somoclu-1.7.2/0000755000175000017500000000000013015657206010210 500000000000000somoclu-1.7.2/README.md0000644000175000017500000003333413015657206011415 00000000000000Somoclu ======= Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs, it is able to rely on MPI for distributing the workload in a cluster, and it can be accelerated by CUDA. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Key features: * Fast execution by parallelization: OpenMP, MPI, and CUDA are supported. * Multi-platform: Linux, OS X, and Windows are supported. * Planar and toroid maps. * Rectangular and hexagonal grids. * Gaussian and bubble neighborhood functions. * Both dense and sparse input data are supported. * Large maps of several hundred thousand neurons are feasible. * Integration with [Databionic ESOM Tools](http://databionic-esom.sourceforge.net/). * [Python](https://somoclu.readthedocs.io/), [R](https://cran.r-project.org/web/packages/Rsomoclu/), [Julia](https://github.com/peterwittek/Somoclu.jl), and [MATLAB](https://github.com/peterwittek/somoclu/tree/master/src/MATLAB) interfaces for the dense CPU and GPU kernels. For more information, refer to the manuscript about the library [1]. Usage === Basic Command Line Use ---------------------- Somoclu takes a plain text input file -- either dense or sparse data. Example files are included. $ [mpirun -np NPROC] somoclu [OPTIONs] INPUT_FILE OUTPUT_PREFIX Arguments: -c FILENAME Specify an initial codebook for the map. -d NUMBER Coefficient in the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)) (default: 0.5) -e NUMBER Maximum number of epochs -g TYPE Grid type: square or hexagonal (default: square) -h, --help This help text -k NUMBER Kernel type 0: Dense CPU 1: Dense GPU 2: Sparse CPU -l NUMBER Starting learning rate (default: 0.1) -L NUMBER Finishing learning rate (default: 0.01) -m TYPE Map type: planar or toroid (default: planar) -n FUNCTION Neighborhood function (bubble or gaussian, default: gaussian) -p NUMBER Compact support for Gaussian neighborhood (0: false, 1: true, default: 0) -r NUMBER Start radius (default: half of the map in direction min(x,y)) -R NUMBER End radius (default: 1) -s NUMBER Save interim files (default: 0): 0: Do not save interim files 1: Save U-matrix only 2: Also save codebook and best matching -t STRATEGY Radius cooling strategy: linear or exponential (default: linear) -T STRATEGY Learning rate cooling strategy: linear or exponential (default: linear) -x, --columns NUMBER Number of columns in map (size of SOM in direction x) -y, --rows NUMBER Number of rows in map (size of SOM in direction y) Examples: $ somoclu data/rgbs.txt data/rgbs $ mpirun -np 4 somoclu -k 0 --rows 20 --columns 20 data/rgbs.txt data/rgbs With random initialization, the initial codebook will be filled with random numbers ranging from 0 to 1. Either supply your own initial codebook or normalize your data to fall in this range. If the range of the values of the features includes negative numbers, the codebook will eventually adjust. It is, however, not advised to have negative values, especially if the codebook is initialized from 0 to 1. This comes from the batch training nature of the parallel implementation. The batch update rule will change the codebook values with weighted averages of the data points, and with negative values, the updates can cancel out. The maps generated by the GPU and the CPU kernels are likely to be different. For computational efficiency, Somoclu uses single-precision floats. This occasionally results in identical distances between a data instance and the neurons. The CPU version will pick the best matching unit with the lowest coordinate values. Such sequentiality cannot be guaranteed in the reduction kernel of the GPU variant. This is not a bug, but it is better to be aware of it. Efficient Parallel and Distributed Execution -------------------------------------------- The CPU kernels use OpenMP to load multicore processors. On a single node, this is more efficient than launching tasks with MPI to match the number of cores. The MPI tasks replicated the codebook, which is especially inefficient for large maps. For instance, given a single node with eight cores, the following execution will use 1/8th of the memory, and will run 10-20% faster: $ somoclu -x 200 -y 200 data/rgbs.txt data/rgbs Or, equivalently: $ OMP_NUM_THREADS=8 somoclu -x 200 -y 200 data/rgbs.txt data/rgbs Avoid the following on a single node: $ OMP_NUM_THREADS=1 mpirun -np 8 somoclu -x 200 -y 200 data/rgbs.txt data/rgbs The same caveats apply for the sparse CPU kernel. Visualisation ------------- The primary purpose of generating a map is visualisation. Apart from the Python interface, Somoclu does not come with its own functions for visualisation, since there are numerous generic tools that are capable of plotting high-quality figures. The R version integrates with [kohonen](https://cran.r-project.org/package=kohonen) and the MATLAB version with [somtoolbox](www.cis.hut.fi/somtoolbox/). The output formats U-matrix and the codebook of the command-line version are compatible with [Databionic ESOM Tools](http://databionic-esom.sourceforge.net/) for more advanced visualisation. Input File Formats ================== One sparse and two dense data formats are supported. All of them are plain text files. The entries can be separated by any white-space character. One row represents one data instance across all formats. Comment lines starting with a hash mark are ignored. The sparse format follows the [libsvm](http://www.csie.ntu.edu.tw/~cjlin/libsvm/) guidelines. The first feature is zero-indexed. For instance, the vector [ 1.2 0 0 3.4] is represented as the following line in the file: 0:1.2 3:3.4. The file is parsed twice: once to get the number of instances and features, and the second time to read the data in the individual threads. The basic dense format includes the coordinates of the data vectors, separated by a white-space. Just like the sparse format, this file is parsed twice to get the basic dimensions right. The .lrn file of [Databionic ESOM Tools](http://databionic-esom.sourceforge.net/) is also accepted and it is parsed only once. The format is described as follows: % n % m % s1 s2 .. sm % var_name1 var_name2 .. var_namem x11 x12 .. x1m x21 x22 .. x2m . . . . . . . . xn1 xn2 .. xnm Here n is the number of rows in the file, that is, the number of data instances. Parameter m defines the number of columns in the file. The next row defines the column mask: the value 1 for a column means the column should be used in the training. Note that the first column in this format is always a unique key, so this should have the value 9 in the column mask. The row with the variable names is ignore by Somoclu. The elements of the matrix follow -- from here, the file is identical to the basic dense format, with the addition of the first column as the unique key. If the input file is sparse, but a dense kernel is invoked, Somoclu will execute and results will be incorrect. Invoking a sparse kernel on a dense input file is likely to lead to a segmentation fault. Interfaces ========== [Python](https://somoclu.readthedocs.io/), [R](https://cran.r-project.org/web/packages/Rsomoclu/), and [MATLAB](https://github.com/peterwittek/somoclu/tree/master/src/MATLAB) interfaces are available for the dense CPU and GPU kernels. MPI and the sparse kernel are not support through the interfaces. For respective examples, see the folders in src. The Python version is also available in [PyPI](https://pypi.python.org/pypi/somoclu). You can install it with $ sudo pip install somoclu Some pre-built binaries in the wheel format or Windows installer are provided at [PyPI Dowloads](https://pypi.python.org/pypi/somoclu#downloads), they are tested with [Anaconda](https://www.continuum.io/downloads) distributions. If you encounter errors like `ImportError: DLL load failed: The specified module could not be found` when `import somoclu`, you may need to use [Dependency Walker](http://www.dependencywalker.com/) as shown [here](http://stackoverflow.com/a/24704384/1136027) on `_somoclu_wrap.pyd` to find out missing DLLs and place them at the write place. Usually right version (32/64bit) of `vcomp90.dll, msvcp90.dll, msvcr90.dll` should be put to `C:\Windows\System32` or `C:\Windows\SysWOW64`. The wheel binaries for OSX are compiled with [clang-omp](http://clang-omp.github.io/), and depend on libiomp5, which you can install by: $ brew install libiomp To get it working with the GPU kernel, you might have to follow the instructions at [Somoclu - Python Interface](https://github.com/peterwittek/somoclu/tree/master/src/Python). The R version is available on CRAN. You can install it with install.packages("Rsomoclu") To get it working with the GPU kernel, download the source zip file and specify your CUDA directory the following way: R CMD INSTALL src/Rsomoclu_version.tar.gz --configure-args=/path/to/cuda The Julia version is available on [GitHub](https://github.com/peterwittek/Somoclu.jl). Clone it with `Pkg.clone("https://github.com/peterwittek/Somoclu.jl")` and build it with `Pkg.build("Somoclu")`. For using the MATLAB toolbox, define the location of your MATLAB install to the configure script: ./configure --without-mpi --with-matlab=/usr/local/MATLAB/R2014a For the GPU kernel, specify the location of your CUDA library for the configure script. More detailed instructions are in the [MATLAB source folder](https://github.com/peterwittek/somoclu/tree/master/src/MATLAB). Compilation & Installation ========================== These are the instructions for compiling the core library and the command line interface. The only dependency is a C++ compiler chain -- GCC, ICC, and VC were tested. Multicore execution is supported through OpenMP -- the compiler must support this. Distributed systems are supported through MPI. The package was tested with OpenMPI. It should also work with other MPI flavours. CUDA support is optional. Linux or Mac OS X ----------------- From GIT repository first run $ ./autogen.sh Then follow the standard POSIX procedure: $ ./configure [options] $ make $ make install Using Intel compilers export CC=/path/of/intel/compiler/icc export CXX=/path/of/intel/compiler/icpc export OMPI_CC=/path/of/intel/compiler/icc export OMPI_CXX=/path/of/intel/compiler/icpc In order to use icc and icpc compilers, you have to set these variables so the mpic++ will invoke icpc instead of the default compiler. Options for configure --prefix=PATH Set directory prefix for installation By default Somoclu is installed into /usr/local. If you prefer a different location, use this option to select an installation directory. --without-mpi Disregard any MPI installation found. --with-mpi=MPIROOT Use MPI root directory. --with-mpi-compilers=DIR or --with-mpi-compilers=yes use MPI compiler (mpicxx) found in directory DIR, or in your PATH if =yes --with-mpi-libs="LIBS" MPI libraries [default "-lmpi"] --with-mpi-incdir=DIR MPI include directory [default MPIROOT/include] --with-mpi-libdir=DIR MPI library directory [default MPIROOT/lib] The above flags allow the identification of the correct MPI library the user wishes to use. The flags are especially useful if MPI is installed in a non-standard location, or when multiple MPI libraries are available. --with-cuda=/path/to/cuda Set path for CUDA Somoclu looks for CUDA in /usr/local/cuda. If your installation is not there, then specify the path with this parameter. If you do not want CUDA enabled, set the parameter to ```--without-cuda```. Windows ------- Use the `somoclu.sln` under `src/Windows/somoclu` as an example Visual Studio 2013 solution. Modify the CUDA version or VC compiler version according to your needs. The default solution enables all of OpenMP, MPI, and CUDA. The default MPI installation path is `C:\Program Files (x86)\Microsoft SDKs\MPI\`, modify the settings if yours is in a different path. The configuration default CUDA version is 7.5. Disable MPI by removing `HAVE_MPI` macro in the project properties (`Properties -> Configuration Properties -> C/C++ -> Preprocessor`). Disable CUDA by removing `CUDA` macro in the solution properties and uncheck CUDA in `Project -> Custom Build Rules`. If you open the solution without CUDA installed, please remove the following sections in `somoclu.vcxproj`: ``` ``` and ``` ``` or change the version number according to which you installed. The usage is identical to the Linux version through command line (see the relevant section). Acknowledgment ============== This work was supported by the European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 PERICLES and by the AWS in Education Machine Learning Grant award. Citation ======== 1. Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2015). Somoclu: An Efficient Parallel Library for Self-Organizing Maps. [arXiv:1305.1422](http://arxiv.org/abs/1305.1422). somoclu-1.7.2/Makefile.in0000644000175000017500000000335013015657206012176 00000000000000# @configure_input@ # Package-related substitution variables package = @PACKAGE_NAME@ version = @PACKAGE_VERSION@ tarname = @PACKAGE_TARNAME@ distdir = $(tarname)-$(version) # Prefix-related substitution variables prefix = @prefix@ exec_prefix = @exec_prefix@ bindir = @bindir@ # VPATH-related substitution variables srcdir = @srcdir@ VPATH = @srcdir@ all clean install uninstall lib libwrapper somoclu python python_doc python_clean python_install r r_clean matlab matlab_clean: $(MAKE) -C src $@ dist: $(distdir).tar.gz $(distdir).tar.gz: FORCE $(distdir) tar chof - $(distdir) | gzip -9 -c >$(distdir).tar.gz rm -rf $(distdir) $(distdir): mkdir -p $(distdir)/src mkdir -p $(distdir)/data cp $(srcdir)/configure $(distdir) cp $(srcdir)/config.h.in $(distdir) cp $(srcdir)/install-sh $(distdir) cp $(srcdir)/Makefile.in $(distdir) cp $(srcdir)/README.md $(distdir) cp $(srcdir)/LICENSE $(distdir) cp $(srcdir)/CHANGES $(distdir) cp $(srcdir)/src/Makefile.in $(distdir)/src cp $(srcdir)/src/*.c* $(srcdir)/src/*.h $(distdir)/src cp $(srcdir)/data/rgbs.* $(distdir)/data cp -r $(srcdir)/src/MATLAB $(distdir)/src/ cp -r $(srcdir)/src/Python $(distdir)/src/ cp -r $(srcdir)/src/R $(distdir)/src/ distcheck: $(distdir).tar.gz gzip -cd $+ | tar xvf - $(MAKE) -C $(distdir) all check $(MAKE) -C $(distdir) DESTDIR=$${PWD}/$(distdir)/_inst install uninstall $(MAKE) -C $(distdir) clean rm -rf $(distdir) @echo "*** Package $(distdir).tar.gz is ready for distribution." Makefile: Makefile.in config.status ./config.status $@ config.status: configure ./config.status --recheck FORCE: -rm -rf $(distdir) &>/dev/null -rm $(distdir).tar.gz &>/dev/null .PHONY: FORCE all clean check dist distcheck install uninstall somoclu-1.7.2/install-sh0000755000175000017500000003452313015657206012143 00000000000000#!/bin/sh # install - install a program, script, or datafile scriptversion=2013-12-25.23; # UTC # This originates from X11R5 (mit/util/scripts/install.sh), which was # later released in X11R6 (xc/config/util/install.sh) with the # following copyright and license. # # Copyright (C) 1994 X Consortium # # 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. 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If we want to # ignore errors from any of these, just make sure not to ignore # errors from the above "$doit $cpprog $src $dsttmp" command. # { test -z "$chowncmd" || $doit $chowncmd "$dsttmp"; } && { test -z "$chgrpcmd" || $doit $chgrpcmd "$dsttmp"; } && { test -z "$stripcmd" || $doit $stripcmd "$dsttmp"; } && { test -z "$chmodcmd" || $doit $chmodcmd $mode "$dsttmp"; } && # If -C, don't bother to copy if it wouldn't change the file. if $copy_on_change && old=`LC_ALL=C ls -dlL "$dst" 2>/dev/null` && new=`LC_ALL=C ls -dlL "$dsttmp" 2>/dev/null` && set -f && set X $old && old=:$2:$4:$5:$6 && set X $new && new=:$2:$4:$5:$6 && set +f && test "$old" = "$new" && $cmpprog "$dst" "$dsttmp" >/dev/null 2>&1 then rm -f "$dsttmp" else # Rename the file to the real destination. $doit $mvcmd -f "$dsttmp" "$dst" 2>/dev/null || # The rename failed, perhaps because mv can't rename something else # to itself, or perhaps because mv is so ancient that it does not # support -f. { # Now remove or move aside any old file at destination location. # We try this two ways since rm can't unlink itself on some # systems and the destination file might be busy for other # reasons. 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Generated from configure.ac by autoheader. */ /* cuda enabled */ #undef CUDA /* define that mpi is being used */ #undef HAVE_MPI /* Define to the address where bug reports for this package should be sent. */ #undef PACKAGE_BUGREPORT /* Define to the full name of this package. */ #undef PACKAGE_NAME /* Define to the full name and version of this package. */ #undef PACKAGE_STRING /* Define to the one symbol short name of this package. */ #undef PACKAGE_TARNAME /* Define to the home page for this package. */ #undef PACKAGE_URL /* Define to the version of this package. */ #undef PACKAGE_VERSION somoclu-1.7.2/src/0000755000175000017500000000000013015657206010777 500000000000000somoclu-1.7.2/src/Makefile.in0000644000175000017500000001142713015657206012771 00000000000000# @configure_input@ # Package-related substitution variables package = @PACKAGE_NAME@ version = @PACKAGE_VERSION@ tarname = @PACKAGE_TARNAME@ distdir = $(tarname)-$(version) # Prefix-related substitution variables prefix = @prefix@ exec_prefix = @exec_prefix@ bindir = @bindir@ libdir = @libdir@ # Tool-related substitution variables CXX = @CXX@ CXXFLAGS = @CXXFLAGS@ LIBS = @LIBS@ DEFS = @DEFS@ INSTALL = @INSTALL@ INSTALL_DATA = @INSTALL_DATA@ INSTALL_PROGRAM= @INSTALL_PROGRAM@ INSTALL_SCRIPT = @INSTALL_SCRIPT@ CUDA_CFLAGS = @CUDA_CFLAGS@ CUDA_LIBS = @CUDA_LIBS@ CUDA_LDFLAGS = @CUDA_LDFLAGS@ NVCC = @NVCC@ MPI_INC = @MPI_INC@ MPI_LIBDIR = @MPI_LIBDIR@ MPI_LIBS = @MPI_LIBS@ MATLAB_ROOT = @MATLAB_ROOT@ MEX_BIN = @MEX_BIN@ # VPATH-related substitution variables srcdir = @srcdir@ VPATH = @srcdir@ LIBOBJS=sparseCpuKernels.o denseCpuKernels.o mapDistanceFunctions.o training.o uMatrix.o MEX_FLAGS=-I./ MEX_LIBS=-lgomp MEX_OBJS=sparseCpuKernels.o denseCpuKernels.o mapDistanceFunctions.o uMatrix.o ifdef CUDA_LIBS LIBOBJS+=denseGpuKernels.cu.co MEX_FLAGS+=-I"$(MATLAB_ROOT)/toolbox/distcomp/gpu/extern/include/" -DCUDA MEX_LIBS+=$(CUDA_LDFLAGS) $(CUDA_LIBS) -lnvblas -lmwgpu MEX_OBJS+=denseGpuKernels.cu.o endif OBJS=$(LIBOBJS) io.o somoclu.o all: somoclu lib libwrapper: $(LIBOBJS) $(CXX) -g $(DEFS) $(CXXFLAGS) $(CUDA_LDFLAGS) ${MPI_LIBDIR} -shared -o libsomoclu.so $^ $(LIBS) $(CUDA_LIBS) ${MPI_LIBS} lib: $(LIBOBJS) io.o $(CXX) -g $(DEFS) $(CXXFLAGS) $(CUDA_LDFLAGS) ${MPI_LIBDIR} -shared -o libsomoclu.so $^ $(LIBS) $(CUDA_LIBS) ${MPI_LIBS} somoclu: CXXFLAGS+= -DCLI somoclu: $(OBJS) $(CXX) $(DEFS) $(CXXFLAGS) $(CUDA_LDFLAGS) ${MPI_LIBDIR} -o $@ $^ $(LIBS) $(CUDA_LIBS) ${MPI_LIBS} %.o: %.cpp $(CXX) $(DEFS) $(CXXFLAGS) ${MPI_INC} -I$(srcdir) -I.. -o $@ -c $< %.cu.co: %.cu $(NVCC) $(DEFS) $(CUDA_CFLAGS) ${MPI_INC} -I$(srcdir) -I.. -o $@ -c $< matlab: libwrapper if [ -e denseGpuKernels.cu.co ]; then cp denseGpuKernels.cu.co denseGpuKernels.cu.o; fi $(MEX_BIN) $(MEX_FLAGS) MATLAB/MexSomoclu.cpp training.cpp $(MEX_OBJS) $(MEX_LIBS) mv MexSomoclu* MATLAB matlab_clean: -rm -f MATLAB/*o python: mkdir -p ./Python/somoclu/src mkdir -p ./Python/somoclu/src/Windows cp ../LICENSE ./Python cp ../Makefile.in ./Python/somoclu/ cp ../configure ./Python/somoclu/ cp ../config.h.in ./Python/somoclu/ cp ../install-sh ./Python/somoclu/ cp ./Makefile.in ./Python/somoclu/src/ cp ./*.h ./Python/somoclu/src/ cp ./mapDistanceFunctions.cpp ./Python/somoclu/src/ cp ./training.cpp ./Python/somoclu/src/ cp ./uMatrix.cpp ./Python/somoclu/src/ cp ./denseCpuKernels.cpp ./Python/somoclu/src/ cp ./denseGpuKernels.cu ./Python/somoclu/src/ cp ./sparseCpuKernels.cpp ./Python/somoclu/src/ cp ./*.cu ./Python/somoclu/src/ swig -c++ -python ./Python/somoclu/somoclu.i cd Python; python setup.py build python_doc: python export PYTHONPATH=`ls -d --color=never $$PWD/Python/build/lib.*| head -c -1`:$$PYTHONPATH; $(MAKE) -C Python/doc html latexpdf python_install: python python_doc cd Python; python setup.py install python_clean: -rm -fr ./Python/somoclu/src ./Python/somoclu.egg* \ ./Python/somoclu/install-sh ./Python/somoclu/Makefile* \ ./Python/somoclu/config* ./Python/somoclu/*so \ ./Python/somoclu/somoclu_wrap* ./Python/somoclu/*pyc \ ./Python/LICENSE ./Python/dist \ ./Python/doc/doctrees ./Python/doc/manual.pdf ./Python/doc/html \ ./Python/doc/latex ./Python/build $(OBJS) 1>/dev/null r_clean: -rm -fr ./R/src/configure ./R/src/config.h.in ./R/src/Makefile.root.in \ ./R/src/Makefile.in ./R/src/denseGpuKernels.cu ./R/src/*h \ ./R/src/mapDistanceFunctions.cpp ./R/src/training.cpp \ ./R/src/io.cpp ./R/src/uMatrix.cpp ./R/src/denseCpuKernels.cpp \ ./R/src/sparseCpuKernels.cpp R/data ./Rsomoclu*gz r: mkdir -p R/data cp ../configure ./R/src/ cp ../config.h.in ./R/src/ cp ../install-sh ./R/src/ cp ../Makefile.in ./R/src/Makefile.root.in cp ./Makefile.in ./R/src/ cp ./*.h ./R/src/ cp ./mapDistanceFunctions.cpp ./R/src/ cp ./training.cpp ./R/src/ cp ./uMatrix.cpp ./R/src/ cp ./denseCpuKernels.cpp ./R/src/ cp ./denseGpuKernels.cu ./R/src/ cp ./sparseCpuKernels.cpp ./R/src/ cp ../data/rgbs.txt ./R/data/ gzip -f ./R/data/rgbs.txt R CMD build R clean: python_clean r_clean matlab_clean -rm -fr $(OBJS) somoclu libsomoclu.so *.cu.o 1>/dev/null install: $(INSTALL) -d $(DESTDIR)$(bindir) -$(INSTALL_PROGRAM) -m 0755 somoclu $(DESTDIR)$(bindir) $(INSTALL) -d $(DESTDIR)$(libdir) $(INSTALL_PROGRAM) -m 0755 libsomoclu.so $(DESTDIR)$(libdir) uninstall: -rm $(DESTDIR)$(bindir)/somoclu &>/dev/null Makefile: Makefile.in ../config.status cd .. && ./config.status $@ ../config.status: ../configure cd .. && ./config.status --recheck .PHONY: all clean install uninstall python somoclu-1.7.2/src/somoclu.h0000644000175000017500000001736413015657206012564 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include using namespace std; #ifndef SOMOCLU_H #define SOMOCLU_H #if HAVE_CONFIG_H #include #endif #ifdef HAVE_MPI #include #endif #ifdef _WIN32 #include #endif #define DENSE_CPU 0 #define DENSE_GPU 1 #define SPARSE_CPU 2 /// The neighbor_fuct value below which we consider /// the impact zero for a given node in the map #define NEIGHBOR_THRESHOLD 0.05 /// Sparse structures and routines struct svm_node { int index; float value; }; float euclideanDistanceOnToroidMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y, const unsigned int nSomX, const unsigned int nSomY); float euclideanDistanceOnPlanarMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y); float euclideanDistanceOnHexagonalToroidMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y, const unsigned int nSomX, const unsigned int nSomY); float euclideanDistanceOnHexagonalPlanarMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y); float getWeight(float distance, float radius, float scaling, bool compact_support, bool gaussian, float std_coeff); int saveCodebook(string cbFileName, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions); float *calculateUMatrix(float* uMatrix, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, string mapType, string gridType); int saveUMatrix(string fname, float *uMatrix, unsigned int nSomX, unsigned int nSomY); int saveBmus(string filename, int *bmus, unsigned int nSomX, unsigned int nSomY, unsigned int nVectors); float *readMatrix(const string inFilename, unsigned int &nRows, unsigned int &nCols); void readSparseMatrixDimensions(const string filename, unsigned int &nRows, unsigned int &nColumns); svm_node** readSparseMatrixChunk(const string filename, unsigned int nRows, unsigned int nRowsToRead, unsigned int rowOffset); void trainOneEpoch(int itask, float *data, svm_node **sparseData, float *codebook, int *globalBmus, unsigned int nEpoch, unsigned int currentEpoch, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius0, float radiusN, string radiusCooling, float scale0, float scaleN, string scaleCooling, unsigned int kernelType, string mapType, string gridType, bool compact_support, bool gaussian, float std_coeff=0.5, bool only_bmus=false); void train(float *data, int data_length, unsigned int nEpoch, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, float radius0, float radiusN, string radiusCooling, float scale0, float scaleN, string scaleCooling, unsigned int kernelType, string mapType, string gridType, bool compact_support, bool gaussian, float std_coeff, float* codebook, int codebook_size, int* globalBmus, int globalBmus_size, float* uMatrix, int uMatrix_size); void train(int itask, float *data, svm_node **sparseData, float *codebook, int *globalBmus, float *uMatrix, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, unsigned int nEpoch, float radius0, float radiusN, string radiusCooling, float scale0, float scaleN, string scaleCooling, unsigned int kernelType, string mapType, string gridType, bool compact_support, bool gaussian, float std_coeff #ifdef CLI , string outPrefix, unsigned int snapshots); #else ); #endif void trainOneEpochDenseCPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, float scale, string mapType, string gridType, bool compact_support, bool gaussian, int *globalBmus, bool only_bmus, float std_coeff); void trainOneEpochSparseCPU(int itask, svm_node **sparseData, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, float scale, string mapType, string gridType, bool compact_support, bool gaussian, int *globalBmus, bool only_bmus, float std_coeff); void initializeCodebook(unsigned int seed, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions); extern "C" { #ifdef CUDA void setDevice(int commRank, int commSize); void freeGpu(); void initializeGpu(float *hostData, int nVectorsPerRank, int nDimensions, int nSomX, int nSomY); void trainOneEpochDenseGPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, float scale, string mapType, string gridType, bool compact_support, bool gaussian, int *globalBmus, bool only_bmus, float std_coeff); #endif void my_abort(string err); void julia_train(float *data, int data_length, unsigned int nEpoch, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, float radius0, float radiusN, unsigned int radiusCooling, float scale0, float scaleN, unsigned int scaleCooling, unsigned int kernelType, unsigned int mapType, unsigned int gridType, bool compact_support, bool gaussian, float std_coeff, float* codebook, int codebook_size, int* globalBmus, int globalBmus_size, float* uMatrix, int uMatrix_size); } #endif // SOMOCLU_H somoclu-1.7.2/src/denseGpuKernels.cu0000644000175000017500000004273313015657206014357 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #undef _GLIBCXX_ATOMIC_BUILTINS #undef _GLIBCXX_USE_INT128 #include #include #include #include #include #include #include #include #include #include #include "somoclu.h" #ifdef _WIN32 #define popen _popen #define pclose _pclose #endif // Error handling macro #define CUDA_CHECK(call) \ if((call) != cudaSuccess) { \ cudaError_t err = cudaGetLastError(); \ stringstream sstm; \ sstm << "CUDA error calling \""#call"\", code is " << err; \ my_abort(sstm.str()); } //Globals cublasHandle_t handle; thrust::device_vector deviceData; thrust::device_vector deviceDataNorms; thrust::device_vector deviceCodebook; thrust::device_vector deviceCodebookNorms; // convert a linear index to a row index template struct linear_index_to_row_index : public thrust::unary_function { T C; // number of columns __host__ __device__ linear_index_to_row_index(T C) : C(C) {} __host__ __device__ T operator()(T i) { return i / C; } }; // note: functor inherits from unary_function template struct square : public thrust::unary_function { __host__ __device__ T operator()(T x) const { return x * x; } }; typedef thrust::tuple argMinType; struct argMin : public thrust::binary_function { __host__ __device__ argMinType operator()(const argMinType& a, const argMinType& b) const { if (thrust::get<1>(a) < thrust::get<1>(b)) { return a; } else { return b; } } }; template thrust::device_vector normsOfRowSpace(thrust::device_vector A, int nRows, int nColumns) { // allocate storage for row sums and indices thrust::device_vector row_sums(nRows); thrust::device_vector row_indices(nRows); // compute row sums by summing values with equal row indices