gfd2.zero(test); test[0] = test[3] = 1; if(val1!=test) { cout << "Failed resize test1 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } // 000111 --> 111 gfd.zero(val1); val1[0] = val1[7] = 1; gfd.resize(val1, (1<<3)); gfd2.initialize(3, l); gfd2.zero(test); test[0] = test[7] = 1; if(val1!=test) { cout << "Failed resize test2 val1: " << gfd.toStrPolyBinary(val1)<< ", result: "<< gfd.toStrPolyBinary(test) << endl; return -1; } return 0; } //Test new multiply (unequal value lenght arrays) //void multiply(value &v, const value &a, const value &b) const; // 000111 * 111 -- linearly dependent // 1100 * 11 -- linearly independent // 1101 * 101011 -- linearly independent int testMultiply() { unsigned k = 7; unsigned l = (unsigned) round_float(log(k)/log(2) + 3, 0); cout << "*Testing multiply* for " << "k: " << k << " l: " << l << endl; GF2l_Z2k_Dynamic_LabelOptimized<> gfdbig, gfdsmall; GF2l_Z2k_Dynamic_LabelOptimized<>::value val1, val2, test; gfdbig.initialize(k,l); gfdbig.zero(test); // 000111 * 111 -- linearly dependent gfdsmall.initialize(3, l); gfdbig.zero(val1); gfdsmall.zero(val2); gfdbig.zero(test); val1[0] = val1[3] = val2[0] = val2[3] = 5; test[0] = test[3] = 0; gfdbig.multiply(val1, val2); if(val1!=test) { cout << "Failed multiply test1 val1: " << gfdbig.toStrPolyBinary(val1)<< ", result: "<< gfdbig.toStrPolyBinary(test) << endl; return -1; } // 1100 * 11 -- linearly independent gfdsmall.initialize(2, l); gfdbig.zero(val1); gfdsmall.zero(val2); gfdbig.zero(test); val1[0] = val1[10] = 3; val2[0] = val2[3] = 3; test[0] = test[3] = test[9] = test[10] = 5; gfdbig.multiply(val1, val2); if(val1!=test) { cout << "Failed multiply test2 val1: " << gfdbig.toStrPolyBinary(val1)<< ", result: "<< gfdbig.toStrPolyBinary(test) << endl; return -1; } // 1101 * 101011 -- linearly independent gfdsmall.initialize(4, l); gfdsmall.zero(val1); gfdbig.zero(val2); gfdbig.zero(test); val1[0] = val1[11] = 5; val2[0] = val2[43] = 5; test[0] = test[11] = test[32] = test[43] = 17; gfdbig.multiply(val2, val1); if(val2!=test) { cout << "Failed multiply test3 val2: " << gfdbig.toStrInt(val2)<< ", result: "<< gfdbig.toStrInt(test) << endl; return -1; } return 0; } int main(int argc, char* argv[]) { //GF2l_Z2k_Dynamic_LabelOptimized::value val1, resultTest; //test random if(testRandom()!=0 || testTransform()!=0 || testClearBits()!=0 || testShiftAfterClear()!=0 || testPermuteBlocks()!=0 || testPermuteBits()!=0 || testResize()!=0 || testMultiply()!=0) return -1; return 0; } mips-1.0/mips/core/group/test/testGF2l_Z2k.cpp0000644002402200254040000001220112520407731020530 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ /******************************************************************* * Unit test for GF2l_Z2k_Dynamic and GF2l_Z2k_Static. ********************************************************************/ #include #include "../GF2l_Z2k.hpp" using namespace std; using namespace mips::groupalgebra; int main(int argc, char* argv[]) { /** Test 1 */ /** Precomputed values val1: (x+1)[011] + (x)[110] val2: (1)[010] + (x)[011] */ /** test static */ cout << "1)" << endl; GF2l_Z2k_Static<3, 2> gf; GF2l_Z2k_Static<3, 2>::value val1, val2, val3, resultS; gf.zero(val1); gf.zero(val2); val1[6] = 2; val1[3] = 3; val2[3] = 2; val2[2] = 1; cout << "static val1: " << gf.toStrInt(val1) << endl; cout << "static val2: " << gf.toStrInt(val2) << endl; //result: (1)[010] + (1)[011] + (x)[110] gf.zero(val3); gf.zero(resultS); resultS[2]=1; resultS[3]=1; resultS[6]=2; gf.add(val3, val1, val2); cout << "val1+val2: " << gf.toStrPolyBinary(val3) << endl; if(!gf.areEqual(val3,resultS)) { cout << "Test1 static failed on addition." << endl; return -1;} //result: (1)[000] + (x+1)[001] + (x)[100] + (x+1)[101] gf.zero(val3); gf.zero(resultS); resultS[0]=1; resultS[1]=3; resultS[4]=2; resultS[5]=3; gf.multiply(val3, val1, val2); cout << "val1*val2: " << gf.toStrPolyBinary(val3) << endl; if(!gf.areEqual(val3,resultS)) {cout << "Test1 static failed on multiplication." << endl; return -1; } /** test dynamic */ cout << endl; GF2l_Z2k_Dynamic gfd; gfd.initialize(3,2); GF2l_Z2k_Dynamic::value vald1, vald2, result, resultD; gfd.zero(vald1); gfd.zero(vald2); vald1[6] = 2; vald1[3] = 3; vald2[3] = 2; vald2[2] = 1; cout << "dynamic val1: " << gfd.toStrPolyBinary(vald1) << endl; cout << "dynamic val2: " << gfd.toStrPolyBinary(vald2) << endl; //result: (1)[010] + (1)[011] + (x)[110] gfd.zero(result); gfd.zero(resultD); resultD[2]=1; resultD[3]=1; resultD[6]=2; gfd.add(result,vald1,vald2); cout << "val1+val2: " << gfd.toStrPolyBinary(result) << endl; if(!(result==resultD)) { cout << "Test1 dynamic failed on addition." << endl; return -1;} //result: (1)[000] + (x+1)[001] + (x)[100] + (x+1)[101] gfd.zero(resultD); resultD[0]=1; resultD[1]=3; resultD[4]=2; resultD[5]=3; gfd.multiply(result,vald1,vald2); cout << "val1*val2: " << gfd.toStrPolyBinary(result) << endl; if(!(result==resultD)) {cout << "Test1 dynamic failed on multiplication." << endl; return -1; } /** Test 2.1 */ /** Precomputed values val1: (x+1)[000] + (x+1)[110] val2: (x)[000] + (x)[110] */ /** test static */ cout << endl << endl; cout << "2)" << endl; gf.zero(val1); gf.zero(val2); val1[0] = 3; val1[6] = 3; val2[0] = 2; val2[6] = 2; cout << "static val1: " << gf.toStrPolyBinary(val1) << endl; cout << "static val2: " << gf.toStrPolyBinary(val2) << endl; //result: 0 gf.zero(val3); gf.zero(resultS); gf.multiply(val3, val1, val2); cout << "val1*val2: " << gf.toStrPolyBinary(val3) << endl; if(!gf.areEqual(val3,resultS)) {cout << "Test2.1 static failed on multiplication." << endl; return -1; } /** Test 2.2 */ /** test dynamic */ cout << endl; gfd.zero(vald1); gfd.zero(vald2); vald1[0] = 3; vald1[6] = 1; vald2[0] = 3; vald2[6] = 1; cout << "dynamic val1: " << gfd.toStrPolyBinary(vald1) << endl; cout << "dynamic val2: " << gfd.toStrPolyBinary(vald2) << endl; //result: 0 gfd.zero(result); gfd.zero(resultD); resultD[0] = 3; gfd.multiply(result, vald1, vald2); cout << "val1*val2: " << gfd.toStrPolyBinary(result) << endl; if(!(result==resultD)) {cout << "Test2.2 dynamic failed on multiplication." << endl; return -1; } /** Test 3 */ /** Precomputed values val1: (x^7+x^3+x)[000] + (x^15+x^7+x+1)[010] + (x^15+x^7+x+1)[011] + (x^7+x^3+x)[100] val2: (x^7+x^3)[000] + (x+1)[010] + (x^7+x+1)[011] + (x^3)[100] */ //(14)[0] + (27)[2] + (27)[3] + (14)[4] //(12)[0] + (3)[2] + (11)[3] + (4)[4] int P_SIZE=3; int k = P_SIZE, l = (int) round_float((log(P_SIZE)/log(2)) + 3, 0); gfd.initialize(k,l); cout << endl; gfd.zero(vald1); gfd.zero(vald2); vald1[0] = vald1[4] = 14; vald1[2] = vald1[3] = 27; //vald2[0] = vald2[4] = 14; //vald2[2] = vald2[3] = 27; vald2[0] = 12; vald2[2] = 3; vald2[3] = 11; vald2[4] = 4; cout << "dynamic val1: " << gfd.toStrPolyBinary(vald1) << endl; cout << "dynamic val2: " << gfd.toStrPolyBinary(vald2) << endl; //result: (x^15+x^7+1)[000] + (x^3+x)[001] + (x^15+x^3+x+1)[010] + (x^7)[011] + (x^15+x^7+x^3+x+1)[100] + (x^15+1)[110] + (x^7+x^3+x)[111] gfd.zero(result); gfd.zero(resultD); resultD[0]=25; resultD[1]=6; resultD[2]=23; resultD[3]=8; resultD[4]=31; resultD[6]=17; resultD[7]=14; gfd.multiply(result, vald1, vald2); cout << "val1*val2: " << gfd.toStrPolyBinary(result) << endl; if(!(result==resultD)) {cout << "Test3 dynamic failed on multiplication." << endl; return -1; } return 0; } mips-1.0/mips/core/group/test/CMakeLists.txt0000644002402200254040000000025412520407731020411 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) CREATE_TEST(NAME testGF2l_Z2k SOURCES testGF2l_Z2k.cpp) CREATE_TEST(NAME testGF2Labels SOURCES testGF2Labels.cpp) mips-1.0/mips/core/group/GF2l_Z2k.hpp0000644002402200254040000006145112520407731016671 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef __CORE_GROUP_GF2L_Z2K_H__ #define __CORE_GROUP_GF2L_Z2K_H__ #include #include "binIrredPolyField.hpp" #include //#include #include #include "mips/core/utils.hpp" #include #include namespace mips { namespace groupalgebra { /* * ******************************************************************** * Implents a ring GF_{2}^{l}Z_{2}^k, where GF_{2}^{l} is a finite * field of polynomials (with of characteristic 2) and Z_{2}_^k is a * group consisting of binary vectors with component-wise addition * modulo 2 as the group operation. * ******************************************************************** */ typedef unsigned long _GFLong; //template //std::string toString(const T &v, const _GFLong size, const Field f, const int switchval); template std::string toString(const T &v, const unsigned k, const _GFLong size, const Field f, const int switchval) { std::stringstream str; bool nonzero=false; for(int i=0; i inbits = i; boost::dynamic_bitset<> inbits(k, i); switch(switchval) { case 1: case 2: str << inbits; break; case 3: default: str << i; } str << ']'; }//end if }//end for if(!nonzero) str << "0"; return str.str(); } template class GF2l_Z2k_Static { private: typedef unsigned long gflong; Field m_field; public: static const gflong twoPowK = 1 << k; typedef Field::value value[twoPowK]; //typedef struct val2 {Field::value x[twoPowK]; Field::value& operator[](const int idx) {return x[idx];} } value2; typedef Field::value fieldVal; typedef boost::random::mt11213b RandomEngine; typedef boost::random::uniform_int_distribution RandomDistribution; typedef boost::random::variate_generator RandomGenerator; private: //boost::random::variate_generator randgen(RandomEngine(), RandomDistribution()); RandomGenerator randgen; unsigned int m_seed; public: GF2l_Z2k_Static() : randgen(RandomEngine(), RandomDistribution()) { m_field = Field(l); //reng = Random(); rdist = RandomDistribution(); //no need for this assert( twoPowK - 1 <= randgen.engine().max()); //randgen = std::bind(rdist, reng); //randgen(reng, rdist); m_seed = randgen.engine().default_seed; } inline unsigned int randomint(unsigned int from, unsigned int to) { return (randgen() % (to - from + 1)) + from; } void setSeed(unsigned seed) { m_seed = seed; randgen.engine().seed(seed); } unsigned getSeed() const { return m_seed; } void zero(value& v) const {for(gflong i=0;i(v, k, twoPowK, m_field, 1); } std::string toStrPolyBinary(const value& v) const { return toString(v, k, twoPowK, m_field, 2); } std::string toStrPoly(const value& v) const { return toString(v, k, twoPowK, m_field, 3); } std::string toStrInt(const value& v) const { return toString(v, k, twoPowK, m_field, 4); } bool areEqual(const value& a, const value& b) { for(gflong i=0; i > class GF2l_Z2k_Dynamic_Generic { public: typedef boost::random::mt11213b RandomEngine; //typedef _RandEng RandomEngine; typedef _RandDist RandomDistribution; typedef boost::random::variate_generator RandomGenerator; //typedef boost::random::variate_generator<_RandEng, _RandDist> Random; typedef std::vector value; typedef Field::value fieldVal; //GF2l_Z2k_val allvectors; protected: Field m_field; unsigned int m_k, m_l; unsigned long m_2powk; unsigned int m_seed; typedef unsigned long gflong; //RandomEngine reng; //RandomDistribution rdist; mutable RandomGenerator randgen; public: GF2l_Z2k_Dynamic_Generic() : randgen(RandomGenerator(RandomEngine(), RandomDistribution())) {} void initialize(unsigned k, unsigned l) { m_k = k; m_l = l; m_2powk = (1 << k); m_field=Field(m_l); //reng = RandomEngine(); //use the default seed. //rdist = RandomDistribution(); m_seed = randgen.engine().default_seed; //reng.default_seed; randgen.engine().seed(m_seed); assert( m_field.getFieldMax() <= randgen.engine().max() ); assert( m_2powk-1 <= randgen.engine().max() ); } inline unsigned int randomInt (unsigned int from, unsigned int to) const { //return ( const_cast< GF2l_Z2k_Dynamic_Generic* >(this)->randgen() % (to - from + 1)) + from; return ( randgen() % (to - from + 1)) + from; //unsigned int toreturn = ( randgen() % (to - from + 1)) + from; //std::cout << "returning rand " << toreturn << " in gf2 " << this << std::endl; //return toreturn; } void setSeed(unsigned seed) { m_seed = seed; // set seed not working correctly here //reng.seed(seed); //std::cout << "Setting seed " << seed << " in gf2 " << this << std::endl; randgen.engine().seed(seed); } unsigned int getSeed() const { return m_seed; } unsigned getFieldSize() const { return m_l; } unsigned getVectorSize() const { return m_k; } //void