CARP-3.1/0000755000076400007640000000000011477233322012030 5ustar volmelnvolmelnCARP-3.1/COPYING0000644000076400007640000010451311356252646013075 0ustar volmelnvolmeln GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. 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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. But first, please read . CARP-3.1/PROC/0000755000076400007640000000000011471371331012570 5ustar volmelnvolmelnCARP-3.1/PROC/runPAM.R0000644000076400007640000000127011463574033014062 0ustar volmelnvolmeln PAM.call <- function(p, K, n, nf, Xname, IDname){ for (i in 1:nf){ x <- matrix(scan(Xname, nlines = n, skip = (i - 1) * n), ncol = p, byrow = T) A <- pam(x, K) if (i == 1) write(x = A$clustering - 1, file = IDname, ncol = n) else write(x = A$clustering - 1, file = IDname, ncol = n, append = TRUE) } } args = (commandArgs(TRUE)) p <- as.numeric(args[1]) K <- as.numeric(args[2]) n <- as.numeric(args[3]) nf <- as.numeric(args[4]) Xname <- args[5] IDname <- args[6] if (!require(cluster)) stop("Package cluster is not installed...\n") if (inherits(try(PAM.call(p, K, n, nf, Xname, IDname)), what = "try-error")) stop("Problem with PAM.call; check the parameters...\n") CARP-3.1/PROC/Mclust.R0000644000076400007640000000120211465325076014165 0ustar volmelnvolmeln Mclust.call <- function(p, k, K, n, nf, Xname, IDname){ for (i in 1:nf){ x <- matrix(scan(Xname, nlines = n, skip = (i - 1) * n), ncol = p, byrow = T) A <- Mclust(x, G = k:K, model = "VVV") if (i == 1) write(x = A$class - 1, file = IDname, ncol = n) else write(x = A$class - 1, file = IDname, ncol = n, append = TRUE) } } p <- 3 k <- 1 K <- 7 n <- 100 nf <- 1 Xname <- "DATA/x.dat" IDname <- "DATA/idEst.dat" if (!require(mclust)) stop("Package mclust is not installed...\n") if (inherits(try(Mclust.call(p, k, K, n, nf, Xname, IDname)), what = "try-error")) stop("Problem with Mclust.call; check the parameters...\n") CARP-3.1/PROC/BHI.R0000644000076400007640000000124411465615706013330 0ustar volmelnvolmeln BHI.call <- function(n, nf, IDtrue, IDest, OutName){ A <- rep(NA, nf) for (i in 1:nf){ id.true <- scan(IDtrue, nlines = 1, skip = i - 1) id.est <- scan(IDest, nlines = 1, skip = i - 1) + 1 K <- max(id.true) + 1 Q <- NULL for (k in 1:K){ Q <- cbind(Q, id.true == k - 1) } A[i] <- BHI(id.est, Q) } write(A, file = OutName, ncol = 1) } n <- 100 nf <- 1 IDtrue <- "DATA/idTrue.dat" IDest <- "DATA/idEst.dat" OutName <- "DATA/Diag.dat" if (!require(clValid)) stop("Package clValid is not installed...\n") if (inherits(try(BHI.call(n, nf, IDtrue, IDest, OutName)), what = "try-error")) stop("Problem with BHI.call; check the parameters...\n") CARP-3.1/PROC/runDiag.R0000644000076400007640000000154411465272042014312 0ustar volmelnvolmeln Diag.call <- function(n, nf, IDtrue, IDest, OutName){ A <- rep(NA, nf) for (i in 1:nf){ id.true <- scan(IDtrue, nlines = 1, skip = i - 1) Nk <- as.vector(table(id.true)) id.est <- scan(IDest, nlines = 1, skip = i - 1) K <- max(id.true) + 1 D <- permn(K) A[i] <- -Inf for (j in 1:length(D)){ AA <- classAgreement(table(rep(D[[j]], Nk), id.est))$diag if (AA > A[i]) A[i] <- AA } } write(A, file = OutName, ncol = 1) } args = (commandArgs(TRUE)) n <- as.numeric(args[1]) nf <- as.numeric(args[2]) IDtrue <- args[3] IDest <- args[4] OutName <- args[5] if (!require(e1071)) stop("Package e1071 is not installed...\n") if (!require(combinat)) stop("Package combinat is not installed...\n") if (inherits(try(Diag.call(n, nf, IDtrue, IDest, OutName)), what = "try-error")) stop("Problem with Diag.call; check the parameters...\n") CARP-3.1/PROC/runKmeans.R0000644000076400007640000000133111463574535014670 0ustar volmelnvolmeln Kmeans.call <- function(p, K, n, nf, Xname, IDname){ for (i in 1:nf){ x <- matrix(scan(Xname, nlines = n, skip = (i - 1) * n), ncol = p, byrow = T) A <- kmeans(x, K, iter.max = 1000, nstart = 10) if (i == 1) write(x = A$cluster - 1, file = IDname, ncol = n) else write(x = A$cluster - 1, file = IDname, ncol = n, append = TRUE) } } args = (commandArgs(TRUE)) p <- as.numeric(args[1]) K <- as.numeric(args[2]) n <- as.numeric(args[3]) nf <- as.numeric(args[4]) Xname <- args[5] IDname <- args[6] if (!require(stats)) stop("Package stats is not installed...\n") if (inherits(try(Kmeans.call(p, K, n, nf, Xname, IDname)), what = "try-error")) stop("Problem with Kmeans.call; check the parameters...\n") CARP-3.1/PROC/runBHI.R0000644000076400007640000000131011465605275014047 0ustar volmelnvolmeln BHI.call <- function(n, nf, IDtrue, IDest, OutName){ A <- rep(NA, nf) for (i in 1:nf){ id.true <- scan(IDtrue, nlines = 1, skip = i - 1) id.est <- scan(IDest, nlines = 1, skip = i - 1) + 1 K <- max(id.true) + 1 Q <- NULL for (k in 1:K){ Q <- cbind(Q, id.true == k - 1) } A[i] <- BHI(id.est, Q) } write(A, file = OutName, ncol = 1) } args = (commandArgs(TRUE)) n <- as.numeric(args[1]) nf <- as.numeric(args[2]) IDtrue <- args[3] IDest <- args[4] OutName <- args[5] if (!require(clValid)) stop("Package clValid is not installed...\n") if (inherits(try(BHI.call(n, nf, IDtrue, IDest, OutName)), what = "try-error")) stop("Problem with BHI.call; check the parameters...\n") CARP-3.1/PROC/runMclust.R0000644000076400007640000000136311465155635014724 0ustar volmelnvolmeln Mclust.call <- function(p, k, K, n, nf, Xname, IDname){ for (i in 1:nf){ x <- matrix(scan(Xname, nlines = n, skip = (i - 1) * n), ncol = p, byrow = T) A <- Mclust(x, G = k:K, model = "VVV") if (i == 1) write(x = A$class - 1, file = IDname, ncol = n) else write(x = A$class - 1, file = IDname, ncol = n, append = TRUE) } } args = (commandArgs(TRUE)) cat(args) p <- as.numeric(args[1]) k <- as.numeric(args[2]) K <- as.numeric(args[3]) n <- as.numeric(args[4]) nf <- as.numeric(args[5]) Xname <- args[6] IDname <- args[7] if (!require(mclust)) stop("Package mclust is not installed...\n") if (inherits(try(Mclust.call(p, k, K, n, nf, Xname, IDname)), what = "try-error")) stop("Problem with Mclust.call; check the parameters...\n") CARP-3.1/PROC/PAM.R0000644000076400007640000000114311465611333013331 0ustar volmelnvolmeln PAM.call <- function(p, K, n, nf, Xname, IDname){ for (i in 1:nf){ x <- matrix(scan(Xname, nlines = n, skip = (i - 1) * n), ncol = p, byrow = T) A <- pam(x, K) if (i == 1) write(x = A$clustering - 1, file = IDname, ncol = n) else write(x = A$clustering - 1, file = IDname, ncol = n, append = TRUE) } } p <- 3 K <- 4 n <- 100 nf <- 1 Xname <- "DATA/x.dat" IDname <- "DATA/idEst.dat" if (!require(cluster)) stop("Package cluster is not installed...\n") if (inherits(try(PAM.call(p, K, n, nf, Xname, IDname)), what = "try-error")) stop("Problem with PAM.call; check the parameters...\n") CARP-3.1/PROC/Diag.R0000644000076400007640000000150111465331262013556 0ustar volmelnvolmeln Diag.call <- function(n, nf, IDtrue, IDest, OutName){ A <- rep(NA, nf) for (i in 1:nf){ id.true <- scan(IDtrue, nlines = 1, skip = i - 1) Nk <- as.vector(table(id.true)) id.est <- scan(IDest, nlines = 1, skip = i - 1) K <- max(id.true) + 1 D <- permn(K) A[i] <- -Inf for (j in 1:length(D)){ AA <- classAgreement(table(rep(D[[j]], Nk), id.est))$diag if (AA > A[i]) A[i] <- AA } } write(A, file = OutName, ncol = 1) } n <- 100 nf <- 1 IDtrue <- "DATA/idTrue.dat" IDest <- "DATA/idEst.dat" OutName <- "DATA/Diag.dat" if (!require(e1071)) stop("Package e1071 is not installed...\n") if (!require(combinat)) stop("Package combinat is not installed...\n") if (inherits(try(Diag.call(n, nf, IDtrue, IDest, OutName)), what = "try-error")) stop("Problem with Diag.call; check the parameters...\n") CARP-3.1/PROC/runRand.R0000644000076400007640000000120011465270717014326 0ustar volmelnvolmeln Rand.call <- function(n, nf, IDtrue, IDest, OutName){ A <- rep(NA, nf) for (i in 1:nf){ id.true <- scan(IDtrue, nlines = 1, skip = i - 1) id.est <- scan(IDest, nlines = 1, skip = i - 1) A[i] <- classAgreement(table(id.true, id.est))$rand } write(A, file = OutName, ncol = 1) } args = (commandArgs(TRUE)) n <- as.numeric(args[1]) nf <- as.numeric(args[2]) IDtrue <- args[3] IDest <- args[4] OutName <- args[5] if (!require(e1071)) stop("Package e1071 is not installed...\n") if (inherits(try(Rand.call(n, nf, IDtrue, IDest, OutName)), what = "try-error")) stop("Problem with Rand.call; check the parameters...\n") CARP-3.1/src/0000755000076400007640000000000011477233257012626 5ustar volmelnvolmelnCARP-3.1/src/GenPars.c0000644000076400007640000001373411472561206014332 0ustar volmelnvolmeln /* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include "array.h" #include "overlap.h" /* genSigma : generates covariance matrix based on (p + 1) observations (unstable covariance matrix) * Parameters: * p - number of dimensions * VC - variance-covariance matrix */ void genSigma(int p, double **VC){ int i,j,k,n; double **x, *mu; n = p + 1; MAKE_MATRIX(x, n, p); MAKE_VECTOR(mu, p); anull(mu, p); for (i=0; i emax){ Anull(L, p, p); for (i=0; i emax){ Anull(L, p, p); for (i=0; i= 1) | (PiLow <= 0)){ /* printf("Warning: PiLow is out of range... generated equal mixing proportions...\n"); */ for (k=0; k. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include "array.h" #include "overlap.h" /* genData: generates a dataset given mixture parameters * Parameters: * p - number of dimensions * K - number of components * Pi - vector of mixing proportions * Mu - set of mean vectors * S - set of covariance matrices * n - number of observations * Y - simulated dataset * Nk - cluster sizes */ void genData(int p, int K, double *Pi, double **Mu, double ***S, int n, double **Y, int *Nk){ int i, j, k; int curr; double **X; MAKE_MATRIX(X, n, p); rmulti(n, K, Pi, Nk); curr = 0; for (k=0; k. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ /* Author of this program: Copyright 1973, 1991 by Stephen L. Moshier Copyleft version. */ /* cephes_eigens.c * * This file was orignially called eigens.c and was found in the cephese * computational library. I edited it to bring it up to the ANSI C * standard and renamed it to cephes_eigens.c. * * --RR * July 2002 * * * Eigenvalues and eigenvectors of a real symmetric matrix * * * * SYNOPSIS: * * int n; * double A[n*(n+1)/2], EV[n*n], E[n]; * void cephes_eigens( A, EV, E, n ); * * * * DESCRIPTION: * * The algorithm is due to J. vonNeumann. * - - * A[] is a symmetric matrix stored in lower triangular form. * That is, A[ row, column ] = A[ (row*row+row)/2 + column ] * or equivalently with row and column interchanged. The * indices row and column run from 0 through n-1. * * EV[] is the output matrix of eigenvectors stored columnwise. * That is, the elements of each eigenvector appear in sequential * memory order. The jth element of the ith eigenvector is * EV[ n*i+j ] = EV[i][j]. * * E[] is the output matrix of eigenvalues. The ith element * of E corresponds to the ith eigenvector (the ith row of EV). * * On output, the matrix A will have been diagonalized and its * orginal contents are destroyed. * * ACCURACY: * * The error is controlled by an internal parameter called RANGE * which is set to 1e-10. After diagonalization, the * off-diagonal elements of A will have been reduced by * this factor. * * EEVOR MESSAGES: * * None. * */ /* Copyright 1973, 1991 by Stephen L. Moshier Copyleft version. */ #include "cephes_eigens.h" void cephes_eigens(double *A, double *EV, double *E, int N) { int IND, L, LL, LM, M, MM, MQ, I, J, IA, LQ; int IQ, IM, IL, NLI, NMI; double ANORM, ANORMX, AIA, THR, ALM, ALL, AMM, X, Y; double