thrust::reduce_by_key (thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)), thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)) + (nRows * nColumns), thrust::make_transform_iterator(A.begin(), square()), row_indices.begin(), row_sums.begin(), thrust::equal_to(), thrust::plus()); return row_sums; } thrust::device_vector minsOfRowSpace(thrust::device_vector A, int nRows, int nColumns) { // allocate storage for row sums and indices thrust::device_vector row_sums(nRows); thrust::device_vector row_indices(nRows); // compute row sums by summing values with equal row indices thrust::reduce_by_key (thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)), thrust::make_transform_iterator(thrust::counting_iterator(0), linear_index_to_row_index(nColumns)) + (nRows * nColumns), thrust::make_zip_iterator(thrust::make_tuple(thrust::counting_iterator(0), A.begin())), row_indices.begin(), row_sums.begin(), thrust::equal_to(), argMin()); return row_sums; } template __global__ void euclidean(float *anorm2, float *bnorm2, float *M, int height, int width) { unsigned int xIndex = blockIdx.x * BLOCK_DIM + threadIdx.x; unsigned int yStartIndex = blockIdx.y * BLOCK_DIM; if (xIndex < width) { float bNormForX = bnorm2[xIndex]; unsigned int yEndIndex = (yStartIndex + BLOCK_DIM < height ? yStartIndex + BLOCK_DIM : height); for (unsigned int yIndex = yStartIndex; yIndex < yEndIndex; yIndex++) { unsigned int index = yIndex * width + xIndex; M[index] = anorm2[yIndex] - 2 * M[index] + bNormForX; } } } template void printMatrix(thrust::device_vector A, int nRows, int nColumns) { for (size_t i = 0; i < nRows; i++) { for (size_t j = 0; j < nColumns; j++) { std::cout << A[i * nColumns + j] << " "; } std::cout << "\n"; } std::cout << "\n"; } /** Clear the device memory and shut down CUBLAS * */ void freeGpu() { deviceData.clear(); deviceDataNorms.clear(); deviceCodebook.clear(); deviceCodebookNorms.clear(); thrust::device_vector().swap(deviceData); thrust::device_vector().swap(deviceDataNorms); thrust::device_vector().swap(deviceCodebook); thrust::device_vector().swap(deviceCodebookNorms); cublasStatus_t status = cublasDestroy(handle); if (status != CUBLAS_STATUS_SUCCESS) { my_abort("CuBLAS shutdown error"); } } /** Find the best matching units -- called from the map function * @param bmus - array of best matching units * @param codebook - the codebook to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance * @param nVectorsPerRank - the number of data points assigned to this GPU */ void getBmusOnGpu(int *bmus, float *codebook, int nSomX, int nSomY, int nDimensions, int nVectorsPerRank) { deviceCodebook = thrust::device_vector(codebook, codebook + nSomX * nSomY * nDimensions); deviceCodebookNorms = normsOfRowSpace(deviceCodebook, nSomX * nSomY, nDimensions); thrust::device_vector deviceGramMatrix(nSomX * nSomY * nVectorsPerRank, 0); //Calculate the inner products of the data vectors and the weight vectors float alpha = 1.0f; float beta = 0.0f; cublasStatus_t status = cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, nSomX * nSomY, nVectorsPerRank, nDimensions, &alpha, thrust::raw_pointer_cast(&deviceCodebook[0]), nDimensions, thrust::raw_pointer_cast(&deviceData[0]), nDimensions, &beta, thrust::raw_pointer_cast(&deviceGramMatrix[0]), nSomX * nSomY); if (status != CUBLAS_STATUS_SUCCESS) { my_abort("Kernel execution error."); } //All components of the vectorized Euclidean distance are available // 32 is a magic number, this is the block size that works best on Tesla C2050 int BLOCK_DIM = 32; dim3 grid((nSomX * nSomY + BLOCK_DIM - 1) / BLOCK_DIM, (nVectorsPerRank + BLOCK_DIM - 1) / BLOCK_DIM, 1); dim3 threads(BLOCK_DIM, 1, 1); if (BLOCK_DIM == 32) { euclidean<32> <<< grid, threads>>>(thrust::raw_pointer_cast(&deviceDataNorms[0]), thrust::raw_pointer_cast(&deviceCodebookNorms[0]), thrust::raw_pointer_cast(&deviceGramMatrix[0]), nVectorsPerRank, nSomX * nSomY); } //Finding minimums thrust::host_vector minsOfA = minsOfRowSpace(deviceGramMatrix, nVectorsPerRank, nSomX * nSomY); CUDA_CHECK(cudaDeviceSynchronize()); //Getting back SOM coordinates from minimums for (int i = 0; i < nVectorsPerRank; i++) { argMinType tmp = minsOfA[i]; int somCoordinate = thrust::get<0>(tmp) % (nSomX * nSomY); bmus[i * 2] = somCoordinate % nSomX; bmus[i * 2 + 1] = somCoordinate / nSomX; } } /** Initialize CUBLAS and device data * @param hostData - the data in the main memory * @param height - number of data points assigned to this GPU * @param width - dimensions of a data instance */ void initializeGpu(float *hostData, int nVectorsPerRank, int nDimensions, int nSomX, int nSomY) { /* Initialize CUBLAS */ cublasStatus_t status = cublasCreate(&handle); if (status != CUBLAS_STATUS_SUCCESS) { my_abort("CuBLAS initialization error"); } deviceData = thrust::device_vector(hostData, hostData + nVectorsPerRank * nDimensions); deviceDataNorms = normsOfRowSpace(deviceData, nVectorsPerRank, nDimensions); deviceCodebook = thrust::device_vector(nSomX * nSomY * nDimensions, 0); deviceCodebookNorms = thrust::device_vector(nSomX * nSomY, 0); } /** Check and initialize a device attached to a node * @param commRank - the MPI rank of this process * @param commSize - the size of MPI comm world */ /// Note that this function was lifted from http://code.google.com/p/gpmr/ void setDevice(int commRank, int commSize) { int devCount; int deviceNum = 0; CUDA_CHECK(cudaGetDeviceCount(&devCount)); #ifdef HAVE_MPI #ifdef _WIN32 FILE * fp = popen("hostname.exe", "r"); #else FILE * fp = popen("/bin/hostname", "r"); #endif char buf[1024]; if (fgets(buf, 1023, fp) == NULL) strcpy(buf, "localhost"); pclose(fp); string host = buf; host = host.substr(0, host.size() - 1); strcpy(buf, host.c_str()); if (commRank == 0) { map > hosts; map devCounts; hosts[buf].push_back(0); devCounts[buf] = devCount; MPI_Status stat; MPI_Request req; for (int i = 1; i < commSize; ++i) { MPI_Recv(buf, 1024, MPI_CHAR, i, 0, MPI_COMM_WORLD, &stat); MPI_Recv(&devCount, 1, MPI_INT, i, 0, MPI_COMM_WORLD, &stat); // check to make sure each process on each node reports the same number of devices. hosts[buf].push_back(i); if (devCounts.find(buf) != devCounts.end()) { if (devCounts[buf] != devCount) { printf("Error, device count mismatch %d != %d on %s\n", devCounts[buf], devCount, buf); fflush(stdout); } } else devCounts[buf] = devCount; } // check to make sure that we don't have more jobs on a node than we have GPUs. for (map >::iterator it = hosts.begin(); it != hosts.end(); ++it) { if (it->second.size() > static_cast(devCounts[it->first])) { stringstream sstm; sstm << "Error, more jobs running on " << it->first.c_str() << " than devices - " << static_cast(it->second.size()) << " jobs > " << devCounts[it->first] << " devices."; my_abort(sstm.str()); } } // send out the device number for each process to use. MPI_Irecv(&deviceNum, 1, MPI_INT, 0, 0, MPI_COMM_WORLD, &req); for (map >::iterator it = hosts.begin(); it != hosts.end(); ++it) { for (unsigned int i = 0; i < it->second.size(); ++i) { int devID = i; MPI_Send(&devID, 1, MPI_INT, it->second[i], 0, MPI_COMM_WORLD); } } MPI_Wait(&req, &stat); } else { // send out the hostname and device count for your local node, then get back the device number you should use. MPI_Status stat; MPI_Send(buf, strlen(buf) + 1, MPI_CHAR, 0, 0, MPI_COMM_WORLD); MPI_Send(&devCount, 1, MPI_INT, 0, 0, MPI_COMM_WORLD); MPI_Recv(&deviceNum, 1, MPI_INT, 0, 0, MPI_COMM_WORLD, &stat); } MPI_Barrier(MPI_COMM_WORLD); #endif CUDA_CHECK(cudaSetDevice(deviceNum)); } /** One epoch on the GPU, dense variant */ void trainOneEpochDenseGPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, float scale, string mapType, string gridType, bool compact_support, bool gaussian, int *globalBmus, bool only_bmus, float std_coeff) { int *bmus; #ifdef HAVE_MPI bmus = new int[nVectorsPerRank * 2]; #else bmus = globalBmus; #endif getBmusOnGpu(bmus, codebook, nSomX, nSomY, nDimensions, nVectorsPerRank); if (only_bmus) { #ifdef HAVE_MPI MPI_Gather(bmus, nVectorsPerRank * 2, MPI_INT, globalBmus, nVectorsPerRank * 2, MPI_INT, 0, MPI_COMM_WORLD); delete [] bmus; #endif return; } #ifdef HAVE_MPI float *localNumerator = new float[nSomY * nSomX * nDimensions]; float *localDenominator = new float[nSomY * nSomX]; #pragma omp for #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif // _WIN32 for (unsigned int som_x = 0; som_x < nSomX; som_x++) { localDenominator[som_y * nSomX + som_x] = 0.0; for (unsigned int d = 0; d < nDimensions; d++) localNumerator[som_y * nSomX * nDimensions + som_x * nDimensions + d] = 0.0; } } #pragma omp parallel default(shared) #else // not HAVE_MPI float *localNumerator; float localDenominator = 0; #pragma omp parallel default(shared) private(localDenominator) private(localNumerator) #endif { #ifndef HAVE_MPI localNumerator = new float[nDimensions]; #endif // HAVE_MPI #pragma omp for #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif for (unsigned int som_x = 0; som_x < nSomX; som_x++) { for (unsigned int n = 0; n < nVectorsPerRank; n++) { if (itask * nVectorsPerRank + n < nVectors) { float dist = 0.0f; if (gridType == "rectangular") { if (mapType == "planar") { dist = euclideanDistanceOnPlanarMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1]); } else if (mapType == "toroid") { dist = euclideanDistanceOnToroidMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1], nSomX, nSomY); } } else { if (mapType == "planar") { dist = euclideanDistanceOnHexagonalPlanarMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1]); } else if (mapType == "toroid") { dist = euclideanDistanceOnHexagonalToroidMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1], nSomX, nSomY); } } float neighbor_fuct = getWeight(dist, radius, scale, compact_support, gaussian, std_coeff); #ifdef HAVE_MPI for (unsigned int d = 0; d < nDimensions; d++) { localNumerator[som_y * nSomX * nDimensions + som_x * nDimensions + d] += 1.0f * neighbor_fuct * (*(data + n * nDimensions + d)); } localDenominator[som_y * nSomX + som_x] += neighbor_fuct; #else // In this case, we can update in place if (n == 0) { localDenominator = neighbor_fuct; for (unsigned int d = 0; d < nDimensions; d++) { localNumerator[d] = 1.0f * neighbor_fuct * (*(data + n * nDimensions + d)); } } else { localDenominator += neighbor_fuct; for (unsigned int d = 0; d < nDimensions; d++) { localNumerator[d] += 1.0f * neighbor_fuct * (*(data + n * nDimensions + d)); } } #endif // HAVE_MPI } } // Looping over data instances #ifndef HAVE_MPI // We update in-place for (unsigned int d = 0; d < nDimensions; d++) { if (localDenominator != 0) { float newWeight = localNumerator[d] / localDenominator; codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] = newWeight; } } #endif } // Looping over som_x } // Looping over som_y #ifndef HAVE_MPI delete [] localNumerator; #endif } // OPENMP #ifdef HAVE_MPI MPI_Reduce(localNumerator, numerator, nSomY * nSomX * nDimensions, MPI_FLOAT, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Reduce(localDenominator, denominator, nSomY * nSomX, MPI_FLOAT, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Gather(bmus, nVectorsPerRank * 2, MPI_INT, globalBmus, nVectorsPerRank * 2, MPI_INT, 0, MPI_COMM_WORLD); delete [] bmus; delete [] localNumerator; delete [] localDenominator; #endif } somoclu-1.7.2/src/R/0000755000175000017500000000000013015657206011200 500000000000000somoclu-1.7.2/src/R/src/0000755000175000017500000000000013015657206011767 500000000000000somoclu-1.7.2/src/R/src/Makevars0000644000175000017500000000030213015657206013376 00000000000000CXX_STD = CXX11 PKG_LIBS = `$(R_HOME)/bin/Rscript -e "Rcpp:::LdFlags()"` $(SHLIB_OPENMP_CXXFLAGS) PKG_CXXFLAGS = `$(R_HOME)/bin/Rscript -e "Rcpp:::CxxFlags()"` $(SHLIB_OPENMP_CXXFLAGS) -DHAVE_R somoclu-1.7.2/src/R/src/Makevars.win0000644000175000017500000000030213015657206014172 00000000000000CXX_STD = CXX11 PKG_LIBS = `$(R_HOME)/bin/Rscript -e "Rcpp:::LdFlags()"` $(SHLIB_OPENMP_CXXFLAGS) PKG_CXXFLAGS = `$(R_HOME)/bin/Rscript -e "Rcpp:::CxxFlags()"` $(SHLIB_OPENMP_CXXFLAGS) -DHAVE_R somoclu-1.7.2/src/R/src/Rsomoclu.cpp0000644000175000017500000000730313015657206014221 00000000000000#include #include using namespace std; using namespace Rcpp; #include"somoclu.h" RcppExport SEXP Rtrain(SEXP data_p, SEXP nEpoch_p, SEXP nSomX_p, SEXP nSomY_p, SEXP radius0_p, SEXP radiusN_p, SEXP radiusCooling_p, SEXP scale0_p, SEXP scaleN_p, SEXP scaleCooling_p, SEXP kernelType_p, SEXP mapType_p, SEXP gridType_p, SEXP compactSupport_p, SEXP neighborhood_p, SEXP stdCoeff_p, SEXP codebook_p) { Rcpp::NumericMatrix dataMatrix(data_p); Rcpp::NumericMatrix codebookMatrix(codebook_p); int nVectors = dataMatrix.rows(); int nDimensions = dataMatrix.cols(); int nEpoch = as(nEpoch_p); unsigned int nSomX = (unsigned int) as (nSomX_p); unsigned int nSomY = (unsigned int) as (nSomY_p); float radius0 = as (radius0_p); float radiusN = as (radiusN_p); string radiusCooling = as(radiusCooling_p); float scale0 = as (scale0_p); float scaleN = as (scaleN_p); float std_coeff = as (stdCoeff_p); string scaleCooling = as (scaleCooling_p); unsigned int kernelType = (unsigned int) as(kernelType_p); bool compactSupport = as(compactSupport_p); string mapType = as(mapType_p); string gridType = as(gridType_p); string neighborhood = as(neighborhood_p); int data_length = nVectors * nDimensions; float* data = new float[data_length]; int uMatrix_size = nSomX * nSomY; // convert matrix to data c float array for(int i = 0; i < nVectors; i++) { for(int j = 0; j < nDimensions; j++) { data[i * nDimensions + j] = (float) dataMatrix(i, j); } } int codebook_size = nSomY * nSomX * nDimensions; float* codebook = new float[codebook_size]; for(int i = 0; i < uMatrix_size; i++) { for(int j = 0; j < nDimensions; j++) { codebook[i * nDimensions + j] = (float) codebookMatrix(i ,j); } } int globalBmus_size = nVectors * 2; int* globalBmus = new int[globalBmus_size]; float* uMatrix = new float[uMatrix_size]; train(data, data_length, nEpoch, nSomX, nSomY, nDimensions, nVectors, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood == "gaussian", std_coeff, codebook, codebook_size, globalBmus, globalBmus_size, uMatrix, uMatrix_size); Rcpp::NumericMatrix globalBmusMatrix(nVectors, 2); Rcpp::NumericMatrix uMatrixMatrix(nSomX, nSomY); if(codebook != NULL) { for(int i = 0; i < uMatrix_size; i++) { for(int j = 0; j < nDimensions; j++) { codebookMatrix(i ,j) = (float) codebook[i * nDimensions + j]; } } } if(globalBmus != NULL) { for(int i = 0; i < nVectors; i++) { for (int j = 0; j < 2; j++) { globalBmusMatrix(i, j) = globalBmus[i * 2 + j]; } } } if(uMatrix != NULL) { for(int i = 0; i < nSomX; i++) { for (int j = 0; j < nSomY; j++) { uMatrixMatrix(i, j) = uMatrix[i * nSomY + j]; } } } delete[] codebook; delete[] globalBmus; delete[] uMatrix; return Rcpp::List::create(Rcpp::Named("codebook") = codebookMatrix, Rcpp::Named("globalBmus") = globalBmusMatrix, Rcpp::Named("uMatrix") = uMatrixMatrix);; } RCPP_MODULE(Rsomoclu) { Rcpp::function("Rtrain", &Rtrain); } somoclu-1.7.2/src/R/tests/0000755000175000017500000000000013015657206012342 500000000000000somoclu-1.7.2/src/R/tests/R_interface_test.R0000644000175000017500000000214513015657206015667 00000000000000library('Rsomoclu') data_file <- system.file("data", "rgbs.txt.gz", package = 'Rsomoclu') input_data <- read.table(data_file) input_data <- data.matrix(input_data) nSomX <- 50 nSomY <- 50 nEpoch <- 10 radius0 <- 0 radiusN <- 0 radiusCooling <- "linear" scale0 <- 0 scaleN <- 0.01 scaleCooling <- "linear" kernelType <- 0 mapType <- "planar" gridType <- "rectangular" compactSupport <- FALSE codebook <- NULL neighborhood <- "gaussian" stdCoeff <- 0.5 res <- Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook) res$codebook res$globalBmus res$uMatrix library('kohonen') sommap = Rsomoclu.kohonen(input_data, res) ## Show 'codebook' plot(sommap, type="codes", main = c("Codes X", "Codes Y")) ## Show 'component planes' plot(sommap, type = "property", property = sommap$codes[,1], main = colnames(sommap$codes)[1]) ## Show 'U-Matrix' plot(sommap, type="dist.neighbours") somoclu-1.7.2/src/R/man/0000755000175000017500000000000013015657206011753 500000000000000somoclu-1.7.2/src/R/man/Rsomoclu.kohonen.Rd0000644000175000017500000000374413015657206015435 00000000000000\name{Rsomoclu.kohonen} \alias{Rsomoclu.kohonen} \title{convert Somoclu train result to kohonen class for plotting} \description{ A function call to convert Somoclu train result to kohonen class for plotting. } \usage{ Rsomoclu.kohonen(input_data, result, n.hood = NULL, toroidal = FALSE) } \arguments{ \item{input_data}{input data, matrix format} \item{result}{The result returned by Rsomoclu.train} \item{n.hood}{Same as in koohonen, the shape of the neighbourhood, either "circular" or "square". The latter is the default for rectangular maps, the former for hexagonal maps.} \item{toroidal}{if TRUE, the edges of the map are joined. Note that in a hexagonal toroidal map, the number of rows must be even.} } \value{ An object of class \code{kohonen} for plotting. } \examples{ library('Rsomoclu') library('kohonen') data_file <- system.file("data", "rgbs.txt.gz", package = 'Rsomoclu') input_data <- read.table(data_file) input_data <- data.matrix(input_data) nSomX <- 20 nSomY <- 20 nEpoch <- 10 radius0 <- 0 radiusN <- 0 radiusCooling <- "linear" scale0 <- 0 scaleN <- 0.01 scaleCooling <- "linear" kernelType <- 0 mapType <- "planar" gridType <- "rectangular" compactSupport <- FALSE codebook <- NULL neighborhood <- "gaussian" stdCoeff <- 0.5 res <- Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook) ## Convert to kohonen object for plotting sommap = Rsomoclu.kohonen(input_data, res) ## Show 'codebook' plot(sommap, type="codes", main = c("Codes X", "Codes Y")) ## Show 'component planes' plot(sommap, type = "property", property = sommap$codes[,1], main = colnames(sommap$codes)[1]) ## Show 'U-Matrix' plot(sommap, type="dist.neighbours") } \seealso{\url{http://www.r-bloggers.com/self-organising-maps-for-customer-segmentation-using-r/}} somoclu-1.7.2/src/R/man/Rsomoclu.train.Rd0000644000175000017500000000604313015657206015104 00000000000000\name{Rsomoclu.train} \alias{Rsomoclu.train} \title{Train function for Somoclu} \description{ A function call to Somoclu to train the Self Organizing Map. } \usage{ Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook) } \arguments{ \item{input_data}{input data, matrix format} \item{nEpoch}{Maximum number of epochs} \item{nSomX}{Number of columns in map (size of SOM in direction x)} \item{nSomY}{Number of rows in map (size of SOM in direction y)} \item{radius0}{Start radius (default: half of the map in direction min(x,y))} \item{radiusN}{End radius (default: 1)} \item{radiusCooling}{Radius cooling strategy: linear or exponential (default: linear)} \item{scale0}{Starting learning rate (default: 1.0)} \item{scaleN}{Finishing learning rate (default: 0.01)} \item{scaleCooling}{Learning rate cooling strategy: linear or exponential (default: linear)} \item{kernelType}{Kernel type 0: Dense CPU 1: Dense GPU 2: Sparse CPU (default: 0)} \item{mapType}{Map type: planar or toroid (default: "planar")} \item{gridType}{Grid type: square or hexagonal (default: "rectangular")} \item{compactSupport}{Compact support for Gaussian neighborhood, (default:TRUE)} \item{neighborhood}{Neighborhood function: gaussian or bubble (default: "gaussian")} \item{codebook}{initial codebook, (default:NULL)} \item{stdCoeff}{The coefficient in the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)), (default:0.5)} %% \item{initialCodebookFilename}{Specify an initial codebook for the map} } \value{ a list including elements \item{codebook}{the codebook} \item{globalBmus}{global Best Matching Unit matrix} \item{uMatrix}{uMatrix} } \references{Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2015). Somoclu: An Efficient Parallel Library for Self-Organizing Maps. arXiv:1305.1422.} \author{Peter Wittek, Shichao Gao} \examples{ library('Rsomoclu') data_file <- system.file("data", "rgbs.txt.gz", package = 'Rsomoclu') input_data <- read.table(data_file) input_data <- data.matrix(input_data) nSomX <- 50 nSomY <- 50 nEpoch <- 10 radius0 <- 0 radiusN <- 0 radiusCooling <- "linear" scale0 <- 0 scaleN <- 0.01 scaleCooling <- "linear" kernelType <- 0 mapType <- "planar" gridType <- "rectangular" compactSupport <- FALSE codebook <- NULL neighborhood <- "gaussian" stdCoeff <- 0.5 res <- Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook) res$codebook res$globalBmus res$uMatrix library('kohonen') sommap = Rsomoclu.kohonen(input_data, res) } somoclu-1.7.2/src/R/man/rgbs.Rd0000644000175000017500000000025613015657206013122 00000000000000\name{rgbs} \docType{data} \alias{rgbs} \title{tiny rgbs data} \description{ tiny rgbs data for testing } \usage{rgbs} \format{matrix in plain text form} \keyword{datasets}somoclu-1.7.2/src/R/R/0000755000175000017500000000000013015657206011401 500000000000000somoclu-1.7.2/src/R/R/Rsomoclu.R0000644000175000017500000000340713015657206013253 00000000000000#require(Rcpp) .onLoad <- function(lib, pkg){ library.dynam("Rsomoclu", pkg, lib) } Rsomoclu.train <- function(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType=0, mapType="planar", gridType="rectangular", compactSupport=TRUE, neighborhood="gaussian", stdCoeff=0.5, codebook=NULL) { if (is.null(codebook)) { codebook <- matrix(data = 0, nrow = nSomX * nSomY, ncol = dim(input_data)[2]) codebook[1, 1] <- 1000 codebook[2, 1] <- 2000 } res <- .Call("Rtrain", input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook) res } Rsomoclu.kohonen <- function(input_data, result, n.hood = NULL, toroidal = FALSE) { mapping <- map(structure(list(codes = result$codebook), class = "kohonen"), newdata = input_data) nSomX = nrow(result$uMatrix) nSomY = ncol(result$uMatrix) grid = somgrid(nSomX, nSomY) if (missing(n.hood)) { n.hood <- switch(grid$topo, hexagonal = "circular", rectangular = "square") } else { n.hood <- match.arg(n.hood, c("circular", "square")) } grid$n.hood <- n.hood sommap = structure(list(data = input_data, grid = grid, codes = result$codebook, changes = NULL, unit.classif = mapping$unit.classif, distances = mapping$distances, toroidal = toroidal, method="som"), class = "kohonen") sommap } somoclu-1.7.2/src/R/inst/0000755000175000017500000000000013015657206012155 500000000000000somoclu-1.7.2/src/R/inst/CITATION0000644000175000017500000000132513015657206013233 00000000000000citHeader("To cite Rsomoclu in publications use:") citEntry(entry = "Article", title = "Somoclu: An Efficient Parallel Library for Self-Organizing Maps", author = personList(as.person("Peter Wittek"), as.person("Shi Chao Gao"), as.person("Ik Soo Lim"), as.person("Li Zhao")), journal = "arXiv:1305.1422", year = "2015", url = "http://arxiv.org/abs/1305.1422", textVersion = paste("Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2015).", "Somoclu: An Efficient Parallel Library for Self-Organizing Maps.", "arXiv:1305.1422.", "URL http://arxiv.org/abs/1305.1422.") ) somoclu-1.7.2/src/R/DESCRIPTION0000644000175000017500000000153613015657206012633 00000000000000Package: Rsomoclu Version: 1.7.2 Date: 2016-10-02 Title: Somoclu Depends: Rcpp Imports: kohonen, class Suggests: Authors@R: c(person(given = "Peter", family = "Wittek", role = c("aut"), email = ""), person(given = "Shichao", family = "Gao", role = c("cre"), email = "xgdgsc@gmail.com")) Description: Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . URL: http://peterwittek.github.io/somoclu/ BugReports: https://github.com/peterwittek/somoclu/issues License: GPL-3 Packaged: 2014-03-16 16:55:16 UTC; Author: Peter Wittek [aut], Shichao Gao [cre] Maintainer: Shichao Gao NeedsCompilation: yes Repository: CRAN SystemRequirements: GNU make somoclu-1.7.2/src/R/NAMESPACE0000644000175000017500000000017413015657206012341 00000000000000useDynLib(Rsomoclu) export(Rsomoclu.train) export(Rsomoclu.kohonen) import(Rcpp) import(kohonen) importFrom(class, somgrid) somoclu-1.7.2/src/R/configure0000755000175000017500000000144613015657206013034 00000000000000#!/bin/bash if [ "$#" -ne 1 ]; then exit 0 fi ARCH=$(uname -m) if [ ${ARCH} = 'i386' ] ; then CUDA_LIB=$1/lib else CUDA_LIB=$1/lib64 fi mkdir -p src/src mv src/*cpp src/*cu src/Makefile.in src/src/ cp src/somoclu.h src/src/ mv src/src/Rsomoclu.cpp src/ mv src/Makefile.root.in src/Makefile.in rm src/Makevars* cd src ./configure --with-cuda=$1 --without-mpi make libwrapper rm Makefile* mv src/*.o ./ mv src/*.co ./ rm -fr src export PKG_LIBS=`${R_HOME}/bin/Rscript -e "Rcpp:::LdFlags()"` export PKG_CXXFLAGS=`${R_HOME}/bin/Rscript -e "Rcpp:::CxxFlags()"`" -DHAVE_R -DCUDA" ${R_HOME}/bin/R CMD SHLIB $PKG_CXXFLAGS Rsomoclu.cpp denseCpuKernels.o sparseCpuKernels.o mapDistanceFunctions.o training.o uMatrix.o denseGpuKernels.cu.co -lgomp -L${CUDA_LIB} -lcudart -lcublas cp Rsomoclu.so ../ cd .. somoclu-1.7.2/src/uMatrix.cpp0000644000175000017500000000722613015657206013063 00000000000000 #include"somoclu.h" #include /** Get weight vector from a codebook using x, y index * @param codebook - the codebook to save * @param som_y - y coordinate of a node in the map * @param som_x - x coordinate of a node in the map * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance * @return the weight vector */ float* get_wvec(float *codebook, unsigned int som_y, unsigned int som_x, unsigned int nSomX, unsigned int nDimensions) { float* wvec = new float[nDimensions]; for (unsigned int d = 0; d < nDimensions; d++) wvec[d] = codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d]; /// CAUTION: (y,x) order return wvec; } /** Euclidean distance between vec1 and vec2 * @param vec1 * @param vec2 * @param nDimensions * @return distance */ float get_distance(const float* vec1, const float* vec2, unsigned int nDimensions) { float distance = 0.0f; float x1 = 0.0f; float x2 = 0.0f; for (unsigned int d = 0; d < nDimensions; d++) { x1 = std::min(vec1[d], vec2[d]); x2 = std::max(vec1[d], vec2[d]); distance += std::abs(x1 - x2) * std::abs(x1 - x2); } return sqrt(distance); } /** Calculate U-matrix * @param codebook - the codebook * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance */ float *calculateUMatrix(float *uMatrix, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, string mapType, string gridType) { float min_dist = 1.5f; for (unsigned int som_y1 = 0; som_y1 < nSomY; som_y1++) { for (unsigned int som_x1 = 0; som_x1 < nSomX; som_x1++) { float dist = 0.0f; unsigned int nodes_number = 0; for (unsigned int som_y2 = 0; som_y2 < nSomY; som_y2++) { for (unsigned int som_x2 = 0; som_x2 < nSomX; som_x2++) { if (som_x1 == som_x2 && som_y1 == som_y2) continue; float tmp = 0.0f; if (gridType == "rectangular") { if (mapType == "planar") { tmp = euclideanDistanceOnPlanarMap(som_x1, som_y1, som_x2, som_y2); } else if (mapType == "toroid") { tmp = euclideanDistanceOnToroidMap(som_x1, som_y1, som_x2, som_y2, nSomX, nSomY); } } else { if (mapType == "planar") { tmp = euclideanDistanceOnHexagonalPlanarMap(som_x1, som_y1, som_x2, som_y2); } else if (mapType == "toroid") { tmp = euclideanDistanceOnHexagonalToroidMap(som_x1, som_y1, som_x2, som_y2, nSomX, nSomY); } } if (tmp <= min_dist) { nodes_number++; float* vec1 = get_wvec(codebook, som_y1, som_x1, nSomX, nDimensions); float* vec2 = get_wvec(codebook, som_y2, som_x2, nSomX, nDimensions); dist += get_distance(vec1, vec2, nDimensions); delete [] vec1; delete [] vec2; } } } dist /= (float)nodes_number; uMatrix[som_y1 * nSomX + som_x1] = dist; } } return uMatrix; } somoclu-1.7.2/src/denseCpuKernels.cpp0000644000175000017500000002152113015657206014516 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #include "somoclu.h" #ifdef HAVE_R #include #endif /** Distance b/w a feature vector and a weight vector * = Euclidean * @param som_y * @param som_x * @param r - row number in the input feature file */ float get_distance(float* codebook, float* data, unsigned int som_y, unsigned int som_x, unsigned int nSomX, unsigned int nDimensions, unsigned int r) { float distance = 0.0f; for (unsigned int d = 0; d < nDimensions; d++) distance += (codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] - * (data + r * nDimensions + d)) * (codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] - * (data + r * nDimensions + d)); return distance; } /** Get node coords for the best matching unit (BMU) * @param coords - BMU coords * @param n - row num in the input feature file */ void get_bmu_coord(float* codebook, float* data, unsigned int nSomY, unsigned int nSomX, unsigned int nDimensions, unsigned int* coords, unsigned int n) { float mindist = 0.0f; float dist = 0.0f; /// Check nSomX * nSomY nodes one by one and compute the distance /// D(W_K, Fvec) and get the mindist and get the coords for the BMU. /// for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { dist = get_distance(codebook, data, som_y, som_x, nSomX, nDimensions, n); if ((som_y == 0 && som_x == 0) || (dist < mindist)) { mindist = dist; coords[0] = som_x; coords[1] = som_y; } } } } void trainOneEpochDenseCPU(int itask, float *data, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, float scale, string mapType, string gridType, bool compact_support, bool gaussian, int *globalBmus, bool only_bmus, float std_coeff) { unsigned int p1[2] = {0, 0}; int *bmus; #ifdef HAVE_MPI bmus = new int[nVectorsPerRank * 2]; #else bmus = globalBmus; #endif #ifdef _OPENMP #pragma omp parallel default(shared) private(p1) #endif { #ifdef _OPENMP #pragma omp for #endif #ifdef _WIN32 for (int n = 0; n < nVectorsPerRank; n++) { #else for (unsigned int n = 0; n < nVectorsPerRank; n++) { #endif if (itask * nVectorsPerRank + n < nVectors) { /// get the best matching unit get_bmu_coord(codebook, data, nSomY, nSomX, nDimensions, p1, n); bmus[2 * n] = p1[0]; bmus[2 * n + 1] = p1[1]; } } } if (only_bmus) { #ifdef HAVE_MPI MPI_Gather(bmus, nVectorsPerRank * 2, MPI_INT, globalBmus, nVectorsPerRank * 2, MPI_INT, 0, MPI_COMM_WORLD); delete [] bmus; #endif return; } #ifdef HAVE_MPI float *localNumerator = new float[nSomY * nSomX * nDimensions]; float *localDenominator = new float[nSomY * nSomX]; #ifdef _OPENMP #pragma omp parallel default(shared) #endif // _OPENMP { #ifdef _OPENMP #pragma omp for #endif // _OPENMP #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif // _WIN32 for (unsigned int som_x = 0; som_x < nSomX; som_x++) { localDenominator[som_y * nSomX + som_x] = 0.0; for (unsigned int d = 0; d < nDimensions; d++) localNumerator[som_y * nSomX * nDimensions + som_x * nDimensions + d] = 0.0; } } } #ifdef _OPENMP #pragma omp parallel default(shared) #endif #else // not HAVE_MPI float *localNumerator; float localDenominator = 0; // Accumulate denoms and numers #ifdef _OPENMP #pragma omp parallel default(shared) private(localDenominator) private(localNumerator) #endif #endif // HAVE_MPI { #ifndef HAVE_MPI localNumerator = new float[nDimensions]; #endif // HAVE_MPI #ifdef _OPENMP #pragma omp for #endif #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif for (unsigned int som_x = 0; som_x < nSomX; som_x++) { for (unsigned int n = 0; n < nVectorsPerRank; n++) { if (itask * nVectorsPerRank + n < nVectors) { float dist = 0.0f; if (gridType == "rectangular") { if (mapType == "planar") { dist = euclideanDistanceOnPlanarMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1]); } else if (mapType == "toroid") { dist = euclideanDistanceOnToroidMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1], nSomX, nSomY); } } else { if (mapType == "planar") { dist = euclideanDistanceOnHexagonalPlanarMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1]); } else if (mapType == "toroid") { dist = euclideanDistanceOnHexagonalToroidMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1], nSomX, nSomY); } } float neighbor_fuct = getWeight(dist, radius, scale, compact_support, gaussian, std_coeff); #ifdef HAVE_MPI localDenominator[som_y * nSomX + som_x] += neighbor_fuct; for (unsigned int d = 0; d < nDimensions; d++) { localNumerator[som_y * nSomX * nDimensions + som_x * nDimensions + d] += 1.0f * neighbor_fuct * (*(data + n * nDimensions + d)); } #else // In this case, we can update in place if (n == 0) { localDenominator = neighbor_fuct; for (unsigned int d = 0; d < nDimensions; d++) { localNumerator[d] = 1.0f * neighbor_fuct * (*(data + n * nDimensions + d)); } } else { localDenominator += neighbor_fuct; for (unsigned int d = 0; d < nDimensions; d++) { localNumerator[d] += 1.0f * neighbor_fuct * (*(data + n * nDimensions + d)); } } #endif // HAVE_MPI } } // Looping over data instances #ifndef HAVE_MPI // We update in-place for (unsigned int d = 0; d < nDimensions; d++) { if (localDenominator != 0) { float newWeight = localNumerator[d] / localDenominator; codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] = newWeight; } } #endif } // Looping over som_x } // Looping over som_y #ifndef HAVE_MPI delete [] localNumerator; #endif } // OPENMP #ifdef HAVE_MPI MPI_Reduce(localNumerator, numerator, nSomY * nSomX * nDimensions, MPI_FLOAT, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Reduce(localDenominator, denominator, nSomY * nSomX, MPI_FLOAT, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Gather(bmus, nVectorsPerRank * 2, MPI_INT, globalBmus, nVectorsPerRank * 2, MPI_INT, 0, MPI_COMM_WORLD); delete [] localNumerator; delete [] localDenominator; delete [] bmus; #endif } somoclu-1.7.2/src/training.cpp0000644000175000017500000003363013015657206013243 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #ifdef CLI #include #include #include #else #include #endif // CLI #ifdef HAVE_R #include #endif // HAVE_R #include "somoclu.h" using namespace std; void my_abort(string err) { #ifdef CLI #ifdef HAVE_MPI int rank = 0; MPI_Comm_rank(MPI_COMM_WORLD, &rank); if (rank == 0) { cerr << "Error: " << err << endl; } MPI_Abort(MPI_COMM_WORLD, 1); #else cerr << "Error: " << err << endl; exit(1); #endif // HAVE_MPI #else throw std::runtime_error(err); #endif // CLI } void train(float *data, int data_length, unsigned int nEpoch, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, float radius0, float radiusN, string radiusCooling, float scale0, float scaleN, string scaleCooling, unsigned int kernelType, string mapType, string gridType, bool compact_support, bool gaussian, float std_coeff, float *codebook, int codebook_size, int *globalBmus, int globalBmus_size, float *uMatrix, int uMatrix_size) { #ifdef HAVE_R #ifndef CUDA if(kernelType == DENSE_GPU){ Rprintf("Error: CUDA kernel not compiled \n"); return; } #endif // CUDA #endif // HAVE_R train(0, data, NULL, codebook, globalBmus, uMatrix, nSomX, nSomY, nDimensions, nVectors, nVectors, nEpoch, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compact_support, gaussian, std_coeff #ifdef CLI , "", 0); #else ); #endif calculateUMatrix(uMatrix, codebook, nSomX, nSomY, nDimensions, mapType, gridType); } void julia_train(float *data, int data_length, unsigned int nEpoch, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, float radius0, float radiusN, unsigned int _radiusCooling, float scale0, float scaleN, unsigned int _scaleCooling, unsigned int kernelType, unsigned int _mapType, unsigned int _gridType, bool compact_support, bool gaussian, float std_coeff, float *codebook, int codebook_size, int *globalBmus, int globalBmus_size, float *uMatrix, int uMatrix_size) { string radiusCooling; string scaleCooling; string mapType; string gridType; if (_radiusCooling == 0) { radiusCooling = "linear"; } else { radiusCooling = "exponential"; } if (_scaleCooling == 0) { scaleCooling = "linear"; } else { scaleCooling = "exponential"; } if (_mapType == 0) { mapType = "planar"; } else { mapType = "toroid"; } if (_gridType == 0) { gridType = "square"; } else { gridType = "hexagonal"; } train(0, data, NULL, codebook, globalBmus, uMatrix, nSomX, nSomY, nDimensions, nVectors, nVectors, nEpoch, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compact_support, gaussian, std_coeff #ifdef CLI , "", 0); #else ); #endif calculateUMatrix(uMatrix, codebook, nSomX, nSomY, nDimensions, mapType, gridType); } void train(int itask, float *data, svm_node **sparseData, float *codebook, int *globalBmus, float *uMatrix, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, unsigned int nEpoch, float radius0, float radiusN, string radiusCooling, float scale0, float scaleN, string scaleCooling, unsigned int kernelType, string mapType, string gridType, bool compact_support, bool gaussian, float std_coeff #ifdef CLI , string outPrefix, unsigned int snapshots) #else ) #endif { int nProcs = 1; #ifdef HAVE_MPI MPI_Comm_size(MPI_COMM_WORLD, &nProcs); #endif #ifdef CUDA if (kernelType == DENSE_GPU) { setDevice(itask, nProcs); initializeGpu(data, nVectorsPerRank, nDimensions, nSomX, nSomY); } #endif // (Re-)Initialize codebook with random values only if requested through // the passed codebook -- meaning that the user did not have an initial // codebook if (codebook[0] == 1000 && codebook[1] == 2000) { initializeCodebook(0, codebook, nSomX, nSomY, nDimensions); } /// /// Parameters for SOM /// if (radius0 == 0) { unsigned int minDim = min(nSomX, nSomY); radius0 = minDim / 2.0f; /// init radius for updating neighbors } if (radiusN == 0) { radiusN = 1; } if (scale0 == 0) { scale0 = 0.1; } /// /// Training /// unsigned int currentEpoch = 0; /// 0...nEpoch-1 while ( currentEpoch < nEpoch ) { #ifdef HAVE_MPI double epoch_time = MPI_Wtime(); #endif trainOneEpoch(itask, data, sparseData, codebook, globalBmus, nEpoch, currentEpoch, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compact_support, gaussian, std_coeff); #ifdef CLI if (snapshots > 0 && itask == 0) { calculateUMatrix(uMatrix, codebook, nSomX, nSomY, nDimensions, mapType, gridType); stringstream sstm; sstm << outPrefix << "." << currentEpoch + 1; saveUMatrix(sstm.str() + string(".umx"), uMatrix, nSomX, nSomY); if (snapshots == 2) { saveBmus(sstm.str() + string(".bm"), globalBmus, nSomX, nSomY, nVectors); saveCodebook(sstm.str() + string(".wts"), codebook, nSomX, nSomY, nDimensions); } } #endif ++currentEpoch; #ifdef CLI #ifdef HAVE_MPI if (itask == 0) { epoch_time = MPI_Wtime() - epoch_time; cerr << "Epoch Time: " << epoch_time << endl; if ( (currentEpoch != nEpoch) && (currentEpoch % (nEpoch / 100 + 1) != 0) ) {} else { float ratio = currentEpoch / (float)nEpoch; int c = ratio * 50 + 1; cout << std::setw(7) << (int)(ratio * 100) << "% ["; for (int x = 0; x < c; x++) cout << "="; for (int x = c; x < 50; x++) cout << " "; cout << "]\n" << flush; } } #endif #endif } trainOneEpoch(itask, data, sparseData, codebook, globalBmus, nEpoch, currentEpoch, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compact_support, gaussian, std_coeff, true); #ifdef CUDA if (kernelType == DENSE_GPU) { freeGpu(); } #endif } float linearCooling(float start, float end, float nEpoch, float epoch) { float diff = (start - end) / (nEpoch-1); return start - (epoch * diff); } float exponentialCooling(float start, float end, float nEpoch, float epoch) { float diff = 0; if (end == 0.0) { diff = -log(0.1) / nEpoch; } else { diff = -log(end / start) / nEpoch; } return start * exp(-epoch * diff); } /** Initialize SOM codebook with random values * @param seed - random seed * @param codebook - the codebook to fill in * @param nSomX - dimensions of SOM map in the currentEpoch direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance */ void initializeCodebook(unsigned int seed, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions) { /// /// Fill initial random weights /// #ifdef HAVE_R GetRNGstate(); #else srand(seed); #endif #pragma omp parallel for #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif for (unsigned int som_x = 0; som_x < nSomX; som_x++) { for (unsigned int d = 0; d < nDimensions; d++) { #ifdef HAVE_R int w = 0xFFF & (int) (RAND_MAX * unif_rand()); #else int w = 0xFFF & rand(); #endif w -= 0x800; codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] = (float)w / 4096.0f; } } } #ifdef HAVE_R PutRNGstate(); #endif } void trainOneEpoch(int itask, float *data, svm_node **sparseData, float *codebook, int *globalBmus, unsigned int nEpoch, unsigned int currentEpoch, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius0, float radiusN, string radiusCooling, float scale0, float scaleN, string scaleCooling, unsigned int kernelType, string mapType, string gridType, bool compact_support, bool gaussian, float std_coeff, bool only_bmus) { float N = (float)nEpoch; float *numerator; float *denominator; float scale = scale0; float radius = radius0; if (itask == 0 && !only_bmus) { #ifdef HAVE_MPI numerator = new float[nSomY * nSomX * nDimensions]; denominator = new float[nSomY * nSomX]; for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { denominator[som_y * nSomX + som_x] = 0.0; for (unsigned int d = 0; d < nDimensions; d++) { numerator[som_y * nSomX * nDimensions + som_x * nDimensions + d] = 0.0; } } } #endif if (radiusCooling == "linear") { radius = linearCooling(radius0, radiusN, N, currentEpoch); } else { radius = exponentialCooling(radius0, radiusN, N, currentEpoch); } if (scaleCooling == "linear") { scale = linearCooling(scale0, scaleN, N, currentEpoch); } else { scale = exponentialCooling(scale0, scaleN, N, currentEpoch); } // << "Epoch: " << currentEpoch << " Radius: " << radius << endl; } #ifdef HAVE_MPI if (!only_bmus) { MPI_Bcast(&radius, 1, MPI_FLOAT, 0, MPI_COMM_WORLD); MPI_Bcast(&scale, 1, MPI_FLOAT, 0, MPI_COMM_WORLD); MPI_Bcast(codebook, nSomY * nSomX * nDimensions, MPI_FLOAT, 0, MPI_COMM_WORLD); } #endif /// 1. Each task fills localNumerator and localDenominator /// 2. MPI_reduce sums up each tasks localNumerator and localDenominator to the root's /// numerator and denominator. switch (kernelType) { default: case DENSE_CPU: trainOneEpochDenseCPU(itask, data, numerator, denominator, codebook, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius, scale, mapType, gridType, compact_support, gaussian, globalBmus, only_bmus, std_coeff); break; case DENSE_GPU: #ifdef CUDA trainOneEpochDenseGPU(itask, data, numerator, denominator, codebook, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius, scale, mapType, gridType, compact_support, gaussian, globalBmus, only_bmus, std_coeff); #else my_abort("Compiled without CUDA!"); #endif break; case SPARSE_CPU: trainOneEpochSparseCPU(itask, sparseData, numerator, denominator, codebook, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, radius, scale, mapType, gridType, compact_support, gaussian, globalBmus, only_bmus, std_coeff); break; } /// 3. Update codebook using numerator and denominator #ifdef HAVE_MPI if (!only_bmus) { MPI_Barrier(MPI_COMM_WORLD); if (itask == 0 && !only_bmus) { #pragma omp parallel for #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif for (unsigned int som_x = 0; som_x < nSomX; som_x++) { float denom = denominator[som_y * nSomX + som_x]; if (denom != 0) { for (unsigned int d = 0; d < nDimensions; d++) { float newWeight = numerator[som_y * nSomX * nDimensions + som_x * nDimensions + d] / denom; codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] = newWeight; } } } } delete [] numerator; delete [] denominator; } } #endif // HAVE_MPI } somoclu-1.7.2/src/MATLAB/0000755000175000017500000000000013015657206011737 500000000000000somoclu-1.7.2/src/MATLAB/README.rst0000644000175000017500000001162713015657206013355 00000000000000Version: 1.7.2 Maintainer: ShichaoGao URL: http://peterwittek.github.io/somoclu/ BugReports: https://github.com/peterwittek/somoclu/issues License: GPL-3 OS_type: unix, windows Somoclu MATLAB Extension Build Guide (Linux/Mac): ================================================= **(OS X users see the Note below first)** 1. Configure the library to compile without MPI support. Optionally, specify the root of your MATLAB installation. E.g.:: $ ./configure --without-mpi --with-matlab=/usr/local/MATLAB/R2014a If you want CUDA support, specify the CUDA directory as well. 2. Build MATLAB Extension by running: :: make matlab 3. Then ``MexSomoclu.mexa64`` or ``MexSomoclu.mexa32`` is generated for use, you can test by installing som-toolbox from https://github.com/ilarinieminen/SOM-Toolbox, and running the ``mex_interface_test.m`` or ``mex_interface_test_gpu.m``. If you encounter errors like: :: /usr/local/MATLAB/R2013a/sys/os/glnx86/libstdc++.so.6: version `GLIBCXX_3.4.20' not found You can rename ``libstdc++.so.6*`` under ``MATLAB_ROOT/sys/os/glnxa64`` to solve this issue. Note for Mac OS X users: ================================ Using GCC --------------- As of OS X 10.10, gcc is just symlink to clang. To build somoclu and this extension using GCC, it is recommended to install gcc using something like: :: $ brew install gcc --without-multilib and set environment using: :: export CC=/usr/local/bin/gcc-5 export CXX=/usr/local/bin/g++-5 export CPP=/usr/local/bin/cpp-5 export LD=/usr/local/bin/gcc-5 alias c++=/usr/local/bin/c++-5 alias g++=/usr/local/bin/g++-5 alias gcc=/usr/local/bin/gcc-5 alias cpp=/usr/local/bin/cpp-5 alias ld=/usr/local/bin/gcc-5 alias cc=/usr/local/bin/gcc-5 before running ``./configure`` . Then follow the instructions at https://github.com/peterwittek/somoclu to build somoclu itself. Using clang-omp --------------- To install clang-omp, follow instructions at http://clang-omp.github.io/. And set environment using: :: export CC=/usr/local/bin/clang-omp export CXX=/usr/local/bin/clang-omp++ export CPP=/usr/local/bin/clang-omp++ export LD=/usr/local/bin/clang-omp alias c++=/usr/local/bin/clang-omp++ alias g++=/usr/local/bin/clang-omp++ alias gcc=/usr/local/bin/clang-omp alias cpp=/usr/local/bin/clang-omp++ alias ld=/usr/local/bin/clang-omp alias cc=/usr/local/bin/clang-omp export PATH=/usr/local/bin/:$PATH export C_INCLUDE_PATH=/usr/local/include/:$C_INCLUDE_PATH export CPLUS_INCLUDE_PATH=/usr/local/include/:$CPLUS_INCLUDE_PATH export LIBRARY_PATH=/usr/local/lib:$LIBRARY_PATH export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH before running ``./configure`` . Building Mex Extension on OS X: =============================== Using GCC --------------- To build the extension on OS X, we need to make mex use gcc instead of the default clang compiler which doesn' t support openmp (As of OSX 10.10.5). We need to copy ``MATLAB_ROOT/bin/mexopts.sh`` to ``~/.matlab/VERSION/mexopts.sh`` , replace ``MATLAB_ROOT`` with your installation path of MATLAB and replace ``VERSION`` with your MATLAB version in that folder. Example: :: cp /Applications/MATLAB_R2013a.app/bin/mexopts.sh ~/.matlab/R2013a/mexopts.sh Then modify ``~/.matlab/VERSION/mexopts.sh`` to use gcc as follows: 1. change ``CC='gcc'`` and comment out all ``CC=`` statements after that. 2. change ``CXX='g++'`` and comment out all ``CXX=`` statements after that. 3. change ``MACOSX_DEPLOYMENT_TARGET='10.9'`` where ``10.9`` is your OS X version number. an example is given at https://gist.github.com/xgdgsc/9832340, then you can follow the instruction step 2 at the top to build the extension and test. Using clang-omp --------------- Similar to above GCC approach, we need to make mex use clang-omp by modifying ``~/.matlab/VERSION/mexopts.sh``, an example is given at https://gist.github.com/xgdgsc/6cfeda967ee44fef4603 . Note ``CXXFLAGS = -std=c++11``, ``LDFLAGS="$LDFLAGS -fopenmp"`` Then you can follow the instruction step 2 at the top to build the extension and test. If you encounter errors including ``libiomp5.dylib`` when running the test after build, renaming the file packed with MATLAB under ``/Applications/MATLAB_R2013a.app/sys/os/maci64/libiomp5.dylib`` would fix it. Building Mex Extension on Windows: =================================== First, you should install some supported version of Visual Studio that includes the Visual C++ compiler by your MATLAB version like on `this `_ page. With MATLAB and Visual Studio installed properly, running ``mex -setup`` in CMD will prompt fpr available compilers and you can choose the appropriate version. Then run the script in this folder makeMex.bat in CMD and the ``MexSomoclu.mexa64`` or ``MexSomoclu.mexa32`` is generated for use, you can test by running the ``mex_interface_test.m``. somoclu-1.7.2/src/MATLAB/mex_interface_test.m0000644000175000017500000000141113015657206015702 00000000000000D = importdata('../../data/rgbs.txt'); msize = [50 50]; sMap = som_randinit(D, 'msize', msize); nEpoch = 100; radius0 = 0; radiusN = 0; radiusCooling = 'linear'; scale0 = 0; scaleN = 0.01; scaleCooling = 'linear'; kernelType = 0; mapType = 'planar'; gridType = 'rectangular'; compactSupport = false; neighborhood = 'gaussian'; stdCoeff = 0.5; [sMap, sTrain, globalBmus, uMatrix] = somoclu_train(sMap, D, 'msize', msize, 'radius0', radius0, ... 'radiusN', radiusN, 'radiusCooling', radiusCooling, ... 'scale0', scale0, 'scaleN', scaleN, 'scaleCooling', scaleCooling, ... 'kernelType', kernelType, 'mapType', mapType, ... 'gridType', gridType, 'compactSupport', compactSupport, ... 'neighborhood', neighborhood, 'stdCoeff', stdCoeff, 'nEpoch', nEpoch); somoclu-1.7.2/src/MATLAB/somoclu_som_demo1.m0000644000175000017500000001560713015657206015472 00000000000000%SOMOCLU_SOM_DEMO1 Basic properties and behaviour of the Self-Organizing Map. % Adapted from SOM Toolbox 2.0 % http://www.cis.hut.fi/projects/somtoolbox/ clf reset; figure(gcf) echo on clc % ========================================================== % SOMOCLU_SOM_DEMO1 - BEHAVIOUR AND PROPERTIES OF SOM % ========================================================== % som_randinit - Create and initialize a SOM. % som_lininit - Create and initialize a SOM. % somoclu_train - Train a SOM. % SELF-ORGANIZING MAP (SOM): % A self-organized map (SOM) is a "map" of the training data, % dense where there is a lot of data and thin where the data % density is low. % The map constitutes of neurons located on a regular map grid. % The lattice of the grid can be either hexagonal or rectangular. subplot(1,2,1) som_cplane('hexa',[10 15],'none') title('Hexagonal SOM grid') subplot(1,2,2) som_cplane('rect',[10 15],'none') title('Rectangular SOM grid') % Each neuron (hexagon on the left, rectangle on the right) has an % associated prototype vector. After training, neighboring neurons % have similar prototype vectors. % The SOM can be used for data visualization, clustering (or % classification), estimation and a variety of other purposes. pause % Strike any key to continue... clf clc % INITIALIZE AND TRAIN THE SELF-ORGANIZING MAP % ============================================ % Here are 28 data points imported from file: D = importdata('../../data/rgbs.txt'); % The map will be a 2-dimensional grid of size 50 x 50. msize = [50 50]; % SOM_RANDINIT and SOM_LININIT can be used to initialize the % prototype vectors in the map. The map size is actually an % optional argument. If omitted, it is determined automatically % based on the amount of data vectors and the principal % eigenvectors of the data set. Below, the random initialization % algorithm is used. sMap = som_randinit(D, 'msize', msize); % Actually, each map unit can be thought as having two sets % of coordinates: % (1) in the input space: the prototype vectors % (2) in the output space: the position on the map % In the two spaces, the map looks like this: subplot(1,3,1) som_grid(sMap) axis([0 50 0 50]), view(0,-90), title('Map in output space') subplot(1,3,2) plot(D(:,1),D(:,2),'+r'), hold on som_grid(sMap,'Coord',sMap.codebook) title('Map in input space') % The black dots show positions of map units, and the gray lines % show connections between neighboring map units. Since the map % was initialized randomly, the positions in in the input space are % completely disorganized. The red crosses are training data. pause % Strike any key to train the SOM... % During training, the map organizes and folds to the training % data. Here, the sequential training algorithm is used: % sMap = som_seqtrain(sMap,D,'radius',[5 1],'trainlen',10); sMap = somoclu_train(sMap, D, 'msize', msize, ... 'nEpoch', 10, 'radius0',0, 'radiusN', 0, 'scale0', 0, 'scaleN', 0.01); % for GPU kernel: % sMap = somoclu_train(sMap, D, 'msize', msize, 'radius',[5 1], ... % 'nEpoch', 100, 'kernelType', 1); subplot(1,3,3) som_grid(sMap,'Coord',sMap.codebook) hold on, plot(D(:,1),D(:,2),'+r') title('Trained map') pause % Strike any key to continue with 3D data... clf clc % 3-dimention Plot subplot(1,3,1), plot3(D(:,1),D(:,2),D(:,3),'+r') view(3), axis on, rotate3d on title('Data') % The ROTATE3D command enables you to rotate the picture by % dragging the pointer above the picture with the leftmost mouse % button pressed down. pause % Strike any key to continue... % Here, the linear initialization is done again, so that % the results can be compared. sMap0 = som_lininit(D); subplot(1,3,2) som_grid(sMap0,'Coord',sMap0.codebook,... 'Markersize',2,'Linecolor','k','Surf',sMap0.codebook(:,3)) axis([0 1 0 1 0 1]), view(-120,-25), title('After initialization') subplot(1,3,3) som_grid(sMap,'Coord',sMap.codebook,... 