setRandom(RandomEngine &eng) { reng = eng; rangen(RandomGenerator(reng, rdist)); } //RandomEngine& getRandom() { return reng; } //void setRandomDist(RandomDistribution &dist) { rdist = dist; rangen(RandomGenerator(reng, rdist)); } //RandomDistribution& getRandomDist() { return rdist; } value zero() const {value v = value(m_2powk, 0); return v;} //return a new zero element void zero(value& v) const { //set a polynomial to zero v.assign(m_2powk, 0); } value one() const { // return a new polynomial: W_0 value v = value(m_2powk, 0); v[0]=1; return v; } void one(value& v) const { //set a polynomial to: W_0 zero(v); v[0]=1; //for(gflong i=1; i b.size() ? b.size() : a.size() ); for(gflong i=0; ia.size()) {big = &b; small = &a; small_size = a.size();} else {big = &a; small = &b; small_size = b.size();} for(gflong i=0; i(v, m_k, m_2powk, m_field, 1); } std::string toStrPolyBinary(const value& v) const { return toString(v, m_k, m_2powk, m_field, 2); } std::string toStrPoly(const value& v) const { return toString(v, m_k, m_2powk, m_field, 3); } std::string toStrInt(const value& v) const { return toString(v, m_k, m_2powk, m_field, 4); } bool areEqual(const value& a, const value& b) { return a==b; } }; class GF2l_Z2k_Dynamic : public GF2l_Z2k_Dynamic_Generic<> {}; /* * Struct for permuting blocks of bits in a vector. * Takes: * size - the number of bits to move * oldpos - old position from left (least significant bit) * newpos - new position to move to (least significant bit) * It is assumed that a list (a std::vector) of such structs * would be provided to permuteBlocks() method of * GF2l_Z2k_Dynamic_LabelOptimized class to move/permute bits. */ struct GF2Permute { int size; int oldpos; int newpos; GF2Permute() : size(0), oldpos(0), newpos(0) {} GF2Permute(int _size, int _oldpos, int _newpos) : size(_size), oldpos(_oldpos), newpos(_newpos) {} friend std::ostream& operator<<(std::ostream& os, const GF2Permute& p) { os << "(size: " << p.size << ", oldpos: " << p.oldpos << ", newpos: " << p.newpos << ")"; return os; } }; /* * GF2l_Z2k optimized for labels. */ template< typename _RandEng = boost::random::mt11213b, typename _RandDist = boost::random::uniform_int_distribution > class GF2l_Z2k_Dynamic_LabelOptimized : public GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist> { public: typedef typename GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::value value; typedef typename GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::gflong gflong; typedef typename GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::fieldVal fieldVal; using GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::random; using GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::randomInt; using GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::setSeed; using GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::getSeed; protected: using GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::m_field; using GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::m_2powk; using GF2l_Z2k_Dynamic_Generic<_RandEng, _RandDist>::m_k; public: void multiply(value &v, const value &a, const value &b) const { //v=a+b; gflong gval; Field::value fval; const value* big; const value* small; unsigned long small_size, big_size; if(b.size()>a.size()) {big = &b; big_size = b.size(); small = &a; small_size = a.size();} else {big = &a; big_size = a.size(); small = &b; small_size = b.size();} v.assign(big_size, 0); for(gflong i=0; i &permutation) const { assert(permutation.size()<=m_k); gflong v1 = 0; value newresult(result.size(), 0); newresult[0] = result[0]; for(gflong i=1; i &permutation) const { gflong v1 = 0; value newresult(result.size(), 0); newresult[0] = result[0]; for(gflong i=1; i 01011 //gfd.shiftAfterClear(val, 2, 1); * 01011 --> 00111 //gfd.shiftAfterClear(val, 3, 2); * Example2: * 110011 --> 1111 //gfd.shiftAfterClear(val1, 4, 2); * */ void shiftAfterClear(value &result, unsigned shiftfrom, unsigned shiftto) { assert(shiftto < shiftfrom); gflong mask = 0, v1 = 0; for(gflong i=1; i> (shiftfrom - shiftto); v1 = v1 & ~mask; v1 = v1 | (i & mask); result[v1] = result[i]; result[i] = 0; } } } /** * Clears bits. * * @param result - GF2lZ2k value to clear bits in (set of 2^k vectors). * @param size - number of bits to clear / zero * @param startpos - start position (least significant bit) to clear from */ void clearbits(value &result, gflong size, gflong startpos) { gflong tmpv1, tmpv2, v2, v1; v1 = 1 << size; gflong mask = v1-1; mask = mask << startpos; v1 = 1 << startpos; // probably wrong - should be (mask-1) mask = ~mask; //v2 = v1 << size; //mask = ~mask; //make scalars //tmpv2 = 1 << size; // -- DISABLE THE SCALARS FOR NOW. //fieldVal scalars[tmpv2]; //for(gflong i=0; i0) { tmpv1 = i & mask; tmpv2 = (tmpv2 >> startpos); //##print 'modifying v2,i',v2,',',i; //result[tmpv1] = addForClear(result[tmpv1], //m_field.multiply(result[tmpv1], scalars[0]), // result[i]); //m_field.multiply(result[i], scalars[tmpv2])); result[tmpv1] = addForClear(result[tmpv1], //m_field.multiply(result[tmpv1], scalars[0]), result[i]); //m_field.multiply(result[i], scalars[tmpv2])); result[i] = 0; } } } //return result; } /** * Clears bits (as above), but takes a list/vector of positions to clear. * * @param result - GF2lZ2k value to clear bits in (set of 2^k vectors). * @param to clear - vector containing a list of size and start positions * (as std::pair(size, startpos)) to clear. */ void clearbits(value &result, const std::vector< std::pair > &toclear) { //clear one by one than in batch to have consistent scalars for(unsigned i=0; i (std::ostream & stream, const typename GF2l_Z2k_Dynamic_LabelOptimized<_RandEng, _RandDist>::value & val); private: /** Assumes all move positions are supplied in permutation. If there are some missing we assume they're empty and can overwrite them.*/ gflong permuteBlocks_internal(const gflong v, const std::vector permutation) const { gflong v2=0, tmpv=0, tmpv2=0, touchedbits=0; int diff; for(std::size_t i=0; i= 0) { tmpv = tmpv << diff; tmpv2 = tmpv2 << diff; } else { tmpv = tmpv >> (-1*diff); tmpv2 = tmpv2 >> (-1*diff); } assert(tmpv!=0); touchedbits = touchedbits | tmpv2; // here we overwrite unused positions (i.e. those unspecified in 'permutation') v2 = v2 | tmpv; } //assert(v2!=0); //set the untouched bits. tmpv = v & ~touchedbits; v2 = v2 | tmpv; return v2; } gflong permuteBits_internal_withbitset(const gflong v, const std::vector &permutation) const { boost::dynamic_bitset<> mask(m_k, 0), res(m_k, 0); //k-bit vectors for(std::size_t i=0; i &permutation) const { gflong mask=0, tmpv=0, result=0; //k-bit vectors int diff; for(std::size_t i=0; i= 0) tmpv = tmpv << diff; else tmpv = tmpv >> (-1*diff); result = result | tmpv; } return result; } }; //template //std::ostream & operator<<(std::ostream & stream, const typename GF2l_Z2k_Dynamic_LabelOptimized<_RandEng, _RandDist>::value & value) //{ // stream << "TV = ["; // int k = (int) round_float(log(value.size())/log(2), 0); // int l = (int) round_float(log(k)/log(2) + 3, 0); // GF2l_Z2k_Dynamic_LabelOptimized<_RandEng, _RandDist> gf2; // gf2.initialize(k, l); // stream << gf2.toStrPolyBinary(value); // stream << "]"; // return stream; //} } //namespace groupalgebra } //namespace mips #endif mips-1.0/mips/core/group/binIrredPolyField.cpp0000644002402200254040000000353012520407731020744 0ustar thomasfdtaig#include "binIrredPolyField.hpp" #include #include //#include #include #include namespace mips { namespace groupalgebra { std::string Field::toStrInt(Field::value val) const { std::stringstream str; str << (unsigned) val; //bad fix return str.str(); } std::string Field::toStrBinary(Field::value val) const { std::stringstream str; //const int n = this->m_l; //std::bitset<32> inbits = val; //need a permanent solution for this hard coded 32 boost::dynamic_bitset<> inbits(this->m_l, val); str << inbits; return str.str(); } std::string Field::toStrPoly_old(Field::value val) const { std::stringstream str; int i=0; while(val !=0) { if(val & 1 != 0) { switch(i) { case 0: str << "1"; break; case 1: str << "+x"; break; default: str << "+" << i << "^x"; break; } } val = val >> 1; i++; } std::string s = str.str(); std::reverse(s.begin(), s.end()); return s; } std::string Field::toStrPoly(Field::value val) const { std::stringstream str; Field::value i = this->highestBit; bool first=true; while(i !=0) { if((val & i) != 0) { if(first) first=false; else str << "+"; switch(i) { case 1: str << "1"; break; case 2: str << "x"; break; default: //str << "+" << i << "^x"; str << "x^" << (i-1); break; } } //val = val >> 1; //i++; i = i >> 1; } std::string s = str.str(); //std::reverse(s.begin(), s.end()); return s; } } //namespace groupalgebra } //namespace mips mips-1.0/mips/core/group/CMakeLists.txt0000755002402200254040000000040512520407731017433 0ustar thomasfdtaiginclude(${PROJECT_SOURCE_DIR}/script/build_system.cmake) set (SOURCES binIrredPolyField.cpp ) set (HEADERS irredPolyList.hpp binIrredPolyField.hpp GF2l_Z2k.hpp ) INCLUDE_ALL_SUBDIRECTORIES_HEADER_SOURCES(SUBDIRECTORIES) add_subdirectory(test) mips-1.0/mips/core/group/irredPolyList.hpp0000644002402200254040000000621112520407731020207 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ /* * ******************************************************************** * Lists precomputed low-weight irreducible polynomials of degree at * most 32. * ******************************************************************** */ /* * Generated from list: * ['2,1', '3,1', '4,1', '5,2', '6,1', '7,1', '8,4,3,1', '9,1', '10,3', * '11,2', '12,3', '13,4,3,1', '14,5', '15,1', '16,5,3,1', '17,3', * '18,3', '19,5,2,1', '20,3', '21,2', '22,1', '23,5', '24,4,3,1', * '25,3', '26,4,3,1', '27,5,2,1', '28,1', '29,2', '30,1', '31,3', * '32,7,3,2'] * E.g.: 3,1 --> x^3 + x^1 +1 */ #ifndef MIPS_CORE_GROUP_IRREDPOLYLIST_HPP #define MIPS_CORE_GROUP_IRREDPOLYLIST_HPP namespace mips { namespace groupalgebra { /** Maximum size field in our irred_poly_list array below. */ static const int POLY_MAX_LENGTH = 31; /** Static offset when accessing the list irred_poly_list. This is to allow for easy constructor calls like 'GF2l_Z2k_Dynamic gf(k,l);' and 'Field f = Field(l);' (where l is the size of the field),instead of 'GF2l_Z2k_Dynamic gf(k,l-2);' and 'Field f = Field(l-2);'. We could have achieved the same by filling the first 2 rows of irred_poly_list with '0', but we want the static list 'irred_poly_list' to be as small as possible. */ static const int POLY_OFFSET = 2; /** List of precomputed IRREDUCIBLE polynomials, from size 2 to size 31. irred_poly_list[][0] -- the irreducible polynomial irred_poly_list[][1] -- the maximum value (biggest polynomial) in this field (it is always the case that irred_poly_list[][1] < irred_poly_list[][0]) */ static const unsigned int irred_poly_list[][2] = { {7u, 3u}, // 0 --> 2 {11u, 7u}, // 1 --> 3 {19u, 15u}, // .... {37u, 31u}, {67u, 63u}, {131u, 127u}, {283u, 255u}, {515u, 511u}, {1033u, 1023u}, {2053u, 2047u}, {4105u, 4095u}, {8219u, 8191u}, {16417u, 16383u}, {32771u, 32767u}, {65579u, 65535u}, {131081u, 131071u}, {262153u, 262143u}, {524327u, 524287u}, {1048585u, 1048575u}, {2097157u, 2097151u}, {4194307u, 4194303u}, {8388641u, 8388607u}, {16777243u, 16777215u}, {33554441u, 33554431u}, {67108891u, 67108863u}, {134217767u, 134217727u}, {268435459u, 268435455u}, {536870917u, 536870911u}, {1073741827u, 1073741823u}, {2147483657u, 2147483647u}, // 29 --> 31 // This should not cause any problem, as max for a uint32_t is 4294967295 //{4294967437u, 4294967295u} }; } //namespace groupalgebra } //namespace mips #endif //MIPS_CORE_GROUP_IRREDPOLYLIST_HPP mips-1.0/mips/core/graph/0000755002402200254040000000000012520407731014636 5ustar thomasfdtaigmips-1.0/mips/core/graph/labeledGraph.hpp0000644002402200254040000001125412520407731017724 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_CORE_GRAPH_LABELEDGRAPH_HPP #define MIPS_CORE_GRAPH_LABELEDGRAPH_HPP #include #include #include #include #include namespace mips { namespace core { /** * @brief The LabeledGraph class is the base class for creating labeled graphs, i.e., a graph together with vertex- and edge-labels. * */ template < typename GraphType, typename VertexLabelMapDescription, typename