SINX, SINX2, COSX, COSX2, SINCS, AIL, AIM; double RLI, RMI; double sqrt(), fabs(); static double RANGE = 1.0e-10; /* 3.0517578e-5; */ /* Initialize identity matrix in EV[] */ for (J = 0; J < N * N; J++) EV[J] = 0.0; MM = 0; for (J = 0; J < N; J++) { EV[MM + J] = 1.0; MM += N; } ANORM = 0.0; for (I = 0; I < N; I++) { for (J = 0; J < N; J++) { if (I != J) { IA = I + (J * J + J) / 2; AIA = A[IA]; ANORM += AIA * AIA; } } } if (ANORM <= 0.0) goto done; ANORM = sqrt(ANORM + ANORM); ANORMX = ANORM * RANGE / N; THR = ANORM; while (THR > ANORMX) { THR = THR / N; do { /* while IND != 0 */ IND = 0; for (L = 0; L < N - 1; L++) { for (M = L + 1; M < N; M++) { MQ = (M * M + M) / 2; LM = L + MQ; ALM = A[LM]; if (fabs(ALM) < THR) continue; IND = 1; LQ = (L * L + L) / 2; LL = L + LQ; MM = M + MQ; ALL = A[LL]; AMM = A[MM]; X = (ALL - AMM) / 2.0; Y = -ALM / sqrt(ALM * ALM + X * X); if (X < 0.0) Y = -Y; SINX = Y/sqrt(2.0*(1.0+sqrt(1.0-Y*Y))); SINX2 = SINX * SINX; COSX = sqrt(1.0 - SINX2); COSX2 = COSX * COSX; SINCS = SINX * COSX; /* ROTATE L AND M COLUMNS */ for (I = 0; I < N; I++) { IQ = (I * I + I) / 2; if ((I != M) && (I != L)) { if (I > M) IM = M + IQ; else IM = I + MQ; if (I >= L) IL = L + IQ; else IL = I + LQ; AIL = A[IL]; AIM = A[IM]; X = AIL * COSX - AIM * SINX; A[IM] = AIL * SINX + AIM * COSX; A[IL] = X; } NLI = N * L + I; NMI = N * M + I; RLI = EV[NLI]; RMI = EV[NMI]; EV[NLI] = RLI * COSX - RMI * SINX; EV[NMI] = RLI * SINX + RMI * COSX; } X = 2.0 * ALM * SINCS; A[LL] = ALL * COSX2 + AMM * SINX2 - X; A[MM] = ALL * SINX2 + AMM * COSX2 + X; A[LM] = (ALL - AMM) * SINCS + ALM * (COSX2 - SINX2); } /* for M=L+1 to N-1 */ } /* for L=0 to N-2 */ } while (IND != 0); } /* while THR > ANORMX */ done: /* Extract eigenvalues from the reduced matrix */ L = 0; for (J = 1; J <= N; J++) { L = L + J; E[J - 1] = A[L - 1]; } } CARP-3.1/src/symmEigValDec.c0000644000076400007640000000633611467026301015460 0ustar volmelnvolmeln /* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include "array.h" #include "cephes_eigens.h" void cephes_symmeigens_down(int p, double *eval, double **A, double (*determinant)) /* input: p - dimension of matrix A - pxp symmetric matrix (only lower triangle used) destroyed on return returns: eval - vector of eigenvalues (in ascending order) evec - matrix of eigenvectors determinant - determinant of the symmetric matrix (as calculated by the product of the eigenvalues */ { int i, j; double *As, *Evec, *Evalues; MAKE_VECTOR(As, p * (p + 1) / 2); for (i = 0; i < p; i++) { for (j = 0; j <= i; j++) As[(i * i + i)/2 + j] = A[i][j]; } MAKE_VECTOR(Evec, p * p); MAKE_VECTOR(Evalues, p); cephes_eigens(As, Evec, Evalues, p); for (i = 0; i < p; i++){ eval[i] = Evalues[p - i - 1]; } for (i = 0; i < p; i++) { for (j = 0; j < p; j++){ A[j][p-i-1] = Evec[p * i + j]; } } /* printf("EigenValues:\n"); for (i = 0; i < p; i++){ printf("EigVal: %lf \n", eval[i]); } printf("EigenVectors:\n"); for (i = 0; i < p; i++) { for (j = 0; j < p; j++){ printf("%lf ", A[i][j]); } printf("\n"); } */ (*determinant)=1.0; for (i = 0; i < p; i++) (*determinant)*=eval[i]; FREE_VECTOR(As); FREE_VECTOR(Evalues); FREE_VECTOR(Evec); return; } CARP-3.1/src/Rand.c0000644000076400007640000001103511467026243013650 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; /* This program computes Rand index for simulated datasets */ int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; int main(int argc, char *argv[]) { int g, nf, i, c, code, errflag; double *clagr; FILE *fname; char runR[500]; char buff[200] = ""; char Path[100] = "DATA"; char IDtrue[100] = "idTrue.dat"; char IDest[100] = "idEst.dat"; char ClAgrName[100] = "Rand.dat"; /* DEFAULT PARAMETERS */ g = 0; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ errflag = 0; if (argc == 1){ exit(1); } while ((c = getopt(argc, argv, ":n:D:I:i:R:#:")) != -1) { switch(c) { case 'n': /* # of observations in simulated datasets */ g = atoi(optarg); g = fabs(g); break; case 'D': /* Name working directory */ strcpy(Path, optarg); break; case 'I': /* true classification */ strcpy(IDtrue, optarg); break; case 'i': /* estimated classification */ strcpy(IDest, optarg); break; case 'R': /* Class Agreement index */ strcpy(ClAgrName, optarg); break; case '#': /* # of simulated mixtures */ nf = atoi(optarg); break; case ':': /* option with no operand */ printf("Operand for option -%c is not specified correctly...\n", optopt); errflag = 1; break; case '?': printf("Unrecognized option: -%c\n", optopt); errflag = 2; } } if (g <= 0){ printf("Operand for option -g is not specified correctly...\n"); errflag = 1; } if (nf < 1){ printf("Operand for option -# is not specified correctly...\n"); errflag = 1; } if (errflag != 0) exit(1); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDtrue); strcpy(IDtrue, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDest); strcpy(IDest, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, ClAgrName); strcpy(ClAgrName, buff); sprintf(runR, "R CMD BATCH --no-save --no-restore '--args %d %d %s %s %s' PROC/runRand.R", g, nf, IDtrue, IDest, ClAgrName); code = system(runR); if (code != 0){ printf("See runRand.Rout for errors...\n"); exit(1); } MAKE_VECTOR(clagr, nf); fname = fopen(ClAgrName,"r"); printf("RAND VALUES:\n"); for (i=0; i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include #include #include "array.h" #include "overlap.h" #include "minunit.h" int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; int main(int argc, char *argv[]) { int k, sum; int p, K, resN, method, lim, fail, sph, hom, g, nf; double emax, PiLow, Lbound, Ubound; double BarOmega, MaxOmega; int *rcMax, *Nk; double *Pi, *pars; double **Mu, **OmegaMap, **Y; double ***S; double tol; int cond, cond1, cond2; MAKE_VECTOR(pars, 2); /* DEFAULT PARAMETERS */ p = 5; K = 7; method = 0; /* 0 - average; 1 - maximum */ BarOmega = 0.0; MaxOmega = 0.0; sph = 0; /* 0 - nonspherical, 1 - spherical */ hom = 0; /* 0 - nonhomogeneous, 1 - homogeneous */ emax = 0.90; /* Max eccentricity */ PiLow = 1.0; /* Smallest mixing proportion */ Lbound = 0.0; /* Means are distributed by U(0,Ubound) */ Ubound = 1.0; /* Means are distributed by U(0,Ubound) */ resN = 100; /* Max # of dataset resimulations */ pars[0] = 0.0000001; /* eps */ pars[1] = 0.0000001; /* acc (Davies) */ lim = 1000000; /* lim (Davies) */ g = 100; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ char PIfname[100] = "TEST/Pi.test"; char MUfname[100] = "TEST/Mu.test"; char Sfname[100] = "TEST/LTSigma.test"; srand(time(NULL)); MAKE_VECTOR(Pi, K); MAKE_MATRIX(Mu, K, p); MAKE_3ARRAY(S, K, p, p); MAKE_MATRIX(OmegaMap, K, K); MAKE_VECTOR(rcMax, 2); MAKE_MATRIX(Y, g, p); MAKE_VECTOR(Nk, K); /* RUNNING A CORRESPONDING FUNCTION */ tol = 0.000001; /* FUNCTION freadParameters */ freadParameters(p, K, Pi, Mu, S, PIfname, MUfname, Sfname, 1); cond = (p == 5) && (K == 7) && (fabs(Pi[0] - 0.1831131) < tol) && (fabs(Pi[6] - 0.1569579) < tol) && (fabs(Mu[0][0] - 0.0012522) < tol) && (fabs(Mu[1][1] - 0.6093170) < tol) && (fabs(Mu[2][2] - 0.3875023) < tol) && (fabs(Mu[3][3] - 0.9270482) < tol) && (fabs(Mu[4][4] - 0.7657742) < tol) && (fabs(Mu[6][4] - 0.7767524) < tol) && (fabs(S[0][0][0] - 0.0162117) < tol) && (fabs(S[1][0][1] + 0.0005859) < tol) && (fabs(S[2][0][2] + 0.0060184) < tol) && (fabs(S[4][3][0] - 0.0047450) < tol) && (fabs(S[5][0][4] - 0.0008476) < tol) && (fabs(S[6][4][4] - 0.0208782) < tol); printf("function \"freadParameters\" - "); func_test(cond, "OK\n", "Test failed\n"); /* FUNCTION ExactOverlap */ ExactOverlap(p, K, Pi, Mu, S, pars, lim, OmegaMap, &BarOmega, &MaxOmega, rcMax); cond = (fabs(BarOmega - 0.017414) < tol) && (fabs(MaxOmega - 0.198721) < tol); printf("function \"ExactOverlap\" - "); func_test(cond, "OK\n", "Test failed\n"); /* FUNCTION genData */ Anull(Y, g, p); genData(p, K, Pi, Mu, S, g, Y, Nk); sum = 0; for (k=0; k < K; k++){ sum = sum + Nk[k]; } cond = (g == sum) && (Y[0][0] != 0.0) && (Y[g-1][p-1] != 0.0); printf("function \"genData\" - "); func_test(cond, "OK\n", "Test failed\n"); /* FUNCTION OmegaClust */ // test 1 sph = 0; method = 1; OmegaClust(0.15, method, p, K, PiLow, Lbound, Ubound, emax, pars, lim, resN, sph, hom, Pi, Mu, S, OmegaMap, &BarOmega, &MaxOmega, rcMax, &fail); cond1 = (fabs(0.15 - MaxOmega) < tol); // test 2 PiLow = 0.1; sph = 1; method = 0; OmegaClust(0.03, method, p, K, PiLow, Lbound, Ubound, emax, pars, lim, resN, sph, hom, Pi, Mu, S, OmegaMap, &BarOmega, &MaxOmega, rcMax, &fail); cond2 = (fabs(0.03 - BarOmega) < tol) && (fabs(S[0][1][0]) < tol) && (fabs(S[1][2][1]) < tol); cond = (cond1 && cond2); printf("function \"OmegaClust\" - "); func_test(cond, "OK\n", "Test failed\n"); /* FUNCTION OmegaClust */ sph = 0; BarOmega = 0.03; MaxOmega = 0.15; OmegaBarOmegaMax(p, K, PiLow, Lbound, Ubound, emax, pars, lim, resN, sph, hom, Pi, Mu, S, OmegaMap, &BarOmega, &MaxOmega, rcMax, &fail); cond = (fabs(BarOmega - 0.03) < tol) && (fabs(MaxOmega - 0.15) < tol); printf("function \"OmegaBarOmegaMax\" - "); func_test(cond, "OK\n", "Test failed\n"); FREE_MATRIX(Y); FREE_VECTOR(Nk); FREE_VECTOR(Pi); FREE_MATRIX(Mu); FREE_3ARRAY(S); FREE_MATRIX(OmegaMap); FREE_VECTOR(rcMax); FREE_VECTOR(pars); return 0; } CARP-3.1/src/overlap.h0000644000076400007640000001432711471404075014446 0ustar volmelnvolmeln /* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #ifndef OVERLAP_H #define OVERLAP_H int CMixSim(int p, int K, double BarOmega, double MaxOmega, int sph, int hom, double emax, double PiLow, double Lbound, double Ubound, int resN, double *pars, int lim, int g, int nf, int v, int w, int o, char *Path, char *PIfname, char *MUfname, char *Sfname, char *overmap, char *overbarmax, char *dataX, char *Nksizes, char *IDtrue, char *Lambda); void genDataset(double *Pi, double **Mu, double ***S, int g, int o, double critval, int p, int w, int v, int K, int i, double Lbound, double Ubound, double *lambda, char *Nksizes, char *IDtrue, char *dataX); void ExactOverlap(int p, int K, double *Pi, double **Mu, double ***S, double *pars, int lim, double **OmegaMap, double (*BarOmega), double (*MaxOmega), int *rcMax); void OmegaClust(double Omega, int method, int p, int K, double PiLow, double Lbound, double Ubound, double emax, double *pars, int lim, int resN, int sph, int hom, double *Pi, double **Mu, double ***S, double **OmegaMap, double (*BarOmega), double (*MaxOmega), int *rcMax, int (*fail)); void OmegaBarOmegaMax(int p, int K, double PiLow, double Lbound, double Ubound, double emax, double *pars, int lim, int resN, int sph, int hom, double *Pi, double **Mu, double ***S, double **OmegaMap, double (*BarOmega), double (*MaxOmega), int *rcMax, int (*fail)); void dsyev_(char *JOBZp, char *UPLOp,int *Np, double *A, int *LDAp, double *Wp, double *WORK, int *LWORK, int *INFOp); int vecMin(double *x, int p, double (*min)); int vecMax(double *x, int p, double (*max)); void Anull(double **X, int ax, int bx); void anulli(int *x, int p); void anull(double *X, int p); void XAXt(double **X, int p, double **A, double **Res); void XAXt2(double **X, int p, double **A, double ***Res, int k); void cpy(double **a, int nrows, int ncols, double **b); void cpy1(double ***a, int k, int nrows, int ncols, double **b); void cpy2(double **a, int nrows, int ncols, double ***b, int k); void tA(double **A, int a, int b, double **Res); double vecNNvec(int p, double *y, double *x); void matxvec(double **a, int arows, int acols, double *x, int