'Markersize',2,'Linecolor','k','Surf',sMap.codebook(:,3)) axis([0 1 0 1 0 1]), view(3), title('After training'), hold on % Here you can see that the 2-dimensional map has folded into the % 3-dimensional space in order to be able to capture the whole data % space. pause % Strike any key to evaluate the quality of maps... clc % BEST-MATCHING UNITS (BMU) % ========================= % Before going to the quality, an important concept needs to be % introduced: the Best-Matching Unit (BMU). The BMU of a data % vector is the unit on the map whose model vector best resembles % the data vector. In practise the similarity is measured as the % minimum distance between data vector and each model vector on the % map. The BMUs can be calculated using function SOM_BMUS. This % function gives the index of the unit. % Here the BMU is searched for the origin point (from the % trained map): bmu = som_bmus(sMap,[0 0 0]); % Here the corresponding unit is shown in the figure. You can % rotate the figure to see better where the BMU is. co = sMap.codebook(bmu,:); text(co(1),co(2),co(3),'BMU','Fontsize',20) plot3([0 co(1)],[0 co(2)],[0 co(3)],'ro-') pause % VISUALIZATION OF CLUSTERS: DISTANCE MATRICES % =============================================== % Distance matrices are typically used to show the cluster % structure of the SOM. They show distances between neighboring % units, and are thus closely related to single linkage clustering % techniques. The most widely used distance matrix technique is % the U-matrix. % Here, the U-matrix of the map is shown (using all three % components in the distance calculation): colormap(1-gray) som_show(sMap,'umat','all'); pause % Strike any key to continue... % VISUALIZATION OF COMPONENTS: COMPONENT PLANES % ================================================ % The component planes visualizations shows what kind of values the % prototype vectors of the map units have for different vector % components. % Here is the U-matrix and the three component planes of the map. som_show(sMap) % Please see demos that installed with som-toolbox for more % visualization examples... pause % Strike any key to continue... % Or You can save the results with functions in Databionic ESOM Tools for further % analysis using Databionic ESOM Tools MatrixFileNamePrefix = 'demo'; saveumx(MatrixFileNamePrefix, uMatrix); savebm(MatrixFileNamePrefix, globalBmus); savewts(MatrixFileNamePrefix, sMap.codebook, msize(1,1), msize(1,2)); background = 'umx'; foreground = 'bm'; OtherFileNames = []; gradient = 'gray'; colors = 10; zoom = 3; bmsize = 3; [img, umx] = esom_picture(MatrixFileNamePrefix, background, foreground, OtherFileNames, gradient, colors, zoom, bmsize); figure; image(img); title(['U-Matrix of ' MatrixFileNamePrefix ' using grayscale with bestmatches']) xlabel('ESOM x coordinates'); ylabel('ESOM y coordinates');somoclu-1.7.2/src/MATLAB/somoclu_train.m0000644000175000017500000001430113015657206014712 00000000000000function [sMap, sTrain, globalBmus, uMatrix] = somoclu_train(sMap, D, varargin) %somoclu_train Use somoclu to train the Self-Organizing Map. % % [sM,sT] = somoclu_train(sM, D, [[argID,] value, ...]) % % Input and output arguments ([]'s are optional): % sM (struct) map struct, the trained and updated map is returned % (matrix) codebook matrix of a self-organizing map % size munits x dim or msize(1) x ... x msize(k) x dim % The trained map codebook is returned. % D (struct) training data; data struct % (matrix) training data, size dlen x dim % [argID, (string) See below. The values which are unambiguous can % value] (varies) be given without the preceeding argID. % % sT (struct) learning parameters used during the training % % Here are the valid argument IDs and corresponding values. The values which % are unambiguous (marked with '*') can be given without the preceeding argID. % 'msize' (vector) map size % 'radius0' 'radius_ini' (scalar) Start radius (default: half of the map in direction min(x,y)) % 'radiusN' 'radius_fin' (scalar) End radius (default: 1) % 'radius' (vector) neighborhood radiuses, length 1, 2 % 'radiusCooling' (string) Radius cooling strategy: linear or exponential (default: linear) % 'scale0' 'alpha_ini' (scalar) Starting learning rate (default: 0.1) % 'scaleN' (scalar)Finishing learning rate (default: 0.01) % 'kernelType' (string) Kernel type % 'mapType' 'shape' (string) Map type: planar or toroid (default: planar) % 'scaleCooling' 'alpha_type' (string) Learning rate cooling strategy: linear or exponential (default: linear) % 'gridType' 'lattice' (string) Grid type: square or hexagonal (default: square) % 'compactSupport' Compact support for map update (0: false, 1: true, default: 0) % 'neighborhood' Neighborhood function (bubble or gaussian, default: gaussian) % 'sdtCoeff' Coefficient in the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)) (default: 0.5) % 'nEpoch' 'trainlen' (scalar) Maximum number of epochs % 'sTrain','som_train ' = 'train' %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Check arguments error(nargchk(2, Inf, nargin)); % check the number of input arguments % map struct_mode = isstruct(sMap); if struct_mode, sTopol = sMap.topol; else orig_size = size(sMap); if ndims(sMap) > 2, si = size(sMap); dim = si(end); msize = si(1:end-1); M = reshape(sMap,[prod(msize) dim]); else msize = [orig_size(1) 1]; dim = orig_size(2); end sMap = som_map_struct(dim,'msize',msize); sTopol = sMap.topol; end [munits dim] = size(sMap.codebook); % data if isstruct(D), data_name = D.name; D = D.data; else data_name = inputname(2); end D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data [dlen ddim] = size(D); % check input dimension if dim ~= ddim, error('Map and data input space dimensions disagree.'); end % varargin sTrain = som_set('som_train','algorithm','seq','neigh', ... sMap.neigh,'mask',sMap.mask,'data_name',data_name); radius = []; alpha = []; tracking = 1; tlen_type = 'epochs'; sTopol.lattice = 'square'; sTopol.shape = 'planar'; sTrain.radius_ini = 0; sTrain.radius_fin = 1; sTrain.radius_cooling = 'linear'; sTrain.alpha_type = 'linear'; sTrain.kernel_type = 0; sTrain.compact_support = true; sTrain.neighborhood = 'gaussian'; sTrain.scale0 = 0.1 sTrain.scaleN = 0.01 sTrain.stdCoeff = 0.5 i=1; while i<=length(varargin), argok = 1; if ischar(varargin{i}), switch varargin{i}, % argument IDs case 'msize', i=i+1; sTopol.msize = varargin{i}; case {'gridType', 'lattice'}, i=i+1; sTopol.lattice = varargin{i}; case {'mapType', 'shape'}, i=i+1; sTopol.shape = varargin{i}; case {'nEpoch', 'trainlen'}, i=i+1; sTrain.trainlen = varargin{i}; case {'radius0', 'radius_ini'}, i=i+1; sTrain.radius_ini = varargin{i}; case 'radiusCooling', i=i+1; sTrain.radius_cooling = varargin{i}; case {'radiusN', 'radius_fin'}, i=i+1; sTrain.radius_fin = varargin{i}; case 'radius', i=i+1; l = length(varargin{i}); if l==1, sTrain.radius_ini = varargin{i}; else sTrain.radius_ini = varargin{i}(1); sTrain.radius_fin = varargin{i}(end); % if l>2, radius = varargin{i}; tlen_type = 'samples'; end end case {'scaleCooling', 'alpha_type'}, i=i+1; sTrain.alpha_type = varargin{i}; case 'neighborhood', i=i+1; sTrain.neighborhood = varargin{i}; case 'stdCoeff', i=i+1; sTrain.stdCoeff = varargin{i}; case {'scale0', 'alpha_ini'}, i=i+1; sTrain.scale0 = varargin{i}; case 'scaleN', i=i+1; sTrain.scaleN = varargin{i}; case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i}; case 'kernelType', i=i+1; sTrain.kernel_type = varargin{i}; case 'compactSupport', i=i+1; sTrain.compact_support = varargin{i}; % unambiguous values % case {'inv','linear','power'}, sTrain.alpha_type = varargin{i}; case {'hexa','rect'}, sTopol.lattice = varargin{i}; case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; otherwise argok=0; end elseif isstruct(varargin{i}) && isfield(varargin{i},'type'), switch varargin{i}(1).type, case 'som_topol', sTopol = varargin{i}; if prod(sTopol.msize) ~= munits, error('Given map grid size does not match the codebook size.'); end case 'som_train', sTrain = varargin{i}; otherwise argok=0; end else argok = 0; end if ~argok, disp(['(som_seqtrain) Ignoring invalid argument #' num2str(i+2)]); end i = i+1; end if strcmp(sTrain.neighborhood, 'gaussian') sTrain.gaussian = 1; else sTrain.gaussian = 0; end [sMap.codebook, globalBmus, uMatrix] = MexSomoclu(D, sTrain.trainlen, ... sTopol.msize(1), sTopol.msize(2), ... sTrain.radius_ini, sTrain.radius_fin, ... sTrain.radius_cooling, sTrain.scale0, sTrain.scaleN, ... sTrain.alpha_type, ... sTrain.kernel_type, sTopol.shape, sTopol.lattice, ... sTrain.compact_support, sTrain.gaussian, sTrain.stdCoeff, sMap.codebook); rowNums=colon(1,size(globalBmus,1))' globalBmus = [rowNums globalBmus] sTrain = som_set(sTrain,'time',datestr(now,0)); somoclu-1.7.2/src/MATLAB/makeMex.bat0000644000175000017500000000026613015657206013742 00000000000000mex -D_OPENMP COMPFLAGS="/openmp $COMPFLAGS" -I../ MexSomoclu.cpp ../denseCpuKernels.cpp ../io.cpp ../sparseCpuKernels.cpp ../training.cpp ../mapDistanceFunctions.cpp ../uMatrix.cpp somoclu-1.7.2/src/MATLAB/mex_interface_test_gpu.m0000644000175000017500000000131313015657206016556 00000000000000D = importdata('../../data/rgbs.txt'); msize = [50 50]; sMap = som_randinit(D, 'msize', msize); nEpoch = 100; radius0 = 0; radiusN = 0; radiusCooling = 'linear'; scale0 = 0; scaleN = 0.01; scaleCooling = 'linear'; kernelType = 1; mapType = 'planar'; gridType = 'square'; compactSupport = false; stdCoeff = 0.5; [sMap, sTrain, globalBmus, uMatrix] = somoclu_train(sMap, D, 'msize', msize, 'radius0', radius0, ... 'radiusN', radiusN, 'radiusCooling', radiusCooling, ... 'scale0', scale0, 'scaleN', scaleN, 'scaleCooling', scaleCooling, ... 'kernelType', kernelType, 'mapType', mapType, ... 'gridType', gridType, 'compactSupport', compactSupport, ... 'stdCoeff', stdCoeff, 'nEpoch', nEpoch); somoclu-1.7.2/src/MATLAB/MexSomoclu.cpp0000644000175000017500000001115513015657206014461 00000000000000#include "mex.h" #include "somoclu.h" #ifdef CUDA #include "gpu/mxGPUArray.h" #endif void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { #ifdef CUDA mxInitGPU(); #endif int i; /* Examine input (right-hand-side) arguments. */ //mexPrintf("\nThere are %d right-hand-side argument(s).", nrhs); // for (i=0; i nrhs) mexErrMsgIdAndTxt( "MATLAB:mexfunction:inputOutputMismatch", "Cannot specify more outputs than inputs.\n"); plhs[0] = mxCreateDoubleMatrix(nSomY * nSomX, nDimensions, mxREAL); double* codebook_p = mxGetPr(plhs[0]); for(int i=0; i < uMatrix_size; i++) { for(int j=0; j < nDimensions; j++) { codebook_p[j * uMatrix_size + i] = codebook[i * nDimensions + j]; } } if(globalBmus != NULL) { plhs[1] = mxCreateDoubleMatrix(nVectors, 2, mxREAL); double* globalBmus_p = mxGetPr(plhs[1]); for(int i=0; i < nVectors; i++) { for(int j=0; j < 2; j++) { globalBmus_p[j * nVectors + i] = (double) globalBmus[i * 2 + j]; } } } else { plhs[1] = mxCreateDoubleMatrix(1, 1, mxREAL); *mxGetPr(plhs[1]) = 1; } plhs[2] = mxCreateDoubleMatrix(nSomX, nSomY, mxREAL); double* uMatrix_p = mxGetPr(plhs[2]); for(int i=0; i < nSomX; i++) { for(int j=0; j < nSomY; j++) { uMatrix_p[j * nSomX + i] = uMatrix[i * nSomY + j]; } } delete[] codebook; delete[] globalBmus; delete[] uMatrix; } somoclu-1.7.2/src/io.cpp0000644000175000017500000002416313015657206012040 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #include #include #include #include "somoclu.h" using namespace std; /** Save a SOM codebook * @param cbFileName - name of the file to save * @param codebook - the codebook to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance */ int saveCodebook(string cbFilename, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions) { FILE* file = fopen(cbFilename.c_str(), "wt"); cout << " Saving Codebook " << cbFilename << endl; fprintf(file, "%%%d %d\n", nSomY, nSomX); fprintf(file, "%%%d\n", nDimensions); if (file != 0) { for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { for (unsigned int d = 0; d < nDimensions; d++) { fprintf(file, "%0.10f ", codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d]); } fprintf(file, "\n"); } } fclose(file); return 0; } else { return 1; } } /** Save best matching units * @param filename - name of the file to save * @param bmus - the best matching units to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nVectors - the number of vectors */ int saveBmus(string filename, int *bmus, unsigned int nSomX, unsigned int nSomY, unsigned int nVectors) { FILE* file = fopen(filename.c_str(), "wt"); cout << " Saving best matching units " << filename << endl; fprintf(file, "%%%d %d\n", nSomY, nSomX); fprintf(file, "%%%d\n", nVectors); if (file != 0) { for (unsigned int i = 0; i < nVectors; ++i) { // ESOM Tools swaps x and y! fprintf(file, "%d %d %d\n", i, bmus[2 * i + 1], bmus[2 * i]); } fclose(file); return 0; } else { return 1; } } /** Save u-matrix * @param fname * @param codebook - the codebook to save * @param nSomX - dimensions of SOM map in the x direction * @param nSomY - dimensions of SOM map in the y direction * @param nDimensions - dimensions of a data instance */ int saveUMatrix(string fname, float *uMatrix, unsigned int nSomX, unsigned int nSomY) { FILE* fp = fopen(fname.c_str(), "wt"); fprintf(fp, "%%"); fprintf(fp, "%d %d", nSomY, nSomX); fprintf(fp, "\n"); if (fp != 0) { for (unsigned int som_y1 = 0; som_y1 < nSomY; som_y1++) { for (unsigned int som_x1 = 0; som_x1 < nSomX; som_x1++) { fprintf(fp, " %f", uMatrix[som_y1 * nSomX + som_x1]); } fprintf(fp, "\n"); } fclose(fp); return 0; } else return -2; } void getMatrixDimensions(string inFilename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(inFilename.c_str()); if (file.is_open()) { string line; float tmp; while(getline(file, line)) { if (line.substr(0, 1) == "#") { continue; } std::istringstream iss(line); if (nRows == 0) { while (iss >> tmp) { nColumns++; } } nRows++; } file.close(); } else { my_abort("Input file could not be opened!"); } } unsigned int *readLrnHeader(string inFilename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(inFilename.c_str()); string line; unsigned int currentColumn = 0; unsigned int nAllColumns = 0; unsigned int *columnMap = NULL; while(getline(file, line)) { if (line.substr(0, 1) == "#") { continue; } if (line.substr(0, 1) == "%") { std::istringstream iss(line.substr(1, line.length())); if (nRows == 0) { iss >> nRows; } else if (nAllColumns == 0) { iss >> nAllColumns; } else if (columnMap == NULL) { columnMap = new unsigned int[nAllColumns]; unsigned int itmp = 0; currentColumn = 0; while (iss >> itmp) { columnMap[currentColumn++] = itmp; if (itmp == 1) { ++nColumns; } } } else { break; } } } file.close(); return columnMap; } unsigned int *readWtsHeader(string inFilename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(inFilename.c_str()); string line; while(getline(file, line)) { if (line.substr(0, 1) == "#") { continue; } if (line.substr(0, 1) == "%") { std::istringstream iss(line.substr(1, line.length())); if (nRows == 0) { iss >> nRows; unsigned int nSomY = 0; iss >> nSomY; nRows = nRows * nSomY; } else if (nColumns == 0) { iss >> nColumns; } else { break; } } } file.close(); unsigned int *columnMap = new unsigned int[nColumns]; for (unsigned int i = 0; i < nColumns; ++i) { columnMap[i] = 1; } return columnMap; } /** Reads a matrix * @param inFilename * @param nRows - returns the number of rows * @param nColumns - returns the number of columns * @return the matrix */ float *readMatrix(string inFilename, unsigned int &nRows, unsigned int &nColumns) { float *data = NULL; unsigned int *columnMap = NULL; if (inFilename.compare(inFilename.size() - 3, 3, "lrn") == 0) { columnMap = readLrnHeader(inFilename, nRows, nColumns); } else if (inFilename.compare(inFilename.size() - 3, 3, "wts") == 0) { columnMap = readWtsHeader(inFilename, nRows, nColumns); } else { getMatrixDimensions(inFilename, nRows, nColumns); columnMap = new unsigned int[nColumns]; for (unsigned int i = 0; i < nColumns; ++i) { columnMap[i] = 1; } } ifstream file; file.open(inFilename.c_str()); string line; float tmp; unsigned int j = 0; unsigned int currentColumn = 0; while(getline(file, line)) { if ( (line.substr(0, 1) == "#") | (line.substr(0, 1) == "%") ) { continue; } if (data == NULL) { data = new float[nRows * nColumns]; } std::istringstream iss(line); currentColumn = 0; while (iss >> tmp) { if (columnMap[currentColumn++] != 1) { continue; } data[j++] = tmp; } } file.close(); delete [] columnMap; return data; } void readSparseMatrixDimensions(string filename, unsigned int &nRows, unsigned int &nColumns) { ifstream file; file.open(filename.c_str()); if (file.is_open()) { string line; int max_index = -1; while(getline(file, line)) { if (line.substr(0, 1) == "#") { continue; } stringstream linestream(line); string value; int dummy_index; while(getline(linestream, value, ' ')) { int separator = value.find(":"); if (separator == -1) // it's a label ? continue; istringstream myStream(value.substr(0, separator)); myStream >> dummy_index; if(dummy_index > max_index) { max_index = dummy_index; } } ++nRows; } nColumns = max_index + 1; file.close(); } else { my_abort("Input file could not be opened!"); } } svm_node** readSparseMatrixChunk(string filename, unsigned int nRows, unsigned int nRowsToRead, unsigned int rowOffset) { ifstream file; file.open(filename.c_str()); string line; for (unsigned int i = 0; i < rowOffset; i++) { getline(file, line); } if (rowOffset + nRowsToRead >= nRows) { nRowsToRead = nRows - rowOffset; } svm_node **x_matrix = new svm_node *[nRowsToRead]; for(unsigned int i = 0; i < nRowsToRead; i++) { getline(file, line); if (line.substr(0, 1) == "#") { --i; continue; } stringstream tmplinestream(line); string value; int elements = 0; while(getline(tmplinestream, value, ' ')) { elements++; } elements++; // To account for the closing dummy node in the row x_matrix[i] = new svm_node[elements]; stringstream linestream(line); int j = 0; while(getline(linestream, value, ' ')) { int separator = value.find(":"); istringstream myStream(value.substr(0, separator)); myStream >> x_matrix[i][j].index; istringstream myStream2(value.substr(separator + 1)); myStream2 >> x_matrix[i][j].value; j++; } x_matrix[i][j].index = -1; } file.close(); return x_matrix; } somoclu-1.7.2/src/Python/0000755000175000017500000000000013015657206012260 500000000000000somoclu-1.7.2/src/Python/somoclu/0000755000175000017500000000000013015657206013741 500000000000000somoclu-1.7.2/src/Python/somoclu/__init__.py0000644000175000017500000000021013015657206015763 00000000000000"""Somoclu ===== Provides massively parallel training of self-organizing maps """ __all__ = ['Somoclu'] from .train import Somoclu somoclu-1.7.2/src/Python/somoclu/somoclu.i0000644000175000017500000000236613015657206015523 00000000000000%module somoclu_wrap %include "std_string.i" %{ #define SWIG_FILE_WITH_INIT #include "src/somoclu.h" %} %include "numpy.i" %init %{ import_array(); %} %apply (float* IN_ARRAY1, int DIM1) {(float* data, int data_length)} %apply (float* INPLACE_ARRAY1, int DIM1) {(float* codebook, int codebook_size)} %apply (int* INPLACE_ARRAY1, int DIM1) {(int* globalBmus, int globalBmus_size)} %apply (float* INPLACE_ARRAY1, int DIM1) {(float* uMatrix, int uMatrix_size)} using namespace std; %exception train { try { $action } catch (runtime_error &e) { PyErr_SetString(PyExc_RuntimeError, const_cast(e.what())); return NULL; } } void train(float *data, int data_length, unsigned int nEpoch, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, float radius0, float radiusN, string radiusCooling, float scale0, float scaleN, string scaleCooling, unsigned int kernelType, string mapType, string gridType, bool compact_support, bool gaussian, float std_coeff, float* codebook, int codebook_size, int* globalBmus, int globalBmus_size, float* uMatrix, int uMatrix_size); somoclu-1.7.2/src/Python/somoclu/numpy.i0000644000175000017500000032416313015657206015214 00000000000000/* -*- C -*- (not really, but good for syntax highlighting) */ /* * Copyright (c) 2005-2015, NumPy Developers. * All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are * met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * * Neither the name of the NumPy Developers nor the names of any * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifdef SWIGPYTHON %{ #ifndef SWIG_FILE_WITH_INIT #define NO_IMPORT_ARRAY #endif #include "stdio.h" #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION #include %} /**********************************************************************/ %fragment("NumPy_Backward_Compatibility", "header") { %#if NPY_API_VERSION < 0x00000007 %#define NPY_ARRAY_DEFAULT NPY_DEFAULT %#define NPY_ARRAY_FARRAY NPY_FARRAY %#define NPY_FORTRANORDER NPY_FORTRAN %#endif } /**********************************************************************/ /* The following code originally appeared in * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was * translated from C++ to C by John Hunter. Bill Spotz has modified * it to fix some minor bugs, upgrade from Numeric to numpy (all * versions), add some comments and functionality, and convert from * direct code insertion to SWIG fragments. */ %fragment("NumPy_Macros", "header") { /* Macros to extract array attributes. */ %#if NPY_API_VERSION < 0x00000007 %#define is_array(a) ((a) && PyArray_Check((PyArrayObject*)a)) %#define array_type(a) (int)(PyArray_TYPE((PyArrayObject*)a)) %#define array_numdims(a) (((PyArrayObject*)a)->nd) %#define array_dimensions(a) (((PyArrayObject*)a)->dimensions) %#define array_size(a,i) (((PyArrayObject*)a)->dimensions[i]) %#define array_strides(a) (((PyArrayObject*)a)->strides) %#define array_stride(a,i) (((PyArrayObject*)a)->strides[i]) %#define array_data(a) (((PyArrayObject*)a)->data) %#define array_descr(a) (((PyArrayObject*)a)->descr) %#define array_flags(a) (((PyArrayObject*)a)->flags) %#define array_enableflags(a,f) (((PyArrayObject*)a)->flags) = f %#else %#define is_array(a) ((a) && PyArray_Check(a)) %#define array_type(a) PyArray_TYPE((PyArrayObject*)a) %#define array_numdims(a) PyArray_NDIM((PyArrayObject*)a) %#define array_dimensions(a) PyArray_DIMS((PyArrayObject*)a) %#define array_strides(a) PyArray_STRIDES((PyArrayObject*)a) %#define array_stride(a,i) PyArray_STRIDE((PyArrayObject*)a,i) %#define array_size(a,i) PyArray_DIM((PyArrayObject*)a,i) %#define array_data(a) PyArray_DATA((PyArrayObject*)a) %#define array_descr(a) PyArray_DESCR((PyArrayObject*)a) %#define array_flags(a) PyArray_FLAGS((PyArrayObject*)a) %#define array_enableflags(a,f) PyArray_ENABLEFLAGS((PyArrayObject*)a,f) %#endif %#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS((PyArrayObject*)a)) %#define array_is_native(a) (PyArray_ISNOTSWAPPED((PyArrayObject*)a)) %#define array_is_fortran(a) (PyArray_ISFORTRAN((PyArrayObject*)a)) } /**********************************************************************/ %fragment("NumPy_Utilities", "header") { /* Given a PyObject, return a string describing its type. */ const char* pytype_string(PyObject* py_obj) { if (py_obj == NULL ) return "C NULL value"; if (py_obj == Py_None ) return "Python None" ; if (PyCallable_Check(py_obj)) return "callable" ; if (PyString_Check( py_obj)) return "string" ; if (PyInt_Check( py_obj)) return "int" ; if (PyFloat_Check( py_obj)) return "float" ; if (PyDict_Check( py_obj)) return "dict" ; if (PyList_Check( py_obj)) return "list" ; if (PyTuple_Check( py_obj)) return "tuple" ; %#if PY_MAJOR_VERSION < 3 if (PyFile_Check( py_obj)) return "file" ; if (PyModule_Check( py_obj)) return "module" ; if (PyInstance_Check(py_obj)) return "instance" ; %#endif return "unkown type"; } /* Given a NumPy typecode, return a string describing the type. */ const char* typecode_string(int typecode) { static const char* type_names[25] = {"bool", "byte", "unsigned byte", "short", "unsigned short", "int", "unsigned int", "long", "unsigned long", "long long", "unsigned long long", "float", "double", "long double", "complex float", "complex double", "complex long double", "object", "string", "unicode", "void", "ntypes", "notype", "char", "unknown"}; return typecode < 24 ? type_names[typecode] : type_names[24]; } /* Make sure input has correct numpy type. This now just calls PyArray_EquivTypenums(). */ int type_match(int actual_type, int desired_type) { return PyArray_EquivTypenums(actual_type, desired_type); } %#ifdef SWIGPY_USE_CAPSULE void free_cap(PyObject * cap) { void* array = (void*) PyCapsule_GetPointer(cap,SWIGPY_CAPSULE_NAME); if (array != NULL) free(array); } %#endif } /**********************************************************************/ %fragment("NumPy_Object_to_Array", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities") { /* Given a PyObject pointer, cast it to a PyArrayObject pointer if * legal. If not, set the python error string appropriately and * return NULL. */ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) { PyArrayObject* ary = NULL; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input), typecode))) { ary = (PyArrayObject*) input; } else if is_array(input) { const char* desired_type = typecode_string(typecode); const char* actual_type = typecode_string(array_type(input)); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. Array of type '%s' given", desired_type, actual_type); ary = NULL; } else { const char* desired_type = typecode_string(typecode); const char* actual_type = pytype_string(input); PyErr_Format(PyExc_TypeError, "Array of type '%s' required. A '%s' was given", desired_type, actual_type); ary = NULL; } return ary; } /* Convert the given PyObject to a NumPy array with the given * typecode. On success, return a valid PyArrayObject* with the * correct type. On failure, the python error string will be set and * the routine returns NULL. */ PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object) { PyArrayObject* ary = NULL; PyObject* py_obj; if (is_array(input) && (typecode == NPY_NOTYPE || PyArray_EquivTypenums(array_type(input),typecode))) { ary = (PyArrayObject*) input; *is_new_object = 0; } else { py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_ARRAY_DEFAULT); /* If NULL, PyArray_FromObject will have set python error value.*/ ary = (PyArrayObject*) py_obj; *is_new_object = 1; } return ary; } /* Given a PyArrayObject, check to see if it is contiguous. If so, * return the input pointer and flag it as not a new object. If it is * not contiguous, create a new PyArrayObject using the original data, * flag it as a new object and return the pointer. */ PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims) { PyArrayObject* result; if (array_is_contiguous(ary)) { result = ary; *is_new_object = 0; } else { result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, array_type(ary), min_dims, max_dims); *is_new_object = 1; } return result; } /* Given a PyArrayObject, check to see if it is Fortran-contiguous. * If so, return the input pointer, but do not flag it as not a new * object. If it is not Fortran-contiguous, create a new * PyArrayObject using the original data, flag it as a new object * and return the pointer. */ PyArrayObject* make_fortran(PyArrayObject* ary, int* is_new_object) { PyArrayObject* result; if (array_is_fortran(ary)) { result = ary; *is_new_object = 0; } else { Py_INCREF(array_descr(ary)); result = (PyArrayObject*) PyArray_FromArray(ary, array_descr(ary), NPY_FORTRANORDER); *is_new_object = 1; } return result; } /* Convert a given PyObject to a contiguous PyArrayObject of the * specified type. If the input object is not a contiguous * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_contiguous(ary1, &is_new2, 0, 0); if ( is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the * specified type. If the input object is not a Fortran-ordered * PyArrayObject, a new one will be created and the new object flag * will be set. */ PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input, int typecode, int* is_new_object) { int is_new1 = 0; int is_new2 = 0; PyArrayObject* ary2; PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, &is_new1); if (ary1) { ary2 = make_fortran(ary1, &is_new2); if (is_new1 && is_new2) { Py_DECREF(ary1); } ary1 = ary2; } *is_new_object = is_new1 || is_new2; return ary1; } } /* end fragment */ /**********************************************************************/ %fragment("NumPy_Array_Requirements", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros") { /* Test whether a python object is contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!array_is_contiguous(ary)) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous. A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Test whether a python object is (C_ or F_) contiguous. If array is * contiguous, return 1. Otherwise, set the python error string and * return 0. */ int require_c_or_f_contiguous(PyArrayObject* ary) { int contiguous = 1; if (!(array_is_contiguous(ary) || array_is_fortran(ary))) { PyErr_SetString(PyExc_TypeError, "Array must be contiguous (C_ or F_). A non-contiguous array was given"); contiguous = 0; } return contiguous; } /* Require that a numpy array is not byte-swapped. If the array is * not byte-swapped, return 1. Otherwise, set the python error string * and return 0. */ int require_native(PyArrayObject* ary) { int native = 1; if (!array_is_native(ary)) { PyErr_SetString(PyExc_TypeError, "Array must have native byteorder. " "A byte-swapped array was given"); native = 0; } return native; } /* Require the given PyArrayObject to have a specified number of * dimensions. If the array has the specified number of dimensions, * return 1. Otherwise, set the python error string and return 0. */ int require_dimensions(PyArrayObject* ary, int exact_dimensions) { int success = 1; if (array_numdims(ary) != exact_dimensions) { PyErr_Format(PyExc_TypeError, "Array must have %d dimensions. Given array has %d dimensions", exact_dimensions, array_numdims(ary)); success = 0; } return success; } /* Require the given PyArrayObject to have one of a list of specified * number of dimensions. If the array has one of the specified number * of dimensions, return 1. Otherwise, set the python error string * and return 0. */ int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) { int success = 0; int i; char dims_str[255] = ""; char s[255]; for (i = 0; i < n && !success; i++) { if (array_numdims(ary) == exact_dimensions[i]) { success = 1; } } if (!success) { for (i = 0; i < n-1; i++) { sprintf(s, "%d, ", exact_dimensions[i]); strcat(dims_str,s); } sprintf(s, " or %d", exact_dimensions[n-1]); strcat(dims_str,s); PyErr_Format(PyExc_TypeError, "Array must have %s dimensions. Given array has %d dimensions", dims_str, array_numdims(ary)); } return success; } /* Require the given PyArrayObject to have a specified shape. If the * array has the specified shape, return 1. Otherwise, set the python * error string and return 0. */ int require_size(PyArrayObject* ary, npy_intp* size, int n) { int i; int success = 1; int len; char desired_dims[255] = "["; char s[255]; char actual_dims[255] = "["; for(i=0; i < n;i++) { if (size[i] != -1 && size[i] != array_size(ary,i)) { success = 0; } } if (!success) { for (i = 0; i < n; i++) { if (size[i] == -1) { sprintf(s, "*,"); } else { sprintf(s, "%ld,", (long int)size[i]); } strcat(desired_dims,s); } len = strlen(desired_dims); desired_dims[len-1] = ']'; for (i = 0; i < n; i++) { sprintf(s, "%ld,", (long int)array_size(ary,i)); strcat(actual_dims,s); } len = strlen(actual_dims); actual_dims[len-1] = ']'; PyErr_Format(PyExc_TypeError, "Array must have shape of %s. Given array has shape of %s", desired_dims, actual_dims); } return success; } /* Require the given PyArrayObject to to be Fortran ordered. If the * the PyArrayObject is already Fortran ordered, do nothing. Else, * set the Fortran ordering flag and recompute the strides. */ int require_fortran(PyArrayObject* ary) { int success = 1; int nd = array_numdims(ary); int i; npy_intp * strides = array_strides(ary); if (array_is_fortran(ary)) return success; /* Set the Fortran ordered flag */ array_enableflags(ary,NPY_ARRAY_FARRAY); /* Recompute the strides */ strides[0] = strides[nd-1]; for (i=1; i < nd; ++i) strides[i] = strides[i-1] * array_size(ary,i-1); return success; } } /* Combine all NumPy fragments into one for convenience */ %fragment("NumPy_Fragments", "header", fragment="NumPy_Backward_Compatibility", fragment="NumPy_Macros", fragment="NumPy_Utilities", fragment="NumPy_Object_to_Array", fragment="NumPy_Array_Requirements") { } /* End John Hunter translation (with modifications by Bill Spotz) */ /* %numpy_typemaps() macro * * This macro defines a family of 75 typemaps that allow C arguments * of the form * * 1. (DATA_TYPE IN_ARRAY1[ANY]) * 2. (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) * 3. (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) * * 4. (DATA_TYPE IN_ARRAY2[ANY][ANY]) * 5. (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 6. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) * 7. (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 8. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) * * 9. (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) * 10. (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 11. (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 12. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) * 13. (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 14. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) * * 15. (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) * 16. (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 17. (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 18. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, , DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) * 19. (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 20. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) * * 21. (DATA_TYPE INPLACE_ARRAY1[ANY]) * 22. (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) * 23. (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) * * 24. (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) * 25. (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 26. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) * 27. (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) * 28. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) * * 29. (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) * 30. (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 31. (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 32. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) * 33. (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) * 34. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) * * 35. (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) * 36. (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 37. (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 38. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4) * 39. (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) * 40. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4) * * 41. (DATA_TYPE ARGOUT_ARRAY1[ANY]) * 42. (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) * 43. (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) * * 44. (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) * * 45. (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) * * 46. (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) * * 47. (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) * 48. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) * * 49. (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 50. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) * 51. (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 52. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) * * 53. (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 54. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) * 55. (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 56. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) * * 57. (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 58. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4) * 59. (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 60. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4) * * 61. (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1) * 62. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1) * * 63. (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 64. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2) * 65. (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) * 66. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2) * * 67. (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 68. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3) * 69. (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) * 70. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3) * * 71. (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 72. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) * 73. (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) * 74. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) * * 75. (DATA_TYPE* INPLACE_ARRAY_FLAT, DIM_TYPE DIM_FLAT) * * where "DATA_TYPE" is any type supported by the NumPy module, and * "DIM_TYPE" is any int-like type suitable for specifying dimensions. * The difference between "ARRAY" typemaps and "FARRAY" typemaps is * that the "FARRAY" typemaps expect Fortran ordering of * multidimensional arrays. In python, the dimensions will not need * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1" * typemaps). The IN_ARRAYs can be a numpy array or any sequence that * can be converted to a numpy array of the specified type. The * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The * ARGOUT_ARRAYs will be returned as new numpy arrays of the * appropriate type. * * These typemaps can be applied to existing functions using the * %apply directive. For example: * * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)}; * double prod(double* series, int length); * * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) * {(int rows, int cols, double* matrix )}; * void floor(int rows, int cols, double* matrix, double f); * * %apply (double IN_ARRAY3[ANY][ANY][ANY]) * {(double tensor[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double low[2][2][2] )}; * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY]) * {(double upp[2][2][2] )}; * void luSplit(double tensor[2][2][2], * double low[2][2][2], * double upp[2][2][2] ); * * or directly with * * double prod(double* IN_ARRAY1, int DIM1); * * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f); * * void luSplit(double IN_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY], * double ARGOUT_ARRAY3[ANY][ANY][ANY]); */ %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE) /************************/ /* Input Array Typemaps */ /************************/ /* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY1[ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY1[ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY1[ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = { -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); } %typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[1] = {-1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY2[ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY2[ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } %typemap(freearg) (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { /* for now, only concerned with lists */ $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL) { npy_intp size[2] = { -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; int is_new_object; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); is_new_object_array = (int *)calloc($2,sizeof(int)); if (array == NULL || object_array == NULL || is_new_object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; is_new_object_array[i] = is_new_object; if (!temp_array || !require_dimensions(temp_array, 2)) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); } if (!require_size(temp_array, size, 2)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; } %typemap(freearg) (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { Py_ssize_t i; if (array$argnum!=NULL) free(array$argnum); /*freeing the individual arrays if needed */ if (object_array$argnum!=NULL) { if (is_new_object_array$argnum!=NULL) { for (i=0; i<$2; i++) { if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i]) { Py_DECREF(object_array$argnum[i]); } } free(is_new_object_array$argnum); } free(object_array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } %typemap(freearg) (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* IN_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4)) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(freearg) (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY]) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1, -1 }; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } %typemap(freearg) (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { /* for now, only concerned with lists */ $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL) { npy_intp size[3] = { -1, -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; int is_new_object; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); is_new_object_array = (int *)calloc($2,sizeof(int)); if (array == NULL || object_array == NULL || is_new_object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; is_new_object_array[i] = is_new_object; if (!temp_array || !require_dimensions(temp_array, 3)) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); size[2] = array_size(temp_array,2); } if (!require_size(temp_array, size, 3)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; $5 = (DIM_TYPE) size[2]; } %typemap(freearg) (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { Py_ssize_t i; if (array$argnum!=NULL) free(array$argnum); /*freeing the individual arrays if needed */ if (object_array$argnum!=NULL) { if (is_new_object_array$argnum!=NULL) { for (i=0; i<$2; i++) { if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i]) { Py_DECREF(object_array$argnum[i]); } } free(is_new_object_array$argnum); } free(object_array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, * DATA_TYPE* IN_ARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1 , -1}; array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1, -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4) | !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } %typemap(freearg) (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, * DATA_TYPE* IN_FARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) { $1 = is_array($input) || PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1 , -1 }; array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } %typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4) { if (is_new_object$argnum && array$argnum) { Py_DECREF(array$argnum); } } /***************************/ /* In-Place Array Typemaps */ /***************************/ /* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY1[ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY1[ANY]) (PyArrayObject* array=NULL) { npy_intp size[1] = { $1_dim0 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* array=NULL, int i=1) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = 1; for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* array=NULL, int i=0) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,1) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = 1; for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i); $2 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY2[ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[2] = { $1_dim0, $1_dim1 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,2) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL) { npy_intp size[2] = { -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); if (array == NULL || object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; if ( !temp_array || !require_dimensions(temp_array, 2) || !require_contiguous(temp_array) || !require_native(temp_array) || !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE) ) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); } if (!require_size(temp_array, size, 2)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; } %typemap(freearg) (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { if (array$argnum!=NULL) free(array$argnum); if (object_array$argnum!=NULL) free(object_array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_ARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY3) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,3) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY]) (PyArrayObject* array=NULL) { npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3 }; array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_size(array, size, 4) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = ($1_ltype) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } /* Typemap suite for (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = PySequence_Check($input); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL) { npy_intp size[3] = { -1, -1, -1 }; PyArrayObject* temp_array; Py_ssize_t i; /* length of the list */ $2 = PyList_Size($input); /* the arrays */ array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *)); object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *)); if (array == NULL || object_array == NULL) { SWIG_fail; } for (i=0; i<$2; i++) { temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE); /* the new array must be stored so that it can be destroyed in freearg */ object_array[i] = temp_array; if ( !temp_array || !require_dimensions(temp_array, 3) || !require_contiguous(temp_array) || !require_native(temp_array) || !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE) ) SWIG_fail; /* store the size of the first array in the list, then use that for comparison. */ if (i == 0) { size[0] = array_size(temp_array,0); size[1] = array_size(temp_array,1); size[2] = array_size(temp_array,2); } if (!require_size(temp_array, size, 3)) SWIG_fail; array[i] = (DATA_TYPE*) array_data(temp_array); } $1 = (DATA_TYPE**) array; $3 = (DIM_TYPE) size[0]; $4 = (DIM_TYPE) size[1]; $5 = (DIM_TYPE) size[2]; } %typemap(freearg) (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { if (array$argnum!=NULL) free(array$argnum); if (object_array$argnum!=NULL) free(object_array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, * DATA_TYPE* INPLACE_ARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } /* Typemap suite for (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, * DIM_TYPE DIM3, DIM_TYPE DIM4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = (DIM_TYPE) array_size(array,0); $3 = (DIM_TYPE) array_size(array,1); $4 = (DIM_TYPE) array_size(array,2); $5 = (DIM_TYPE) array_size(array,3); } /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, * DATA_TYPE* INPLACE_FARRAY4) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4) (PyArrayObject* array=NULL) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_dimensions(array,4) || !require_contiguous(array) || !require_native(array) || !require_fortran(array)) SWIG_fail; $1 = (DIM_TYPE) array_size(array,0); $2 = (DIM_TYPE) array_size(array,1); $3 = (DIM_TYPE) array_size(array,2); $4 = (DIM_TYPE) array_size(array,3); $5 = (DATA_TYPE*) array_data(array); } /*************************/ /* Argout Array Typemaps */ /*************************/ /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY1[ANY]) (PyObject* array = NULL) { npy_intp dims[1] = { $1_dim0 }; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) (PyObject* array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $2 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $2; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) */ %typemap(in,numinputs=1, fragment="NumPy_Fragments") (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) (PyObject* array = NULL) { npy_intp dims[1]; if (!PyInt_Check($input)) { const char* typestring = pytype_string($input); PyErr_Format(PyExc_TypeError, "Int dimension expected. '%s' given.", typestring); SWIG_fail; } $1 = (DIM_TYPE) PyInt_AsLong($input); dims[0] = (npy_intp) $1; array = PyArray_SimpleNew(1, dims, DATA_TYPECODE); if (!array) SWIG_fail; $2 = (DATA_TYPE*) array_data(array); } %typemap(argout) (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) (PyObject* array = NULL) { npy_intp dims[2] = { $1_dim0, $1_dim1 }; array = PyArray_SimpleNew(2, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) (PyObject* array = NULL) { npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 }; array = PyArray_SimpleNew(3, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) */ %typemap(in,numinputs=0, fragment="NumPy_Backward_Compatibility,NumPy_Macros") (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) (PyObject* array = NULL) { npy_intp dims[4] = { $1_dim0, $1_dim1, $1_dim2, $1_dim3 }; array = PyArray_SimpleNew(4, dims, DATA_TYPECODE); if (!array) SWIG_fail; $1 = ($1_ltype) array_data(array); } %typemap(argout) (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY]) { $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum); } /*****************************/ /* Argoutview Array Typemaps */ /*****************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEW_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_ARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_FARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; $result = SWIG_Python_AppendOutput($result,obj); } /*************************************/ /* Managed Argoutview Array Typemaps */ /*************************************/ /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp) { $1 = &data_temp; $2 = &dim_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1) { npy_intp dims[1] = { *$2 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEWM_ARRAY1) (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim_temp; $2 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1) { npy_intp dims[1] = { *$1 }; PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_ARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2) { npy_intp dims[2] = { *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_FARRAY2) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2) { npy_intp dims[2] = { *$1, *$2 }; PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_ARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj= PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[3] = { *$2, *$3, *$4 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_FARRAY3) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3) { npy_intp dims[3] = { *$1, *$2, *$3 }; PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) */ %typemap(in,numinputs=0) (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 ) (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp) { $1 = &data_temp; $2 = &dim1_temp; $3 = &dim2_temp; $4 = &dim3_temp; $5 = &dim4_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4) { npy_intp dims[4] = { *$2, *$3, *$4 , *$5 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) */ %typemap(in,numinputs=0) (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4) (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL ) { $1 = &dim1_temp; $2 = &dim2_temp; $3 = &dim3_temp; $4 = &dim4_temp; $5 = &data_temp; } %typemap(argout, fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements,NumPy_Utilities") (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4) { npy_intp dims[4] = { *$1, *$2, *$3 , *$4 }; PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5)); PyArrayObject* array = (PyArrayObject*) obj; if (!array || !require_fortran(array)) SWIG_fail; %#ifdef SWIGPY_USE_CAPSULE PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap); %#else PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free); %#endif %#if NPY_API_VERSION < 0x00000007 PyArray_BASE(array) = cap; %#else PyArray_SetBaseObject(array,cap); %#endif $result = SWIG_Python_AppendOutput($result,obj); } /**************************************/ /* In-Place Array Typemap - flattened */ /**************************************/ /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY_FLAT, DIM_TYPE DIM_FLAT) */ %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY, fragment="NumPy_Macros") (DATA_TYPE* INPLACE_ARRAY_FLAT, DIM_TYPE DIM_FLAT) { $1 = is_array($input) && PyArray_EquivTypenums(array_type($input), DATA_TYPECODE); } %typemap(in, fragment="NumPy_Fragments") (DATA_TYPE* INPLACE_ARRAY_FLAT, DIM_TYPE DIM_FLAT) (PyArrayObject* array=NULL, int i=1) { array = obj_to_array_no_conversion($input, DATA_TYPECODE); if (!array || !require_c_or_f_contiguous(array) || !require_native(array)) SWIG_fail; $1 = (DATA_TYPE*) array_data(array); $2 = 1; for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i); } %enddef /* %numpy_typemaps() macro */ /* *************************************************************** */ /* Concrete instances of the %numpy_typemaps() macro: Each invocation * below applies all of the typemaps above to the specified data type. */ %numpy_typemaps(signed char , NPY_BYTE , int) %numpy_typemaps(unsigned char , NPY_UBYTE , int) %numpy_typemaps(short , NPY_SHORT , int) %numpy_typemaps(unsigned short , NPY_USHORT , int) %numpy_typemaps(int , NPY_INT , int) %numpy_typemaps(unsigned int , NPY_UINT , int) %numpy_typemaps(long , NPY_LONG , int) %numpy_typemaps(unsigned long , NPY_ULONG , int) %numpy_typemaps(long long , NPY_LONGLONG , int) %numpy_typemaps(unsigned long long, NPY_ULONGLONG, int) %numpy_typemaps(float , NPY_FLOAT , int) %numpy_typemaps(double , NPY_DOUBLE , int) /* *************************************************************** * The follow macro expansion does not work, because C++ bool is 4 * bytes and NPY_BOOL is 1 byte * * %numpy_typemaps(bool, NPY_BOOL, int) */ /* *************************************************************** * On my Mac, I get the following warning for this macro expansion: * 'swig/python detected a memory leak of type 'long double *', no destructor found.' * * %numpy_typemaps(long double, NPY_LONGDOUBLE, int) */ #ifdef __cplusplus %include %numpy_typemaps(std::complex, NPY_CFLOAT , int) %numpy_typemaps(std::complex, NPY_CDOUBLE, int) #endif #endif /* SWIGPYTHON */ somoclu-1.7.2/src/Python/somoclu/train.py0000644000175000017500000007526613015657206015370 00000000000000# -*- coding: utf-8 -*- """ The module contains the Somoclu class that trains and visualizes self-organizing maps and emergent self-organizing maps. Created on Sun July 26 15:07:47 2015 @author: Peter Wittek """ from __future__ import division, print_function import sys import matplotlib.cm as cm import matplotlib.pyplot as plt import matplotlib.collections as mcoll import numpy as np from scipy.spatial.distance import cdist try: import seaborn as sns from sklearn.metrics.pairwise import pairwise_distances have_heatmap = True except ImportError: have_heatmap = False try: from .somoclu_wrap import train as wrap_train except ImportError: print("Warning: training function cannot be imported. Only pre-trained " "maps can be analyzed.") if sys.platform.startswith('win'): print("If you installed Somoclu with pip on Windows, this typically " "means missing DLLs. Please refer to the documentation and to " "this issue:") print("https://github.com/peterwittek/somoclu/issues/28") elif sys.platform.startswith('darwin'): print("If you installed Somoclu with pip on OS X, this typically " "means missing libiomp. Please refer to the documentation and to" " this issue:") print("https://github.com/peterwittek/somoclu/issues/28") class Somoclu(object): """Class for training and visualizing a self-organizing map. :param n_columns: The number of columns in the map. :type n_columns: int. :param n_rows: The number of rows in the map. :type n_rows: int. :param data: Optional parameter to provide training data. It is not necessary if the map is otherwise trained outside Python, e.g., on a GPU cluster. :type data: 2D numpy.array of float32. :param initialcodebook: Optional parameter to start the training with a given codebook. :type initialcodebook: 2D numpy.array of float32. :param kerneltype: Optional parameter to specify which kernel to use: * 0: dense CPU kernel (default) * 1: dense GPU kernel (if compiled with it) :type kerneltype: int. :param maptype: Optional parameter to specify the map topology: * "planar": Planar map (default) * "toroid": Toroid map :type maptype: str. :param gridtype: Optional parameter to specify the grid form of the nodes: * "rectangular": rectangular neurons (default) * "hexagonal": hexagonal neurons :type gridtype: str. :param compactsupport: Optional parameter to cut off map updates beyond the training radius with the Gaussian neighborhood. Default: True. :type compactsupport: bool. :param neighborhood: Optional parameter to specify the neighborhood: * "gaussian": Gaussian neighborhood (default) * "bubble": bubble neighborhood function :type neighborhood: str. :param std_coeff: Optional parameter to set the coefficient in the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)) Default: 0.5 :type std_coeff: float. :param initialization: Optional parameter to specify the initalization: * "random": random weights in the codebook * "pca": codebook is initialized from the first subspace spanned by the first two eigenvectors of the correlation matrix :type initialization: str. """ def __init__(self, n_columns, n_rows, data=None, initialcodebook=None, kerneltype=0, maptype="planar", gridtype="rectangular", compactsupport=True, neighborhood="gaussian", std_coeff=0.5, initialization=None): """Constructor for the class. """ self._n_columns, self._n_rows = n_columns, n_rows self._kernel_type = kerneltype self._map_type = maptype self._grid_type = gridtype self._compact_support = compactsupport self._neighborhood = neighborhood self._std_coeff = std_coeff self._check_parameters() self.activation_map = None if initialcodebook is not None and initialization is not None: raise Exception("An initial codebook is given but initilization" " is also requested") self.bmus = None self.umatrix = np.zeros(n_columns * n_rows, dtype=np.float32) self.codebook = initialcodebook if initialization is None or initialization == "random": self._initialization = "random" elif initialization == "pca": self._initialization = "pca" else: raise Exception("Unknown initialization method") self.n_vectors = 0 self.n_dim = 0 self.clusters = None self._data = None if data is not None: self.update_data(data) def load_bmus(self, filename): """Load the best matching units from a file to the Somoclu object. :param filename: The name of the file. :type filename: str. """ self.bmus = np.loadtxt(filename, comments='%', usecols=(1, 2)) if self.n_vectors != 0 and len(self.bmus) != self.n_vectors: raise Exception("The number of best matching units does not match " "the number of data instances") else: self.n_vectors = len(self.bmus) tmp = self.bmus[:, 0].copy() self.bmus[:, 0] = self.bmus[:, 1].copy() self.bmus[:, 1] = tmp if max(self.bmus[:, 0]) > self._n_columns - 1 or \ max(self.bmus[:, 1]) > self._n_rows - 1: raise Exception("The dimensions of the best matching units do not " "match that of the map") def load_umatrix(self, filename): """Load the umatrix from a file to the Somoclu object. :param filename: The name of the file. :type filename: str. """ self.umatrix = np.loadtxt(filename, comments='%') if self.umatrix.shape != (self._n_rows, self._n_columns): raise Exception("The dimensions of the U-matrix do not " "match that of the map") def load_codebook(self, filename): """Load the codebook from a file to the Somoclu object. :param filename: The name of the file. :type filename: str. """ self.codebook = np.loadtxt(filename, comments='%') if self.n_dim == 0: self.n_dim = self.codebook.shape[1] if self.codebook.shape != (self._n_rows*self._n_columns, self.n_dim): raise Exception("The dimensions of the codebook do not " "match that of the map") self.codebook.shape = (self._n_rows, self._n_columns, self.n_dim) def train(self, epochs=10, radius0=0, radiusN=1, radiuscooling="linear", scale0=0.1, scaleN=0.01, scalecooling="linear"): """Train the map on the current data in the Somoclu object. :param epochs: The number of epochs to train the map for. :type epochs: int. :param radius0: The initial radius on the map where the