EdgeLabelMapDescription, template class StoragePolicy = mips::utilities::StoreBySmartPointer > class LabeledGraph : public StoragePolicy { public: typedef typename detail::TransformIfNoLabelMap::type _VertexLabelMapDescription; typedef typename detail::TransformIfNoLabelMap::type _EdgeLabelMapDescription; typedef detail::LabelMapConfig VertexConfig; typedef detail::LabelMapConfig EdgeConfig; typedef StoragePolicy GraphStorage; public: typedef typename VertexConfig::label_map_accessor VertexLabelMapAccessor; typedef typename EdgeConfig::label_map_accessor EdgeLabelMapAccessor; typedef GraphType Graph; typedef typename VertexConfig::property_map VertexLabelMap; typedef typename VertexConfig::const_property_map ConstVertexLabelMap; typedef typename EdgeConfig::property_map EdgeLabelMap; typedef typename EdgeConfig::const_property_map ConstEdgeLabelMap; typedef typename boost::property_traits::value_type VertexLabel; typedef typename boost::property_traits::value_type EdgeLabel; /** * @brief LabeledGraph This constructor creates a new labeled graph by using the type supplied * @param graph */ template explicit LabeledGraph(const LabeledGraphSetType & graph) : GraphStorage(graph) { } template LabeledGraph(const LabeledGraphSetType & graph, const VertexLabelMapData & vmapData, const EdgeLabelMapData & emapData) : GraphStorage(graph), vmap_accessor_(vmapData), emap_accessor_(emapData) { } LabeledGraph() : GraphStorage(GraphStorage::SafeInitialise()) { } const Graph & graph() const { return GraphStorage::get(); } Graph & graph() { return GraphStorage::get(); } template void setGraph(const GraphSetType & graph) { GraphStorage::set(graph); } ConstVertexLabelMap vertexLabelMap() const { return vmap_accessor_.generateConstPropertyMap(graph()); } VertexLabelMap vertexLabelMap() { return vmap_accessor_.generatePropertyMap(graph()); } ConstEdgeLabelMap edgeLabelMap() const { return emap_accessor_.generateConstPropertyMap(graph()); } EdgeLabelMap edgeLabelMap() { return emap_accessor_.generatePropertyMap(graph()); } const VertexLabelMapAccessor & vertexLabelMapAccessor() const { return vmap_accessor_; } VertexLabelMapAccessor & vertexLabelMapAccessor() { return vmap_accessor_; } const EdgeLabelMapAccessor & edgeLabelMapAccessor() const { return emap_accessor_; } EdgeLabelMapAccessor & edgeLabelMapAccessor() { return emap_accessor_; } private: typename VertexConfig::label_map_accessor vmap_accessor_; typename EdgeConfig::label_map_accessor emap_accessor_; }; } // core namespace } // mips namespace #endif // MIPS_CORE_GRAPH_LABELEDGRAPH_HPP mips-1.0/mips/core/graph/graphio.hpp0000644002402200254040000003253312520407731017006 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_GRAPH_IO_GRAPHIO_HPP #define MIPS_GRAPH_IO_GRAPHIO_HPP #include #include #include #include #include #include #include #include #include #include #include #include namespace mips { namespace graph { /*! * \namespace mips::graph::io * \brief Functions for graph input and output reside in this namespace. */ namespace io { template< typename Derived > struct GraphFormatter{ template< typename Graph > bool writeGraph( std::ostream & os, const Graph & g, std::ostream & err ){ return static_cast(this)->writeGraph(os,g,err); } template< typename Graph > bool readGraph( std::istream & is, Graph & g, std::ostream & err ){ return static_cast(this)->readGraph(is,g, err ); } template< typename Graph, typename VertexLabelMap, typename EdgeLabelMap > bool writeGraph( std::ostream & os, const Graph & g, VertexLabelMap const & vlm, EdgeLabelMap const & elm, std::ostream & err ){ return static_cast(this)->writeGraph(os,g,vlm,elm,err); } template< typename Graph , typename VertexLabelMap, typename EdgeLabelMap > bool readGraph( std::istream & is, Graph & g, VertexLabelMap & vlm, EdgeLabelMap & elm, std::ostream & err ){ return static_cast(this)->readGraph(is,g, vlm, elm, err ); } }; /*! * \brief Writes a graph to an output stream using the format supplied by the formatter. * * \tparam Format The format that should be used to write the graph out. * \tparam Graph The type of the graph used. * \tparam VertexLabelMap The type of map container. * \tparam EdgeLabelMap The type of the map container * * \param[in] os An output stream * \param[in] g A graph object * \param[in] vlm A map of vertex identifiers to labels. * \param[in] elm A map of edge identifier to labels. * \param[in] err An error stream. ( Optional ) Defaults to standard error. * * \return true if graph was successfully written, false otherwise. * */ template< typename Format, typename Graph, typename VertexLabelMap, typename EdgeLabelMap > bool writeGraph( std::ostream & os , const Graph & g, VertexLabelMap const & vlm, EdgeLabelMap const & elm, std::ostream & err = std::cerr ){ GraphFormatter formatter; return formatter.writeGraph(os,g,vlm, elm,err); } /*! * \brief Writes a graph to an output stream using the format supplied by the formatter. * * \param[in] os An output stream * \param[in] g A graph object * \param[in] err An error stream. ( Optional ) Defaults to standard error. * * \tparam Format The format that should be used to write the graph out. * \tparam Graph The type of the graph used. * * \return true if graph was successfully written, false otherwise. */ template< typename Format, typename Graph > bool writeGraph( std::ostream & os , const Graph & g, std::ostream & err = std::cerr ){ typedef typename boost::vertex_bundle_type::type VertexBundle; typedef typename boost::edge_bundle_type::type EdgeBundle; typename boost::property_map::const_type vlm = get(&VertexBundle::label,g); typename boost::property_map::const_type elm = get(&EdgeBundle::label,g); return writeGraph(os,g,vlm,elm,err); } /*! * \brief Writes graphs to an output stream using the format supplied by the formatter. * * \param[in] os An output stream * \param[in] container A container of graphs. * \param[in] err An error stream. ( Optional ) Defaults to standard error. * * \tparam Format The format that should be used to write the graph out. * \tparam Graph The type of the graph used. * \tparam GraphContainer The type of the graph container, must provide access to value_type. * * \return true if graph was successfully written, false otherwise. */ template< typename Format, typename GraphContainer > int writeGraphs( std::ostream & os , const GraphContainer & container, std::ostream & err = std::cerr ){ unsigned count = 0; BOOST_FOREACH( typename GraphContainer::value_type g, container ){ if(writeGraph(os,g,err)) count++; } return count; } /*! * \brief Writes a graph to an output stream using the format supplied by the formatter. * * \tparam Format The format that should be used to write the graph out. * \tparam GraphContainer The type of the graph container, must provide access to value_type. * \tparam VertexLabelMapContainer The container type of vertex label maps. * \tparam EdgeLabelMap The container type of edge label maps. * * \param[in] os An output stream * \param[in] container A container of graphs. * \param[in] vlm A collection of vertex label maps. * \param[in] elm A collection of edge label maps. * \param[in] err An error stream. ( Optional ) Defaults to standard error. * * \return true if graph was successfully written, false otherwise. * */ template< typename Format, typename GraphContainer, typename VertexLabelMapContainer, typename EdgeLabelMapContainer > int writeGraphs( std::ostream & os , const GraphContainer & container, VertexLabelMapContainer const & vlm, EdgeLabelMapContainer const & elm, std::ostream & err = std::cerr ){ unsigned count = 0; typedef typename GraphContainer::const_iterator graph_iterator; typedef typename VertexLabelMapContainer::const_iterator vertex_map_iterator; typedef typename EdgeLabelMapContainer::const_iterator edge_map_iterator; graph_iterator begin; vertex_map_iterator v_begin; edge_map_iterator e_begin; for( begin = container.begin(), v_begin = vlm.begin(), e_begin = elm.begin(); begin != container.end(); ++begin,++v_begin,++e_begin){ if(writeGraph(os,*begin,*v_begin,*e_begin,err)) count++; } return count; } /*! * \brief Read in a graph from the given inputstream using the format supplied by the formatter. * * \tparam Format The format that should be used to write the graph out. * \tparam Graph The type of the graph used. * \tparam VertexLabelMap The type of map container. * \tparam EdgeLabelMap The type of the map container * * \param[in] is An input stream. * \param[out] g A graph object. * \param[out] vlm A map of vertex identifiers to labels * \param[in] elm A map of edge identifier to labels. * \param[in] err An error stream. ( Optional ) Defaults to standard error. * * \return true if graph was successfully read, false otherwise. */ template< typename Format, typename Graph, typename VertexLabelMap, typename EdgeLabelMap > bool readGraph( std::istream & is , Graph & g, VertexLabelMap vlm, EdgeLabelMap elm, std::ostream & err = std::cerr ){ GraphFormatter formatter; return formatter.readGraph(is,g,vlm,elm,err); } /*! * \brief Read in a graph from the given inputstream using the format supplied by the formatter. * * \tparam Format The format that should be used to write the graph out. * \tparam Graph The type of the graph used. * * \param[in] is An input stream. * \param[out] g A graph object. * * \return true if graph was successfully read, false otherwise. * */ template< typename Format, typename Graph > bool readGraph( std::istream & is , Graph & g, std::ostream & err = std::cerr ){ typedef typename boost::vertex_bundle_type::type VertexBundle; typedef typename boost::edge_bundle_type::type EdgeBundle; typename boost::property_map::type vlm = get(&VertexBundle::label,g); typename boost::property_map::type elm = get(&EdgeBundle::label,g); return readGraph(is,g,vlm,elm,err); } //There is no need for this function, and should be removed - SR //No this is required to handle graphs which are passed as pointers. - SR template< typename Format, typename Graph > bool readGraph( std::istream & is , Graph * g, std::ostream & err = std::cerr ){ return readGraph(is,*g,err); } //There is no need for this function, and should be removed - SR //No this is required to handle graphs which are passed as pointers. -SR template< typename Format, typename Graph, typename VertexLabelMap, typename EdgeLabelMap > bool readGraph( std::istream & is , Graph * g, VertexLabelMap vlm, EdgeLabelMap elm, std::ostream & err = std::cerr ){ return readGraph(is,*g,vlm,elm,err); } /*! * \brief Reads in graphs from the given inputstream using the format supplied by the formatter. * * \tparam Format The format that should be used to write the graph out. * \tparam GraphContainer The type of container for graph. GraphContainer must contain a typedef value_type. * * \param[in] is An input stream. * \param[out] container A graph object. * \param[in] err An error stream. ( Optional ) Defaults to standard error. * * \return true if graph was successfully read, false otherwise. * */ template< typename Format, typename GraphContainer > int readGraphs( std::istream & is , GraphContainer & container, std::ostream & err = std::cerr ){ std::size_t count = 0; //Bad idea, use peek instead. while( !is.eof() ){ typename GraphContainer::value_type g; if(readGraph(is,g,err)){ container.push_back(g); count++; } } return count; } /*! * \brief Reads in graphs from the given inputstream using the format supplied by the formatter. * * \tparam Format The format that should be used to write the graph out. * \tparam GraphContainer The type of container for graph. GraphContainer must contain a typedef value_type. * \tparam VertexLabelMap The type of map container. * \tparam EdgeLabelMap The type of the map container * * \param[in] is An input stream. * \param[out] container A graph object. * \param[out] vlm A map of vertex identifiers to labels * \param[in] elm A map of edge identifier to labels. * \param[in] err An error stream. ( Optional ) Defaults to standard error. * * \return true if graph was successfully read, false otherwise. * */ template< typename Format, typename GraphContainer, typename VertexLabelMapContainer, typename EdgeLabelMapContainer > int readGraphs( std::istream & is , GraphContainer & container, VertexLabelMapContainer & vlm, EdgeLabelMapContainer & elm, std::ostream & err = std::cerr ){ std::size_t count = 0; typename GraphContainer::value_type g; typename VertexLabelMapContainer::value_type vertex_label_map; typename EdgeLabelMapContainer::value_type edge_label_map; while(readGraph(is,g,vertex_label_map,edge_label_map,err)){ container.push_back(g); vlm.push_back(vertex_label_map); elm.push_back(edge_label_map); g = typename GraphContainer::value_type(); vertex_label_map = typename VertexLabelMapContainer::value_type(); edge_label_map = typename EdgeLabelMapContainer::value_type(); count++; } return count; } } // namespace io } // namespace graph } // namespace mips #endif // MIPS_GRAPH_IO_GRAPHIO mips-1.0/mips/core/graph/compactadjacencylist.hpp0000644002402200254040000020060412520407731021535 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef MIPS_CORE_GRAPH_COMPACTADJACENCYLIST_HPP #define MIPS_CORE_GRAPH_COMPACTADJACENCYLIST_HPP #include #include #include #include #include #include #include #include #include #include #define CAL_GRAPH_TEMPLATE_PARAMS typename Directed, typename VertexProperties, typename EdgeProperties, \ typename GraphProperties, typename IndexType #define CAL_GRAPH_CLASS_PARAMS mips::graph::compact_adjacency_list #define CAL_DIR_GRAPH_TEMPLATE_PARAMS typename VertexProperties, typename EdgeProperties, \ typename GraphProperties, typename IndexType #define CAL_DIR_GRAPH_CLASS_PARAMS mips::graph::compact_adjacency_list #define CAL_UDIR_GRAPH_TEMPLATE_PARAMS typename VertexProperties, typename EdgeProperties, \ typename GraphProperties, typename IndexType #define CAL_UDIR_GRAPH_CLASS_PARAMS mips::graph::compact_adjacency_list namespace mips { namespace graph { namespace detail{ /*! * \brief Edge iterator class */ template< typename GraphType > class cal_edge_iterator : public boost::iterator_facade< cal_edge_iterator, typename GraphType::edge_descriptor, std::forward_iterator_tag, typename GraphType::edge_descriptor > { private: typedef typename GraphType::vertex_iterator vertex_iterator; typedef typename GraphType::edge_descriptor edge_descriptor; typedef typename GraphType::edges_size_type EdgeIndex; typedef typename GraphType::VertexRecord VertexRecord; public: cal_edge_iterator() : m_vertex_array(0), m_current_index(0),m_degree(0) {} cal_edge_iterator( vertex_iterator source, std::size_t num_vertices,VertexRecord const vertex_array[] ) : m_source(source), m_num_vertices(num_vertices), m_vertex_array(vertex_array), m_current_index(0) { m_degree = m_vertex_array[*(m_source)+1].first_edge_index - m_vertex_array[*m_source].first_edge_index; } friend class boost::iterator_core_access; const edge_descriptor dereference() const { return edge_descriptor(*m_source,m_current_index); } bool equal(const cal_edge_iterator & rhs) const { return m_source == rhs.m_source; } void increment() { m_current_index++; //Increment the current index while((m_degree == 0 || m_current_index >= m_vertex_array[*(m_source)].first_edge_index + m_degree) && *m_source < m_num_vertices ){ //If current edge index equals the starting index of the adj. list of the source vertex + degree of the soruce ++m_source; //Move to the next vertex //Compute the degree of the vertex if(m_vertex_array[*m_source].first_edge_index > 0 ) m_degree = m_vertex_array[*(m_source)+1].first_edge_index - m_vertex_array[*m_source].first_edge_index; else m_degree = 0; } } VertexRecord const * m_vertex_array; EdgeIndex m_current_index; EdgeIndex m_degree; vertex_iterator m_source; std::size_t m_num_vertices; }; //Undirected edge iterator template< typename GraphType > class cal_undirected_edge_iterator : public boost::iterator_facade< cal_undirected_edge_iterator, typename GraphType::edge_descriptor, std::forward_iterator_tag, typename GraphType::edge_descriptor > { private: typedef typename GraphType::vertex_iterator vertex_iterator; typedef typename GraphType::edge_descriptor edge_descriptor; typedef typename GraphType::edges_size_type EdgeIndex; typedef typename GraphType::VertexRecord VertexRecord; typedef typename GraphType::EdgeRecord EdgeRecord; public: cal_undirected_edge_iterator() : m_vertex_array(0), m_current_index(0),m_out_degree(0) {} cal_undirected_edge_iterator( vertex_iterator source, VertexRecord const vertex_array[]) : m_source(source), m_vertex_array(vertex_array), m_current_index(0) { m_out_degree = m_vertex_array[*(m_source)+1].first_edge_index - m_vertex_array[*m_source].first_out_edge_index; } friend class boost::iterator_core_access; const edge_descriptor dereference() const { return edge_descriptor(*m_source,m_current_index); } bool equal(const cal_undirected_edge_iterator & rhs) const { return m_source == rhs.m_source; } void increment() { m_current_index++; //Increment the current index if(m_current_index >= m_vertex_array[*(m_source)].first_out_edge_index + m_out_degree ){ //If current edge index equals the starting index of the adj. list of the source vertex + degree of the soruce ++m_source; //Move to the next vertex // while((m_out_degree = m_vertex_array[*(m_source)+1].first_edge_index - m_vertex_array[*m_source].first_out_edge_index) == 0 ) // ++m_source; //Move to the next vertex //Compute the degree of the vertex m_out_degree = m_vertex_array[*(m_source)+1].first_edge_index - m_vertex_array[*m_source].first_out_edge_index; //Move current edge index to first out edge. m_current_index = m_vertex_array[*m_source].first_out_edge_index; } } VertexRecord const * m_vertex_array; EdgeIndex m_current_index; EdgeIndex m_out_degree; vertex_iterator m_source; }; /*! * \brief Out edge iterator */ template< typename GraphType > class cal_out_edge_iterator : public boost::iterator_facade< cal_out_edge_iterator, typename GraphType::edge_descriptor, std::forward_iterator_tag, typename GraphType::edge_descriptor > { private: typedef typename GraphType::vertex_descriptor vertex_descriptor; typedef typename GraphType::edge_descriptor edge_descriptor; typedef typename GraphType::edges_size_type EdgeIndex; typedef typename GraphType::VertexRecord VertexRecord; public: cal_out_edge_iterator() : m_edge_descriptor(0,0) {} cal_out_edge_iterator( EdgeIndex edge_index , vertex_descriptor source ) : m_edge_descriptor(source,edge_index){} friend class boost::iterator_core_access; const edge_descriptor dereference() const { return m_edge_descriptor; } bool equal(const cal_out_edge_iterator & rhs) const { return m_edge_descriptor.second >= rhs.m_edge_descriptor.second; } void increment() { m_edge_descriptor.second++;} void advance( int n ){ m_edge_descriptor.second += n; } edge_descriptor m_edge_descriptor; }; template struct cal_edge_index_map{ typedef EdgeIndex value_type; typedef EdgeIndex reference; typedef std::pair key_type; typedef boost::readable_property_map_tag category; inline const reference operator[]( const std::pair & e ) const { return get(*this); } }; template< typename Vertex,typename EdgeIndex > inline const EdgeIndex get( const cal_edge_index_map &, const std::pair & e ){ return e.second; } }//namespace detail; /*! * \brief A compact adjacency list representation of a graph * * The compact graph adjacency list, CGAL, is a data structure for storing a * large graph efficiently in memory. * This data structure conforms with boost graph concepts to allow it to be * used with boost graph algorithms. */ template< typename Directed, typename VertexProperties, typename EdgeProperties, typename GraphProperties, typename IndexType > class compact_adjacency_list; // Generic class //Specialisation for directed graphs. template< typename VertexProperties, typename EdgeProperties, typename GraphProperties, typename IndexType > class compact_adjacency_list{ public: typedef IndexType VertexIndex; typedef IndexType EdgeIndex; //Required by Graph concept typedef VertexIndex vertex_descriptor; typedef std::pair edge_descriptor; typedef boost::directedS directed_category; //This has to be fixed. Edge parallel is based on the underlying container for a boost::Adjacency graph typedef void * edge_parallel_category; typedef std::forward_iterator_tag traversal_category; //Required by IncidenceGraph concept typedef typename detail::cal_out_edge_iterator out_edge_iterator; typedef std::size_t degree_size_type; //Required by VertexListGraph concept typedef boost::integer_range VertexList; typedef typename VertexList::const_iterator vertex_iterator; typedef std::size_t vertices_size_type; //Required by EdgeListGraph concept typedef typename detail::cal_edge_iterator edge_iterator; typedef std::size_t edges_size_type; //Required by BidirectionalGraph concept //NOT IMPLEMENTED YET typedef void * in_edge_iterator; //Required by AdjacencyGraph concept class cgal_adjacency_iterator {}; // only for bidirectional graphs; typedef cgal_adjacency_iterator adjacency_iterator; //Property Graph. typedef GraphProperties graph_property_type; typedef typename boost::lookup_one_property::type graph_bundled; graph_property_type m_property; public: struct VertexRecord { IndexType first_edge_index; //VertexProperties properties; }; //Vertex Property storage. std::vector m_vertex_properties; typedef boost::no_property vertex_property_type; typedef VertexProperties vertex_bundled; typedef boost::iterator_property_map< typename std::vector::iterator, typename boost::typed_identity_property_map > vertex_property_map_type; typedef boost::iterator_property_map< typename std::vector::const_iterator, typename boost::typed_identity_property_map > const_vertex_property_map_type; /* * EXPERIMENTAL IMPLEMENTATION OF INTERNAL PROPERTY MAPS * * */ // typedef boost::vector_property_map > internal_vertex_property_map; // internal_vertex_property_map m_vertex_properties; // typedef internal_vertex_property_map vertex_property_map_type; // typedef const internal_vertex_property_map const_vertex_property_map_type; typedef boost::no_property edge_property_type; typedef EdgeProperties edge_bundled; typedef boost::iterator_property_map< typename std::vector::iterator, typename detail::cal_edge_index_map > edge_property_map_type; typedef boost::iterator_property_map< typename std::vector::const_iterator, typename detail::cal_edge_index_map > const_edge_property_map_type; //typedef typename boost::lookup_one_property::type vertex_bundled; private: IndexType m_vertex_count; VertexRecord * m_vertices; VertexList m_vertex_index; // to be constructed with new VertexRecord[m_vertex_count+1]; // the last vertex with index m_vertex_count is only intended // to be able to compute the degree of vertex m_vertex_count-1 // with m_vertices[m_vertex_count-1].first_edge_index - m_vertices[m_vertex_count-2] public: struct EdgeRecord { IndexType target_node; //EdgeProperties properties; }; private: IndexType m_edge_count; IndexType m_current_edge_index; EdgeRecord * m_edges; std::vector m_edge_properties; // public: // class edge_iterator { // const compact_adjacency_list* m_graph; // IndexType m_vertex_index; // IndexType m_edge_index; // // operators ++ -- * ... // }; // class vertex_iterator { // IndexType m_vertex_index; // // I don't think we need a reference to the graph to implement // // ++ -- * ... // }; //typedef EdgeRecord * out_edge_iterator; public: compact_adjacency_list( IndexType vertex_count, IndexType edge_count); template compact_adjacency_list( const boost::adjacency_list & src ); //compact_adjacency_list( compact_adjacency_list< directed_category,VertexProperties,EdgeProperties,GraphProperties,IndexType > const & src ); compact_adjacency_list( std::size_t num_vertices, std::size_t num_edges, VertexRecord * vlist, EdgeRecord * elist ); template< typename VPropIterator, typename EPropIterator > explicit compact_adjacency_list( std::size_t num_vertices, std::size_t num_edges, VertexRecord * vlist, EdgeRecord * elist, VPropIterator vprop, EPropIterator eprop); template< typename DegreeIterator, typename EdgeIterator > explicit compact_adjacency_list( IndexType vertex_count, IndexType edge_count, DegreeIterator dbegin, DegreeIterator dend, EdgeIterator begin, EdgeIterator end); template< typename DegreeSequence, typename EdgeIterator, typename EdgePropIterator > explicit compact_adjacency_list(IndexType vertex_count, IndexType edge_count,DegreeSequence dbegin,DegreeSequence dend, EdgeIterator begin, EdgeIterator end, EdgePropIterator