xrows, double *y); void multiply(double **a, int arows, int acols, double **b, int brows, int bcols, double **c); void cephes_symmeigens_down(int p, double *eval, double **A, double (*determinant)); void cxS(int p, int K, double ***S, double c); double qfc(double* lb1, double* nc1, int* n1, int *r1in, double *sigmain, double *c1in, int *lim1in, double *accin, double* trace, int* ifault); void genPi(int K, double PiLow, double *Pi); void genMu(int p, int K, double **Mu, double Lbound, double Ubound); void genSigmaEcc(int p, int K, double emax, double ***S, int hom); void genSphSigma(int p, int K, double ***S, int hom); void fprintOverlap(int K, double **OmegaMap, double BarOmega, double MaxOmega, int *rcMax, int fileN, char *overmap, char *overbarmax); void printOverlap(int K, double **OmegaMap, double BarOmega, double MaxOmega, int *rcMax); void fprintParameters(int p, int K, double *Pi, double **Mu, double ***S, char *PIfname, char *MUfname, char *Sfname, int fileN); void printParameters(int p, int K, double *Pi, double **Mu, double ***S); void freadParameters(int p, int K, double *Pi, double **Mu, double ***S, char *PIfname, char *MUfname, char *Sfname, int fileN); void fprintData(int n, int p, int K, double **x, int *Nk, int fileN, char *dataX, char *Nksizes, char *IDtrue); void array1to2(int a, int b, double *y, double **x); void array1to3(int a, int b, int c, double *y, double ***x); void array2to1(int a, int b, double *y, double **x); void array3to1(int a, int b, int c, double *y, double ***x); void array3to1(int a, int b, int c, double *y, double ***x); void setseed(unsigned int s); long genseed(void); double runir(double a,double b); double rnor(double mu,double sd); double rgamma(double alpha); void rmulti(int n, int K, double *pi, int *Nk); void rMVN(int n, int p, double *MU, double **S, double **X); float gammln(float xx); void gser(float *gamser, float a, float x, float *gln); float gammq(float a, float x); void genData(int p, int K, double *Pi, double **Mu, double ***S, int n, double **Y, int *id); void genNoise(int p, int w, double Lbound, double Ubound, int n, double **Y); void genOutliers(int p, int K, double *Pi, double **Mu, double ***S, int n, int o, double critval, double **Y, double Lbound, double Ubound); void invBoxCox(int n, int p, double *lambda, double **Y); #endif /* OVERLAP_H */ CARP-3.1/src/Overlap.c0000644000076400007640000005546011472561447014414 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include "array.h" #define Inf 1e+140 #include "overlap.h" /* computes parameters needed for computing overlap * p - dimensionality * K - number of components * Pi - mixing proportions * Mu - mean vectors * S - covariance matrices * li, di, const1 - parameters needed for computing overlap (see theory of method) */ void ComputePars(int p, int K, double *Pi, double **Mu, double ***S, double ***li, double ***di, double **const1){ int i, j, k; double dtmt; double *m1, *m2, *Eig, *detS; double **Ga, **L, **Ga2, **L2, **Si; double ***Sinv, ***Sh; MAKE_VECTOR(detS, K); MAKE_3ARRAY(Sinv, K, p, p); MAKE_3ARRAY(Sh, K, p, p); MAKE_VECTOR(m1, p); MAKE_VECTOR(m2, p); MAKE_VECTOR(Eig, p); MAKE_MATRIX(Ga, p, p); MAKE_MATRIX(L, p, p); MAKE_MATRIX(Ga2, p, p); MAKE_MATRIX(L2, p, p); MAKE_MATRIX(Si, p, p); for (k=0; k (*MaxOmega)){ (*MaxOmega) = OmegaOverlap; rcMax[0] = i; rcMax[1] = j; } if (j < (K - 1)){ j = j + 1; } else { i = i + 1; j = i + 1; } } } if (asympt == 1){ while (i < (K-1)){ if (const1[i][j] > 0){ OmegaMap[i][j] = 1; OmegaMap[j][i] = 0; } if (const1[i][j] < 0){ OmegaMap[i][j] = 0; OmegaMap[j][i] = 1; } if (const1[i][j] == 0){ OmegaMap[i][j] = 0.5; OmegaMap[j][i] = 0.5; } OmegaOverlap = OmegaMap[i][j] + OmegaMap[j][i]; TotalOmega = TotalOmega + OmegaOverlap; if (OmegaOverlap > (*MaxOmega)){ (*MaxOmega) = OmegaOverlap; rcMax[0] = i; rcMax[1] = j; } if (j < (K - 1)){ j = j + 1; } else { i = i + 1; j = i + 1; } } } } if (hom == 0){ /* heterogeneous clusters */ sigma = 0.0; if (asympt == 0){ while (i < (K-1)){ if (fix[i] == 1){ for (k=0; k (*MaxOmega)){ (*MaxOmega) = OmegaOverlap; rcMax[0] = i; rcMax[1] = j; } if (j < (K - 1)){ j = j + 1; } else { i = i + 1; j = i + 1; } } } if (asympt == 1){ while (i < (K-1)){ if (fix[i] == 1){ if (fix[j] == 1){ s = 0; for (k=0; k (*MaxOmega)){ (*MaxOmega) = OmegaOverlap; rcMax[0] = i; rcMax[1] = j; } if (j < (K - 1)){ j = j + 1; } else { i = i + 1; j = i + 1; } } } } (*BarOmega) = TotalOmega / (K * (K - 1) / 2.0); for (k=0; k eps){ (*c) = (lower + upper) / 2.0; asympt = 0; GetOmegaMap((*c), p, K, li, di, const1, fix, pars, lim, asympt, OmegaMap, &(*BarOmega), &(*MaxOmega), rcMax); if (method == 0){ if ((*BarOmega) < Omega){ /* clusters are too far */ lower = (*c); } else { upper = (*c); } diff = (*BarOmega) - Omega; } else { if ((*MaxOmega) < Omega){ /* clusters are too far */ lower = (*c); } else { upper = (*c); } diff = (*MaxOmega) - Omega; } sch = sch + 1; if (sch == stopIter){ (*c) = 0.0; /* printf("Error: required overlap was not reached in %i iterations...\n", stopIter); */ break; } } } /* run the procedure when average or maximum overlap is specified * * Omega - overlap value * method - average or maximum overlap * p - dimensionality * K - number of components * PiLow - smallest mixing proportion allowed * Lbound - lower bound for uniform hypercube at which mean vectors at simulated * Ubound - upper bound for uniform hypercube at which mean vectors at simulated * emax - maximum eccentricity * pars, lim - parameters for qfc function * resN - number of resamplings allowed * sph - sperical covariance matrices * hom - homogeneous covariance matrices * Pi - mixing proportions * Mu - mean vectors * S - covariance matrices * OmegaMap - map of misclassification probabilities * BarOmega - average overlap * MaxOmega - maximum overlap * rcMax - contains the pair of components producing the highest overlap * fail - flag indicating if the process failed */ void OmegaClust(double Omega, int method, int p, int K, double PiLow, double Lbound, double Ubound, double emax, double *pars, int lim, int resN, int sph, int hom, double *Pi, double **Mu, double ***S, double **OmegaMap, double (*BarOmega), double (*MaxOmega), int *rcMax, int (*fail)){ int asympt, sch; double c, diff, lower, upper, eps, acc, Balpha, Malpha; int *fix; double **const1; double ***li, ***di; MAKE_3ARRAY(li, K, K, p); MAKE_3ARRAY(di, K, K, p); MAKE_MATRIX(const1, K, K); MAKE_VECTOR(fix, K); anulli(fix, K); eps = pars[0]; acc = pars[1]; Balpha = (*BarOmega); Malpha = (*MaxOmega); sch = 0; do{ (*fail) = 0; /* generate parameters */ /* printf("Simulating dataset...\n"); */ genPi(K, PiLow, Pi); genMu(p, K, Mu, Lbound, Ubound); if (sph == 0){ genSigmaEcc(p, K, emax, S, hom); } else { genSphSigma(p, K, S, hom); } /* prepare parameters */ ComputePars(p, K, Pi, Mu, S, li, di, const1); /* check if desired overlap is reachable */ asympt = 1; c = 0.0; GetOmegaMap(c, p, K, li, di, const1, fix, pars, lim, asympt, OmegaMap, &Balpha, &Malpha, rcMax); if (method == 0){ diff = Balpha - Omega; } else { diff = Malpha - Omega; } if (diff < -eps){ /* not reachable */ /* printf("Warning: the desired overlap cannot be reached...\n"); */ (*fail) = 1; } else { lower = 0.0; upper = pow(2.0, 2); do{ FindC(lower, upper, Omega, method, p, K, li, di, const1, fix, pars, lim, &c, OmegaMap, &Balpha, &Malpha, rcMax); lower = upper; upper = upper * upper; if (upper > 1000000){ /* printf("Warning: the desired overlap cannot be reached...\n"); printf("Simulating another dataset...\n"); */ (*fail) = 1; break; } /* (upper > 1000000) prevents nonstopping loops */ } while(c == 0); } if ((*fail) == 0){ /* correct covariances by multiplier C */ cxS(p, K, S, c); break; } sch = sch + 1; if (sch == resN){ printf("Error: the desired overlap has not been reached in %i simulations...\n", resN); printf("Increase the number of simulations allowed (option -r) or change the value of overlap...\n"); (*fail) = 1; break; } } while ((*fail) == 1); (*BarOmega) = Balpha; (*MaxOmega) = Malpha; FREE_3ARRAY(li); FREE_3ARRAY(di); FREE_MATRIX(const1); FREE_VECTOR(fix); } /* run the procedure when average and maximum overlaps are both specified * * p - dimensionality * K - number of components * PiLow - smallest mixing proportion allowed * Lbound - lower bound for uniform hypercube at which mean vectors at simulated * Ubound - upper bound for uniform hypercube at which mean vectors at simulated * emax - maximum eccentricity * pars, lim - parameters for qfc function * resN - number of resamplings allowed * sph - sperical covariance matrices * hom - homogeneous covariance matrices * Pi - mixing proportions * Mu - mean vectors * S - covariance matrices * OmegaMap - map of misclassification probabilities * BarOmega - average overlap * MaxOmega - maximum overlap * rcMax - contains the pair of components producing the highest overlap * fail - flag indicating if the process failed */ void OmegaBarOmegaMax(int p, int K, double PiLow, double Lbound, double Ubound, double emax, double *pars, int lim, int resN, int sph, int hom, double *Pi, double **Mu, double ***S, double **OmegaMap, double (*BarOmega), double (*MaxOmega), int *rcMax, int (*fail)){ int i, j, k, asympt, sch, rowN, colN, method; double c, diff, lower = 0, upper = 0, eps, acc, Balpha, Malpha; int *fix, *fix2; double **const1, **const12, **OmegaMap2; double ***li, ***di, ***li2, ***di2; MAKE_3ARRAY(li, K, K, p); MAKE_3ARRAY(di, K, K, p); MAKE_MATRIX(const1, K, K); MAKE_3ARRAY(li2, 2, 2, p); MAKE_3ARRAY(di2, 2, 2, p); MAKE_MATRIX(const12, 2, 2); MAKE_MATRIX(OmegaMap2, 2, 2); MAKE_VECTOR(fix, K); MAKE_VECTOR(fix2, 2); anulli(fix, K); anulli(fix2, 2); eps = pars[0]; acc = pars[1]; Balpha = (*BarOmega); Malpha = (*MaxOmega); (*fail) = 1; if ((Malpha < Balpha) | (Malpha > Balpha * K * (K - 1) / 2.0)){ /* wrong parameters*/ printf("Error: incorrect values of average and maximum overlaps...\n"); printf("Both conditions should hold:\n1. MaxOverlap > AverOverlap\n2. MaxOverlap < AverOverlap * K (K - 1) / 2\n"); } else { sch = 0; do{ /* generate parameters */ /* printf("Simulating dataset...\n"); */ genPi(K, PiLow, Pi); genMu(p, K, Mu, Lbound, Ubound); if (sph == 0){ genSigmaEcc(p, K, emax, S, hom); } else { genSphSigma(p, K, S, hom); } /* prepare parameters */ ComputePars(p, K, Pi, Mu, S, li, di, const1); /* check if maximum overlap is reachable */ asympt = 1; c = 0.0; GetOmegaMap(c, p, K, li, di, const1, fix, pars, lim, asympt, OmegaMap, &Balpha, &Malpha, rcMax); diff = Malpha - (*MaxOmega); if (diff >= -eps){ /* reachable */ lower = 0.0; upper = pow(2.0, 10); do{ /* find C for two currently largest clusters */ rowN = rcMax[0]; colN = rcMax[1]; for (i=0; i<2; i++){ for (j=0; j<2; j++){ for (k=0; k. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; /* This procedure computes Adjusted Rand index * * Parameters: * N - number of observations * TRUK - true number of clusters * PREDK - estimated number of clusters * trcl - true classification vector * prcl - estimated classification vector * Rand - Rand index * adjRand - Adjusted Rand index * Eindex - E index */ void RRand(int N, int TRUK, int PREDK,int *trcl, int *prcl, double *Rand,double *adjRand,double *Eindex) { int i,j,n[TRUK][PREDK]; double sumtr[TRUK],sumpr[PREDK],sumprsq,sumtrsq,sumsq,discordant, sumtrprsq; double term1, term2, term3; double nij2sum, nidot2sum, ndotj2sum, Wallace; for (i=0;i estK) estK = idest[i]; } estK++; trueK = 0; for (i=0; i trueK) trueK = idtrue[i]; } trueK++; printf("Ktrue = %d Kest = %d : ", trueK, estK); RRand(g, trueK, estK, idtrue, idest, &Rand, &adjRand, &Eindex); printf("%lf\n", adjRand); fprintf(fout, "%lf ", adjRand); } fclose(fout); fclose(fidtrue); fclose(fidest); FREE_VECTOR(idtrue); FREE_VECTOR(idest); return 0; } CARP-3.1/src/array.h0000644000076400007640000003001211467025320014076 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ /* Author: Rouben Rostamian, June 2000 Revised Jul 2000 Revised Sep 2002 Revised Jun 2003 - macros don't call exit anymore changed macro names to all-caps Revised Aug 2003 - changed reserved names to all caps Revised Dec 2003 - changed array index types to size_t (were int) - added an "out of memory" message, printed to stderr - most FREE* macros now come before MAKE* macros, for possible improved efficiency in preprocessing */ /* array.h o This file defines the following macros: MAKE_1ARRAY(a,n) make a 1D array of length "n" MAKE_2ARRAY(a,m,n) make a 2D array of dimensions "m x n" MAKE_3ARRAY(a,l,m,n) make a 3D array of dimensions "l x m x n" MAKE_4ARRAY(a,k,l,m,n) make a 4D array of dimensions "k x l x m x n" FREE_1ARRAY(a) free memory allocated by MAKE_1ARRAY() FREE_2ARRAY(a) free memory allocated by MAKE_2ARRAY() FREE_3ARRAY(a) free memory allocated by MAKE_3ARRAY() FREE_4ARRAY(a) free memory allocated by MAKE_4ARRAY() o Additionally, it defines the following convenience macros as synonyms: MAKE_VECTOR(a,n) same as MAKE_1ARRAY(a,n) FREE_VECTOR(a) same as FREE_1ARRAY(a) MAKE_MATRIX(a,m,n) same as MAKE_2ARRAY(a,m,n) FREE_MATRIX(a) same as FREE_2ARRAY(a) o Additionally, it declares and uses the identifiers: ARRAY_H2RESERVED ARRAY_H3RESERVED ARRAY_H4RESERVED within local blocks within the macros. THESE IDENTIFIERS ARE RESERVED. The user should not use identifiers with this names within the scope of these macros. o vector/matrix/array elements can be of _any_ type. o If malloc() fails during the execution of any of the MAKE_* macros: . An "out of memory" message is printed to stderr. . The macro's first argument is set to NULL. o After a call to any of the FREE_*() macros, the macro's argument is set to NULL. o The FREE_*() macros can be applied to previously FREE_*()-ed arrays with no ill effect. o Note that macro arguments are evaluated more than once. o Customization When malloc() returns NULL, MAKE_1ARRAY prints a message on stderr and the program continues. The user can alter this behavior by redefining the MAKE_1ARRAY macro. For instance, to call exit() whenever malloc() fails, the user can do: #undef MAKE_1ARRAY #define MAKE_1ARRAY(a,n) do { \ (a) = malloc((n) * sizeof *(a)); \ if ((a)==NULL) { \ fprintf(stderr, "*** in file %s, function %s(), line %d: " \ "out of memory!\n", __FILE__, __func__, __LINE__); \ exit(EXIT_FAILURE); \ } \ } while (0) Since only MAKE_1ARRAY calls malloc() explicitly, this change affects the behavior of not only MAKE_1ARRAY but all other MAKE_* macros as well. ---- SAMPLE USAGE ------------- #include "array.h" int main(void) { float ***a; // can use any other type instead of "float" size_t p=3, q=4, r=5; // will make a 3x4x5 3-D array of float MAKE_3ARRAY(a, p, q, r); if (a==NULL) return EXIT_FAILURE; a[2][0][1] = 3.14; printf("%g \n", a[2][0][1]); FREE_3ARRAY(a); return EXIT_SUCCESS; } ---- END OF SAMPLE USAGE ------- Author: Rouben Rostamian, June 2000 Revised Jul 2000 Revised Sep 2002 Revised Jun 2003 - macros don't call exit anymore changed macro names to all-caps Revised Aug 2003 - changed reserved names to all caps Revised Dec 2003 - changed array index types to size_t (were int) - added an "out of memory" message, printed to stderr - most FREE* macros now come before MAKE* macros, for possible improved efficiency in preprocessing */ #ifndef H_ARRAY_ #define H_ARRAY_ #include #include /* ---------- 1D arrays ---------------------- */ #define MAKE_1ARRAY(a,n) do { \ (a) = malloc((n) * sizeof *(a)); \ if ((a)==NULL) \ fprintf(stderr, "*** in file %s, function %s(), line %d: " \ "out of memory!\n", __FILE__, __func__, __LINE__); \ } while (0) #define FREE_1ARRAY(a) do { \ free(a); \ a = NULL; \ } while (0) /* ---------- 2D arrays ---------------------- */ #define FREE_2ARRAY(a) do { \ size_t ARRAY_H2RESERVED; \ if (a==NULL) \ break; \ for (ARRAY_H2RESERVED=0; (a)[ARRAY_H2RESERVED]!=NULL; ARRAY_H2RESERVED++)\ FREE_1ARRAY((a)[ARRAY_H2RESERVED]); \ FREE_1ARRAY(a); \ } while (0) /* note: parenthesize first arg because it may be given as `*a' */ #define MAKE_2ARRAY(a,m,n) do { \ size_t ARRAY_H2RESERVED; \ MAKE_1ARRAY(a,(m)+1); \ if (a==NULL) \ break; \ (a)[m] = NULL; \ for (ARRAY_H2RESERVED=0; ARRAY_H2RESERVED<(m); ARRAY_H2RESERVED++) { \ MAKE_1ARRAY((a)[ARRAY_H2RESERVED],(n)); \ if ((a)[ARRAY_H2RESERVED]==NULL) { \ FREE_2ARRAY(a); \ break; \ } \ } \ } while (0) /* ---------- 3D arrays ---------------------- */ #define FREE_3ARRAY(a) do { \ size_t ARRAY_H3RESERVED; \ if (a==NULL) \ break; \ for (ARRAY_H3RESERVED=0; (a)[ARRAY_H3RESERVED]!=NULL; ARRAY_H3RESERVED++)\ FREE_2ARRAY((a)[ARRAY_H3RESERVED]); \ FREE_1ARRAY(a); \ } while (0) #define MAKE_3ARRAY(a,p,q,r) do { \ size_t ARRAY_H3RESERVED; \ MAKE_1ARRAY(a,(p)+1); \ if (a==NULL) \ break; \ (a)[p] = NULL; \ for (ARRAY_H3RESERVED=0; ARRAY_H3RESERVED<(p); ARRAY_H3RESERVED++) { \ MAKE_2ARRAY((a)[ARRAY_H3RESERVED],(q),(r)); \ if ((a)[ARRAY_H3RESERVED]==NULL) { \ FREE_3ARRAY(a); \ break; \ } \ } \ } while (0) /* ---------- 3D arrays ---------------------- */ #define FREE_4ARRAY(a) do { \ size_t ARRAY_H4RESERVED; \ if (a==NULL) \ break; \ for (ARRAY_H4RESERVED=0; (a)[ARRAY_H4RESERVED]!=NULL; ARRAY_H4RESERVED++)\ FREE_3ARRAY((a)[ARRAY_H4RESERVED]); \ FREE_1ARRAY(a); \ } while (0) #define MAKE_4ARRAY(a,p,q,r,s) do { \ size_t ARRAY_H4RESERVED; \ MAKE_1ARRAY(a,(p)+1); \ if (a==NULL) \ break; \ (a)[p] = NULL; \ for (ARRAY_H4RESERVED=0; ARRAY_H4RESERVED<(p); ARRAY_H4RESERVED++) { \ MAKE_3ARRAY((a)[ARRAY_H4RESERVED],(q),(r),(s)); \ if ((a)[ARRAY_H4RESERVED]==NULL) { \ FREE_4ARRAY(a); \ break; \ } \ } \ } while (0) /* ---------- synonyms ---------------------- */ #define MAKE_VECTOR(a,n) MAKE_1ARRAY(a,n) #define MAKE_MATRIX(a,m,n) MAKE_2ARRAY(a,m,n) #define FREE_VECTOR(a) FREE_1ARRAY(a) #define FREE_MATRIX(a) FREE_2ARRAY(a) #endif /* H_ARRAY_ */ CARP-3.1/src/QF.c0000644000076400007640000003227311467026222013276 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ /* Author of this program: Robert Davies, */ #define UseDouble 0 /* all floating point double */ #include #include #include #include #define TRUE 1 #define FALSE 0 typedef int BOOL; #define pi 3.14159265358979 #define log28 .0866 /* log(2.0) / 8.0 */ static double sigsq, lmax, lmin, mean, c; static double intl, ersm; static int count, r, lim; static BOOL ndtsrt, fail; static int *n,*th; static double *lb,*nc; static jmp_buf env; static double exp1(double x) /* to avoid underflows */ { return x < -50.0 ? 0.0 : exp(x); } static void counter(void) /* count number of calls to errbd, truncation, cfe */ { count = count + 1; if ( count > lim ) longjmp(env,1); } static double square(double x) { return x*x; } static double cube(double x) { return x*x*x; } static double log1(double x, BOOL first) /* if (first) log(1 + x) ; else log(1 + x) - x */ { if (fabs(x) > 0.1) { return (first ? log(1.0 + x) : (log(1.0 + x) - x)); } else { double s, s1, term, y, k; y = x / (2.0 + x); term = 2.0 * cube(y); k = 3.0; s = (first ? 2.0 : - x) * y; y = square(y); for (s1 = s + term / k; s1 != s; s1 = s + term / k) { k = k + 2.0; term = term * y; s = s1; } return s; } } static void order(void) /* find order of absolute values of lb */ { int j, k; double lj; for ( j=0; j=0; k--) { if ( lj > fabs(lb[th[k]]) ) th[k + 1] = th[k]; else goto l1; } k = -1; l1 : th[k + 1] = j; } ndtsrt = FALSE; } static double errbd(double u, double* cx) /* find bound on tail probability using mgf, cutoff point returned to *cx */ { double sum1, lj, ncj, x, y, xconst; int j, nj; counter(); xconst = u * sigsq; sum1 = u * xconst; u = 2.0 * u; for (j=r-1; j>=0; j--) { nj = n[j]; lj = lb[j]; ncj = nc[j]; x = u * lj; y = 1.0 - x; xconst = xconst + lj * (ncj / y + nj) / y; sum1 = sum1 + ncj * square(x / y) + nj * (square(x) / y + log1(-x, FALSE )); } *cx = xconst; return exp1(-0.5 * sum1); } static double ctff(double accx, double* upn) /* find ctff so that p(qf > ctff) < accx if (upn > 0, p(qf < ctff) < accx otherwise */ { double u1, u2, u, rb, xconst, c1, c2; u2 = *upn; u1 = 0.0; c1 = mean; rb = 2.0 * ((u2 > 0.0) ? lmax : lmin); for (u = u2 / (1.0 + u2 * rb); errbd(u, &c2) > accx; u = u2 / (1.0 + u2 * rb)) { u1 = u2; c1 = c2; u2 = 2.0 * u2; } for (u = (c1 - mean) / (c2 - mean); u < 0.9; u = (c1 - mean) / (c2 - mean)) { u = (u1 + u2) / 2.0; if (errbd(u / (1.0 + u * rb), &xconst) > accx) { u1 = u; c1 = xconst; } else { u2 = u; c2 = xconst; } } *upn = u2; return c2; } static double truncation(double u, double tausq) /* bound integration error due to truncation at u */ { double sum1, sum2, prod1, prod2, prod3, lj, ncj, x, y, err1, err2; int j, nj, s; counter(); sum1 = 0.0; prod2 = 0.0; prod3 = 0.0; s = 0; sum2 = (sigsq + tausq) * square(u); prod1 = 2.0 * sum2; u = 2.0 * u; for (j=0; j 1.0) { prod2 = prod2 + nj * log(x); prod3 = prod3 + nj * log1(x, TRUE ); s = s + nj; } else prod1 = prod1 + nj * log1(x, TRUE ); } sum1 = 0.5 * sum1; prod2 = prod1 + prod2; prod3 = prod1 + prod3; x = exp1(-sum1 - 0.25 * prod2) / pi; y = exp1(-sum1 - 0.25 * prod3) / pi; err1 = ( s == 0 ) ? 1.0 : x * 2.0 / s; err2 = ( prod3 > 1.0 ) ? 2.5 * y : 1.0; if (err2 < err1) err1 = err2; x = 0.5 * sum2; err2 = ( x <= y ) ? 1.0 : y / x; return ( err1 < err2 ) ? err1 : err2; } static void findu(double* utx, double accx) /* find u such that truncation(u) < accx and truncation(u / 1.2) > accx */ { double u, ut; int i; static double divis[]={2.0,1.4,1.2,1.1}; ut = *utx; u = ut / 4.0; if ( truncation(u, 0.0) > accx ) { for ( u = ut; truncation(u, 0.0) > accx; u = ut) ut = ut * 4.0; } else { ut = u; for ( u = u / 4.0; truncation(u, 0.0) <= accx; u = u / 4.0 ) ut = u; } for ( i=0;i<4;i++) { u = ut/divis[i]; if ( truncation(u, 0.0) <= accx ) ut = u; } *utx = ut; } static void integrate(int nterm, double interv, double tausq, BOOL mainx) /* carry out integration with nterm terms, at stepsize interv. if (! mainx) multiply integrand by 1.0-exp(-0.5*tausq*u^2) */ { double inpi, u, sum1, sum2, sum3, x, y, z; int k, j, nj; inpi = interv / pi; for ( k = nterm; k>=0; k--) { u = (k + 0.5) * interv; sum1 = - 2.0 * u * c; sum2 = fabs(sum1); sum3 = - 0.5 * sigsq * square(u); for ( j = r-1; j>=0; j--) { nj = n[j]; x = 2.0 * lb[j] * u; y = square(x); sum3 = sum3 - 0.25 * nj * log1(y, TRUE ); y = nc[j] * x / (1.0 + y); z = nj * atan(x) + y; sum1 = sum1 + z; sum2 = sum2 + fabs(z); sum3 = sum3 - 0.5 * x * y; } x = inpi * exp1(sum3) / u; if ( ! mainx ) x = x * (1.0 - exp1(-0.5 * tausq * square(u))); sum1 = sin(0.5 * sum1) * x; sum2 = 0.5 * sum2 * x; intl = intl + sum1; ersm = ersm + sum2; } } static double cfe(double x) /* coef of tausq in error when convergence factor of exp1(-0.5*tausq*u^2) is used when df is evaluated at x */ { double axl, axl1, axl2, sxl, sum1, lj; int j, k, t; counter(); if (ndtsrt) order(); axl = fabs(x); sxl = (x>0.0) ? 