update happens around a best matching unit. Default value of 0 will trigger a value of min(n_columns, n_rows)/2. :type radius0: float. :param radiusN: The radius on the map where the update happens around a best matching unit in the final epoch. Default: 1. :type radiusN: float. :param radiuscooling: The cooling strategy between radius0 and radiusN: * "linear": Linear interpolation (default) * "exponential": Exponential decay :param scale0: The initial learning scale. Default value: 0.1. :type scale0: float. :param scaleN: The learning scale in the final epoch. Default: 0.01. :type scaleN: float. :param scalecooling: The cooling strategy between scale0 and scaleN: * "linear": Linear interpolation (default) * "exponential": Exponential decay :type scalecooling: str. """ _check_cooling_parameters(radiuscooling, scalecooling) if self._data is None: raise Exception("No data was provided!") self._init_codebook() self.umatrix.shape = (self._n_rows*self._n_columns, ) self.bmus.shape = (self.n_vectors*2, ) wrap_train(np.ravel(self._data), epochs, self._n_columns, self._n_rows, self.n_dim, self.n_vectors, radius0, radiusN, radiuscooling, scale0, scaleN, scalecooling, self._kernel_type, self._map_type, self._grid_type, self._compact_support, self._neighborhood == "gaussian", self._std_coeff, self.codebook, self.bmus, self.umatrix) self.umatrix.shape = (self._n_rows, self._n_columns) self.bmus.shape = (self.n_vectors, 2) self.codebook.shape = (self._n_rows, self._n_columns, self.n_dim) def update_data(self, data): """Change the data set in the Somoclu object. It is useful when the data is updated and the training should continue on the new data. :param data: The training data. :type data: 2D numpy.array of float32. """ oldn_dim = self.n_dim if data.dtype != np.float32: print("Warning: data was not float32. A 32-bit copy was made") self._data = np.float32(data) else: self._data = data self.n_vectors, self.n_dim = data.shape if self.n_dim != oldn_dim and oldn_dim != 0: raise Exception("The dimension of the new data does not match!") self.bmus = np.zeros(self.n_vectors*2, dtype=np.intc) def view_component_planes(self, dimensions=None, figsize=None, colormap=cm.Spectral_r, colorbar=False, bestmatches=False, bestmatchcolors=None, labels=None, zoom=None, filename=None): """Observe the component planes in the codebook of the SOM. :param dimensions: Optional parameter to specify along which dimension or dimensions should the plotting happen. By default, each dimension is plotted in a sequence of plots. :type dimension: int or list of int. :param figsize: Optional parameter to specify the size of the figure. :type figsize: (int, int) :param colormap: Optional parameter to specify the color map to be used. :type colormap: matplotlib.colors.Colormap :param colorbar: Optional parameter to include a colormap as legend. :type colorbar: bool. :param bestmatches: Optional parameter to plot best matching units. :type bestmatches: bool. :param bestmatchcolors: Optional parameter to specify the color of each best matching unit. :type bestmatchcolors: list of int. :param labels: Optional parameter to specify the label of each point. :type labels: list of str. :param zoom: Optional parameter to zoom into a region on the map. The first two coordinates of the tuple are the row limits, the second tuple contains the column limits. :type zoom: ((int, int), (int, int)) :param filename: If specified, the plot will not be shown but saved to this file. :type filename: str. """ if self.codebook is None: raise Exception("The codebook is not available. Either train a map" " or load a codebook from a file") if dimensions is None: dimensions = range(self.n_dim) for i in dimensions: plt = self._view_matrix(self.codebook[:, :, i], figsize, colormap, colorbar, bestmatches, bestmatchcolors, labels, zoom, filename) return plt def view_umatrix(self, figsize=None, colormap=cm.Spectral_r, colorbar=False, bestmatches=False, bestmatchcolors=None, labels=None, zoom=None, filename=None): """Plot the U-matrix of the trained map. :param figsize: Optional parameter to specify the size of the figure. :type figsize: (int, int) :param colormap: Optional parameter to specify the color map to be used. :type colormap: matplotlib.colors.Colormap :param colorbar: Optional parameter to include a colormap as legend. :type colorbar: bool. :param bestmatches: Optional parameter to plot best matching units. :type bestmatches: bool. :param bestmatchcolors: Optional parameter to specify the color of each best matching unit. :type bestmatchcolors: list of int. :param labels: Optional parameter to specify the label of each point. :type labels: list of str. :param zoom: Optional parameter to zoom into a region on the map. The first two coordinates of the tuple are the row limits, the second tuple contains the column limits. :type zoom: ((int, int), (int, int)) :param filename: If specified, the plot will not be shown but saved to this file. :type filename: str. """ if self.umatrix is None: raise Exception("The U-matrix is not available. Either train a map" " or load a U-matrix from a file") return self._view_matrix(self.umatrix, figsize, colormap, colorbar, bestmatches, bestmatchcolors, labels, zoom, filename) def view_activation_map(self, data_vector=None, data_index=None, activation_map=None, figsize=None, colormap=cm.Spectral_r, colorbar=False, bestmatches=False, bestmatchcolors=None, labels=None, zoom=None, filename=None): """Plot the activation map of a given data instance or a new data vector :param data_vector: Optional parameter for a new vector :type data_vector: numpy.array :param data_index: Optional parameter for the index of the data instance :type data_index: int. :param activation_map: Optional parameter to pass the an activation map :type activation_map: numpy.array :param figsize: Optional parameter to specify the size of the figure. :type figsize: (int, int) :param colormap: Optional parameter to specify the color map to be used. :type colormap: matplotlib.colors.Colormap :param colorbar: Optional parameter to include a colormap as legend. :type colorbar: bool. :param bestmatches: Optional parameter to plot best matching units. :type bestmatches: bool. :param bestmatchcolors: Optional parameter to specify the color of each best matching unit. :type bestmatchcolors: list of int. :param labels: Optional parameter to specify the label of each point. :type labels: list of str. :param zoom: Optional parameter to zoom into a region on the map. The first two coordinates of the tuple are the row limits, the second tuple contains the column limits. :type zoom: ((int, int), (int, int)) :param filename: If specified, the plot will not be shown but saved to this file. :type filename: str. """ if data_vector is None and data_index is None: raise Exception("Either specify a vector to see its activation " "or give an index of the training data instances") if data_vector is not None and data_index is not None: raise Exception("You cannot specify both a data vector and the " "index of a training data instance") if data_vector is not None and activation_map is not None: raise Exception("You cannot pass a previously computated" "activation map with a data vector") if data_vector is not None: try: d1, _ = data_vector.shape w = data_vector.copy() except ValueError: d1, _ = data_vector.shape w = data_vector.reshape(1, d1) if w.shape[1] == 1: w = w.T matrix = cdist(self.codebook.reshape((self.codebook.shape[0] * self.codebook.shape[1], self.codebook.shape[2])), w, 'euclidean').T matrix.shape = (self.codebook.shape[0], self.codebook.shape[1]) else: if activation_map is None and self.activation_map is None: self.get_surface_state() if activation_map is None: activation_map = self.activation_map matrix = activation_map[data_index].reshape((self.codebook.shape[0], self.codebook.shape[1])) return self._view_matrix(matrix, figsize, colormap, colorbar, bestmatches, bestmatchcolors, labels, zoom, filename) def _view_matrix(self, matrix, figsize, colormap, colorbar, bestmatches, bestmatchcolors, labels, zoom, filename): """Internal function to plot a map with best matching units and labels. """ if zoom is None: zoom = ((0, self._n_rows), (0, self._n_columns)) if figsize is None: figsize = (8, 8/float(zoom[1][1]/zoom[0][1])) fig = plt.figure(figsize=figsize) if self._grid_type == "hexagonal": offsets = _hexplot(matrix[zoom[0][0]:zoom[0][1], zoom[1][0]:zoom[1][1]], fig, colormap) filtered_bmus = self._filter_array(self.bmus, zoom) filtered_bmus[:, 0] = filtered_bmus[:, 0] - zoom[1][0] filtered_bmus[:, 1] = filtered_bmus[:, 1] - zoom[0][0] bmu_coords = np.zeros(filtered_bmus.shape) for i, (row, col) in enumerate(filtered_bmus): bmu_coords[i] = offsets[col*zoom[1][1] + row] else: plt.imshow(matrix[zoom[0][0]:zoom[0][1], zoom[1][0]:zoom[1][1]], aspect='auto') plt.set_cmap(colormap) bmu_coords = self._filter_array(self.bmus, zoom) bmu_coords[:, 0] = bmu_coords[:, 0] - zoom[1][0] bmu_coords[:, 1] = bmu_coords[:, 1] - zoom[0][0] if colorbar: cmap = cm.ScalarMappable(cmap=colormap) cmap.set_array(matrix) plt.colorbar(cmap, orientation='horizontal', shrink=0.5) if bestmatches: if bestmatchcolors is None: if self.clusters is None: colors = "white" else: colors = [] for bm in self.bmus: colors.append(self.clusters[bm[1], bm[0]]) colors = self._filter_array(colors, zoom) else: colors = self._filter_array(bestmatchcolors, zoom) plt.scatter(bmu_coords[:, 0], bmu_coords[:, 1], c=colors) if labels is not None: for label, col, row in zip(self._filter_array(labels, zoom), bmu_coords[:, 0], bmu_coords[:, 1]): if label is not None: plt.annotate(label, xy=(col, row), xytext=(10, -5), textcoords='offset points', ha='left', va='bottom', bbox=dict(boxstyle='round,pad=0.3', fc='white', alpha=0.8)) plt.axis('off') if filename is not None: plt.savefig(filename) else: plt.show() return plt def _filter_array(self, a, zoom): filtered_array = [] for index, bmu in enumerate(self.bmus): if bmu[0] >= zoom[1][0] and bmu[0] < zoom[1][1] and \ bmu[1] >= zoom[0][0] and bmu[1] < zoom[0][1]: filtered_array.append(a[index]) return np.array(filtered_array) def _check_parameters(self): """Internal function to verify the basic parameters of the SOM. """ if self._map_type != "planar" and self._map_type != "toroid": raise Exception("Invalid parameter for _map_type: " + self._map_type) if self._grid_type != "rectangular" and self._grid_type != "hexagonal": raise Exception("Invalid parameter for _grid_type: " + self._grid_type) if self._neighborhood != "gaussian" and self._neighborhood != "bubble": raise Exception("Invalid parameter for neighborhood: " + self._neighborhood) if self._kernel_type != 0 and self._kernel_type != 1: raise Exception("Invalid parameter for kernelTye: " + self._kernel_type) def _pca_init(self): try: from sklearn.decomposition import PCA pca = PCA(n_components=2, svd_solver="randomized") except: from sklearn.decomposition import RandomizedPCA pca = RandomizedPCA(n_components=2) coord = np.zeros((self._n_columns*self._n_rows, 2)) for i in range(self._n_columns*self._n_rows): coord[i, 0] = int(i / self._n_columns) coord[i, 1] = int(i % self._n_columns) coord = coord/[self._n_rows-1, self._n_columns-1] coord = (coord - .5)*2 me = np.mean(self._data, 0) self.codebook = np.tile(me, (self._n_columns*self._n_rows, 1)) pca.fit(self._data - me) eigvec = pca.components_ eigval = pca.explained_variance_ norms = np.linalg.norm(eigvec, axis=1) eigvec = ((eigvec.T/norms)*eigval).T for j in range(self._n_columns*self._n_rows): for i in range(eigvec.shape[0]): self.codebook[j, :] = self.codebook[j, :] + \ coord[j, i] * eigvec[i, :] def _init_codebook(self): """Internal function to set the codebook or to indicate it to the C++ code that it should be randomly initialized. """ codebook_size = self._n_columns * self._n_rows * self.n_dim if self.codebook is None: if self._initialization == "random": self.codebook = np.zeros(codebook_size, dtype=np.float32) self.codebook[0:2] = [1000, 2000] else: self._pca_init() elif self.codebook.size != codebook_size: raise Exception("Invalid size for initial codebook") else: if self.codebook.dtype != np.float32: print("Warning: initialcodebook was not float32. A 32-bit " "copy was made") self.codebook = np.float32(self.codebook) self.codebook.shape = (codebook_size, ) def cluster(self, algorithm=None): """Cluster the codebook. The clusters of the data instances can be assigned based on the BMUs. The method populates the class variable Somoclu.clusters. If viewing methods are called after clustering, but without colors for best matching units, colors will be automatically assigned based on cluster membership. :param algorithm: Optional parameter to specify a scikit-learn clustering algorithm. The default is K-means with eight clusters. :type filename: sklearn.base.ClusterMixin. """ import sklearn.base if algorithm is None: import sklearn.cluster algorithm = sklearn.cluster.KMeans() elif not isinstance(algorithm, sklearn.base.ClusterMixin): raise Exception("Cannot use algorithm of type " + type(algorithm)) original_shape = self.codebook.shape self.codebook.shape = (self._n_columns*self._n_rows, self.n_dim) linear_clusters = algorithm.fit_predict(self.codebook) self.codebook.shape = original_shape self.clusters = np.zeros((self._n_rows, self._n_columns), dtype=int) for i, c in enumerate(linear_clusters): self.clusters[i // self._n_columns, i % self._n_columns] = c def get_surface_state(self, data=None): """Return the Euclidean distance between codebook and data. :param data: Optional parameter to specify data, otherwise the data used previously to train the SOM is used. :type data: 2D numpy.array of float32. :returns: The the dot product of the codebook and the data. :rtype: 2D numpy.array """ if data is None: d = self._data else: d = data am = cdist(self.codebook.reshape((self.codebook.shape[0] * self.codebook.shape[1], self.codebook.shape[2])), d, 'euclidean').T if data is None: self.activation_map = am return am def get_bmus(self, activation_map): """Returns Best Matching Units indexes of the activation map. :param activation_map: Activation map computed with self.get_surface_state() :type activation_map: 2D numpy.array :returns: The bmus indexes corresponding to this activation map (same as self.bmus for the training samples). :rtype: 2D numpy.array """ Y, X = np.unravel_index(activation_map.argmin(axis=1), (self._n_rows, self._n_columns)) return np.vstack((X,Y)).T def view_similarity_matrix(self, data=None, labels=None, figsize=None, filename=None): """Plot the similarity map according to the activation map :param data: Optional parameter for data points to calculate the similarity with :type data: numpy.array :param figsize: Optional parameter to specify the size of the figure. :type figsize: (int, int) :param labels: Optional parameter to specify the label of each point. :type labels: list of str. :param filename: If specified, the plot will not be shown but saved to this file. :type filename: str. """ if not have_heatmap: raise Exception("Import dependencies missing for viewing " "similarity matrix. You must have seaborn and " "scikit-learn") if data is None and self.activation_map is None: self.get_surface_state() if data is None: X = self.activation_map else: X = data # Calculate the pairwise correlations as a metric for similarity corrmat = 1-pairwise_distances(X, metric="correlation") # Set up the matplotlib figure if figsize is None: figsize = (12, 9) f, ax = plt.subplots(figsize=figsize) # Y axis has inverted labels (seaborn default, no idea why) yticklabels = np.atleast_2d(labels) yticklabels = np.fliplr(yticklabels)[0] # Draw the heatmap using seaborn sns.heatmap(corrmat, vmax=1, vmin=-1, square=True, xticklabels=labels, yticklabels=labels, cmap="RdBu_r", center=0) f.tight_layout() # This sets the ticks to a readable angle plt.yticks(rotation=0) plt.xticks(rotation=90) # This sets the labels for the two axes ax.set_yticklabels(yticklabels, ha='right', va='center', size=8) ax.set_xticklabels(labels, ha='center', va='top', size=8) # Save and close the figure if filename is not None: plt.savefig(filename, bbox_inches='tight') else: plt.show() return plt def _check_cooling_parameters(radiuscooling, scalecooling): """Helper function to verify the cooling parameters of the training. """ if radiuscooling != "linear" and radiuscooling != "exponential": raise Exception("Invalid parameter for radiuscooling: " + radiuscooling) if scalecooling != "linear" and scalecooling != "exponential": raise Exception("Invalid parameter for scalecooling: " + scalecooling) def _hexplot(matrix, fig, colormap): """Internal function to plot a hexagonal map. """ umatrix_min = matrix.min() umatrix_max = matrix.max() n_rows, n_columns = matrix.shape cmap = plt.get_cmap(colormap) offsets = np.zeros((n_columns*n_rows, 2)) facecolors = [] for row in range(n_rows): for col in range(n_columns): if row % 2 == 0: offsets[row*n_columns + col] = [col+0.5, 2*n_rows-2*row] facecolors.append(cmap((matrix[row, col]-umatrix_min) / (umatrix_max)*255)) else: offsets[row*n_columns + col] = [col, 2*n_rows-2*row] facecolors.append(cmap((matrix[row, col]-umatrix_min) / (umatrix_max)*255)) polygon = np.zeros((6, 2), float) polygon[:, 0] = 1.1 * np.array([0.5, 0.5, 0.0, -0.5, -0.5, 0.0]) polygon[:, 1] = 1.1 * np.array([-np.sqrt(3)/6, np.sqrt(3)/6, np.sqrt(3)/2+np.sqrt(3)/6, np.sqrt(3)/6, -np.sqrt(3)/6, -np.sqrt(3)/2-np.sqrt(3)/6]) polygons = np.expand_dims(polygon, 0) + np.expand_dims(offsets, 1) ax = fig.gca() collection = mcoll.PolyCollection( polygons, offsets=offsets, facecolors=facecolors, edgecolors=facecolors, linewidths=1.0, offset_position="data") ax.add_collection(collection, autolim=False) corners = ((-0.5, -0.5), (n_columns + 0.5, 2*n_rows + 0.5)) ax.update_datalim(corners) ax.autoscale_view(tight=True) return offsets somoclu-1.7.2/src/Python/README.rst0000644000175000017500000001424213015657206013672 00000000000000Somoclu - Python Interface ================================ Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution and it can be accelerated by CUDA. The topology of map is either planar or toroid, the grid is rectangular or hexagonal. Currently a subset of the command line version is supported with this Python module. Key features of the Python interface: * Fast execution by parallelization: OpenMP and CUDA are supported. * Multi-platform: Linux, OS X, and Windows are supported. * Planar and toroid maps. * Rectangular and hexagonal grids. * Gaussian or bubble neighborhood functions. * Visualization of maps, including those that were trained outside of Python. The documentation is available on `Read the Docs `_. Further details are found in the manuscript describing the library [1]. Usage ----- A simple example is below. For more example, please refer to the `documentation `_ and a more thorough ipython notebook example at `Somoclu in Python.ipynb `_. :: import somoclu import numpy as np import matplotlib.pyplot as plt c1 = np.random.rand(50, 2)/5 c2 = (0.2, 0.5) + np.random.rand(50, 2)/5 c3 = (0.4, 0.1) + np.random.rand(50, 2)/5 data = np.float32(np.concatenate((c1, c2, c3))) colors = ["red"] * 50 colors.extend(["green"] * 50) colors.extend(["blue"] * 50) labels = list(range(150)) n_rows, n_columns = 30, 50 som = somoclu.Somoclu(n_columns, n_rows, data=data, maptype="planar", gridtype="rectangular") som.train(epochs=10) som.view_umatrix(bestmatches=True, bestmatchcolors=colors, labels=labels) Installation ------------ The code is available on PyPI, hence it can be installed by :: $ sudo pip install somoclu Some pre-built binaries in the wheel format or windows installer are provided at `PyPI Dowloads `_, they are tested with `Anaconda `_ distributions. If you encounter errors like `ImportError: DLL load failed: The specified module could not be found` when `import somoclu`, you may need to use `Dependency Walker `_ as shown `here `_ on ``_somoclu_wrap.pyd`` to find out missing DLLs and place them at the write place. Usually right version (32/64bit) of ``vcomp90.dll, msvcp90.dll, msvcr90.dll`` should be put to ``C:\Windows\System32`` or ``C:\Windows\SysWOW64``. The wheel binaries for OSX are compiled with `clang-omp `_ , and depend on libiomp5, which you can install by: :: $ brew install libiomp If you want the latest git version, first git clone the repo, install `swig `_ and run: :: $ ./autogen.sh $ ./configure [options] $ make $ make python to generate python interface files. Then follow the standard procedure for installing Python modules: :: $ sudo python setup.py install Build on Mac OS X -------------------- Using GCC --------------- Since OS X 10.9, gcc is just symlink to clang. To build somoclu and this extension correctly, it is recommended to install gcc using something like: :: $ brew install gcc --without-multilib and set environment using: :: export CC=/usr/local/bin/gcc-5 export CXX=/usr/local/bin/g++-5 export CPP=/usr/local/bin/cpp-5 export LD=/usr/local/bin/gcc-5 alias c++=/usr/local/bin/c++-5 alias g++=/usr/local/bin/g++-5 alias gcc=/usr/local/bin/gcc-5 alias cpp=/usr/local/bin/cpp-5 alias ld=/usr/local/bin/gcc-5 alias cc=/usr/local/bin/gcc-5 Using clang-omp --------------- To install clang-omp, follow instructions at http://clang-omp.github.io/. And set environment using: :: export CC=/usr/local/bin/clang-omp export CXX=/usr/local/bin/clang-omp++ export CPP=/usr/local/bin/clang-omp++ export LD=/usr/local/bin/clang-omp alias c++=/usr/local/bin/clang-omp++ alias g++=/usr/local/bin/clang-omp++ alias gcc=/usr/local/bin/clang-omp alias cpp=/usr/local/bin/clang-omp++ alias ld=/usr/local/bin/clang-omp alias cc=/usr/local/bin/clang-omp export PATH=/usr/local/bin/:$PATH export C_INCLUDE_PATH=/usr/local/include/:$C_INCLUDE_PATH export CPLUS_INCLUDE_PATH=/usr/local/include/:$CPLUS_INCLUDE_PATH export LIBRARY_PATH=/usr/local/lib:$LIBRARY_PATH export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH Before building the module manually with: :: $ python setup.py build Build with CUDA support on Linux and OS X: ------------------------------------------ If the ``CUDAHOME`` variable is set, the usual install command will build and install the library: :: $ sudo python setup.py install Build with CUDA support on Windows: -------------------------------------- You should first follow the instructions to `build the Windows binary `_ with ``HAVE_MPI`` and ``CLI`` disabled with the same version Visual Studio as your Python is built with.(Since currently Python is built by VS2008 by default and CUDA v6.5 removed VS2008 support, you may use CUDA 6.0 with VS2008 or find a Python prebuilt with VS2010. And remember to install VS2010 or Windows SDK7.1 to get the option in Platform Toolset if you use VS2013.) The recommended configuration is VS2010 Platform Toolset with Python 3.4. Then you should copy the .obj files generated in the release build path to the ``Python\somoclu\src`` folder. Then modify the environment variable ``CUDA_PATH`` or ``win_cuda_dir`` in ``setup.py`` to your CUDA path and run the install command :: $ sudo python setup.py install Then it should be able to build and install the module. Citation -------- 1. Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2015). Somoclu: An Efficient Parallel Library for Self-Organizing Maps. `arXiv:1305.1422 `_. somoclu-1.7.2/src/Python/MANIFEST.in0000644000175000017500000000065313015657206013742 00000000000000include *.py include CITATION include LICENSE include README.rst include doc/Makefile include doc/manual.pdf recursive-include doc/html * recursive-include doc/source * include somoclu/*.py include somoclu/configure include somoclu/config.h.in include somoclu/install-sh include somoclu/Makefile.in include somoclu/src/Makefile.in include somoclu/src/*.h include somoclu/src/*.cpp include somoclu/src/*.cu include somoclu/m4/* somoclu-1.7.2/src/Python/CITATION0000644000175000017500000000124613015657206013340 00000000000000To cite Somoclu in publications, please use: Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2015). Somoclu: An Efficient Parallel Library for Self-Organizing Maps. arXiv:1305.1422. A BibTeX entry for LaTeX users is @Article{wittek2015somoclu, author = {Wittek, Peter and Gao, Shi Chao and Lim, Ik Soo and Zhao, Li}, title = {Somoclu: An Efficient Parallel Library for Self-Organizing Maps}, journal = {arXiv:1305.1422}, year = {2015} } You may also consider adding a link to the code. Please cite the PyPI version: https://pypi.python.org/pypi/somoclu/ This ensures that Depsy (http://depsy.org/) can aggregate the usage statistics of the library. somoclu-1.7.2/src/Python/tests/0000755000175000017500000000000013015657206013422 500000000000000somoclu-1.7.2/src/Python/tests/test_somoclu.py0000644000175000017500000000165413015657206016442 00000000000000import unittest import numpy as np from somoclu import Somoclu class DeterministicCodebook(unittest.TestCase): def test_deterministic_codebook(self): n_rows, n_columns = 2, 2 codebook = np.zeros((2*2, 2), dtype=np.float32) data = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32) som = Somoclu(n_columns, n_rows, data=data, initialcodebook=codebook, compactsupport=False) som.train() correct_codebook = np.array([[[ 0.2 , 0.30000001], [ 0.10359724, 0.20359723]], [[ 0.29640275, 0.39640275], [ 0.2 , 0.30000001]]], dtype=np.float32) self.assertTrue(sum(codebook.reshape((n_rows*n_columns*2)) - correct_codebook.reshape((n_rows*n_columns*2))) < 10e-8) if __name__ == '__main__': test_main() somoclu-1.7.2/src/Python/tests/__init__.py0000644000175000017500000000000013015657206015441 00000000000000somoclu-1.7.2/src/Python/setup.py0000644000175000017500000001775713015657206013733 00000000000000#!