prop_begin ); ~compact_adjacency_list(); inline vertices_size_type vertex_size() const { return m_vertex_count; } inline edges_size_type edges_size() const { return m_edge_count; } inline const VertexList & vertex_set() const { return m_vertex_index; } inline std::pair edge_set() const { return std::make_pair(edge_iterator(m_vertex_index.begin(),m_vertex_count, m_vertices),edge_iterator(m_vertex_index.end(),m_vertex_count,m_vertices)); } inline std::pair out_edge_set(vertex_descriptor const v ) const{ return std::make_pair(out_edge_iterator(m_vertices[v].first_edge_index,v),out_edge_iterator(m_vertices[v+1].first_edge_index,v)); } inline const VertexRecord & vertexRecords() const { return *m_vertices; } inline const EdgeRecord & edgeRecords(){ return *m_edges; } /*! * \brief hasEdge * \param s Source vertex * \param t Destination vertex * \return A pair of edge_descriptor and a boolean value. * * This method returns an edge descriptor and boolean value. If a edge exists between vertices s & t then a pair of * consisting of the edge_descriptor and true is returned. If no edge was found then the boolean is set to false, * the edge descriptor is undefined. * * This method is provided for convenience only and is linear in the degree of the soruce vertex. * */ inline std::pair< edge_descriptor, bool> hasEdge( vertex_descriptor s, vertex_descriptor t ) const { EdgeIndex idx = m_vertices[s].first_edge_index; bool found = false; unsigned int deg = m_vertices[s+1].first_edge_index - m_vertices[s].first_edge_index; while( idx < m_vertices[s].first_edge_index + deg && found == false ){ found = m_edges[idx].target_node == t ? true : idx++; } return std::make_pair(std::make_pair(s,idx),found); } inline vertex_descriptor target ( edge_descriptor const e ) const { return m_edges[e.second].target_node; } inline vertex_property_map_type get_vertex_bundle( boost::typed_identity_property_map indexMap = boost::typed_identity_property_map()) { return vertex_property_map_type(m_vertex_properties.begin(), indexMap); } inline const_vertex_property_map_type get_vertex_bundle( boost::typed_identity_property_map const indexMap = boost::typed_identity_property_map()) const { return const_vertex_property_map_type(m_vertex_properties.begin(), indexMap); } inline edge_property_map_type get_edge_bundle( detail::cal_edge_index_map indexMap = detail::cal_edge_index_map()){ return edge_property_map_type(m_edge_properties.begin(),indexMap); } inline const_edge_property_map_type get_edge_bundle( detail::cal_edge_index_map const indexMap = detail::cal_edge_index_map()) const { return const_edge_property_map_type(m_edge_properties.begin(),indexMap); } inline degree_size_type out_degree( vertex_descriptor const v ) const { return m_vertices[v+1].first_edge_index - m_vertices[v].first_edge_index;} static vertex_descriptor null_vertex(){ return std::numeric_limits::max(); } }; //Specialisation for undirected graphs template< typename VertexProperties, typename EdgeProperties, typename GraphProperties, typename IndexType > class compact_adjacency_list{ public: typedef IndexType VertexIndex; typedef IndexType EdgeIndex; //Required by Graph concept typedef VertexIndex vertex_descriptor; typedef std::pair edge_descriptor; typedef boost::undirectedS directed_category; //This has to be fixed. Edge parallel is based on the underlying container for a boost::Adjacency graph typedef void * edge_parallel_category; typedef std::forward_iterator_tag traversal_category; //Required by IncidenceGraph concept typedef typename detail::cal_out_edge_iterator out_edge_iterator; typedef std::size_t degree_size_type; //Required by VertexListGraph concept typedef boost::integer_range VertexList; typedef typename VertexList::const_iterator vertex_iterator; typedef std::size_t vertices_size_type; //Required by EdgeListGraph concept //typedef typename detail::cal_undirected_edge_iterator edge_iterator; typedef typename detail::cal_edge_iterator edge_iterator; typedef std::size_t edges_size_type; //Required by BidirectionalGraph concept typedef edge_iterator in_edge_iterator; //Required by AdjacencyGraph concept class cgal_adjacency_iterator {}; // only for bidirectional graphs; typedef cgal_adjacency_iterator adjacency_iterator; //Property Graph. typedef GraphProperties graph_property_type; typedef typename boost::lookup_one_property::type graph_bundled; graph_property_type m_property; public: struct VertexRecord { IndexType first_edge_index; IndexType first_out_edge_index; //VertexProperties properties; }; //Vertex Property storage. std::vector m_vertex_properties; typedef boost::no_property vertex_property_type; typedef VertexProperties vertex_bundled; typedef boost::iterator_property_map< typename std::vector::iterator, typename boost::typed_identity_property_map > vertex_property_map_type; typedef boost::iterator_property_map< typename std::vector::const_iterator, typename boost::typed_identity_property_map > const_vertex_property_map_type; typedef boost::no_property edge_property_type; typedef EdgeProperties edge_bundled; typedef boost::iterator_property_map< typename std::vector::iterator, typename detail::cal_edge_index_map > edge_property_map_type; typedef boost::iterator_property_map< typename std::vector::const_iterator, typename detail::cal_edge_index_map > const_edge_property_map_type; //typedef typename boost::lookup_one_property::type vertex_bundled; private: IndexType m_vertex_count; VertexRecord * m_vertices; VertexList m_vertex_index; // to be constructed with new VertexRecord[m_vertex_count+1]; // the last vertex with index m_vertex_count is only intended // to be able to compute the degree of vertex m_vertex_count-1 // with m_vertices[m_vertex_count-1].first_edge_index - m_vertices[m_vertex_count-2] public: struct EdgeRecord { IndexType target_node; //EdgeProperties properties; }; private: IndexType m_edge_count; IndexType m_current_edge_index; EdgeRecord * m_edges; std::vector m_edge_properties; // public: // class edge_iterator { // const compact_adjacency_list* m_graph; // IndexType m_vertex_index; // IndexType m_edge_index; // // operators ++ -- * ... // }; // class vertex_iterator { // IndexType m_vertex_index; // // I don't think we need a reference to the graph to implement // // ++ -- * ... // }; //typedef EdgeRecord * out_edge_iterator; public: compact_adjacency_list( IndexType vertex_count, IndexType edge_count); template compact_adjacency_list( const boost::adjacency_list & src ); //compact_adjacency_list( compact_adjacency_list< directed_category,VertexProperties,EdgeProperties,GraphProperties,IndexType > const & src ); compact_adjacency_list( std::size_t num_vertices, std::size_t num_edges, VertexRecord * vlist, EdgeRecord * elist ); template< typename VPropIterator, typename EPropIterator > explicit compact_adjacency_list( std::size_t num_vertices, std::size_t num_edges, VertexRecord * vlist, EdgeRecord * elist, VPropIterator vprop, EPropIterator eprop); template< typename DegreeIterator, typename EdgeIterator > explicit compact_adjacency_list( IndexType vertex_count, IndexType edge_count, DegreeIterator dbegin, DegreeIterator dend, EdgeIterator begin, EdgeIterator end); template< typename DegreeSequence, typename EdgeIterator, typename EdgePropIterator > explicit compact_adjacency_list(IndexType vertex_count, IndexType edge_count,DegreeSequence dbegin,DegreeSequence dend, EdgeIterator begin, EdgeIterator end, EdgePropIterator prop_begin ); ~compact_adjacency_list(); inline vertices_size_type vertex_size() const { return m_vertex_count; } inline edges_size_type edges_size() const { return m_edge_count; } inline const VertexList & vertex_set() const { return m_vertex_index; } inline std::pair edge_set() const { return std::make_pair(edge_iterator(m_vertex_index.begin(),m_vertex_count,m_vertices),edge_iterator(m_vertex_index.end(),m_vertex_count,m_vertices)); } inline std::pair out_edge_set(vertex_descriptor const v ) const{ return std::make_pair(out_edge_iterator(m_vertices[v].first_edge_index,v),out_edge_iterator(m_vertices[v+1].first_edge_index,v)); } inline const VertexRecord & vertexRecords() const { return *m_vertices; } inline const EdgeRecord & edgeRecords() const { return *m_edges; } /*! * \brief hasEdge * \param s Source vertex * \param t Destination vertex * \return A pair of edge_descriptor and a boolean value. * * This method returns an edge descriptor and boolean value. If a edge exists between vertices s & t then a pair of * consisting of the edge_descriptor and true is returned. If no edge was found then the boolean is set to false, * the edge descriptor is undefined. * * This method is provided for convenience only and is linear in the degree of the soruce vertex. * */ inline std::pair< edge_descriptor, bool> hasEdge( vertex_descriptor s, vertex_descriptor t ) const { EdgeIndex idx = m_vertices[s].first_edge_index; bool found = false; unsigned int deg = m_vertices[s+1].first_edge_index - m_vertices[s].first_edge_index; while( idx < m_vertices[s].first_edge_index + deg && found == false ){ found = m_edges[idx].target_node == t ? true : idx++; } return std::make_pair(std::make_pair(s,idx),found); } inline vertex_descriptor target ( edge_descriptor const e ) const { return m_edges[e.second].target_node; } inline vertex_property_map_type get_vertex_bundle( boost::typed_identity_property_map indexMap = boost::typed_identity_property_map()) { return vertex_property_map_type(m_vertex_properties.begin(), indexMap); } inline const_vertex_property_map_type get_vertex_bundle( boost::typed_identity_property_map const indexMap = boost::typed_identity_property_map()) const { return const_vertex_property_map_type(m_vertex_properties.begin(), indexMap); } inline edge_property_map_type get_edge_bundle( detail::cal_edge_index_map indexMap = detail::cal_edge_index_map()){ return edge_property_map_type(m_edge_properties.begin(),indexMap); } inline const_edge_property_map_type get_edge_bundle( detail::cal_edge_index_map const indexMap = detail::cal_edge_index_map()) const { return const_edge_property_map_type(m_edge_properties.begin(),indexMap); } inline degree_size_type out_degree( vertex_descriptor v ){ return m_vertices[v+1].first_edge_index - m_vertices[v].first_edge_index;} inline degree_size_type out_degree( vertex_descriptor const v ) const { return m_vertices[v+1].first_edge_index - m_vertices[v].first_edge_index;} static vertex_descriptor null_vertex(){ return std::numeric_limits::max(); } }; } // namespace graph } // namespace mips template< CAL_DIR_GRAPH_TEMPLATE_PARAMS > CAL_DIR_GRAPH_CLASS_PARAMS:: compact_adjacency_list(IndexType vertex_count, IndexType edge_count) :m_vertex_count(vertex_count), m_current_edge_index(0), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertex_properties(vertex_count), m_edge_properties(edge_count) { m_vertices = new VertexRecord[m_vertex_count+1]; m_edges = new EdgeRecord[m_edge_count]; for(unsigned int i = 0; i < m_vertex_count; i++ ) m_vertices[i].first_edge_index = std::numeric_limits::max(); m_vertices[m_vertex_count].first_edge_index = m_edge_count; } template< CAL_DIR_GRAPH_TEMPLATE_PARAMS> template< typename DegreeSequence, typename EdgeIterator > CAL_DIR_GRAPH_CLASS_PARAMS:: compact_adjacency_list(IndexType vertex_count, IndexType edge_count,DegreeSequence dbegin,DegreeSequence dend, EdgeIterator begin, EdgeIterator end ) :m_vertex_count(vertex_count), m_current_edge_index(0), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertex_properties(vertex_count), m_edge_properties(edge_count) { m_vertices = new VertexRecord[m_vertex_count+1]; m_edges = new EdgeRecord[m_edge_count]; std::size_t edge_index = 0; DegreeSequence tmp_begin = dbegin; while(dbegin != dend){ m_vertices[ dbegin - tmp_begin ].first_edge_index = edge_index; for( std::size_t count = 0; count < *dbegin; count++){ m_edges[ edge_index ].target_node = begin->second; ++begin; edge_index++; } dbegin++; } m_vertices[m_vertex_count].first_edge_index = m_edge_count; } template< CAL_DIR_GRAPH_TEMPLATE_PARAMS> template< typename DegreeSequence, typename EdgeIterator, typename EdgePropIterator > CAL_DIR_GRAPH_CLASS_PARAMS:: compact_adjacency_list(IndexType vertex_count, IndexType edge_count,DegreeSequence dbegin,DegreeSequence dend, EdgeIterator