1.0 : -1.0; sum1 = 0.0; for ( j = r-1; j>=0; j-- ) { t = th[j]; if ( lb[t] * sxl > 0.0 ) { lj = fabs(lb[t]); axl1 = axl - lj * (n[t] + nc[t]); axl2 = lj / log28; if ( axl1 > axl2 ) axl = axl1 ; else { if ( axl > axl2 ) axl = axl2; sum1 = (axl - axl1) / lj; for ( k = j-1; k>=0; k--) sum1 = sum1 + (n[th[k]] + nc[th[k]]); goto l; } } } l: if (sum1 > 100.0) { fail = TRUE; return 1.0; } else return pow(2.0,(sum1 / 4.0)) / (pi * square(axl)); } double qfc(double* lb1, double* nc1, int* n1, int *r1in, double *sigmain, double *c1in, int *lim1in, double *accin, double* trace, int* ifault) /* distribution function of a linear combination of non-central chi-squared random variables : input: lb[j] coefficient of j-th chi-squared variable nc[j] non-centrality parameter n[j] degrees of freedom j = 0, 2 ... r-1 sigma coefficient of standard normal variable c point at which df is to be evaluated lim maximum number of terms in integration acc maximum error output: ifault = 1 required accuracy NOT achieved 2 round-off error possibly significant 3 invalid parameters 4 unable to locate integration parameters 5 out of memory trace[0] absolute sum trace[1] total number of integration terms trace[2] number of integrations trace[3] integration interval in final integration trace[4] truncation point in initial integration trace[5] s.d. of initial convergence factor trace[6] cycles to locate integration parameters */ { int r1 = r1in[0], lim1 = lim1in[0]; double sigma = sigmain[0], c1 = c1in[0], acc = accin[0]; int j, nj, nt, ntm; double acc1, almx, xlim, xnt, xntm; double utx, tausq, sd, intv, intv1, x, up, un, d1, d2, lj, ncj; double qfval = 0; static int rats[]={1,2,4,8}; if (setjmp(env) != 0) { *ifault=4; goto endofproc; } r=r1; lim=lim1; c=c1; n=n1; lb=lb1; nc=nc1; for ( j = 0; j<7; j++ ) trace[j] = 0.0; *ifault = 0; count = 0; intl = 0.0; ersm = 0.0; qfval = -1.0; acc1 = acc; ndtsrt = TRUE; fail = FALSE; xlim = (double)lim; th=(int*)malloc(r*(sizeof(int))); if (! th) { *ifault=5; goto endofproc; } /* find mean, sd, max and min of lb, check that parameter values are valid */ sigsq = square(sigma); sd = sigsq; lmax = 0.0; lmin = 0.0; mean = 0.0; for (j=0; j lj) lmin = lj; } if ( sd == 0.0 ) { qfval = (c > 0.0) ? 1.0 : 0.0; goto endofproc; } if ( lmin == 0.0 && lmax == 0.0 && sigma == 0.0 ) { *ifault = 3; goto endofproc; } sd = sqrt(sd); almx = (lmax < - lmin) ? - lmin : lmax; /* starting values for findu, ctff */ utx = 16.0 / sd; up = 4.5 / sd; un = - up; /* truncation point with no convergence factor */ findu(&utx, .5 * acc1); /* does convergence factor help */ if (c != 0.0 && (almx > 0.07 * sd)) { tausq = .25 * acc1 / cfe(c); if (fail) fail = FALSE ; else if (truncation(utx, tausq) < .2 * acc1) { sigsq = sigsq + tausq; findu(&utx, .25 * acc1); trace[5] = sqrt(tausq); } } trace[4] = utx; acc1 = 0.5 * acc1; /* find RANGE of distribution, quit if outside this */ l1: d1 = ctff(acc1, &up) - c; if (d1 < 0.0) { qfval = 1.0; goto endofproc; } d2 = c - ctff(acc1, &un); if (d2 < 0.0) { qfval = 0.0; goto endofproc; } /* find integration interval */ intv = 2.0 * pi / ((d1 > d2) ? d1 : d2); /* calculate number of terms required for main and auxillary integrations */ xnt = utx / intv; xntm = 3.0 / sqrt(acc1); if (xnt > xntm * 1.5) { /* parameters for auxillary integration */ if (xntm > xlim) { *ifault = 1; goto endofproc; } ntm = (int)floor(xntm+0.5); intv1 = utx / ntm; x = 2.0 * pi / intv1; if (x <= fabs(c)) goto l2; /* calculate convergence factor */ tausq = .33 * acc1 / (1.1 * (cfe(c - x) + cfe(c + x))); if (fail) goto l2; acc1 = .67 * acc1; /* auxillary integration */ integrate(ntm, intv1, tausq, FALSE ); xlim = xlim - xntm; sigsq = sigsq + tausq; trace[2] = trace[2] + 1; trace[1] = trace[1] + ntm + 1; /* find truncation point with new convergence factor */ findu(&utx, .25 * acc1); acc1 = 0.75 * acc1; goto l1; } /* main integration */ l2: trace[3] = intv; if (xnt > xlim) { *ifault = 1; goto endofproc; } nt = (int)floor(xnt+0.5); integrate(nt, intv, 0.0, TRUE ); trace[2] = trace[2] + 1; trace[1] = trace[1] + nt + 1; qfval = 0.5 - intl; trace[0] = ersm; /* test whether round-off error could be significant allow for radix 8 or 16 machines */ up=ersm; x = up + acc / 10.0; for (j=0;j<4;j++) { if (rats[j] * x == rats[j] * up) *ifault = 2; } endofproc : free((char*)th); trace[6] = (double)count; return qfval; } CARP-3.1/src/hierclust.c0000644000076400007640000002747111467025707015005 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; /* Given a hierarchical clustering through a sequence of agglomerations, derive the assignment for the top (lev-1)th level of the hierarchy. Parameters: n: number of observations ia,ib: vectors of dimension N defining the agglomerations lev: number of clusters in partition. iclass: n-dimensional vector of cluster assignments C code written by Ranjan Maitra, Baltimore (07/02/02) */ void hclass(int n, int *ia, int *ib, int lev, int *iclass) { int i,j,k; for (i=0;i=0;k--) { if (iclass[ia[k]]==j) { iclass[ib[k]]=j; } } j--; } j=0; iclass[ia[n-2]]=j; for (k=(n-lev-1);k>=0;k--) { if (iclass[ia[k]]==j) { iclass[ib[k]]=j; } } return; } /* HIERARCHICAL CLUSTERING using (user-specified) criterion. Parameters: n,m number of observations and coordinates data(n,m) input data matrix, iopt clustering criterion to be used, IA, IB, CRIT history of agglomerations; dimension N-1 each C code rewritten by Ranjan Maitra, Baltimore (07/02/02) from f77 code of F. Murtagh, ESA/ESO/STECF, Garching (1986). Note that I use the true distances, not squared Euclidean which FM used. */ int ioffset(int n, int i, int j) { /* Map (i,j)th element of upper half */ return(j+i*n-(i+1)*(i+2)/2); /* diagonal symmetric matrix to vector */ } int imin(int aa, int bb) { if (aa1) { /* Next, determine least diss. using list of NNs */ int jj=0,jm=0,im=0; dmin=inf; for (i= 0;i<(n-1);++i) { if (flag[i]==1) { if (disnn[i]. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ /* This is the main program that reads parameters and runs the following 3 steps: 1. C-MixSim which generates mixtures with prespecified level of overlap and datasets from these mixtures. 2. External clustering algorithm provided by user. 3. External index for evaluating simulated datasets. */ #include #include #include #include #include #include #include "array.h" #include "overlap.h" void printHelp(){ printf("Usage:\n> ./CARP \n\nParameters:\n"); printf("-b : average overlap (no default value)\n"); printf("-m : maximum overlap (no default value)\n"); printf("-p : number of dimensions (2 by default)\n"); printf("-G : number of mixing components (2 by default)\n"); printf("-s : spherical covariance matrix structure (non-spherical by default if option is unspecified)\n"); printf("-H : homogeneous covariance matrices (non-homogeneous by default if option is unspecified)\n"); printf("-e : maximum eccentricity (0.90 by default)\n"); printf("-z : smallest mixing proportion (equal mixing proportions 1/K by default)\n"); printf("-L : lower bound for Uniform(lower-bound, upper-bound ) distribution from which mean vectors are generated\n"); printf("-U : upper bound for Uniform(lower-bound, upper-bound ) distribution from which mean vectors are generated\n"); printf("-r : maximum number of resimulations (100 by default)\n"); printf("-a : accuracy of estimation (1e-06 by default)\n"); printf("-l : maximum number of integration terms (1e06 by default)\n"); printf("-P : name of the file containing mixing proportions (“Pi.dat” by default)\n"); printf("-M : name of the file containing mean vectors (“Mu.dat” by default)\n"); printf("-S : name of the file containing covariance matrices in triangular form (“LTSigma.dat” by default)\n"); printf("-D : name of working directory (“DATA” by default)\n"); printf("-N : name of the file containing cluster sizes (“Nk.dat” by default)\n"); printf("-I : name of the file containing numbers of observations generated from every cluster (“idTrue.dat” by default)\n"); printf("-i : name of the file containing estimated classifications (“idEst.dat” by default)\n"); printf("-X : name of the file containing simulated datasets (“x.dat” by default)\n"); printf("-W : name of the file containing maps of pairwise overlaps (“overMap.dat” by default)\n"); printf("-O : name of the file containing characteristics of simulated mixtures (“overBarMax.dat” by default)\n"); printf("-R : name of the file containing index values (Adjusted Rand index and “AR.dat” by default)\n\n"); printf("-n : number of observations generated from every mixture (0 by default)\n"); printf("-# : number of simulated mixtures (1 by default)\n"); printf("-V : name of the file (if file does not exist random transformation is applied)\n"); printf("-w : number of noise variables (0 by default)\n"); printf("-o : number of outliers for generated datasets (0 by default)\n"); printf("-A : clustering program (no default name)\n"); printf("-c : provide command line interface for option -A (FALSE by default)\n"); printf("-E : partition analyzing program (“AdjRand” by default)\n"); printf("-C : provide command line interface for option -E (FALSE by default)\n"); printf("-h : help\n"); } int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; int main(int argc, char *argv[]){ if (argc == 1){ printHelp(); exit(1); } int c, errflag, errflag0, errflag1, code, p, G, w, o, v, ClustEval, RunIndex; int resN, method, lim, sph, hom, g, nf, k, K, flagK; double emax, PiLow, Lbound, Ubound; double BarOmega, MaxOmega; double *pars; MAKE_VECTOR(pars, 2); /* DEFAULT PARAMETERS */ method = 0; /* 0 - average; 1 - maximum */ BarOmega = 0.0; MaxOmega = 0.0; sph = 0; /* 0 - nonspherical, 1 - spherical */ hom = 0; /* 0 - nonhomogeneous, 1 - homogeneous */ emax = 0.90; /* Max eccentricity */ PiLow = 1.0; /* Smallest mixing proportion */ Lbound = 0; /* Means are distributed by U(Lbound, Ubound) */ Ubound = 1; /* Means are distributed by U(Lbound, Ubound) */ resN = 1000; /* Max # of dataset resimulations */ pars[0] = 0.0000001; /* eps */ pars[1] = 0.0000001; /* acc (Davies) */ lim = 1000000; /* lim (Davies) */ g = 0; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ v = 0; /* =0 no transformations, !