/usr/bin/env python from setuptools import setup, Extension from setuptools.command.build_ext import build_ext import numpy import os import sys import platform import traceback win_cuda_dir = None def find_cuda(): if 'CUDAHOME' in os.environ: home = os.environ['CUDAHOME'] nvcc = os.path.join(home, 'bin', 'nvcc') else: nvcc = None for dir in os.environ['PATH'].split(os.pathsep): binpath = os.path.join(dir, 'nvcc') if os.path.exists(binpath): nvcc = os.path.abspath(binpath) if nvcc is None: raise EnvironmentError('The nvcc binary could not be located in ' 'your $PATH. Either add it to your path, or' 'set $CUDAHOME') home = os.path.dirname(os.path.dirname(nvcc)) cudaconfig = {'home': home, 'nvcc': nvcc, 'include': os.path.join(home, 'include')} for k, v in cudaconfig.items(): if not os.path.exists(v): raise EnvironmentError('The CUDA %s path could not be located in ' '%s' % (k, v)) libdir = os.path.join(home, 'lib') arch = int(platform.architecture()[0][0:2]) if sys.platform.startswith('win'): os.path.join(libdir, "x"+str(arch)) if os.path.exists(os.path.join(home, libdir + "64")): cudaconfig['lib'] = libdir + "64" elif os.path.exists(os.path.join(home, libdir)): cudaconfig['lib'] = libdir else: raise EnvironmentError('The CUDA libraries could not be located') return cudaconfig try: CUDA = find_cuda() except EnvironmentError: CUDA = None print("Proceeding without CUDA") try: numpy_include = numpy.get_include() except AttributeError: numpy_include = numpy.get_numpy_include() def customize_compiler_for_nvcc(self): '''This is a verbatim copy of the NVCC compiler extension from https://github.com/rmcgibbo/npcuda-example ''' self.src_extensions.append('.cu') default_compiler_so = self.compiler_so super = self._compile def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): if os.path.splitext(src)[1] == '.cu': self.set_executable('compiler_so', CUDA['nvcc']) postargs = extra_postargs['nvcc'] else: postargs = extra_postargs['cc'] super(obj, src, ext, cc_args, postargs, pp_opts) self.compiler_so = default_compiler_so self._compile = _compile # run the customize_compiler class custom_build_ext(build_ext): def build_extensions(self): customize_compiler_for_nvcc(self.compiler) build_ext.build_extensions(self) cmdclass = {} if sys.platform.startswith('win') and win_cuda_dir is not None: if win_cuda_dir == "": if 'CUDA_PATH' in os.environ: win_cuda_dir = os.environ['CUDA_PATH'] elif os.path.exists(win_cuda_dir): pass else: win_cuda_dir = None if win_cuda_dir: arch = int(platform.architecture()[0][0:2]) somoclu_module = Extension('_somoclu_wrap', sources=['somoclu/somoclu_wrap.cxx'], extra_objects=[ 'somoclu/src/denseCpuKernels.obj', 'somoclu/src/sparseCpuKernels.obj', 'somoclu/src/training.obj', 'somoclu/src/mapDistanceFunctions.obj', 'somoclu/src/uMatrix.obj', 'somoclu/src/denseGpuKernels.cu.obj'], define_macros=[('CUDA', None)], library_dirs=[win_cuda_dir+"/lib/x"+str(arch)], libraries=['cudart', 'cublas'], include_dirs=[numpy_include]) else: sources_files = ['somoclu/src/denseCpuKernels.cpp', 'somoclu/src/sparseCpuKernels.cpp', 'somoclu/src/mapDistanceFunctions.cpp', 'somoclu/src/training.cpp', 'somoclu/src/uMatrix.cpp', 'somoclu/somoclu_wrap.cxx'] if sys.platform.startswith('win'): extra_compile_args = ['-openmp'] cmdclass = {} somoclu_module = Extension('_somoclu_wrap', sources=sources_files, include_dirs=[numpy_include, 'src'], extra_compile_args=extra_compile_args, ) else: extra_compile_args = ['-fopenmp'] if 'CC' in os.environ and 'clang-omp' in os.environ['CC']: openmp = 'iomp5' else: openmp = 'gomp' cmdclass = {'build_ext': custom_build_ext} somoclu_module = Extension('_somoclu_wrap', sources=sources_files, include_dirs=[numpy_include, 'src'], extra_compile_args={'cc': extra_compile_args}, libraries=[openmp], ) if CUDA is not None: somoclu_module.sources.append('somoclu/src/denseGpuKernels.cu') somoclu_module.define_macros = [('CUDA', None)] somoclu_module.include_dirs.append(CUDA['include']) somoclu_module.library_dirs = [CUDA['lib']] somoclu_module.libraries += ['cudart', 'cublas'] somoclu_module.runtime_library_dirs = [CUDA['lib']] somoclu_module.extra_compile_args['nvcc']=['-use_fast_math', '--ptxas-options=-v', '-c', '--compiler-options','-fPIC ' + extra_compile_args[0]] try: setup(name='somoclu', version='1.7.2', license='GPL3', author="Peter Wittek, Shi Chao Gao", author_email="", maintainer="shichaogao", maintainer_email="xgdgsc@gmail.com", url="https://somoclu.readthedocs.io/", platforms=["unix", "windows"], description="Massively parallel implementation of self-organizing maps", ext_modules=[somoclu_module], packages=["somoclu"], install_requires=['numpy', 'matplotlib'], classifiers=[ 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', 'Operating System :: OS Independent', 'Development Status :: 5 - Production/Stable', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Scientific/Engineering :: Visualization', 'Intended Audience :: Science/Research', 'Programming Language :: Python', 'Programming Language :: C++' ], cmdclass=cmdclass, test_suite="tests" ) except: traceback.print_exc() setup(name='somoclu', version='1.7.2', license='GPL3', author="Peter Wittek, Shi Chao Gao", author_email="", maintainer="shichaogao", maintainer_email="xgdgsc@gmail.com", url="https://somoclu.readthedocs.io/", platforms=["unix", "windows"], description="Massively parallel implementation of self-organizing maps", packages=["somoclu"], classifiers=[ 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', 'Operating System :: OS Independent', 'Development Status :: 5 - Production/Stable', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Scientific/Engineering :: Visualization', 'Intended Audience :: Science/Research', 'Programming Language :: Python', 'Programming Language :: C++' ], install_requires=['numpy', 'matplotlib'], test_suite="tests" ) somoclu-1.7.2/src/Python/doc/0000755000175000017500000000000013015657206013025 500000000000000somoclu-1.7.2/src/Python/doc/source/0000755000175000017500000000000013015657206014325 500000000000000somoclu-1.7.2/src/Python/doc/source/reference.rst0000644000175000017500000002700213015657206016736 00000000000000****************** Function Reference ****************** .. py:class:: Somoclu(n_columns, n_rows, data=None, initialcodebook=None, kerneltype=0, maptype='planar', gridtype='rectangular', compactsupport=False, neighborhood='gaussian', std_coeff=0.5, initialization=None) Class for training and visualizing a self-organizing map. :param n_columns: The number of columns in the map. :type n_columns: int. :param n_rows: The number of rows in the map. :type n_rows: int. :param data: Optional parameter to provide training data. It is not necessary if the map is otherwise trained outside Python, e.g., on a GPU cluster. :type data: 2D numpy.array of float32. :param initialcodebook: Optional parameter to start the training with a given codebook. :type initialcodebook: 2D numpy.array of float32. :param kerneltype: Optional parameter to specify which kernel to use: * 0: dense CPU kernel (default) * 1: dense GPU kernel (if compiled with it) :type kerneltype: int. :param maptype: Optional parameter to specify the map topology: * "planar": Planar map (default) * "toroid": Toroid map :type maptype: str. :param gridtype: Optional parameter to specify the grid form of the nodes: * "rectangular": rectangular neurons (default) * "hexagonal": hexagonal neurons :type gridtype: str. :param compactsupport: Optional parameter to cut off map updates beyond the training radius with the Gaussian neighborhood. Default: True. :type compactsupport: bool. :param neighborhood: Optional parameter to specify the neighborhood: * "gaussian": Gaussian neighborhood (default) * "bubble": bubble neighborhood function :type neighborhood: str. :param std_coeff: Optional parameter to set the coefficient in the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)) Default: 0.5 :type std_coeff: float. :param initialization: Optional parameter to specify the initalization: * "random": random weights in the codebook * "pca": codebook is initialized from the first subspace spanned by the first two eigenvectors of the correlation matrix :type initialization: str. .. py:method:: Somoclu.cluster(algorithm=None) Cluster the codebook. The clusters of the data instances can be assigned based on the BMUs. The method populates the class variable Somoclu.clusters. If viewing methods are called after clustering, but without colors for best matching units, colors will be automatically assigned based on cluster membership. :param algorithm: Optional parameter to specify a scikit-learn clustering algorithm. The default is K-means with eight clusters. :type filename: sklearn.base.ClusterMixin. .. py:method:: Somoclu.get_surface_state(data=None)) Return the dot product of the codebook and the data. :param data: Optional parameter to specify data, otherwise the data used previously to train the SOM is used. :type data: 2D numpy.array of float32. :returns: The the dot product of the codebook and the data. :rtype: 2D numpy.array .. py:method:: Somoclu.get_bmus(activation_map) Return Best Matching Unit indexes of the activation map. :param activation_map: Activation map computed with self.get_surface_state() :type activation_map: 2D numpy.array :returns: The bmus indexes corresponding to this activation map (same as self.bmus for the training samples). :rtype: 2D numpy.array .. py:method:: Somoclu.load_bmus(filename) Load the best matching units from a file to the Somoclu object. :param filename: The name of the file. :type filename: str. .. py:method:: Somoclu.load_codebook(filename) Load the codebook from a file to the Somoclu object. :param filename: The name of the file. :type filename: str. .. py:method:: Somoclu.load_umatrix(filename) Load the umatrix from a file to the Somoclu object. :param filename: The name of the file. :type filename: str. .. py:method:: Somoclu.train(epochs=10, radius0=0, radiusN=1, radiuscooling='linear', scale0=0.1, scaleN=0.01, scalecooling='linear') Train the map on the current data in the Somoclu object. :param epochs: The number of epochs to train the map for. :type epochs: int. :param radius0: The initial radius on the map where the update happens around a best matching unit. Default value of 0 will trigger a value of min(n_columns, n_rows)/2. :type radius0: float. :param radiusN: The radius on the map where the update happens around a best matching unit in the final epoch. Default: 1. :type radiusN: float. :param radiuscooling: The cooling strategy between radius0 and radiusN: * "linear": Linear interpolation (default) * "exponential": Exponential decay :param scale0: The initial learning scale. Default value: 0.1. :type scale0: float. :param scaleN: The learning scale in the final epoch. Default: 0.01. :type scaleN: float. :param scalecooling: The cooling strategy between scale0 and scaleN: * "linear": Linear interpolation (default) * "exponential": Exponential decay :type scalecooling: str. .. py:method:: Somoclu.update_data(data) Change the data set in the Somoclu object. It is useful when the data is updated and the training should continue on the new data. :param data: The training data. :type data: 2D numpy.array of float32. .. py:method:: Somoclu.view_activation_map(data_vector=None, data_index=None, activation_map=None, figsize=None, colormap=cm.Spectral_r, colorbar=False, bestmatches=False, bestmatchcolors=None, labels=None, zoom=None, filename=None) Plot the activation map of a given data instance or a new data vector :param data_vector: Optional parameter for a new vector :type data_vector: numpy.array :param data_index: Optional parameter for the index of the data instance :type data_index: int. :param activation_map: Optional parameter to pass the an activation map :type activation_map: numpy.array :param figsize: Optional parameter to specify the size of the figure. :type figsize: (int, int) :param colormap: Optional parameter to specify the color map to be used. :type colormap: matplotlib.colors.Colormap :param colorbar: Optional parameter to include a colormap as legend. :type colorbar: bool. :param bestmatches: Optional parameter to plot best matching units. :type bestmatches: bool. :param bestmatchcolors: Optional parameter to specify the color of each best matching unit. :type bestmatchcolors: list of int. :param labels: Optional parameter to specify the label of each point. :type labels: list of str. :param zoom: Optional parameter to zoom into a region on the map. The first two coordinates of the tuple are the row limits, the second tuple contains the column limits. :type zoom: ((int, int), (int, int)) :param filename: If specified, the plot will not be shown but saved to this file. :type filename: str. .. py:method:: Somoclu.view_component_planes(dimensions=None, figsize=None, colormap=cm.Spectral_r, colorbar=False, bestmatches=False, bestmatchcolors=None, labels=None, zoom=None, filename=None) Observe the component planes in the codebook of the SOM. :param dimensions: Optional parameter to specify along which dimension or dimensions should the plotting happen. By default, each dimension is plotted in a sequence of plots. :type dimension: int or list of int. :param figsize: Optional parameter to specify the size of the figure. :type figsize: (int, int) :param colormap: Optional parameter to specify the color map to be used. :type colormap: matplotlib.colors.Colormap :param colorbar: Optional parameter to include a colormap as legend. :type colorbar: bool. :param bestmatches: Optional parameter to plot best matching units. :type bestmatches: bool. :param bestmatchcolors: Optional parameter to specify the color of each best matching unit. :type bestmatchcolors: list of int. :param labels: Optional parameter to specify the label of each point. :type labels: list of str. :param zoom: Optional parameter to zoom into a region on the map. The first two coordinates of the tuple are the row limits, the second tuple contains the column limits. :type zoom: ((int, int), (int, int)) :param filename: If specified, the plot will not be shown but saved to this file. :type filename: str. .. py:method:: Somoclu.view_similarity_matrix(data=None, labels=None, figsize=None, filename=None) Plot the similarity map according to the activation map :param data: Optional parameter for data points to calculate the similarity with :type data: numpy.array :param figsize: Optional parameter to specify the size of the figure. :type figsize: (int, int) :param labels: Optional parameter to specify the label of each point. :type labels: list of str. :param filename: If specified, the plot will not be shown but saved to this file. :type filename: str. .. py:method:: Somoclu.view_umatrix(figsize=None, colormap=`_, containing the source, documentation, and examples. The latest development version is available on `GitHub `_. Dependencies ============ The module requires `Numpy `_ and `matplotlib `_. The code is compatible with both Python 2 and 3. On Linux, you need a standard GCC compile chain. Other compiler chains, for instance, ICC, are known to work too. On OS X, you must have ``libiomp`` installed. See `this issue `_ if you encounter problems. If you build it from source, please refer to the section below. On Windows, having ``MSVCP90.DLL`` and ``VCOMP90.DLL`` is usually sufficient. See `this issue `_ if you have problems. Installation ------------ The code is available on PyPI, hence it can be installed by :: $ sudo pip install somoclu If you want the latest git version, clone the repository, make the Python target, and follow the standard procedure for installing Python modules: :: $ git clone https://github.com/peterwittek/somoclu.git $ cd somoclu $ ./autogen.sh $ ./configure $ make python $ cd src/python $ sudo python setup.py install Build on Mac OS X -------------------- GCC should be installed first. As of OS X 10.9, gcc is just symlink to clang. To build somoclu and this extension correctly, it is recommended to install gcc using something like: :: $ brew install gcc48 and set environment using: :: export CC=/usr/local/bin/gcc export CXX=/usr/local/bin/g++ export CPP=/usr/local/bin/cpp export LD=/usr/local/bin/gcc alias c++=/usr/local/bin/c++ alias g++=/usr/local/bin/g++ alias gcc=/usr/local/bin/gcc alias cpp=/usr/local/bin/cpp alias ld=/usr/local/bin/gcc alias cc=/usr/local/bin/gcc Then you can issue :: $ sudo pip install somoclu Build with CUDA support on Linux and OS X: ------------------------------------------ If the ``CUDAHOME`` variable is set, the usual install command will build and install the library: :: $ sudo python setup.py install Build with CUDA support on Windows: -------------------------------------- You should first follow the instructions to `build the Windows binary `_ with MPI disabled with the same version Visual Studio as your Python is built with.(Since currently Python is built by VS2008 by default and CUDA v6.5 removed VS2008 support, you may use CUDA 6.0 with VS2008 or find a Python prebuilt with VS2010. And remember to install VS2010 or Windows SDK7.1 to get the option in Platform Toolset if you use VS2013.) Then you should copy the .obj files generated in the release build path to the Python/src folder. Then modify the win_cuda_dir in setup.py to your CUDA path and run the install command :: $ sudo python setup.py install Then it should be able to build and install the module. somoclu-1.7.2/src/Python/doc/source/conf.py0000644000175000017500000002130013015657206015540 00000000000000# -*- coding: utf-8 -*- # # somoclu documentation build configuration file, created by # sphinx-quickstart2 on Fri Dec 12 16:39:13 2014. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import mock MOCK_MODULES = ['numpy', 'matplotlib', 'matplotlib.cm', 'matplotlib.pyplot', 'matplotlib.collections'] for mod_name in MOCK_MODULES: sys.modules[mod_name] = mock.Mock() # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.ifconfig', 'sphinx.ext.mathjax', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Somoclu' copyright = u'2015, Peter Wittek, Shi Chao Gao' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1.7.2' # The full version, including alpha/beta/rc tags. release = '1.7.2' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [''] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # The style sheet to use for HTML and HTML Help pages. A file of that name # must exist either in Sphinx' static/ path, or in one of the custom paths # given in html_static_path. html_theme_path = ["_themes", ] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. html_extra_path = ['.nojekyll'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. html_show_sourcelink = False # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'somocludoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. 'preamble': '\usepackage{amssymb}', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('indexlatex', 'manual.tex', u'Somoclu Python Documentation', u'Peter Wittek, Shi Chao Gao', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'somoclu', u'Somoclu Python Documentation', [u'Peter Wittek, Shi Chao Gao'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'somoclu', u'Somoclu Python Documentation', u'Peter Wittek, Shi Chao Gao', 'somoclu', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'http://docs.python.org/': None} somoclu-1.7.2/src/Python/doc/source/figures/0000755000175000017500000000000013015657206015771 500000000000000somoclu-1.7.2/src/Python/doc/source/figures/output_28_0.png0000600000175000017500000050670113015657206020510 00000000000000‰PNG  IHDRê3þ\@ÝsBIT|dˆ pHYs  ÒÝ~ü IDATxœìwœTÕùÿßçÜ{gf{ßeYz)¢¢X°$j°`EcÔˆ±ÅhK5jì(죉1Ö ‚X5v Qz/[ØÝ)÷Þs~Ü23K[`Éן¯ùøZg˜™{î¹çž{žöyž#´ÖäC9äC?NÈÿëäC9äCGNPçC9äÃ9AC9äC?bäu9äC9üˆ‘Ô9äC9äð#FNPçC9äÃ9AC9äC?bäu9äC9üˆ‘Ô9äC9äð#FNPçC9äÃ9AC9äC?b˜ÿ×È!‡~B  (ߊÛ€åZkÕ±½Ú¾B˜@g  ÕÀJ Aç6\øÑÀŸÃ@e6kË´Ö­[Û@NPçCÛ !DQUUÕ_ËÊʆVVV:Þz×>h­ihh0V¯^½XqœÖzåvìj‡A1¨¦¦æï555ùEEE[¤`(¥X¾|y¤±±ñy!Ä´Öîöêgíƒ"¯ªªêÁÒÒÒ‘UUUö–ÌáM!™LŠeË–Ñhô²d2ùÔÖ´‘Ô9äÃ6A©®®~ì7¿ùÍàn¸¡AÊ­‹¨Ýu×]]¯¿þú©Bˆ1ZëÆîf‡BÑ»k×®ÿüóŸÿ9è ƒÖmM‰D¢õ¤“N:ìµ×^SÀ…ÜŶBYYYùÐqÇ·ëäÉ“ ÃèÐö¿ÿþ{먣Žº1&“Éäó[Ü¿œ×%‡ö@Q‰DN(// ¥ŒtD›ZëÔÚµkç&“ÉüÿbEm B!¥<¢¼¼|d$)Û’cµÖnccã¼ÖÖÖ)ZëùÛ©‹ÛBˆA#GŽ|öwÞiÚZ!àØc-zꩧ~§µ~­ƒº·]`YÖ™W\qÅ%W^yåêmi'‹¾}û–-Y²dÖ:ÑQýËaË „è>dÈW?ùä“ÆŽÒ^}õÕ‚SN9å›… ¹¥Çæ,ê6 !DYuuõ??þøþ#GŽ´c±X‡ÄmÛŸ|òɘ‡~øh!Ä1ZëåÑîÿ„¢¸¸øâwÞùŒqãÆéÒÒÒ-reº®+¾ýöÛÈ=÷ÜóK,~Ø^}ݨêÖ­›»­B gÏžP³í]Ú¾(--íÖ½{wg[ÛÉËËÓö’%K*Åе¶UuuuÎöÒÝ»w·N[slNPo!„Q 蘆G4X©µv…EÀYb›A+°f[É*ÕÕÕ7œuÖY}®¸âŠÕ±gbìرTWW×Nœ8ñà—Úøÿû 2äô©S§6–——oµ"Ó«W¯âóÎ;ïa`ߎëÚv‡Øм¨««RXXèJ)1MSñÅ_\ýõÕùË_ª,ËÒûï¿ã½÷Þ»$8Æ4Mè¸gk»Ásžd_óçŸýå/Ù[¡µÖbñâÅ‘ &,½üòËWnꚥ”šÿ®yK!„xäÂèêà­‰Û¬m„B{ì±ÝgÎœYZQQaÏ™3ç+€3Ï<³ËôéÓK"‘ˆîÞ½{ò‰'ž˜W^^®ž}öÙ¢Ë/¿¼‹mÛ²,=qâÄÅcÆŒÉ ‰˜¦¹Õ÷9'¨Û !„,--½ª®®îÄŠŠ Û0Œ‰$“I¹zõj*++WvïÞ½¶¬¬¬ÃH ÍÍÍFcccƒâ­õœ­m'=î¸ãZ:ZH8á„ÖMš4iðviüÓ4û3ÆØ! 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somoclu-1.7.2/src/Python/doc/source/reference_autodoc.rst0000644000175000017500000000045313015657206020455 00000000000000****************** Function Reference ****************** Somoclu Class ============= .. autoclass:: somoclu.Somoclu :members: get_surface_state, load_bmus, load_umatrix, load_codebook, train, update_data, view_activation_map, view_component_planes, view_similarity_matrix, view_umatrix, cluster somoclu-1.7.2/src/Python/doc/source/example.rst0000644000175000017500000002060713015657206016437 00000000000000******** Examples ******** Self-organizing maps are computationally intensive to train, especially if the original space is high-dimensional or the map is large. Very large maps where the number of neurons is at least five times the number of data points are sometimes called emergent-self organizing maps -- these are especially demanding to train. Somoclu is a highly efficient, parallel and distributed algorithm to train such maps, and its Python interface was recently updated. This enables fast training of self-organizing maps on multicore CPUs or a GPU from Python, albeit only on dense data, and the distributed computing capability is also not exposed. The Python interface also lets you process the output files of the command-line version, so if the data is sparse or the map was trained on a cluster, you can still use the module for visualization. Here we take a quick look at how to train and visualize a small map. First, we import the necessary modules: .. code:: python import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import somoclu %matplotlib inline Then we generate and plot some random data in three categories: .. code:: python c1 = np.random.rand(50, 3)/5 c2 = (0.6, 0.1, 0.05) + np.random.rand(50, 3)/5 c3 = (0.4, 0.1, 0.7) + np.random.rand(50, 3)/5 data = np.float32(np.concatenate((c1, c2, c3))) colors = ["red"] * 50 colors.extend(["green"] * 50) colors.extend(["blue"] * 50) fig = plt.figure() ax = Axes3D(fig) ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=colors) labels = range(150) .. image:: figures/output_3_0.png Planar maps ----------- We train Somoclu with default parameter settings, asking for a large map that qualifies as an emergent self-organizing map for this data: .. code:: python n_rows, n_columns = 100, 160 som = somoclu.Somoclu(n_columns, n_rows, data=data, compactsupport=False) %time som.train() .. parsed-literal:: CPU times: user 6.99 s, sys: 3.33 ms, total: 6.99 s Wall time: 5.21 s We plot the component planes of the trained codebook of the ESOM: .. code:: python som.view_component_planes() .. image:: figures/output_7_0.png .. image:: figures/output_7_1.png .. image:: figures/output_7_2.png .. parsed-literal:: We can plot the U-Matrix, together with the best matching units for each data point. We color code the units with the classes of the data points and also add the labels of the data points. .. code:: python som.view_umatrix(bestmatches=True, bestmatchcolors=colors, labels=labels) .. image:: figures/output_9_0.png .. parsed-literal:: We can also zoom into a region of interest, for instance, the dense lower right corner: .. code:: python som.view_umatrix(bestmatches=True, bestmatchcolors=colors, labels=labels, zoom=((50, n_rows), (100, n_columns))) .. image:: figures/output_11_0.png .. parsed-literal:: Toroid topology, hexagonal grid ------------------------------- We can repeat the above with a toroid topology by specifying the map type as follows: .. code:: python som = somoclu.Somoclu(n_columns, n_rows, data=data, maptype="toroid", compactsupport=False) som.train() som.view_umatrix(bestmatches=True, bestmatchcolors=colors) .. image:: figures/output_13_0.png .. parsed-literal:: Notice how the edges of the map connect to the other side. Hexagonal neurons are also implemented: .. code:: python som = somoclu.Somoclu(n_columns, n_rows, data=data, gridtype="hexagonal", compactsupport=False) som.train() som.view_umatrix(bestmatches=True, bestmatchcolors=colors) .. image:: figures/output_15_0.png .. parsed-literal:: The separation of the individual points is more marked with these neurons. Initialization with principal component analysis and clustering the results --------------------------------------------------------------------------- We can pass an initial codebook of our choice, but we can also ask Somoclu to initialize the codebook with vectors from the subspace spanned by the first two eigenvalues of the correlation matrix. To do this, we need to pass an optional argument to the constructor: .. code:: python som = somoclu.Somoclu(n_columns, n_rows, data=data, maptype="toroid", compactsupport=False, initialization="pca") som.train() som.view_umatrix(bestmatches=True, bestmatchcolors=colors) .. image:: figures/output_18_0.png .. parsed-literal:: While one would expect entirely deterministic results on repeated runs with the initialization based on PCA, this is not the case. The order in which the data instances arrive matters: since Somoclu uses multiple cores, there is no control over the order of each batch, hence the maps will show small variation even with a PCA initalization. We can also postprocess the codebook with an arbitrary clustering algorithm that is included in `scikit-learn `__. The default algorithm is K-means with eight clusters. After clustering, the labels for each node are available in the SOM object in the ``clusters`` class variable. If we do not pass colors to the matrix viewing functions and clustering is already done, the plotting routines automatically color the best matching units according to the clustering structure. .. code:: python som.cluster() som.view_umatrix(bestmatches=True) .. image:: figures/output_20_0.png .. parsed-literal:: We can, of course, choose another clustering algorithm, but topological clustering methods will make little sense with their default parameterization. DBSCAN, for instance, will assign the same class to all nodes: .. code:: python from sklearn.cluster import DBSCAN algorithm = DBSCAN() som.cluster(algorithm=algorithm) som.view_umatrix(bestmatches=True) .. image:: figures/output_22_0.png .. parsed-literal:: Evolving maps ------------- One of the great advantages of self-organizing maps is that they are incremental, they can be updated with new data. This is especially interesting if the data points retain their old label, that is, the properties of the vectors change in the high-dimensional space. Let us train again a toroid rectangular emergent map on the same data: .. code:: python som = somoclu.Somoclu(n_columns, n_rows, data=data, maptype="toroid") som.train() Next, let us assume that the green cluster moves to the left, the other points remaining invariant: .. code:: python c2_shifted = c2 - 0.2 updated_data = np.float32(np.concatenate((c1, c2_shifted, c3))) fig = plt.figure() ax = Axes3D(fig) ax.scatter(updated_data[:, 0], updated_data[:, 1], updated_data[:, 2], c=colors) .. parsed-literal:: .. image:: figures/output_26_1.png We can update the map to reflect this shift. We plot the map before and after continuing the training: .. code:: python som.view_umatrix(bestmatches=True, bestmatchcolors=colors, labels=labels) som.update_data(updated_data) som.train(epochs=2, radius0=20, scale0=0.02) som.view_umatrix(bestmatches=True, bestmatchcolors=colors, labels=labels) .. image:: figures/output_28_0.png .. image:: figures/output_28_1.png .. parsed-literal:: As a result of the shift, the blue points do not move around much. On the other hand, the relationship of the red and green clusters is being redefined as their coordinates inched closer in the original space. somoclu-1.7.2/src/Python/doc/source/.nojekyll0000644000175000017500000000000013015657206016063 00000000000000somoclu-1.7.2/src/Python/doc/source/introduction.rst0000644000175000017500000000344513015657206017526 00000000000000============ Introduction ============ Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution and it can be accelerated by CUDA. The topology of map is either planar or toroid, the grid is rectangular or hexagonal. Currently a subset of the command line version is supported with this Python module. Key features of the Python interface: * Fast execution by parallelization: OpenMP and CUDA are supported. * Multi-platform: Linux, OS X, and Windows are supported. * Planar and toroid maps. * Rectangular and hexagonal grids. * Gaussian or bubble neighborhood functions. * Visualization of maps, including those that were trained outside of Python. The documentation is available online. Further details are found in the following paper: Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2015). Somoclu: An Efficient Parallel Library for Self-Organizing Maps. `arXiv:1305.1422 `_. Copyright and License --------------------- Somoclu is free software; you can redistribute it and/or modify it under the terms of the `GNU General Public License `_ as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. Somoclu is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the `GNU General Public License `_ for more details. Acknowledgment -------------- This work is supported by the European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 `PERICLES `_ and by the AWS in Education Machine Learning Grant award. somoclu-1.7.2/src/Python/doc/Makefile0000644000175000017500000001533213015657206014411 00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." xml: $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." somoclu-1.7.2/src/mapDistanceFunctions.cpp0000644000175000017500000000667513015657206015562 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #include #include "somoclu.h" float euclideanDistanceOnPlanarMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y) { unsigned int x1 = std::min(som_x, x); unsigned int y1 = std::min(som_y, y); unsigned int x2 = std::max(som_x, x); unsigned int y2 = std::max(som_y, y); unsigned int xdist = x2 - x1; unsigned int ydist = y2 - y1; return sqrt(float(xdist * xdist + ydist * ydist)); } float euclideanDistanceOnToroidMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y, const unsigned int nSomX, const unsigned int nSomY) { unsigned int x1 = std::min(som_x, x); unsigned int y1 = std::min(som_y, y); unsigned int x2 = std::max(som_x, x); unsigned int y2 = std::max(som_y, y); unsigned int xdist = std::min(x2 - x1, x1 + nSomX - x2); unsigned int ydist = std::min(y2 - y1, y1 + nSomY - y2); return sqrt(float(xdist * xdist + ydist * ydist)); } float euclideanDistanceOnHexagonalPlanarMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y) { unsigned int x1 = std::min(som_x, x); unsigned int y1 = std::min(som_y, y); unsigned int x2 = std::max(som_x, x); unsigned int y2 = std::max(som_y, y); unsigned int xdist = x2 - x1; unsigned int ydist = y2 - y1; if (ydist & 1) { xdist += ((y1 & 1) ? -0.5 : 0.5); } return sqrt(float(xdist * xdist + ydist * ydist)); } float euclideanDistanceOnHexagonalToroidMap(const unsigned int som_x, const unsigned int som_y, const unsigned int x, const unsigned int y, const unsigned int nSomX, const unsigned int nSomY) { unsigned int x1 = std::min(som_x, x); unsigned int y1 = std::min(som_y, y); unsigned int x2 = std::max(som_x, x); unsigned int y2 = std::max(som_y, y); unsigned int xdist = std::min(x2 - x1, x1 + nSomX - x2); unsigned int ydist = std::min(y2 - y1, y1 + nSomY - y2); if (ydist & 1) { xdist += ((y1 & 1) ? -0.5 : 0.5); } return sqrt(float(xdist * xdist + ydist * ydist)); } float gaussianNeighborhood(float distance, float radius, float std_coeff) { float std = radius * std_coeff; return exp((- distance * distance) / (2 * std * std)); } float getWeight(float distance, float radius, float scaling, bool compact_support = false, bool gaussian = true, float std_coeff=0.5) { float result = 0.0; if (gaussian) { if (!compact_support || distance <= radius) { result = gaussianNeighborhood(distance, radius, std_coeff); } } else { if (distance <= radius) { result = 1.0; } } return scaling * result; } somoclu-1.7.2/src/somoclu.cpp0000644000175000017500000004217713015657206013117 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include #include #include #ifdef _WIN32 #include "Windows/unistd.h" #include "Windows/getopt.h" #else #include #include #endif #include "somoclu.h" using namespace std; /// For synchronized timing #ifndef MPI_WTIME_IS_GLOBAL #define MPI_WTIME_IS_GLOBAL 1 #endif // Default parameters #define N_EPOCH 10 #define N_SOM_X 50 #define N_SOM_Y 50 #define KERNEL_TYPE 0 #define SNAPSHOTS 0 void printUsage() { cout << "Usage:\n" \ " [mpirun -np NPROC] somoclu [OPTIONs] INPUT_FILE OUTPUT_PREFIX\n" \ "Arguments:\n" \ " -c FILENAME Specify an initial codebook for the map.