begin, EdgeIterator end, EdgePropIterator prop_begin ) :m_vertex_count(vertex_count), m_current_edge_index(0), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertex_properties(vertex_count), m_edge_properties(edge_count) { m_vertices = new VertexRecord[m_vertex_count+1]; m_edges = new EdgeRecord[m_edge_count]; std::size_t edge_index = 0; DegreeSequence tmp_begin = dbegin; while(dbegin != dend){ m_vertices[ dbegin - tmp_begin ].first_edge_index = edge_index; for( std::size_t count = 0; count < *dbegin; count++){ m_edges[ edge_index ].target_node = begin->second; m_edge_properties[ edge_index ].label = *prop_begin; edge_index++; } dbegin++; } m_vertices[m_vertex_count].first_edge_index = m_edge_count; } template< CAL_DIR_GRAPH_TEMPLATE_PARAMS > CAL_DIR_GRAPH_CLASS_PARAMS::compact_adjacency_list( std::size_t vertex_count, std::size_t edge_count, typename CAL_DIR_GRAPH_CLASS_PARAMS::VertexRecord * vlist, typename CAL_DIR_GRAPH_CLASS_PARAMS::EdgeRecord * elist ): m_vertex_count(vertex_count), m_current_edge_index(edge_count), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertex_properties(vertex_count), m_edge_properties(edge_count), m_vertices(vlist), m_edges(elist){} template< CAL_DIR_GRAPH_TEMPLATE_PARAMS > template< typename VPropIterator, typename EPropIterator > CAL_DIR_GRAPH_CLASS_PARAMS::compact_adjacency_list( std::size_t vertex_count, std::size_t edge_count, typename CAL_DIR_GRAPH_CLASS_PARAMS::VertexRecord * vlist, typename CAL_DIR_GRAPH_CLASS_PARAMS::EdgeRecord * elist, VPropIterator vprop, EPropIterator eprop): m_vertex_count(vertex_count), m_current_edge_index(edge_count), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertices(vlist), m_edges(elist) { m_vertex_properties.assign(vprop,vprop+vertex_count); m_edge_properties.assign(eprop,eprop+edge_count); } template< CAL_DIR_GRAPH_TEMPLATE_PARAMS > CAL_DIR_GRAPH_CLASS_PARAMS:: ~compact_adjacency_list(){ delete m_vertices; delete m_edges; } /* * UNDIRECTED COMPACT ADJACENCY LIST * */ template< CAL_UDIR_GRAPH_TEMPLATE_PARAMS > CAL_UDIR_GRAPH_CLASS_PARAMS:: compact_adjacency_list(IndexType vertex_count, IndexType edge_count) :m_vertex_count(vertex_count), m_current_edge_index(0), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()) { m_vertices = new VertexRecord[m_vertex_count+1]; //Assign 2 times the number of edges for undirected edges. m_edges = new EdgeRecord[2*m_edge_count]; for(unsigned int i = 0; i < m_vertex_count; i++ ) m_vertices[i].first_edge_index = std::numeric_limits::max(); m_vertices[m_vertex_count].first_edge_index = m_edge_count; } template< CAL_UDIR_GRAPH_TEMPLATE_PARAMS> template< typename DegreeSequence, typename EdgeIterator > CAL_UDIR_GRAPH_CLASS_PARAMS:: compact_adjacency_list(IndexType vertex_count, IndexType edge_count,DegreeSequence dbegin,DegreeSequence dend, EdgeIterator begin, EdgeIterator end ) :m_vertex_count(vertex_count), m_current_edge_index(0), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertex_properties(vertex_count), m_edge_properties(edge_count) { m_vertices = new VertexRecord[m_vertex_count+1]; m_edges = new EdgeRecord[2*m_edge_count]; std::size_t edge_index = 0; DegreeSequence tmp_begin = dbegin; std::queue< std::pair > scratchpad; bool firstNewEdge = true; while( dbegin != dend ){ m_vertices[dbegin - tmp_begin ].first_edge_index = edge_index; m_vertices[dbegin - tmp_begin ].first_out_edge_index = edge_index + *dbegin; for( std::size_t count = 0; count < *dbegin; count++){ if(! scratchpad.empty() && scratchpad.front().first == (dbegin - tmp_begin) ){ firstNewEdge = true; m_edges[edge_index].target_node = scratchpad.front().second; scratchpad.pop(); } else{ if(firstNewEdge){ //This is a new edge that has not been added before. m_vertices[dbegin - tmp_begin ].first_out_edge_index = edge_index; firstNewEdge = false; } m_edges[edge_index].target_node = begin->second; scratchpad.push(std::make_pair(begin->second,begin->first)); ++begin; } edge_index++; } assert((edge_index - m_vertices[dbegin - tmp_begin].first_edge_index) == *dbegin ); ++dbegin; } m_vertices[m_vertex_count].first_edge_index = 2*m_edge_count; } template< CAL_UDIR_GRAPH_TEMPLATE_PARAMS> template< typename DegreeSequence, typename EdgeIterator, typename EdgePropIterator > CAL_UDIR_GRAPH_CLASS_PARAMS:: compact_adjacency_list(IndexType vertex_count, IndexType edge_count,DegreeSequence dbegin,DegreeSequence dend, EdgeIterator begin, EdgeIterator end, EdgePropIterator prop_begin ) :m_vertex_count(vertex_count), m_current_edge_index(0), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertex_properties(vertex_count), m_edge_properties(edge_count) { m_vertices = new VertexRecord[m_vertex_count+1]; m_edges = new EdgeRecord[2*m_edge_count]; std::size_t edge_index = 0; DegreeSequence tmp_begin = dbegin; std::queue< std::pair > scratchpad; bool firstNewEdge = true; while( dbegin != dend ){ m_vertices[dbegin - tmp_begin ].first_edge_index = edge_index; m_vertices[dbegin - tmp_begin ].first_out_edge_index = 0; for( std::size_t count = 0; count < *dbegin; count++){ if(! scratchpad.empty() && scratchpad.front().first == (dbegin - tmp_begin) ){ firstNewEdge = true; m_edges[edge_index].target_node = scratchpad.front().second; scratchpad.pop(); } else{ if(firstNewEdge){ //This is a new edge that has not been added before. m_vertices[dbegin - tmp_begin ].first_out_edge_index = edge_index; firstNewEdge = false; } m_edges[edge_index].target_node = begin->second; scratchpad.push(std::make_pair(begin->second,begin->first)); m_edge_properties[edge_index] = *prop_begin; ++begin; ++prop_begin; } edge_index++; } assert((edge_index - m_vertices[dbegin - tmp_begin].first_edge_index) == *dbegin ); ++dbegin; } m_vertices[m_vertex_count].first_edge_index = m_edge_count; } template< CAL_UDIR_GRAPH_TEMPLATE_PARAMS > CAL_UDIR_GRAPH_CLASS_PARAMS::compact_adjacency_list( std::size_t vertex_count, std::size_t edge_count, typename CAL_UDIR_GRAPH_CLASS_PARAMS::VertexRecord * vlist, typename CAL_UDIR_GRAPH_CLASS_PARAMS::EdgeRecord * elist ): m_vertex_count(vertex_count), m_current_edge_index(edge_count), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertex_properties(vertex_count), m_edge_properties(edge_count), m_vertices(vlist), m_edges(elist){ } template< CAL_UDIR_GRAPH_TEMPLATE_PARAMS > template< typename VPropIterator, typename EPropIterator > CAL_UDIR_GRAPH_CLASS_PARAMS::compact_adjacency_list( std::size_t vertex_count, std::size_t edge_count, typename CAL_UDIR_GRAPH_CLASS_PARAMS::VertexRecord * vlist, typename CAL_UDIR_GRAPH_CLASS_PARAMS::EdgeRecord * elist, VPropIterator vprop, EPropIterator eprop): m_vertex_count(vertex_count), m_current_edge_index(edge_count), m_edge_count(edge_count), m_vertex_index(0,vertex_count), m_property(GraphProperties()), m_vertices(vlist), m_edges(elist) { m_vertex_properties.assign(vprop,vprop+vertex_count); m_edge_properties.assign(eprop,eprop+edge_count); } template< CAL_UDIR_GRAPH_TEMPLATE_PARAMS > CAL_UDIR_GRAPH_CLASS_PARAMS:: ~compact_adjacency_list(){ delete m_vertices; delete m_edges; } //=========================================================== //Implementation of boost methods for compactadjacencylist namespace boost { template< CAL_GRAPH_TEMPLATE_PARAMS > inline typename CAL_GRAPH_CLASS_PARAMS::vertices_size_type num_vertices( CAL_GRAPH_CLASS_PARAMS const & g ){ return g.vertex_size(); } template< CAL_GRAPH_TEMPLATE_PARAMS > inline typename CAL_GRAPH_CLASS_PARAMS:: vertices_size_type num_edges( CAL_GRAPH_CLASS_PARAMS const & g ){ return g.edges_size(); } template< CAL_GRAPH_TEMPLATE_PARAMS > inline std::pair< typename CAL_GRAPH_CLASS_PARAMS::vertex_iterator, typename CAL_GRAPH_CLASS_PARAMS::vertex_iterator > vertices( CAL_GRAPH_CLASS_PARAMS const & g ){ return std::make_pair(g.vertex_set().begin(),g.vertex_set().end()); } template< CAL_GRAPH_TEMPLATE_PARAMS > inline std::pair< typename CAL_GRAPH_CLASS_PARAMS::edge_descriptor, bool > edge( typename CAL_GRAPH_CLASS_PARAMS::vertex_descriptor s, typename CAL_GRAPH_CLASS_PARAMS::vertex_descriptor t, CAL_GRAPH_CLASS_PARAMS const & g ){ return g.hasEdge(s,t); } template< CAL_GRAPH_TEMPLATE_PARAMS > inline std::pair< typename CAL_GRAPH_CLASS_PARAMS::edge_iterator, typename CAL_GRAPH_CLASS_PARAMS::edge_iterator > edges( CAL_GRAPH_CLASS_PARAMS const & g ){ return g.edge_set(); } template< CAL_GRAPH_TEMPLATE_PARAMS > inline std::pair< typename CAL_GRAPH_CLASS_PARAMS::out_edge_iterator, typename CAL_GRAPH_CLASS_PARAMS::out_edge_iterator > out_edges( typename CAL_GRAPH_CLASS_PARAMS::vertex_descriptor vertex, CAL_GRAPH_CLASS_PARAMS const & g ){ return g.out_edge_set(vertex); } template< CAL_GRAPH_TEMPLATE_PARAMS > typename CAL_GRAPH_CLASS_PARAMS::degree_size_type out_degree( typename CAL_GRAPH_CLASS_PARAMS::vertex_descriptor vertex, CAL_GRAPH_CLASS_PARAMS const & g ){ return g.out_degree(vertex); } template< CAL_GRAPH_TEMPLATE_PARAMS > typename CAL_GRAPH_CLASS_PARAMS::vertex_descriptor vertex( std::size_t v,CAL_GRAPH_CLASS_PARAMS const & g ){ return v; } template< typename T, CAL_GRAPH_TEMPLATE_PARAMS > T source( std::pair e,CAL_GRAPH_CLASS_PARAMS const & g ){ return e.first; } template< typename T, CAL_GRAPH_TEMPLATE_PARAMS > T target( std::pair e,CAL_GRAPH_CLASS_PARAMS const & g ){ return g.target(e); } //The following segement has been taken from the boost::compressed_row_graph implementation. There is not much //that can be improved or is significantly different from my chosen approach. // For Property Graph // Graph properties template inline void set_property(CAL_GRAPH_CLASS_PARAMS & g, Tag tag, const Value& value) { get_property_value(g.m_property, tag) = value; } template inline typename graph_property::type& get_property(CAL_GRAPH_CLASS_PARAMS & g, Tag tag) { return get_property_value(g.m_property, tag); } template inline const typename graph_property::type& get_property(const CAL_GRAPH_CLASS_PARAMS & g, Tag tag) { return get_property_value(g.m_property, tag); } template struct cal_property_map_helper {}; // Kind == void for invalid property tags, so we can use that to SFINAE out template struct cal_property_map_helper { typedef vertex_all_t all_tag; typedef typename property_traits::type>::key_type key_type; typedef VertexProperties plist_type; typedef typename property_map::type all_type; typedef typename property_map::const_type all_const_type; typedef transform_value_property_map, all_type> type; typedef transform_value_property_map, all_const_type> const_type; }; template struct cal_property_map_helper { typedef edge_all_t all_tag; typedef typename property_traits::type>::key_type key_type; typedef EdgeProperties plist_type; typedef typename property_map::type all_type; typedef typename property_map::const_type all_const_type; typedef transform_value_property_map, all_type> type; typedef transform_value_property_map, all_const_type> const_type; }; template struct cal_property_map_helper { typedef graph_all_t all_tag; typedef CAL_GRAPH_CLASS_PARAMS* key_type; typedef GraphProperties plist_type; typedef typename property_map::type all_type; typedef typename property_map::const_type all_const_type; typedef transform_value_property_map, all_type> type; typedef transform_value_property_map, all_const_type> const_type; }; template struct property_map: cal_property_map_helper< CAL_GRAPH_CLASS_PARAMS, Tag, typename detail::property_kind_from_graph ::type> {}; template typename property_map::type get(Tag tag, CAL_GRAPH_CLASS_PARAMS & g) { return typename property_map::type(tag, get(typename property_map::all_tag(), g)); } template typename property_map::const_type get(Tag tag, const CAL_GRAPH_CLASS_PARAMS & g) { return typename property_map::const_type(tag, get(typename property_map::all_tag(), g)); } template typename property_traits::type>::reference get(Tag tag, CAL_GRAPH_CLASS_PARAMS& g, typename property_map::key_type k) { typedef typename property_map::all_tag all_tag; typedef typename property_map::type outer_pm; return lookup_one_property::value_type, Tag>::lookup(get(all_tag(), g, k), tag); } template typename property_traits::const_type>::reference get(Tag tag, const CAL_GRAPH_CLASS_PARAMS& g, typename property_map::key_type k) { typedef typename property_map::all_tag all_tag; typedef typename property_map::const_type outer_pm; return