=0 coefficient of transformation */ w = 0; /* # of noise coordinates (columns) */ o = 0; /* # of outliers */ k = 0; /* min # of components */ K = 0; /* max # of components */ char **pp; pp = NULL; char Path[100] = "DATA"; char PIfname[100] = "Pi.dat"; char MUfname[100] = "Mu.dat"; char Sfname[100] = "LTSigma.dat"; char overmap[100] = "overMap.dat"; char overbarmax[100] = "overBarMax.dat"; char dataX[100] = "x.dat"; char Nksizes[100] = "Nk.dat"; char IDtrue[100] = "idTrue.dat"; char Lambda[100] = ""; char file2[500] = "./"; char file3[500] = "./"; char options2[500] = ""; char options3[500] = ""; char buff1[500] = ""; char buff2[500] = ""; ClustEval = 0; /* run with passing parameters; 1 otherwise */ RunIndex = 0; /* run with passing parameters; 1 otherwise */ errflag = 0; errflag0 = 1; errflag1 = 1; flagK = 0; p = 0; G = 0; /* DEALING WITH PARAMETERS */ while ((c = getopt(argc, argv, ":b:m:p:G:sHhe:z:k:K:L:U:r:a:l:P:M:S:D:N:I:i:X:W:O:R:n:#:V:w:o:cCA:E:")) != -1) { switch(c) { case 'A': /* clustering program */ strcat(buff1, optarg); errflag0 = 0; break; case 'E': /* indexing program */ strcat(buff2, optarg); errflag1 = 0; break; case 'c': /* command line at stage 2 */ ClustEval = 1; break; case 'C': /* command line at stage 3 */ RunIndex = 1; break; case 'h': /* help */ printHelp(); exit(1); break; case 'b': /* average overlap */ BarOmega = strtod(optarg, pp); break; case 'm': /* maximum overlap */ MaxOmega = strtod(optarg, pp); break; case 'p': /* # of dimensions */ p = atoi(optarg); break; case 'G': /* # of components */ G = atoi(optarg); break; case 's': /* spherical cluster */ sph = 1; break; case 'H': /* homogeneous cluster */ hom = 1; break; case 'e': /* maximum eccentricity */ emax = strtod(optarg, pp); break; case 'z': /* smallest mixing proportion */ PiLow = strtod(optarg, pp); break; case 'k': /* min # of components */ k = atoi(optarg); strcat(options2, " "); strcat(options2, argv[optind-1]); flagK++; break; case 'K': /* max # of components */ K = atoi(optarg); strcat(options2, " "); strcat(options2, argv[optind-1]); flagK++; break; case 'L': /* lower bound for Uniform */ Lbound = strtod(optarg, pp); break; case 'U': /* upper bound for Uniform */ Ubound = strtod(optarg, pp); break; case 'r': /* # of resimulations */ resN = atoi(optarg); break; case 'a': /*epsilon / accuracy */ pars[0] = strtod(optarg, pp); pars[1] = strtod(optarg, pp); break; case 'l': /* max # of integration terms */ lim = atoi(optarg); break; case 'P': /* Name of the file of mixing proportions */ strcpy(PIfname, optarg); break; case 'M': /* Name of the file with means */ strcpy(MUfname, optarg); break; case 'S': /* Name of the file with covariance matrices */ strcpy(Sfname, optarg); break; case 'D': /* Name of the working directory */ strcpy(Path, optarg); strcat(options2, " "); strcat(options2, argv[optind-1]); strcat(options3, " "); strcat(options3, argv[optind-1]); break; case 'N': /* Name of the file with true sample sizes */ strcpy(Nksizes, optarg); break; case 'I': /* Name of the file with true sample sizes */ strcpy(IDtrue, optarg); strcat(options3, " "); strcat(options3, argv[optind-1]); break; case 'i': /* Name of the file with estimated classifications */ strcat(options2, " "); strcat(options2, argv[optind-1]); strcat(options3, " "); strcat(options3, argv[optind-1]); break; case 'X': /* Name of the output file */ strcpy(dataX, optarg); strcat(options2, " "); strcat(options2, argv[optind-1]); break; case 'W': /* Name of the file with overlap map */ strcpy(overmap, optarg); break; case 'O': /* Name of the file with overlap characteristics */ strcpy(overbarmax, optarg); break; case 'R': /* Name of the file with index values */ strcat(options3, " "); strcat(options3, argv[optind-1]); break; case 'n': /* # of observations in simulated datasets */ g = atoi(optarg); break; case '#': /* # of simulated mixtures */ nf = atoi(optarg); strcat(options2, " "); strcat(options2, argv[optind-1]); strcat(options3, " "); strcat(options3, argv[optind-1]); break; case 'V': /* Name of the file with transformation coefficients */ strcpy(Lambda, optarg); break; case 'w': /* # of noise coordinates */ w = atoi(optarg); break; case 'o': /* # of outliers */ o = atoi(optarg); break; case ':': /* option with no operand */ printf("Operand for option -%c is not specified correctly...\n", optopt); errflag = 1; break; case '?': printf("Unrecognized option: -%c\n", optopt); errflag = 2; break; } } if (errflag0 == 0){ if (ClustEval == 0){ strcat(file2, buff1); } else { strcpy(file2, buff1); } } if (errflag1 == 0){ if (RunIndex == 0){ strcat(file3, buff2); } else { strcpy(file3, buff2); } } /* DELAING WITH WRONG INPUT */ if ((p < 1) | (p > 1000)){ printf("Operand for option -p is not specified correctly...\n"); errflag = 1; } if (G < 1){ printf("Operand for option -G is not specified correctly...\n"); errflag = 1; } if ((sph != 0) & (sph != 1)){ printf("Operand for option -s is not specified correctly...\n"); errflag = 1; } if ((hom != 0) & (hom != 1)){ printf("Operand for option -H is not specified correctly...\n"); errflag = 1; } if ((emax <= 0.0) | (emax >= 1.0)){ printf("Operand for option -e is not specified correctly...\n"); errflag = 1; } if ((PiLow <= 0.0) | (PiLow > 1.0)){ printf("Operand for option -z is not specified correctly...\n"); errflag = 1; } if (k < 0){ printf("Operand for option -k is not specified correctly...\n"); errflag = 1; } if (K < 0){ printf("Operand for option -K is not specified correctly...\n"); errflag = 1; } if (K < k){ printf("Operands for options -k and -K are not specified correctly; use k < K...\n"); errflag = 1; } if (Ubound <= Lbound){ printf("Operands for options -L/-U is not specified correctly...\n"); errflag = 1; } if (resN <= 0){ printf("Operand for option -r is not specified correctly...\n"); errflag = 1; } if (pars[0] <= 0.0){ printf("Operand for option -a is not specified correctly...\n"); errflag = 1; } if (lim <= 0){ printf("Operand for option -l is not specified correctly...\n"); errflag = 1; } if ((g < 0) & ((MaxOmega != 0.0) | (BarOmega != 0.0))){ printf("Operand for option -n is not specified correctly...\n"); errflag = 1; } if (nf < 1){ printf("Operand for option -# is not specified correctly...\n"); errflag = 1; } if (w < 0){ printf("Operand for option -w is not specified correctly...\n"); errflag = 1; } if (o < 0){ printf("Operand for option -o is not specified correctly...\n"); errflag = 1; } if ((hom == 1) & ((MaxOmega != 0.0) & (BarOmega != 0.0))){ printf("Operand -H cannot be used when both options -b and -m are specified...\n"); errflag = 1; } if (errflag != 0) exit(1); /* Preparing parameters for the 2nd step */ if (ClustEval == 0){ if (w != 0) p = p + w; strcpy(buff1, options2); strcat(buff1, " -p"); sprintf(buff2, "%d", p); strcpy(options2, buff1); strcat(options2, buff2); if (o != 0) g = g + o; strcpy(buff1, options2); strcat(buff1, " -n"); sprintf(buff2, "%d", g); strcpy(options2, buff1); strcat(options2, buff2); if (flagK == 0){ if (o != 0) G++; strcpy(buff1, options2); strcat(buff1, " -K"); sprintf(buff2, "%d", G); strcpy(options2, buff1); strcat(options2, buff2); if (o != 0) G--; } strcat(file2, options2); if (w != 0) p = p - w; if (o != 0) g = g - o; } /* Preparing parameters for the 3rd step */ if (RunIndex == 0){ if (o != 0) g = g + o; strcpy(buff1, options3); strcat(buff1, " -n"); sprintf(buff2, "%d", g); strcpy(options3, buff1); strcat(options3, buff2); if (o != 0) g = g - o; /* default analyzing program */ if (errflag1 != 0){ strcat(file3, "AdjRand"); } strcat(file3, options3); } /* STEP 1 - Mixture simulation */ printf("\n\nSTEP 1: Mixture simulation\n\n"); code = CMixSim(p, G, BarOmega, MaxOmega, sph, hom, emax, PiLow, Lbound, Ubound, resN, pars, lim, g, nf, v, w, o, Path, PIfname, MUfname, Sfname, overmap, overbarmax, dataX, Nksizes, IDtrue, Lambda); if (code != 0){ printf("ERROR: Unable to fulfill STEP 1\n"); exit(1); } if (errflag0 == 0){ /* STEP 2 - Run of clustering algorithm */ printf("\n\n\nSTEP 2: Running %s\n\n\n", file2); code = system(file2); if (code != 0){ printf("ERROR: Unable to fulfill STEP 2\n"); exit(1); } /* STEP 3 - Calculation of Adjusted Rand index */ printf("\n\n\nSTEP 3: Running %s\n\n\n", file3); code = system(file3); if (code != 0){ printf("ERROR: Unable to fulfill STEP 3\n"); exit(1); } } FREE_VECTOR(pars); return 0; } CARP-3.1/src/minunit.h0000644000076400007640000000414111467026055014455 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ /* file: minunit.h */ #ifndef MINUNIT_H #define MINUNIT_H #define func_test(test, message1, message2) do { \ if (test) printf(message1); \ else printf(message2); \ } while (0) #endif /* MINUNIT_H */ CARP-3.1/src/BHI.c0000644000076400007640000001106111467025421013362 0ustar volmelnvolmeln /*/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; /* This program computes Biological Homogeneity Index for simulated datasets */ int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; int main(int argc, char *argv[]) { int g, nf, i, c, code, errflag; double *clagr; FILE *fname; char runR[500]; char buff[200] = ""; char Path[100] = "DATA"; char IDtrue[100] = "idTrue.dat"; char IDest[100] = "idEst.dat"; char ClAgrName[100] = "BHI.dat"; /* DEFAULT PARAMETERS */ g = 0; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ errflag = 0; if (argc == 1){ exit(1); } while ((c = getopt(argc, argv, ":K:n:D:I:i:R:#:")) != -1) { switch(c) { case 'n': /* # of observations in simulated datasets */ g = atoi(optarg); g = fabs(g); break; case 'D': /* Name working directory */ strcpy(Path, optarg); break; case 'I': /* true classification */ strcpy(IDtrue, optarg); break; case 'i': /* estimated classification */ strcpy(IDest, optarg); break; case 'R': /* Class Agreement index */ strcpy(ClAgrName, optarg); break; case '#': /* # of simulated mixtures */ nf = atoi(optarg); break; case ':': /* option with no operand */ printf("Operand for option -%c is not specified correctly...\n", optopt); errflag = 1; break; case '?': printf("Unrecognized option: -%c\n", optopt); errflag = 2; } } if (g <= 0){ printf("Operand for option -g is not specified correctly...\n"); errflag = 1; } if (nf < 1){ printf("Operand for option -# is not specified correctly...\n"); errflag = 1; } if (errflag != 0) exit(1); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDtrue); strcpy(IDtrue, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDest); strcpy(IDest, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, ClAgrName); strcpy(ClAgrName, buff); sprintf(runR, "R CMD BATCH --no-save --no-restore '--args %d %d %s %s %s' PROC/runBHI.R", g, nf, IDtrue, IDest, ClAgrName); code = system(runR); if (code != 0){ printf("See runBHI.Rout for errors...\n"); exit(1); } MAKE_VECTOR(clagr, nf); fname = fopen(ClAgrName,"r"); printf("BHI VALUES:\n"); for (i=0; i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; /* This porgram obtains classifications for simulated datasets using PAM */ int main(int argc, char *argv[]) { int p, K, g, nf, c, k, i, errflag, code; int *id; char runR[500]; char buff[200] = ""; char Path[100] = "DATA"; char Xname[100] = "x.dat"; char IDname[100] = "idEst.dat"; FILE *fdata; /* DEFAULT PARAMETERS */ p = 2; K = 2; g = 0; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ errflag = 0; /* DEALING WITH PARAMETERS */ if (argc == 1){ exit(1); } while ((c = getopt(argc, argv, ":p:K:n:D:i:X:#:")) != -1) { switch(c) { case 'p': /* # of dimensions */ p = atoi(optarg); break; case 'K': /* # of components */ K = atoi(optarg); break; case 'n': /* # of observations in datasets */ g = atoi(optarg); g = fabs(g); break; case 'D': /* Name of the working directory */ strcpy(Path, optarg); break; case 'i': /* Name for estimated classification */ strcpy(IDname, optarg); break; case 'X': /* Name for the file with covariance matrices */ strcpy(Xname, optarg); break; case '#': /* # of simulated mixtures */ nf = atoi(optarg); break; case ':': /* option with no operand */ printf("Operand for option -%c is not specified correctly...\n", optopt); errflag = 1; break; case '?': printf("Unrecognized option: -%c\n", optopt); errflag = 2; } } if (p < 1){ printf("Operand for option -p is not specified correctly...\n"); errflag = 1; } if (K < 1){ printf("Operand for option -K is not specified correctly...\n"); errflag = 1; } if (g < 0){ printf("Operand for option -g is not specified correctly...\n"); errflag = 1; } if (nf < 1){ printf("Operand for option -# is not specified correctly...\n"); errflag = 1; } if (errflag != 0) exit(1); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, Xname); strcpy(Xname, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDname); strcpy(IDname, buff); /* Run Pam clustering */ sprintf(runR, "R CMD BATCH --no-save --no-restore '--args %d %d %d %d %s %s' PROC/runPAM.R", p, K, g, nf, Xname, IDname); code = system(runR); if (code != 0){ printf("See runPAM.Rout for errors...