\n" \ " -d NUMBER Coefficient in the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)) (default: 0.5)\n" \ " -e NUMBER Maximum number of epochs (default: " << N_EPOCH << ")\n" \ " -g TYPE Grid type: rectangular or hexagonal (default: rectangular)\n"\ " -h, --help This help text\n" \ " -k NUMBER Kernel type (default: " << KERNEL_TYPE << "): \n" \ " 0: Dense CPU\n" \ " 1: Dense GPU\n" \ " 2: Sparse CPU\n" \ " -l NUMBER Starting learning rate (default: 0.1)\n" \ " -L NUMBER Finishing learning rate (default: 0.01)\n" \ " -m TYPE Map type: planar or toroid (default: planar) \n" \ " -n FUNCTION Neighborhood function (bubble or gaussian, default: gaussian)\n"\ " -p NUMBER Compact support for Gaussian neighborhood (0: false, 1: true, default: 1)\n"\ " -r NUMBER Start radius (default: half of the map in direction min(x,y))\n" \ " -R NUMBER End radius (default: 1)\n" \ " -s NUMBER Save interim files (default: 0):\n" \ " 0: Do not save interim files\n" \ " 1: Save U-matrix only\n" \ " 2: Also save codebook and best matching neurons\n" \ " -t STRATEGY Radius cooling strategy: linear or exponential (default: linear)\n" \ " -T STRATEGY Learning rate cooling strategy: linear or exponential (default: linear)\n" \ " -x, --columns NUMBER Number of columns in map (size of SOM in direction x) (default: " << N_SOM_X << ")\n" \ " -y, --rows NUMBER Number of rows in map (size of SOM in direction y) (default: " << N_SOM_Y << ")\n" \ "Examples:\n" \ " somoclu data/rgbs.txt data/rgbs\n" " mpirun -np 4 somoclu -k 0 -x 20 -y 20 data/rgbs.txt data/rgbs\n"; } void processCommandLine(int argc, char** argv, string *inFilename, string* outPrefix, unsigned int *nEpoch, float *radius0, float *radiusN, string *radiusCooling, float *scale0, float *scaleN, string *scaleCooling, unsigned int *nSomX, unsigned int *nSomY, unsigned int *kernelType, string *mapType, unsigned int *snapshots, string *gridType, unsigned int *compactSupport, unsigned int *gaussian, float *std_coeff, string *initialCodebookFilename) { // Setting default values *nEpoch = N_EPOCH; *nSomX = N_SOM_X; *nSomY = N_SOM_Y; *kernelType = KERNEL_TYPE; *snapshots = SNAPSHOTS; *mapType = "planar"; *radius0 = 0; *radiusN = 0; *radiusCooling = "linear"; *scale0 = 0.0; *scaleN = 0.01; *scaleCooling = "linear"; *gridType = "rectangular"; *compactSupport = 1; *gaussian = 1; *std_coeff = 0.5; string neighborhood_function = "gaussian"; static struct option long_options[] = { {"help", no_argument, 0, 'h'}, {"rows", required_argument, 0, 'y'}, {"columns", required_argument, 0, 'x'}, {0, 0, 0, 0} }; int c; extern int optind, optopt; int option_index = 0; while ((c = getopt_long (argc, argv, "hx:y:d:e:g:k:l:m:n:p:r:s:t:c:L:R:T:", long_options, &option_index)) != -1) { switch (c) { case 'c': *initialCodebookFilename = optarg; break; case 'd': *std_coeff = atof(optarg); if (*std_coeff <= 0) { my_abort("The argument of option -l should be a positive float."); } break; case 'e': *nEpoch = atoi(optarg); if (*nEpoch <= 0) { my_abort("The argument of option -e should be a positive integer."); } break; case 'h': printUsage(); exit(0); break; case 'k': *kernelType = atoi(optarg); if (*kernelType > SPARSE_CPU) { my_abort("The argument of option -k should be a valid kernel."); } break; case 'n': neighborhood_function = optarg; if (neighborhood_function == "bubble") { *gaussian = 0; } else if (neighborhood_function == "gaussian") { *gaussian = 1; } else { my_abort("The argument of option -n should be either bubble or Gaussian."); } break; case 'p': *compactSupport = atoi(optarg); if (*compactSupport != 0 && *compactSupport != 1) { my_abort("The argument of option -g should be either 0 (false) or 1 (true)."); } break; case 'm': *mapType = optarg; if (*mapType != "planar" && *mapType != "toroid") { my_abort("The argument of option -m should be either planar or toroid."); } break; case 'g': *gridType = optarg; if (*gridType != "rectangular" && *gridType != "hexagonal") { my_abort("The argument of option -h should be either rectangular or hexagonal."); } break; case 'r': *radius0 = atof(optarg); if (*radius0 <= 0) { my_abort("The argument of option -r should be a positive integer."); } break; case 'R': *radiusN = atof(optarg); if (*radiusN <= 0) { my_abort("The argument of option -R should be a positive integer."); } break; case 't': *radiusCooling = optarg; if (*radiusCooling != "linear" && *radiusCooling != "exponential") { my_abort("The argument of option -t should be linear or exponential."); } break; case 'l': *scale0 = atof(optarg); if (*scale0 <= 0) { my_abort("The argument of option -l should be a positive float."); } break; case 'L': *scaleN = atof(optarg); if (*scaleN <= 0) { my_abort("The argument of option -L should be a positive float."); } break; case 'T': *scaleCooling = optarg; if (*scaleCooling != "linear" && *scaleCooling != "exponential") { my_abort("The argument of option -T should be linear or exponential."); } break; case 's': *snapshots = atoi(optarg); if (*snapshots > 2) { my_abort("The argument of option -s should be 0, 1, or 2."); } break; case 'x': *nSomX = atoi(optarg); if (*nSomX <= 0) { my_abort("The argument of option -x should be a positive integer."); } break; case 'y': *nSomY = atoi(optarg); if (*nSomY <= 0) { my_abort("The argument of option -y should be a positive integer."); } break; case '?': if (optopt == 'e' || optopt == 'k' || optopt == 's' || optopt == 'x' || optopt == 'y') { stringstream sstm; sstm << "Option -" << optopt << " requires an argument."; my_abort(sstm.str()); } else if (isprint (optopt)) { stringstream sstm; sstm << "Unknown option -" << optopt; my_abort(sstm.str()); } else { stringstream sstm; sstm << "Unknown option character `\\x" << optopt << "'"; my_abort(sstm.str()); } default: abort (); } } if (argc - optind != 2) { my_abort("Incorrect number of mandatory parameters"); } *inFilename = argv[optind++]; *outPrefix = argv[optind++]; } /* -------------------------------------------------------------------------- */ int main(int argc, char** argv) /* -------------------------------------------------------------------------- */ { int rank = 0; int nProcs = 1; #ifdef HAVE_MPI /// /// MPI init /// MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &nProcs); MPI_Barrier(MPI_COMM_WORLD); #endif unsigned int nEpoch = 0; unsigned int nSomX = 0; unsigned int nSomY = 0; unsigned int kernelType = 0; string mapType; string gridType; unsigned int compactSupport; unsigned int gaussian; float radius0 = 0; float radiusN = 0; string radiusCooling; float scale0 = 0.0; float scaleN = 0.0; string scaleCooling; unsigned int snapshots = 0; string inFilename; string initialCodebookFilename; string outPrefix; float std_coeff = 0.0; if (rank == 0) { processCommandLine(argc, argv, &inFilename, &outPrefix, &nEpoch, &radius0, &radiusN, &radiusCooling, &scale0, &scaleN, &scaleCooling, &nSomX, &nSomY, &kernelType, &mapType, &snapshots, &gridType, &compactSupport, &gaussian, &std_coeff, &initialCodebookFilename); #ifndef CUDA if (kernelType == DENSE_GPU) { my_abort("Somoclu was compile without GPU support!"); } #endif } #ifdef HAVE_MPI MPI_Bcast(&nEpoch, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&radius0, 1, MPI_FLOAT, 0, MPI_COMM_WORLD); MPI_Bcast(&nSomX, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&nSomY, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&kernelType, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&compactSupport, 1, MPI_INT, 0, MPI_COMM_WORLD); char *inFilenameCStr = new char[255]; if (rank == 0) { strcpy(inFilenameCStr, inFilename.c_str()); } MPI_Bcast(inFilenameCStr, 255, MPI_CHAR, 0, MPI_COMM_WORLD); inFilename = inFilenameCStr; char *mapTypeCStr = new char[255]; if (rank == 0) { strcpy(mapTypeCStr, mapType.c_str()); } MPI_Bcast(mapTypeCStr, 255, MPI_CHAR, 0, MPI_COMM_WORLD); mapType = mapTypeCStr; char *gridTypeCStr = new char[255]; if (rank == 0) { strcpy(gridTypeCStr, gridType.c_str()); } MPI_Bcast(gridTypeCStr, 255, MPI_CHAR, 0, MPI_COMM_WORLD); gridType = gridTypeCStr; double profile_time = MPI_Wtime(); #endif float * dataRoot = NULL; unsigned int nDimensions = 0; unsigned int nVectors = 0; if(rank == 0 ) { if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { dataRoot = readMatrix(inFilename, nVectors, nDimensions); } else { readSparseMatrixDimensions(inFilename, nVectors, nDimensions); } } #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); MPI_Bcast(&nVectors, 1, MPI_INT, 0, MPI_COMM_WORLD); MPI_Bcast(&nDimensions, 1, MPI_INT, 0, MPI_COMM_WORLD); #endif unsigned int nVectorsPerRank = ceil(nVectors / (1.0 * nProcs)); // Allocate a buffer on each node float* data = NULL; svm_node **sparseData; sparseData = NULL; if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { #ifdef HAVE_MPI // Dispatch a portion of the input data to each node data = new float[nVectorsPerRank * nDimensions]; MPI_Scatter(dataRoot, nVectorsPerRank * nDimensions, MPI_FLOAT, data, nVectorsPerRank * nDimensions, MPI_FLOAT, 0, MPI_COMM_WORLD); #else data = dataRoot; #endif } else { int currentRankProcessed = 0; while (currentRankProcessed < nProcs) { if (rank == currentRankProcessed) { sparseData = readSparseMatrixChunk(inFilename, nVectors, nVectorsPerRank, rank * nVectorsPerRank); } currentRankProcessed++; #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); #endif } } if(rank == 0) { // No need for root data any more if compiled with MPI #ifdef HAVE_MPI if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { delete [] dataRoot; } #endif cout << "nVectors: " << nVectors << " "; cout << "nVectorsPerRank: " << nVectorsPerRank << " "; cout << "nDimensions: " << nDimensions << " "; cout << endl; } /// /// Codebook /// float *codebook = new float[nSomY * nSomX * nDimensions]; int *globalBmus = NULL; float *uMatrix = NULL; if (rank == 0) { globalBmus = new int[nVectorsPerRank * int(ceil(nVectors / (double)nVectorsPerRank)) * 2]; uMatrix = new float[nSomX * nSomY]; if (initialCodebookFilename.empty()) { initializeCodebook(0, codebook, nSomX, nSomY, nDimensions); } else { unsigned int nSomXY = 0; unsigned int tmpNDimensions = 0; delete [] codebook; codebook = readMatrix(initialCodebookFilename, nSomXY, tmpNDimensions); if (tmpNDimensions != nDimensions) { my_abort("Dimension of initial codebook does not match data!"); } else if (nSomXY / nSomY != nSomX) { my_abort("Dimension of initial codebook does not match specified SOM grid!"); } cout << "Read initial codebook: " << initialCodebookFilename << "\n"; } } #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); double training_time = MPI_Wtime(); #endif // TRAINING train(rank, data, sparseData, codebook, globalBmus, uMatrix, nSomX, nSomY, nDimensions, nVectors, nVectorsPerRank, nEpoch, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport == 1, gaussian == 1, std_coeff #ifdef CLI , outPrefix, snapshots); #else ); #endif #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); training_time = MPI_Wtime() - training_time; if (rank == 0) { cerr << "Total training Time: " << training_time << endl; } #endif /// /// Save SOM map and u-mat /// if (rank == 0) { /// /// Save U-mat /// calculateUMatrix(uMatrix, codebook, nSomX, nSomY, nDimensions, mapType, gridType); int ret = saveUMatrix(outPrefix + string(".umx"), uMatrix, nSomX, nSomY); if (ret < 0) cout << " Failed to save u-matrix. !" << endl; else { cout << " Done!" << endl; } saveBmus(outPrefix + string(".bm"), globalBmus, nSomX, nSomY, nVectors); /// /// Save codebook /// saveCodebook(outPrefix + string(".wts"), codebook, nSomX, nSomY, nDimensions); } #ifdef HAVE_MPI MPI_Barrier(MPI_COMM_WORLD); #endif if (kernelType == DENSE_CPU || kernelType == DENSE_GPU) { delete [] data; } else { delete [] sparseData; } delete [] codebook; delete [] globalBmus; delete [] uMatrix; #ifdef HAVE_MPI profile_time = MPI_Wtime() - profile_time; if (rank == 0) { cerr << "Total Execution Time: " << profile_time << endl; } #endif #ifdef HAVE_MPI MPI_Finalize(); #endif return 0; } /// EOF somoclu-1.7.2/src/sparseCpuKernels.cpp0000644000175000017500000002205613015657206014721 00000000000000/** * Self-Organizing Maps on a cluster * Copyright (C) 2013 Peter Wittek * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . * */ #include #include "somoclu.h" #ifdef HAVE_R #include #endif /** Distance b/w a feature vector and a weight vector * = Euclidean * @param som_y * @param som_x * @param r - row number in the input feature file */ float get_distance(float* codebook, svm_node **sparseData, unsigned int som_y, unsigned int som_x, unsigned int nSomX, unsigned int nDimensions, unsigned int r) { float distance = 0.0f; unsigned int j = 0; for ( unsigned int d = 0; d < nDimensions; d++ ) { if ( (int) d == sparseData[r][j].index ) { distance += (codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] - sparseData[r][j].value) * (codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] - sparseData[r][j].value); ++j; } else { distance += codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] * codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d]; } } return distance; } /** Get node coords for the best matching unit (BMU) * @param coords - BMU coords * @param n - row num in the input feature file */ void get_bmu_coord(float* codebook, svm_node **sparseData, unsigned int nSomY, unsigned int nSomX, unsigned int nDimensions, int* coords, unsigned int n) { float mindist = 0.0f; float dist = 0.0f; /// Check nSomX * nSomY nodes one by one and compute the distance /// D(W_K, Fvec) and get the mindist and get the coords for the BMU. /// for (unsigned int som_y = 0; som_y < nSomY; som_y++) { for (unsigned int som_x = 0; som_x < nSomX; som_x++) { dist = get_distance(codebook, sparseData, som_y, som_x, nSomX, nDimensions, n); if ((som_y == 0 && som_x == 0) || (dist < mindist)) { mindist = dist; coords[0] = som_x; coords[1] = som_y; } } } } void trainOneEpochSparseCPU(int itask, svm_node **sparseData, float *numerator, float *denominator, float *codebook, unsigned int nSomX, unsigned int nSomY, unsigned int nDimensions, unsigned int nVectors, unsigned int nVectorsPerRank, float radius, float scale, string mapType, string gridType, bool compact_support, bool gaussian, int *globalBmus, bool only_bmus, float std_coeff) { int p1[2] = {0, 0}; int *bmus; #ifdef HAVE_MPI bmus = new int[nVectorsPerRank * 2]; #else bmus = globalBmus; #endif #ifdef _OPENMP #pragma omp parallel default(shared) private(p1) #endif { #ifdef _OPENMP #pragma omp for #endif #ifdef _WIN32 for (int n = 0; n < nVectorsPerRank; n++) { #else for (unsigned int n = 0; n < nVectorsPerRank; n++) { #endif if (itask * nVectorsPerRank + n < nVectors) { /// get the best matching unit get_bmu_coord(codebook, sparseData, nSomY, nSomX, nDimensions, p1, n); bmus[2 * n] = p1[0]; bmus[2 * n + 1] = p1[1]; } } } if (only_bmus) { #ifdef HAVE_MPI MPI_Gather(bmus, nVectorsPerRank * 2, MPI_INT, globalBmus, nVectorsPerRank * 2, MPI_INT, 0, MPI_COMM_WORLD); delete [] bmus; #endif return; } #ifdef HAVE_MPI float *localNumerator = new float[nSomY * nSomX * nDimensions]; float *localDenominator = new float[nSomY * nSomX]; #ifdef _OPENMP #pragma omp parallel default(shared) #endif // _OPENMP { #ifdef _OPENMP #pragma omp for #endif // _OPENMP #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif // _WIN32 for (unsigned int som_x = 0; som_x < nSomX; som_x++) { localDenominator[som_y * nSomX + som_x] = 0.0; for (unsigned int d = 0; d < nDimensions; d++) localNumerator[som_y * nSomX * nDimensions + som_x * nDimensions + d] = 0.0; } } } #ifdef _OPENMP #pragma omp parallel default(shared) #endif #else // not HAVE_MPI float *localNumerator; float localDenominator; // Accumulate denoms and numers #ifdef _OPENMP #pragma omp parallel default(shared) private(localDenominator) private(localNumerator) #endif #endif // HAVE_MPI { #ifndef HAVE_MPI localNumerator = new float[nDimensions]; #endif // HAVE_MPI #ifdef _OPENMP #pragma omp for #endif #ifdef _WIN32 for (int som_y = 0; som_y < nSomY; som_y++) { #else for (unsigned int som_y = 0; som_y < nSomY; som_y++) { #endif for (unsigned int som_x = 0; som_x < nSomX; som_x++) { #ifndef HAVE_MPI localDenominator = 0; for (unsigned int d = 0; d < nDimensions; d++) localNumerator[d] = 0.0; #endif for (unsigned int n = 0; n < nVectorsPerRank; n++) { if (itask * nVectorsPerRank + n < nVectors) { float dist = 0.0f; if (gridType == "rectangular") { if (mapType == "planar") { dist = euclideanDistanceOnPlanarMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1]); } else if (mapType == "toroid") { dist = euclideanDistanceOnToroidMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1], nSomX, nSomY); } } else { if (mapType == "planar") { dist = euclideanDistanceOnHexagonalPlanarMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1]); } else if (mapType == "toroid") { dist = euclideanDistanceOnHexagonalToroidMap(som_x, som_y, bmus[2 * n], bmus[2 * n + 1], nSomX, nSomY); } } float neighbor_fuct = getWeight(dist, radius, scale, compact_support, gaussian, std_coeff); #ifdef HAVE_MPI unsigned int j = 0; while ( sparseData[n][j].index != -1 ) { localNumerator[som_y * nSomX * nDimensions + som_x * nDimensions + sparseData[n][j].index] += 1.0f * neighbor_fuct * sparseData[n][j].value; ++j; } localDenominator[som_y * nSomX + som_x] += neighbor_fuct; #else // In this case, we can update in place unsigned int j = 0; localDenominator += neighbor_fuct; while ( sparseData[n][j].index != -1 ) { localNumerator[sparseData[n][j].index] += 1.0f * neighbor_fuct * sparseData[n][j].value; ++j; } #endif } } // Looping over data instances #ifndef HAVE_MPI // We update in-place for (unsigned int d = 0; d < nDimensions; d++) { if (localDenominator != 0) { float newWeight = localNumerator[d] / localDenominator; codebook[som_y * nSomX * nDimensions + som_x * nDimensions + d] = newWeight; } } #endif } // Looping over som_x } // Looping over som_y #ifndef HAVE_MPI delete [] localNumerator; #endif } // OPENMP #ifdef HAVE_MPI MPI_Reduce(localNumerator, numerator, nSomY * nSomX * nDimensions, MPI_FLOAT, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Reduce(localDenominator, denominator, nSomY * nSomX, MPI_FLOAT, MPI_SUM, 0, MPI_COMM_WORLD); MPI_Gather(bmus, nVectorsPerRank * 2, MPI_INT, globalBmus, nVectorsPerRank * 2, MPI_INT, 0, MPI_COMM_WORLD); delete [] localDenominator; delete [] localNumerator; delete [] bmus; #endif } somoclu-1.7.2/data/0000755000175000017500000000000013015657206011121 500000000000000somoclu-1.7.2/data/rgbs.svm0000644000175000017500000000076213015657206012532 000000000000000:0.01 1:1.0 1:0.97 2:0.01 1:0.99 2:0.02 0:0.01 1:0.94 2:0.03 1:0.91 2:0.01 0:0.01 1:1.0 1:0.98 2:0.02 0:1.0 1:0.01 0:0.97 1:0.01 0:0.97 1:0.02 0:0.99 1:0.02 0:0.94 1:0.03 2:0.01 0:0.91 2:0.01 0:1.0 1:0.01 2:0.01 0:0.98 1:0.02 2:0.01 0:0.97 1:0.01 2:0.02 0:0.92 1:0.03 2:0.01 0:0.01 2:1.0 0:0.01 2:0.97 0:0.02 2:0.97 0:0.02 2:0.99 0:0.03 1:0.01 2:0.94 1:0.01 2:0.91 0:0.01 1:0.01 2:1.0 0:0.02 1:0.01 2:0.98 0:0.01 1:0.02 2:0.97 0:0.03 1:0.01 2:0.92 0:0.01 1:0.02 2:0.97 somoclu-1.7.2/data/rgbs.txt0000644000175000017500000000067013015657206012542 000000000000000.01 1.00 0.0 0.0 0.97 0.01 0.0 0.99 0.02 0.01 0.94 0.03 0.0 0.91 0.01 0.01 1.00 0.0 0.0 0.98 0.02 1.00 0.01 0.0 0.97 0.01 0.0 0.97 0.02 0.0 0.99 0.02 0.0 0.94 0.03 0.01 0.91 0.0 0.01 1.00 0.01 0.01 0.98 0.02 0.01 0.97 0.01 0.02 0.92 0.03 0.01 0.01 0.0 1.00 0.01 0.0 0.97 0.02 0.0 0.97 0.02 0.0 0.99 0.03 0.01 0.94 0.0 0.01 0.91 0.01 0.01 1.00 0.02 0.01 0.98 0.01 0.02 0.97 0.03 0.01 0.92 0.01 0.02 0.97 somoclu-1.7.2/data/rgbs.lrn0000644000175000017500000000112613015657206012513 00000000000000# Example file for ESOM lrn format % 28 % 3 % 9 1 1 1 % Seq data1 data2 data3 1 0.01 1.00 0.0 2 0.0 0.97 0.01 3 0.0 0.99 0.02 4 0.01 0.94 0.03 5 0.0 0.91 0.01 6 0.01 1.00 0.0 7 0.0 0.98 0.02 8 1.00 0.01 0.0 9 0.97 0.01 0.0 10 0.97 0.02 0.0 11 0.99 0.02 0.0 12 0.94 0.03 0.01 13 0.91 0.0 0.01 14 1.00 0.01 0.01 15 0.98 0.02 0.01 16 0.97 0.01 0.02 17 0.92 0.03 0.01 18 0.01 0.0 1.00 19 0.01 0.0 0.97 20 0.02 0.0 0.97 21 0.02 0.0 0.99 22 0.03 0.01 0.94 23 0.0 0.01 0.91 24 0.01 0.01 1.00 25 0.02 0.01 0.98 26 0.01 0.02 0.97 27 0.03 0.01 0.92 28 0.01 0.02 0.97 somoclu-1.7.2/LICENSE0000644000175000017500000010575513015657206011152 00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation. If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program. Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version. 15. Disclaimer of Warranty. THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. 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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. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. 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Compiler: ${CXX} ${CXXFLAGS} ${MPI_INC} ${MPI_LIBDIR} ${MPI_LIBS} Package features: OpenMP enabled: ${openmp_enabled} MPI enabled: ${mpi_enabled} CUDA enabled: ${cuda_enabled} Now type 'make []' where the optional is: all - build all binaries install - install everything --------------------------------------------------" somoclu-1.7.2/CHANGES0000644000175000017500000001143013015657206011122 00000000000000Version 1.7.2: 2016-11-24 - New: The coefficient of the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)) is now exposed in all interfaces as a parameter. - New: `get_bmu` function in the Python interface to get the best matching units given an activation map. - Changed: Updated PCA initialization in the Python interface to work with `sk-learn` 0.18 onwards. - Changed: Radii can be float values. - Fixed: Only positive values were written back to codebook during update. - Fixed: Sparse data is read correctly when there are class labels. Version 1.7.1: 2016-10-02 - Fixed: macOS build works again. Version 1.7.0: 2016-09-30 - New: Julia interface is available (https://github.com/peterwittek/Somoclu.jl). - New: Method `get_surface_state` of the `Somoclu` object in Python calculates the activation map for all data instances. - New: Method `view_activation_map` of the `Somoclu` object in Python allows plotting the activation map for the training data instances or for a new data instance. - New: Method `view_similarity_matrix` of the `Somoclu` object in Python visualizes the similarity matrix of data points according to their distance to the nodes in the map. - Fixed: CRAN-friendliness improved. Version 1.6.2: 2016-08-09 - Changed: In-place codebook updates when compiled without MPI. This improves update speed and substantially cuts memory use. - Changed: Compatible with Visual Studio 15. - Fixed: The BMUs returned after training were from before the last epoch. Now another round of BMU search is done. - Fixed: Training can continue on the same data in the Python wrapper. - Fixed: GPU memory allocation problem on Windows. Version 1.6.1: 2016-02-22 - New: Option for PCA initialization is added to the Python interface. - New: Clustering of the codebook with arbitrary clustering algorithm in scikit-learn is now possible in the Python interface. Version 1.6: 2016-01-10 - New: R wrapper integrates with kohonen package. - New: MATLAB wrapper integrates with soomtoolbox. - New: Better handling of CUDA compilation in the Python interface. - Changed: Throws an exception if GPU kernel is requested, but it was compiled without it. The earlier behaviour quietly defaulted to the CPU kernel. Version 1.5.1: 2015-12-02 - New: Neighborhood function can be chosen between Gaussian and bubble. - Fixed: R wrapper passes arrays with correct orientation. - Fixed: `io.cpp` is no longer required in the wrappers. An exception is thrown when needed. Version 1.5: 2015-09-30 - New: Python interface has visual capabilities. - New: Option for hexagonal grid. - New: Option for requesting compact support in updating the map. - New: Python, R, and MATLAB interfaces now allow passing an initial codebook. - Changed: Reduced memory use in calculating U-matrices. - Changed: Build system rebuilt and simplified. Version 1.4.1: 2015-01-28 - Better support for ICC. - Faster code when compiling with GCC. - Building instructions and documentation improved. - Bug fixes: portability for R, using native R random number generator. Version 1.4: 2014-09-05 - Better Windows support. - Completed CUDA support for Python and R interfaces. - Faster compilation by removing unnecessary flags for nvcc and support for CUDA 6.5. - Bug fixes: R version no longer needs separate code. Version 1.3.1: 2014-04-10 - Better I/O separation for the Python, R, and MATLAB interfaces. - Initial Windows support through GCC on Windows. - Bug fixes: major MPI initialization bug fixed. Version 1.3: 2014-03-31 - Learning rate parameter included. - Linear and exponential cooling strategies added for radius and learning rate. - CLI interface made more user-friendly. - Default radius depends on both X and Y of the map. - Bug fixes: CUDA build without MPI, best matching unit passing without MPI, coordinate order in best matching unit file. - Python, R, and MATLAB interfaces added. Version 1.2: 2013-12-17 - Massive improvements in OpenMP parallelization. - MPI libraries are no longer mandatory. - Best matching units are saved. - Option for specifying an initial codebook for the map. - ESOM .lrn input format added. - Parsing of white-space characters corrected. - Long-named command line switches for specifying SOM dimensions. - Fine-grained control of which interim files to save across epochs - Option in Makefile for building shared library. Version 1.1.2: 2013-11-28 - Toroid maps were added. - Initial radius is exposed as a parameter via the command line interface. - Formats of codebook and U-matrix export are compatible with Databionic ESOM Tools for advanced visualisation. - Bug fixes: codebook update with a compact support was removed, NaN entry no longer appears in U-matrices. Version 1.0: 2013-05-14 - Initial release.