lookup_one_property::value_type, Tag>::lookup(get(all_tag(), g, k), tag); } template void put( Tag tag, CAL_GRAPH_CLASS_PARAMS& g, typename property_map::key_type k, typename lookup_one_property::plist_type, Tag>::type val) { typedef typename property_map::all_tag all_tag; typedef typename property_map::type outer_pm; lookup_one_property::plist_type, Tag>::lookup(get(all_tag(), g, k), tag) = val; } template struct property_map { typedef typed_identity_property_map type; typedef type const_type; }; template struct property_map { //cal_edge_index_map typedef mips::graph::detail::cal_edge_index_map< IndexType, IndexType > type; typedef type const_type; }; template struct property_map { typedef typename CAL_GRAPH_CLASS_PARAMS::vertex_property_map_type type; typedef typename CAL_GRAPH_CLASS_PARAMS::const_vertex_property_map_type const_type; }; template struct property_map { typedef typename CAL_GRAPH_CLASS_PARAMS::edge_property_map_type type; typedef typename CAL_GRAPH_CLASS_PARAMS::const_edge_property_map_type const_type; }; template struct property_map { typedef boost::ref_property_map type; typedef boost::ref_property_map const_type; }; template inline typed_identity_property_map get(vertex_index_t, const CAL_GRAPH_CLASS_PARAMS&) { return typed_identity_property_map(); } template inline IndexType get(vertex_index_t, const CAL_GRAPH_CLASS_PARAMS&, IndexType v) { return v; } template inline typed_identity_property_map get(vertex_index_t, CAL_GRAPH_CLASS_PARAMS&) { return typed_identity_property_map(); } template inline IndexType get(vertex_index_t, CAL_GRAPH_CLASS_PARAMS&, IndexType v) { return v; } template inline typename property_map::const_type get(edge_index_t, CAL_GRAPH_CLASS_PARAMS&) { typedef typename property_map::const_type result_type; return result_type(); } template inline typename property_map::const_type get(edge_index_t, const CAL_GRAPH_CLASS_PARAMS&) { typedef typename property_map::const_type result_type; return result_type(); } template inline IndexType //This referes to the edge index. get(edge_index_t, CAL_GRAPH_CLASS_PARAMS&, typename CAL_GRAPH_CLASS_PARAMS::edge_descriptor e) { return e.second; } template inline IndexType //This index referes to the edge index. get(edge_index_t, const CAL_GRAPH_CLASS_PARAMS&, typename CAL_GRAPH_CLASS_PARAMS::edge_descriptor e) { return e.second; } template inline typename property_map::type get(vertex_all_t, CAL_GRAPH_CLASS_PARAMS& g) { return g.get_vertex_bundle(get(vertex_index, g)); } template inline typename property_map::const_type get(vertex_all_t, const CAL_GRAPH_CLASS_PARAMS& g) { return g.get_vertex_bundle(get(vertex_index, g)); } template inline VertexProperties& get(vertex_all_t, CAL_GRAPH_CLASS_PARAMS& g, IndexType v) { return get(vertex_all, g)[v]; } template inline const VertexProperties& get(vertex_all_t, const CAL_GRAPH_CLASS_PARAMS& g, IndexType v) { return get(vertex_all, g)[v]; } template inline void put(vertex_all_t, CAL_GRAPH_CLASS_PARAMS& g, IndexType v, const VertexProperties& val) { put(get(vertex_all, g), v, val); } template inline typename property_map::type get(edge_all_t, CAL_GRAPH_CLASS_PARAMS& g) { return g.get_edge_bundle(get(edge_index, g)); } template inline typename property_map::const_type get(edge_all_t, const CAL_GRAPH_CLASS_PARAMS& g) { return g.get_edge_bundle(get(edge_index, g)); } template inline EdgeProperties& get(edge_all_t, CAL_GRAPH_CLASS_PARAMS& g, const typename CAL_GRAPH_CLASS_PARAMS::edge_descriptor& e) { return get(edge_all, g)[e]; } template inline const EdgeProperties& get(edge_all_t, const CAL_GRAPH_CLASS_PARAMS& g, const typename CAL_GRAPH_CLASS_PARAMS::edge_descriptor& e) { return get(edge_all, g)[e]; } template inline void put(edge_all_t, CAL_GRAPH_CLASS_PARAMS& g, const typename CAL_GRAPH_CLASS_PARAMS::edge_descriptor& e, const EdgeProperties& val) { put(get(edge_all, g), e, val); } template inline typename property_map::type get(graph_all_t, CAL_GRAPH_CLASS_PARAMS& g) { return typename property_map::type(g.m_property); } template inline typename property_map::const_type get(graph_all_t, const CAL_GRAPH_CLASS_PARAMS& g) { return typename property_map::const_type(g.m_property); } template inline GraphProperties& get(graph_all_t, CAL_GRAPH_CLASS_PARAMS& g, CAL_GRAPH_CLASS_PARAMS*) { return g.m_property; } template inline const GraphProperties& get(graph_all_t, const CAL_GRAPH_CLASS_PARAMS& g, CAL_GRAPH_CLASS_PARAMS*) { return g.m_property; } template inline void put(graph_all_t, CAL_GRAPH_CLASS_PARAMS& g, CAL_GRAPH_CLASS_PARAMS*, const GraphProperties& val) { g.m_property = val; } } // namespace boost // Free functions namespace mips { namespace graph { struct graph_format{}; struct nci_format:public graph_format{}; struct g_format:public graph_format{}; struct m_format:public graph_format{}; template CAL_GRAPH_CLASS_PARAMS * format_creator(std::istream & stream, graph_format format ); /***************** * Factory methods for reading a graph into a compact ajacency list. * *****************/ template CAL_GRAPH_CLASS_PARAMS * format_creator(std::istream & stream, VertexPropertiesMap & vmap, EdgePropertiesMap & emap, graph_format format ); template CAL_GRAPH_CLASS_PARAMS * format_creator(std::istream & stream, nci_format){ typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::VertexRecord > VertexList; typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord > EdgeList; std::size_t num_vertices; std::size_t num_edges; std::size_t graph_id; VertexList * vertexlist = new VertexList(); EdgeList * edgelist = new EdgeList(); typedef typename boost::property_map::type VertexIdMap; typedef typename boost::property_map::type EdgeIdMap; typedef boost::vector_property_map< typename VertexProperties::value_type,VertexIdMap > VertexPropertiesMap; typedef boost::vector_property_map< typename EdgeProperties::value_type,EdgeIdMap > EdgePropertiesMap; VertexPropertiesMap * vmap = new VertexPropertiesMap(); EdgePropertiesMap * emap = new EdgePropertiesMap(); mips::graph::formats::NCIFormat format; format.readGraph,std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord >,VertexPropertiesMap,EdgePropertiesMap > (stream,num_vertices,num_edges,*vertexlist,*edgelist,*vmap,*emap,std::cerr,graph_id, Directed()); typename CAL_GRAPH_CLASS_PARAMS::VertexRecord lastRecord; lastRecord.first_edge_index = edgelist->size(); vertexlist->push_back(lastRecord); CAL_GRAPH_CLASS_PARAMS * tmp = new CAL_GRAPH_CLASS_PARAMS(num_vertices, num_edges, &((*vertexlist)[0]), &((*edgelist)[0]),vmap->storage_begin(),emap->storage_begin()); return tmp; } template CAL_GRAPH_CLASS_PARAMS * format_creator(std::istream & stream, VertexPropertiesMap & vmap, EdgePropertiesMap & emap, nci_format){ typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::VertexRecord > VertexList; typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord > EdgeList; std::size_t num_vertices; std::size_t num_edges; std::size_t graph_id; VertexList * vertexlist = new VertexList(); EdgeList * edgelist = new EdgeList(); mips::graph::formats::NCIFormat format; format.readGraph,std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord >,VertexPropertiesMap,EdgePropertiesMap > (stream,num_vertices,num_edges,*vertexlist,*edgelist,vmap,emap,std::cerr,graph_id, Directed()); typename CAL_GRAPH_CLASS_PARAMS::VertexRecord lastRecord; lastRecord.first_edge_index = edgelist->size(); vertexlist->push_back(lastRecord); CAL_GRAPH_CLASS_PARAMS * tmp = new CAL_GRAPH_CLASS_PARAMS(num_vertices, num_edges, &((*vertexlist)[0]), &((*edgelist)[0])); return tmp; } template CAL_GRAPH_CLASS_PARAMS * format_creator(std::istream & stream, VertexPropertiesMap & vmap, EdgePropertiesMap & emap, g_format){ typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::VertexRecord > VertexList; typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord > EdgeList; std::size_t num_vertices; std::size_t num_edges; std::size_t graph_id; VertexList * vertexlist = new VertexList(); EdgeList * edgelist = new EdgeList(); mips::graph::formats::GFormat format; if(!format.readGraph,std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord >,VertexPropertiesMap,EdgePropertiesMap > (stream,num_vertices,num_edges,*vertexlist,*edgelist,vmap,emap,std::cerr,graph_id, Directed())){ std::cerr<<"There was a problem reading the graph from file. Perhaps the graph is not given in NCI-Format ? "<size(); vertexlist->push_back(lastRecord); CAL_GRAPH_CLASS_PARAMS * tmp = new CAL_GRAPH_CLASS_PARAMS(num_vertices, num_edges, &((*vertexlist)[0]), &((*edgelist)[0])); return tmp; } template CAL_GRAPH_CLASS_PARAMS * format_creator(std::istream & stream, g_format){ typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::VertexRecord > VertexList; typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord > EdgeList; std::size_t num_vertices; std::size_t num_edges; std::size_t graph_id; VertexList * vertexlist = new VertexList(); EdgeList * edgelist = new EdgeList(); typedef typename boost::property_map::type VertexIdMap; typedef typename boost::property_map::type EdgeIdMap; typedef boost::vector_property_map< typename VertexProperties::value_type,VertexIdMap > VertexPropertiesMap; typedef boost::vector_property_map< typename EdgeProperties::value_type,EdgeIdMap > EdgePropertiesMap; VertexPropertiesMap * vmap = new VertexPropertiesMap(); EdgePropertiesMap * emap = new EdgePropertiesMap(); mips::graph::formats::GFormat format; if(!format.readGraph,std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord >,VertexPropertiesMap,EdgePropertiesMap > (stream,num_vertices,num_edges,*vertexlist,*edgelist,*vmap,*emap,std::cerr,graph_id, Directed())){ std::cerr<<"There was a problem reading the graph from file. Perhaps the graph is not given in GFormat ? "<size(); vertexlist->push_back(lastRecord); CAL_GRAPH_CLASS_PARAMS * tmp = new CAL_GRAPH_CLASS_PARAMS(num_vertices, num_edges, &((*vertexlist)[0]), &((*edgelist)[0]),vmap->storage_begin(),emap->storage_begin()); return tmp; } template CAL_GRAPH_CLASS_PARAMS * format_creator(std::istream & stream, m_format){ typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::VertexRecord > VertexList; typedef std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord > EdgeList; std::size_t num_vertices; std::size_t num_edges; std::size_t graph_id; VertexList * vertexlist = new VertexList(); EdgeList * edgelist = new EdgeList(); typedef typename boost::property_map::type VertexIdMap; typedef typename boost::property_map::type EdgeIdMap; typedef boost::vector_property_map< typename VertexProperties::value_type,VertexIdMap > VertexPropertiesMap; typedef boost::vector_property_map< typename EdgeProperties::value_type,EdgeIdMap > EdgePropertiesMap; VertexPropertiesMap * vmap = new VertexPropertiesMap(); EdgePropertiesMap * emap = new EdgePropertiesMap(); mips::graph::formats::MFormat format; if(!format.readGraph,std::vector< typename CAL_GRAPH_CLASS_PARAMS::EdgeRecord >,VertexPropertiesMap,EdgePropertiesMap > (stream,num_vertices,num_edges,*vertexlist,*edgelist,*vmap,*emap,std::cerr,graph_id, Directed())){ std::cerr<<"There was a problem reading the graph from file. Perhaps the graph is not given in MFormat ? "<size(); vertexlist->push_back(lastRecord); CAL_GRAPH_CLASS_PARAMS * tmp = new CAL_GRAPH_CLASS_PARAMS(num_vertices, num_edges, &((*vertexlist)[0]), &((*edgelist)[0]),vmap->storage_begin(),emap->storage_begin()); return tmp; } template CAL_GRAPH_CLASS_PARAMS * make_compact_adjacency_list( std::istream & stream ){ return format_creator (stream,GraphFormat()); } template CAL_GRAPH_CLASS_PARAMS * make_compact_adjacency_list( std::istream & stream, VertexPropertiesMap & vmap, EdgePropertiesMap & emap ){ return format_creator (stream,vmap,emap,GraphFormat()); } } // namespace graph } // namespace mips #undef CAL_GRAPH_CLASS_PARAMS #undef CAL_GRAPH_TEMPLATE_PARAMS #undef CAL_DIR_GRAPH_CLASS_PARAMS #undef CAL_DIR_GRAPH_TEMPLATE_PARAMS #endif // MIPS_CORE_GRAPH_COMPACTADJACENCYLIST_HPP mips-1.0/mips/core/graph/defaultLabelTags.hpp0000644002402200254040000000321612520407731020554 0ustar thomasfdtaig/******************************************************************* * Copyright (c) 2010-2013 MiGraNT - DTAI.cs.kuleuven.be * License details in the root directory of this distribution in * the LICENSE file ********************************************************************/ #ifndef _DEFAULT_LABEL_TAGS_HPP #define _DEFAULT_LABEL_TAGS_HPP // Default label tags are vertex_label_t and edge_label_t // In principle, all code will use template parameters for label tags // Other header files can provide support for default label tags // by checking first if MIPS_DEFAULT_LABEL_TAGS has been defined #define MIPS_DEFAULT_LABEL_TAGS namespace boost { enum edge_label_t { edge_label }; enum vertex_label_t { vertex_label }; BOOST_INSTALL_PROPERTY(vertex, label); BOOST_INSTALL_PROPERTY(edge, label); } template inline typename boost::property_map::type stdVertexLabelMap(GraphType& g) { return get(boost::vertex_label, g); } template inline typename boost::property_map::const_type stdConstVertexLabelMap(const GraphType & g) { return get(boost::vertex_label, g); } template inline typename boost::property_map::type stdEdgeLabelMap(GraphType& g) { return get(boost::edge_label, g); } template inline typename boost::property_map::const_type stdConstEdgeLabelMap(const GraphType& g) { return get(boost::edge_label, g); } #ifdef ALPHABET_GRAPH_HPP #include #endif #endif // _DEFAULT_LABEL_TAGS_HPP mips-1.0/mips/core/graph/format/0000755002402200254040000000000012520407731016126 5ustar thomasfdtaigmips-1.0/mips/core/graph/format/snapformat.hpp0000644002402200254040000003645212520407731021023 0ustar thomasfdtaig#ifndef MIPS_GRAPH_IO_FORMAT_SNAPFORMAT_HPP #define MIPS_GRAPH_IO_FORMAT_SNAPFORMAT_HPP #include #include namespace mips { namespace graph { /*! * \namespace mips::graph::formats * \brief Formats for reading and writing graphs reside in this namespace */ namespace formats { /*! * \brief Provides methods for reading and writing files given in the following format: * * The graph is written in the SNAP library format, an example is shown below : * * \code * [] * \endcode * * I.e., a list of edges with an optional edge label. * * \note Currently, we assume the edge label is of type char. * */ struct SNAPFormat : public io::GraphFormatter< SNAPFormat > { private: template< typename Graph, typename VertexLabelMap> std::size_t getAddVertex(std::map &id_map, std::size_t vId, std::size_t &vCount, Graph &g, VertexLabelMap & vlm, typename boost::property_traits::value_type vLabel) { if(id_map.find(vId) == id_map.end()) { typename boost::graph_traits::vertex_descriptor v = add_vertex(g); vlm[v] = vLabel; id_map[vId] = vCount; return ++vCount - 1; } else { return id_map[vId]; } } std::size_t getVertexID(std::map &id_map, std::size_t vId, std::size_t &vCount) { if(id_map.find(vId) == id_map.end()) { id_map[vId] = vCount; return ++vCount - 1; } else return id_map[vId]; } /* template< typename Graph, typename EdgeLabelMap > void addEdge(Graph & g, EdgeLabelMap & elm, std::size_t vertexSrc, std::size_t vertexDst, typename boost::property_traits::value_type eLabel, boost::directed_tag){ typename boost::graph_traits::edge_descriptor edge; edge = boost::add_edge(boost::vertex(vertexSrc,g),boost::vertex(vertexDst,g),g).first; elm[edge] = eLabel; } template< typename Graph, typename EdgeLabelMap > void addEdge(Graph & g, EdgeLabelMap & elm, std::size_t vertexSrc, std::size_t vertexDst, typename boost::property_traits::value_type eLabel, boost::undirected_tag){ typename boost::graph_traits::edge_descriptor edge; if(vertexSrc < vertexDst) { edge = boost::add_edge(boost::vertex(vertexSrc,g),boost::vertex(vertexDst,g),g).first; elm[edge] = eLabel; } } */ public: template< typename Graph, typename VertexLabelMap, typename EdgeLabelMap > bool readGraph(std::istream & is, Graph & g, VertexLabelMap & vlm, EdgeLabelMap & elm, std::ostream & err){ typename boost::graph_traits::edge_descriptor edge; typename boost::property_traits::value_type vertexLabel = '0'; //use default labels for vertices typename boost::property_traits::value_type edgeLabel='0'; std::size_t vSrc, vDst, idSrc, idDst, vertex_count=0; //std::size_t vertex_count=0; //, edge_count=0; std::string line; std::map nodeid_map; bool firstEdge=true, hasLabels=false; while(getline(is, line) and is.good()) { std::stringstream sstream; //Declaration needs to be inside the loop, an extra space at the end of a line does not go down well with str method below sstream.str(line); //rtrim would be a bit expensive for this format //std::cout << "read >>" << line << "<< "; if(line[0]=='#' or line[0]=='%' or line[0]=='\n') { //ignoring comment lines continue; } else if('0' <= line[0] && line[0] <='9') { if(!(sstream >> vSrc >> vDst)) return false; //std::cout << "found edge " << vSrc << ' ' << vDst << std::endl; idSrc = getAddVertex(nodeid_map, vSrc, vertex_count, g, vlm, vertexLabel); idDst = getAddVertex(nodeid_map, vDst, vertex_count, g, vlm, vertexLabel); if(firstEdge) { if(!(sstream >> edgeLabel)) {hasLabels=false; edgeLabel = '0';} else hasLabels=true; firstEdge = false; } else if(hasLabels) { if (!(sstream >> edgeLabel)) return false; } //addEdge(g, elm, idSrc, idDst, edgeLabel, typename boost::graph_traits::directed_category()); edge = add_edge(idSrc,idDst,g).first; elm[edge] = edgeLabel; //edge_count++; } else if( !(line[0]==' ' || line[0]=='\t') ) { //ignore whitespace return false; } }//end while return true; } template bool readGraph( std::istream & is,std::size_t & num_vertices, std::size_t & num_edges, VertexList & vlist, EdgeList & elist, VertexLabelMap & vlm, EdgeLabelMap & elm, std::ostream & err, std::size_t & graphId, boost::directedS ){ typedef IndexType vertex_descriptor; typedef IndexType edge_descriptor; typedef std::vector DegreeSequence; typedef typename boost::property_traits::value_type VertexLabel; typedef typename boost::property_traits::value_type EdgeLabel; VertexLabel vlbl='0'; EdgeLabel elbl='0'; DegreeSequence degreeList; degreeList.reserve(1000); //we want to do memory alloc in big chunks std::map nodeid_map; bool firstEdge = true, edgeHasLabels=false; std::size_t vSrc, vDst, idSrc, idDst; std::string line; while(getline(is, line) and is.good()) { std::stringstream sstream; //Declaration needs to be inside the loop, an extra space at the end of a line does not go down well with str method below sstream.str(line); //rtrim would be a bit expensive for this format //std::cout << "read >>" << line << "<< "; if(line[0]=='#' or line[0]=='%' or line[0]=='\n') { //ignoring comment lines continue; } else if('0' <= line[0] && line[0] <='9') { if(!(sstream >> vSrc >> vDst)) return false; //std::cout << "found edge " << vSrc << ' ' << vDst << std::endl; idSrc = getVertexID(nodeid_map, vSrc, num_vertices); idDst = getVertexID(nodeid_map, vDst, num_vertices); if(degreeList.capacity() < num_vertices) { degreeList.reserve(num_vertices*1.2); //reserve 20% more than our numVertices } if(degreeList.size() < num_vertices) { degreeList.resize(num_vertices, 0); } if(firstEdge) { if(!(sstream >> elbl)) { edgeHasLabels=false; elbl = '0'; } else edgeHasLabels=true; firstEdge = false; } else if(edgeHasLabels) { if (!(sstream >> elbl)) return false; } degreeList[idSrc]++; num_edges++; boost::put(vlm,idSrc,vlbl); boost::put(vlm,idDst,vlbl); } else if( !(line[0]==' ' || line[0]=='\t') ) { //ignore whitespace return false; } }//end while //Rewind stream to beginning is.clear(); is.seekg(0); //rewind stream to beginning //Remove the eofbit flag. Otherwise, we cannot rewind the stream. is.clear(); //Reset to starting position of stream is.seekg(0); //Assign space for the vertex and edge list. vlist.resize(num_vertices); elist.resize(num_edges); IndexType edge_index = 0; for( typename DegreeSequence::iterator begin = degreeList.begin(); begin != degreeList.end(); begin++ ){ vlist[begin - degreeList.begin()].first_edge_index = edge_index; for( std::size_t count = 0; count < *begin; count++){ std::size_t u,v; //typename boost::property_traits::value_type edge_label; EdgeLabel edge_label; if(edgeHasLabels) { if(!(is>>u>>v>>edge_label)) { return false; } } else if(!(is>>u>>v)) { return false; } elist[edge_index].target_node = v; boost::put(elm,std::make_pair(u,edge_index),edge_label); edge_index++; } assert((edge_index - vlist[begin - degreeList.begin()].first_edge_index) == *begin ); } return true; } template bool readGraph( std::istream & is,std::size_t & num_vertices, std::size_t & num_edges, VertexList & vlist, EdgeList & elist, VertexLabelMap & vlm, EdgeLabelMap & elm, std::ostream & err, std::size_t & graphId, boost::undirectedS ){ typedef IndexType vertex_descriptor; typedef IndexType edge_descriptor; typedef std::vector DegreeSequence; typedef typename boost::property_traits::value_type VertexLabel; typedef typename boost::property_traits::value_type EdgeLabel; VertexLabel vlbl ='0'; EdgeLabel elbl = '0'; DegreeSequence degreeList; degreeList.reserve(1000); //we want to do memory alloc in big chunks std::map nodeid_map; bool firstEdge = true, edgeHasLabels=false; std::size_t vSrc, vDst, idSrc, idDst; std::string line; while(getline(is, line) and is.good()) { std::stringstream sstream; //Declaration needs to be inside the loop, an extra space at the end of a line does not go down well with str method below sstream.str(line); //rtrim would be a bit expensive for this format //std::cout << "read >>" << line << "<< "; if(line[0]=='#' or line[0]=='%' or line[0]=='\n') { //ignoring comment lines continue; } else if('0' <= line[0] && line[0] <='9') { if(!(sstream >> vSrc >> vDst)) return false; //std::cout << "found edge " << vSrc << ' ' << vDst << std::endl; idSrc = getVertexID(nodeid_map, vSrc, num_vertices); idDst = getVertexID(nodeid_map, vDst, num_vertices); if(degreeList.capacity() < num_vertices) { degreeList.reserve(num_vertices*1.2); //reserve 20% more than our numVertices } if(degreeList.size() < num_vertices) { degreeList.resize(num_vertices, 0); } if(firstEdge) { if(!(sstream >> elbl)) { edgeHasLabels=false; elbl = '0'; } else edgeHasLabels=true; firstEdge = false; } else if(edgeHasLabels) { if (!(sstream >> elbl)) return false; } degreeList[idSrc]++; degreeList[idDst]++; num_edges++; boost::put(vlm,idSrc,vlbl); boost::put(vlm,idDst,vlbl); } else if( !(line[0]==' ' || line[0]=='\t') ) { //ignore whitespace return false; } }//end while //Rewind stream is.clear(); is.seekg(0); //rewind stream //Remove the eofbit flag. Otherwise, we cannot rewind the stream. is.clear(); //Reset to starting position of stream. is.seekg(0); //Assign space for the vertex and edge list. vlist.resize(num_vertices); elist.resize(2*num_edges); std::queue< std::pair > scratchpad; IndexType edge_index = 0; //Sentinel variable to mark the starting index of an undirected edge that has not been added before. bool firstNewEdge = true; for( typename DegreeSequence::iterator begin = degreeList.begin(); begin != degreeList.end(); begin++ ){ vlist[begin - degreeList.begin()].first_edge_index = edge_index; for( std::size_t count = 0; count < *begin; count++){ std::size_t u,v; if( !scratchpad.empty() && scratchpad.front().first == (begin - degreeList.begin()) ){ firstNewEdge = true; elist[edge_index].target_node = scratchpad.front().second; scratchpad.pop(); } else{ if(firstNewEdge){ //This is a new edge that has not been added before. vlist[begin - degreeList.begin()].first_out_edge_index = edge_index; firstNewEdge = false; } EdgeLabel edge_label; if(edgeHasLabels) { if(!(is>>u>>v>>edge_label)) { return false; } } else if(!(is>>u>>v)) return false; elist[edge_index].target_node = v; scratchpad.push(std::make_pair(v,u)); boost::put(elm,std::make_pair(u,edge_index),edge_label); edge_index++; } assert((edge_index - vlist[begin - degreeList.begin()].first_edge_index) == *begin ); } } return true; } template< typename Graph, typename VertexLabelMap, typename EdgeLabelMap > bool writeGraph(std::ostream & os, const Graph & g, VertexLabelMap & vlm, EdgeLabelMap & elm, std::ostream & err){ os << "# Writing snap format graph " << std::endl; for( typename boost::graph_traits::edge_iterator eIter = edges(g).first; eIter != edges(g).second; ++eIter ){ os< #include namespace mips { namespace graph { /*! * \namespace mips::graph::formats * \brief Formats for reading and writing graphs reside in this namespace */ namespace formats { /*! * \brief Provides methods for reading and writing files given in the following format: * * The graph is written in the G format, an example is shown below : * * \code * t # * v