\n"); exit(1); } /* Print obtained classifications */ MAKE_VECTOR(id, g); if (!(fdata = fopen(IDname, "r"))){ fprintf(stderr, "Error: could not open file %s...\n", IDname); exit(EXIT_FAILURE); } printf("OBTAINED CLASSIFICATIONS:\n"); for (k=1; k<=nf; k++){ if (nf != 1){ printf("Dataset #%i:\n", k); } for (i=0; i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #ifndef CEPHES_EIGENS_H #define CEPHES_EIGENS_H void cephes_eigens(double *A, double *EV, double *E, int N); #endif /* CEPHES_EIGENS_H */ CARP-3.1/src/Matrix.c0000644000076400007640000002111511467025766014241 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include "array.h" #include "overlap.h" /* Multiplies matrices a and b and puts the result in c which should be pre-allocated */ void multiply(double **a, int arows, int acols, double **b, int brows, int bcols, double **c) { int i, j, k; for (i=0; i (*max)){ (*max) = x[i]; maxN = i; } } return maxN; } /* multiplies x by x' */ int vec11vecSQ(double *y, int p, double **Res){ int i,j; for (i=0;i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; /* This porgram obtains classifications for simulated datasets using k-means */ int main(int argc, char *argv[]) { int p, K, g, nf, c, k, i, errflag, code; int *id; char runR[500]; char buff[200] = ""; char Path[100] = "DATA"; char Xname[100] = "x.dat"; char IDname[100] = "idEst.dat"; FILE *fdata; /* DEFAULT PARAMETERS */ p = 2; K = 2; g = 0; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ errflag = 0; /* DEALING WITH PARAMETERS */ if (argc == 1){ exit(1); } while ((c = getopt(argc, argv, ":p:K:n:D:i:X:#:")) != -1) { switch(c) { case 'p': /* # of dimensions */ p = atoi(optarg); break; case 'K': /* # of components */ K = atoi(optarg); break; case 'n': /* # of observations in datasets */ g = atoi(optarg); g = fabs(g); break; case 'D': /* Name of the working directory */ strcpy(Path, optarg); break; case 'i': /* Name for estimated classification */ strcpy(IDname, optarg); break; case 'X': /* Name for the file with covariance matrices */ strcpy(Xname, optarg); break; case '#': /* # of simulated mixtures */ nf = atoi(optarg); break; case ':': /* option with no operand */ printf("Operand for option -%c is not specified correctly...\n", optopt); errflag = 1; break; case '?': printf("Unrecognized option: -%c\n", optopt); errflag = 2; } } if (p < 1){ printf("Operand for option -p is not specified correctly...\n"); errflag = 1; } if (K < 1){ printf("Operand for option -K is not specified correctly...\n"); errflag = 1; } if (g < 0){ printf("Operand for option -g is not specified correctly...\n"); errflag = 1; } if (nf < 1){ printf("Operand for option -# is not specified correctly...\n"); errflag = 1; } if (errflag != 0) exit(1); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, Xname); strcpy(Xname, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDname); strcpy(IDname, buff); /* Run Mclust clustering */ sprintf(runR, "R CMD BATCH --no-save --no-restore '--args %d %d %d %d %s %s' PROC/runKmeans.R", p, K, g, nf, Xname, IDname); code = system(runR); if (code != 0){ printf("See runKmeans.Rout for errors...\n"); exit(1); } /* Print obtained classifications */ MAKE_VECTOR(id, g); if (!(fdata = fopen(IDname, "r"))){ fprintf(stderr, "Error: could not open file %s...\n", IDname); exit(EXIT_FAILURE); } printf("OBTAINED CLASSIFICATIONS:\n"); for (k=1; k<=nf; k++){ if (nf != 1){ printf("Dataset #%i:\n", k); } for (i=0; i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include "array.h" #include "overlap.h" #define PI 3.141592653589793 #define EE 2.718281828459046 long int random(void); /* declare in order to avoid undef with ISO */ void srandom(unsigned int seed); /* declare in order to avoid undef with ISO */ double rxxx1; int ixxx1=0; /*************************************/ void setseed(unsigned int s) { srandom(s); } /*************************************/ long genseed(void) { return((long)random()); } /*************************************/ double runi(void) { return ((double)random() + 0.5)/((double)RAND_MAX + 1.0); } /*************************************/ double runir(double a,double b) { return (b-a)*runi()+a; } /*************************************/ double rnor(double mu,double sd) { double e,v1,v2,w; if(ixxx1==0){ do{ v1=2*runi()-1.; v2=2*runi()-1.; w=v1*v1+v2*v2; }while(w>1.); e=sqrt((-2.*log(w))/w); rxxx1=v1*e; ixxx1=1; return v2*e*sd+mu; } else{ ixxx1=0; return rxxx1*sd+mu; } } /*************************************/ double rexp(double lambda) { return -log(runi())/lambda; } /*************************************/ double rgamma(double alpha) { double r1,r2,aa,x,w,c1,c2,c3,c4,c5; if(alpha<=0.)return 0.; if(alpha == 1.)return rexp(1.); if(alpha<1){ aa=(alpha+EE)/EE; do{ r1=runi(); r2=runi(); if(r1>1./aa){ x = -log(aa*(1.-r1)/alpha); if(r22.5)r1=r2+c5*(1.-1.86*r1); }while(r1<=0 || r1 >= 1); w=c2*r2/r1; if(c3*r1+w+1/w <= c4)return c1*w; if(c3*log(r1)-log(w)+w<1)return c1*w; }while(r2<2); } exit(1); } /*************************************/ /* Generation of random realizations from Multinomial distribution * n - number of trials * K - dimensionality * pi - multinomial proportions * Nk - produced random realization */ void rmulti(int n, int K, double *pi, int *Nk){ int i, k; double X; double *cumPI; MAKE_VECTOR(cumPI, K); for(k=0; k < K; k++){ Nk[k] = 0; } cumPI[0] = pi[0]; for(k=1; k < K; k++){ cumPI[k] = cumPI[k-1] + pi[k]; } for (i = 0; i < n; i++){ X = runi(); for(k=0; k < K; k++){ if (X < cumPI[k]){ Nk[k] = Nk[k] + 1; break; } } } FREE_VECTOR(cumPI); } /*************************************/ /* Generation of random realizations from multivariate Gaussian distribution * n - sample size * K - dimensionality * MU - mean vector * S - covariance matrix * X - produced random realizations */ void rMVN(int n, int p, double *MU, double **S, double **X){ int i, j; double dtmt; double *Eig; double **Ga, **L, **Sh, **Z; MAKE_MATRIX(Sh, p, p); MAKE_VECTOR(Eig, p); MAKE_MATRIX(Ga, p, p); MAKE_MATRIX(L, p, p); MAKE_MATRIX(Z, n, p); cpy(S, p, p, Ga); cephes_symmeigens_down(p, Eig, Ga, &dtmt); Anull(L, p, p); for (i=0; i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include #include #include "array.h" #include "overlap.h" void genDataset(double *Pi, double **Mu, double ***S, int g, int o, double critval, int p, int w, int v, int K, int i, double Lbound, double Ubound, double *lambda, char *Nksizes, char *IDtrue, char *dataX){ int k, j; double **Y; int *Nk; MAKE_MATRIX(Y, g+o, p+w); if (o == 0){ MAKE_VECTOR(Nk, K); } else { MAKE_VECTOR(Nk, K+1); } /* dataset generation and printing parameters */ genData(p, K, Pi, Mu, S, g, Y, Nk); if (o != 0){ genOutliers(p, K, Pi, Mu, S, g, o, critval, Y, Lbound, Ubound); Nk[K] = o; } if (w != 0) genNoise(p, w, Lbound, Ubound, g+o, Y); if (v != 0) { for (k=0; k 0){ FILE *fchsq; if (!(fchsq = fopen(ChSq, "r"))){ fprintf(stderr, "Error: could not open file %s...\n", ChSq); exit(EXIT_FAILURE); } i = 0; while (i < 1000){ if(!fscanf(fchsq, "%lf\n", &critval)){ fprintf(stderr, "Error: invalid format in %s...\n", ChSq); exit(EXIT_FAILURE); } if ((i+1) == p){ break; } else { i = i + 1; } } fclose(fchsq); } /* RUNNING A CORRESPONDING FUNCTION */ for (i=1; i <= nf; i++){ if (nf != 1){ printf("\n\nMIXTURE MODEL #%i:\n\n", i); } if (BarOmega == 0.0){ if (MaxOmega == 0.0){ /* AVERAGE AND MAXIMUM OVERLAPS ARE NOT SPECIFIED */ freadParameters(p, K, Pi, Mu, S, PIfname, MUfname, Sfname, i); ExactOverlap(p, K, Pi, Mu, S, pars, lim, OmegaMap, &BarOmega, &MaxOmega, rcMax); if ((BarOmega > MaxOmega) | (BarOmega*K*(K-1.0)/2.0 + pars[0] < MaxOmega) | (BarOmega < 0.0)){ printf("Incorrect parameters. "); printf("Check the options -p and -G...\n"); exit(2); } printOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax); fprintOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax, i, overmap, overbarmax); BarOmega = 0.0; MaxOmega = 0.0; if (g > 0){ genDataset(Pi, Mu, S, g, o, critval, p, w, v, K, i, Lbound, Ubound, lambda, Nksizes, IDtrue, dataX); } } else { /* ONLY MAXIMUM OVERLAP IS SPECIFIED */ method = 1; OmegaClust(MaxOmega, method, p, K, PiLow, Lbound, Ubound, emax, pars, lim, resN, sph, hom, Pi, Mu, S, OmegaMap, &BarOmega, &MaxOmega, rcMax, &fail); if (fail != 0){ printf("The desired overlap has not been met...\n"); exit(2); } printf("The desired overlap has been met...\n"); printOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax); printParameters(p, K, Pi, Mu, S); fprintOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax, i, overmap, overbarmax); fprintParameters(p, K, Pi, Mu, S, PIfname, MUfname, Sfname, i); BarOmega = 0.0; if (g != 0){ genDataset(Pi, Mu, S, g, o, critval, p, w, v, K, i, Lbound, Ubound, lambda, Nksizes, IDtrue, dataX); } } } else { if (MaxOmega == 0.0){ /* ONLY AVERAGE OVERLAP IS SPECIFIED */ method = 0; OmegaClust(BarOmega, method, p, K, PiLow, Lbound, Ubound, emax, pars, lim, resN, sph, hom, Pi, Mu, S, OmegaMap, &BarOmega, &MaxOmega, rcMax, &fail); if (fail != 0){ printf("The desired overlap has not been met...\n"); exit(2); } printf("The desired overlap has been met...\n"); printOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax); printParameters(p, K, Pi, Mu, S); fprintOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax, i, overmap, overbarmax); fprintParameters(p, K, Pi, Mu, S, PIfname, MUfname, Sfname, i); MaxOmega = 0.0; if (g != 0){ genDataset(Pi, Mu, S, g, o, critval, p, w, v, K, i, Lbound, Ubound, lambda, Nksizes, IDtrue, dataX); } } else { /* AVERAGE AND MAXIMUM OVERLAPS ARE BOTH SPECIFIED */ OmegaBarOmegaMax(p, K, PiLow, Lbound, Ubound, emax, pars, lim, resN, sph, hom, Pi, Mu, S, OmegaMap, &BarOmega, &MaxOmega, rcMax, &fail); if (fail != 0){ printf("The desired overlap has not been met...\n"); exit(2); } printf("The desired overlap has been met...\n"); printOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax); printParameters(p, K, Pi, Mu, S); fprintOverlap(K, OmegaMap, BarOmega, MaxOmega, rcMax, i, overmap, overbarmax); fprintParameters(p, K, Pi, Mu, S, PIfname, MUfname, Sfname, i); if (g != 0){ genDataset(Pi, Mu, S, g, o, critval, p, w, v, K, i, Lbound, Ubound, lambda, Nksizes, IDtrue, dataX); } } } } FREE_VECTOR(lambda); FREE_VECTOR(Pi); FREE_MATRIX(Mu); FREE_3ARRAY(S); FREE_MATRIX(OmegaMap); FREE_VECTOR(rcMax); return 0; } CARP-3.1/src/Mclust.c0000644000076400007640000001244411467026034014236 0ustar volmelnvolmeln/* CARP: Clustering Algorithms' Referee Package Copyright (C) 2010 Volodymyr Melnykov and Ranjan Maitra DISCLAIMER: THIS SOURCE CODE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR'S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR'S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR's publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. 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 . Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; /* This porgram obtains classifications for simulated datasets using Mclust */ int main(int argc, char *argv[]) { int p, k, K, g, nf, c, i, j, errflag, code; int *id; char runR[500]; char buff[200] = ""; char Path[100] = "DATA"; char Xname[100] = "x.dat"; char IDname[100] = "idEst.dat"; FILE *fdata; /* DEFAULT PARAMETERS */ p = 0; k = 0; K = 0; g = 0; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ errflag = 0; /* DEALING WITH PARAMETERS */ if (argc == 1){ exit(1); } while ((c = getopt(argc, argv, ":p:k:K:n:D:i:X:#:")) != -1) { switch(c) { case 'p': /* # of dimensions */ p = atoi(optarg); break; case 'k': /* min # of components */ k = atoi(optarg); break; case 'K': /* max # of components */ K = atoi(optarg); break; case 'n': /* # of observations in datasets */ g = atoi(optarg); g = fabs(g); break; case 'D': /* Name of the working directory */ strcpy(Path, optarg); break; case 'i': /* Name for estimated classification */ strcpy(IDname, optarg); break; case 'X': /* Name for the file with covariance matrices */ strcpy(Xname, optarg); break; case '#': /* # of simulated mixtures */ nf = atoi(optarg); break; case ':': /* option with no operand */ printf("Operand for option -%c is not specified correctly...\n", optopt); errflag = 1; break; case '?': printf("Unrecognized option: -%c\n", optopt); errflag = 2; } } if (p < 1){ printf("Operand for option -p is not specified correctly...\n"); errflag = 1; } if (k < 1){ printf("Operand for option -k is not specified correctly...\n"); errflag = 1; } if (K < 1){ printf("Operand for option -K is not specified correctly...\n"); errflag = 1; } if (g < 0){ printf("Operand for option -g is not specified correctly...\n"); errflag = 1; } if (nf < 1){ printf("Operand for option -# is not specified correctly...\n"); errflag = 1; } if (errflag != 0) exit(1); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, Xname); strcpy(Xname, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDname); strcpy(IDname, buff); /* Run Mclust clustering */ sprintf(runR, "R CMD BATCH --no-save --no-restore '--args %d %d %d %d %d %s %s' PROC/runMclust.R", p, k, K, g, nf, Xname, IDname); code = system(runR); if (code != 0){ printf("See runMclust.Rout for errors...\n"); exit(1); } /* Print obtained classifications */ MAKE_VECTOR(id, g); if (!(fdata = fopen(IDname, "r"))){ fprintf(stderr, "Error: could not open file %s...\n", IDname); exit(EXIT_FAILURE); } printf("OBTAINED CLASSIFICATIONS:\n"); for (j=1; j<=nf; j++){ if (nf != 1){ printf("Dataset #%i:\n", j); } for (i=0; i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include "array.h" /* fprintOverlap : writes overlaps to files * Parameters: * K - number of components * OmegaMap - map of misclassification probabilities * BarOmega - average overlap * MaxOmega - maximum overlap * rcMax - pair of components producing maximum overlap * fileN - number of the dataset * overmap - name of file with the map of misclassification probabilities * overbarmax - name of file with overlap characteristics */ void fprintOverlap(int K, double **OmegaMap, double BarOmega, double MaxOmega, int *rcMax, int fileN, char *overmap, char *overbarmax){ int i, j; FILE *fout; if (fileN == 1){ if (!(fout = fopen(overmap, "w"))){ fprintf(stderr, "Error: could not open file %s...\n", overmap); exit(EXIT_FAILURE); } } else { if (!(fout = fopen(overmap, "a"))){ fprintf(stderr, "Error: could not open file %s...\n", overmap); exit(EXIT_FAILURE); } } for (i=0; i. Authors' contact information: Volodymyr Melnykov Ranjan Maitra Volodymyr.Melnykov@ndsu.edu maitra@iastate.edu Department of Statistics Department of Statistics North Dakota State University Iowa State University Fargo, ND 58102 Ames, IA 50011 */ #include #include #include #include #include "array.h" #define inf 1e+40; /* This program computes proportion of correctly classified observations for simulated datasets */ int getopt(int argc, char *const argv[], const char *optstring); extern char *optarg; extern int optind, optopt; int main(int argc, char *argv[]) { int K, g, nf, i, c, code, errflag; double *clagr; FILE *fname; char runR[500]; char buff[200] = ""; char Path[100] = "DATA"; char IDtrue[100] = "idTrue.dat"; char IDest[100] = "idEst.dat"; char ClAgrName[100] = "Diag.dat"; /* DEFAULT PARAMETERS */ K = 2; g = 0; /* sample size for generated datasets */ nf = 1; /* # of simulated mixtures */ errflag = 0; if (argc == 1){ exit(1); } while ((c = getopt(argc, argv, ":n:D:I:i:R:#:")) != -1) { switch(c) { case 'n': /* # of observations in simulated datasets */ g = atoi(optarg); g = fabs(g); break; case 'D': /* Name working directory */ strcpy(Path, optarg); break; case 'I': /* true classification */ strcpy(IDtrue, optarg); break; case 'i': /* estimated classification */ strcpy(IDest, optarg); break; case 'R': /* Class Agreement index */ strcpy(ClAgrName, optarg); break; case '#': /* # of simulated mixtures */ nf = atoi(optarg); break; case ':': /* option with no operand */ printf("Operand for option -%c is not specified correctly...\n", optopt); errflag = 1; break; case '?': printf("Unrecognized option: -%c\n", optopt); errflag = 2; } } if (g <= 0){ printf("Operand for option -g is not specified correctly...\n"); errflag = 1; } if (nf < 1){ printf("Operand for option -# is not specified correctly...\n"); errflag = 1; } if (errflag != 0) exit(1); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDtrue); strcpy(IDtrue, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, IDest); strcpy(IDest, buff); strcpy(buff, Path); strcat(buff, "/"); strcat(buff, ClAgrName); strcpy(ClAgrName, buff); sprintf(runR, "R CMD BATCH --no-save --no-restore '--args %d %d %s %s %s' PROC/runDiag.R", g, nf, IDtrue, IDest, ClAgrName); code = system(runR); if (code != 0){ printf("See runDiag.Rout for errors...\n"); exit(1); } MAKE_VECTOR(clagr, nf); fname = fopen(ClAgrName,"r"); printf("DIAG VALUES:\n"); for (i=0; i> endobj 8 0 obj (Description) endobj 9 0 obj << /S /GoTo /D (subsection.1.1) >> endobj 12 0 obj (Project homepage) endobj 13 0 obj << /S /GoTo /D (subsection.1.2) >> endobj 16 0 obj (Dependencies) endobj 17 0 obj << /S /GoTo /D (section.2) >> endobj 20 0 obj (Installation) endobj 21 0 obj << /S /GoTo /D (section.3) >> endobj 24 0 obj (Usage) endobj 25 0 obj << /S /GoTo /D (subsection.3.1) >> endobj 28 0 obj (Parameters) endobj 29 0 obj << /S /GoTo /D (subsection.3.2) >> endobj 32 0 obj (Details) endobj 33 0 obj << /S /GoTo /D (subsubsection.3.2.1) >> 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0.000 rg 296.00 180.86 m 325.33 208.49 l 325.33 209.66 l 296.00 182.45 l h f /GS292 gs 1.000 0.000 0.000 rg 325.33 204.35 m 354.67 207.80 l 354.67 206.69 l 325.33 203.18 l h f /GS293 gs 1.000 0.000 0.000 rg 354.67 207.80 m 384.00 211.25 l 384.00 210.20 l 354.67 206.69 l h f /GS294 gs 1.000 0.000 0.000 rg 384.00 211.25 m 413.33 214.70 l 413.33 213.71 l 384.00 210.20 l h f /GS282 gs 1.000 0.000 0.000 rg 413.33 214.70 m 442.67 218.14 l 442.67 217.22 l 413.33 213.71 l h f /GS268 gs 1.000 0.000 0.000 rg 442.67 218.14 m 472.00 221.59 l 472.00 220.73 l 442.67 217.22 l h f /GS292 gs 1.000 0.000 0.000 rg 325.33 208.49 m 354.67 211.72 l 354.67 212.83 l 325.33 209.66 l h f /GS293 gs 1.000 0.000 0.000 rg 354.67 211.72 m 384.00 214.95 l 384.00 216.00 l 354.67 212.83 l h f /GS294 gs 1.000 0.000 0.000 rg 384.00 214.95 m 413.33 218.18 l 413.33 219.16 l 384.00 216.00 l h f /GS282 gs 1.000 0.000 0.000 rg 413.33 218.18 m 442.67 221.41 l 442.67 222.33 l 413.33 219.16 l h f /GS268 gs 1.000 0.000 0.000 rg 442.67 221.41 m 472.00 224.64 l 472.00 225.50 l 442.67 222.33 l h f /GS289 gs 1.000 0.000 0.000 rg 32.00 302.56 m 61.33 253.13 l 61.33 250.84 l 32.00 300.68 l h f /GS276 gs 1.000 0.000 0.000 rg 61.33 253.13 m 90.67 203.71 l 90.67 201.00 l 61.33 250.84 l h f /GS288 gs 1.000 0.000 0.000 rg 90.67 203.71 m 120.00 154.28 l 120.00 151.16 l 90.67 201.00 l h f /GS295 gs 1.000 0.000 0.000 rg 120.00 154.28 m 149.33 104.85 l 149.33 101.31 l 120.00 151.16 l h f /GS296 gs 1.000 0.000 0.000 rg 149.33 104.85 m 178.67 55.42 l 178.67 51.47 l 149.33 101.31 l h f /GS289 gs 1.000 0.000 0.000 rg 32.00 311.21 m 61.33 263.69 l 61.33 265.98 l 32.00 313.09 l h f /GS276 gs 1.000 0.000 0.000 rg 61.33 263.69 m 90.67 216.17 l 90.67 218.88 l 61.33 265.98 l h f /GS288 gs 1.000 0.000 0.000 rg 90.67 216.17 m 120.00 168.65 l 120.00 171.77 l 90.67 218.88 l h f /GS295 gs 1.000 0.000 0.000 rg 120.00 168.65 m 149.33 121.14 l 149.33 124.67 l 120.00 171.77 l h f /GS296 gs 1.000 0.000 0.000 rg 149.33 121.14 m 178.67 73.62 l 178.67 77.57 l 149.33 124.67 l h f /GS297 gs 1.000 0.000 0.000 rg 178.67 55.42 m 208.00 84.97 l 208.00 81.53 l 178.67 51.47 l h f /GS298 gs 1.000 0.000 0.000 rg 208.00 84.97 m 237.33 114.52 l 237.33 111.59 l 208.00 81.53 l h f /GS260 gs 1.000 0.000 0.000 rg 237.33 114.52 m 266.67 144.08 l 266.67 141.65 l 237.33 111.59 l h f /GS299 gs 1.000 0.000 0.000 rg 266.67 144.08 m 296.00 173.63 l 296.00 171.71 l 266.67 141.65 l h f /GS300 gs 1.000 0.000 0.000 rg 296.00 173.63 m 325.33 203.18 l 325.33 201.77 l 296.00 171.71 l h f /GS297 gs 1.000 0.000 0.000 rg 178.67 73.62 m 208.00 100.83 l 208.00 104.27 l 178.67 77.57 l h f /GS298 gs 1.000 0.000 0.000 rg 208.00 100.83 m 237.33 128.03 l 237.33 130.97 l 208.00 104.27 l h f /GS260 gs 1.000 0.000 0.000 rg 237.33 128.03 m 266.67 155.24 l 266.67 157.67 l 237.33 130.97 l h f /GS299 gs 1.000 0.000 0.000 rg 266.67 155.24 m 296.00 182.45 l 296.00 184.37 l 266.67 157.67 l h f /GS300 gs 1.000 0.000 0.000 rg 296.00 182.45 m 325.33 209.66 l 325.33 211.06 l 296.00 184.37 l h f /GS301 gs 1.000 0.000 0.000 rg 325.33 203.18 m 354.67 206.69 l 354.67 205.36 l 325.33 201.77 l h f /GS302 gs 1.000 0.000 0.000 rg 354.67 206.69 m 384.00 210.20 l 384.00 208.94 l 354.67 205.36 l h f /GS303 gs 1.000 0.000 0.000 rg 384.00 210.20 m 413.33 213.71 l 413.33 212.53 l 384.00 208.94 l h f /GS304 gs 1.000 0.000 0.000 rg 413.33 213.71 m 442.67 217.22 l 442.67 216.11 l 413.33 212.53 l h f /GS305 gs 1.000 0.000 0.000 rg 442.67 217.22 m 472.00 220.73 l 472.00 219.70 l 442.67 216.11 l h f /GS301 gs 1.000 0.000 0.000 rg 325.33 209.66 m 354.67 212.83 l 354.67 214.16 l 325.33 211.06 l h f /GS302 gs 1.000 0.000 0.000 rg 354.67 212.83 m 384.00 216.00 l 384.00 217.25 l 354.67 214.16 l h f /GS303 gs 1.000 0.000 0.000 rg 384.00 216.00 m 413.33 219.16 l 413.33 220.35 l 384.00 217.25 l h f /GS304 gs 1.000 0.000 0.000 rg 413.33 219.16 m 442.67 222.33 l 442.67 223.44 l 413.33 220.35 l h f /GS305 gs 1.000 0.000 0.000 rg 442.67 222.33 m 472.00 225.50 l 472.00 226.54 l 442.67 223.44 l h f /GS306 gs 1.000 0.000 0.000 rg 32.00 300.68 m 61.33 250.84 l 61.33 247.59 l 32.00 298.02 l h f /GS307 gs 1.000 0.000 0.000 rg 61.33 250.84 m 90.67 201.00 l 90.67 197.16 l 61.33 247.59 l h f /GS308 gs 1.000 0.000 0.000 rg 90.67 201.00 m 120.00 151.16 l 120.00 146.73 l 90.67 197.16 l h f /GS309 gs 1.000 0.000 0.000 rg 120.00 151.16 m 149.33 101.31 l 149.33 96.30 l 120.00 146.73 l h f /GS310 gs 1.000 0.000 0.000 rg 149.33 101.31 m 178.67 51.47 l 178.67 45.87 l 149.33 96.30 l h f /GS306 gs 1.000 0.000 0.000 rg 32.00 313.09 m 61.33 265.98 l 61.33 269.23 l 32.00 315.75 l h f /GS307 gs 1.000 0.000 0.000 rg 61.33 265.98 m 90.67 218.88 l 90.67 222.72 l 61.33 269.23 l h f /GS308 gs 1.000 0.000 0.000 rg 90.67 218.88 m 120.00 171.77 l 120.00 176.20 l 90.67 222.72 l h f /GS309 gs 1.000 0.000 0.000 rg 120.00 171.77 m 149.33 124.67 l 149.33 129.69 l 120.00 176.20 l h f /GS310 gs 1.000 0.000 0.000 rg 149.33 124.67 m 178.67 77.57 l 178.67 83.17 l 149.33 129.69 l h f /GS311 gs 1.000 0.000 0.000 rg 178.67 51.47 m 208.00 81.53 l 208.00 76.65 l 178.67 45.87 l h f /GS312 gs 1.000 0.000 0.000 rg 208.00 81.53 m 237.33 111.59 l 237.33 107.43 l 208.00 76.65 l h f /GS313 gs 1.000 0.000 0.000 rg 237.33 111.59 m 266.67 141.65 l 266.67 138.21 l 237.33 107.43 l h f /GS314 gs 1.000 0.000 0.000 rg 266.67 141.65 m 296.00 171.71 l 296.00 168.99 l 266.67 138.21 l h f /GS315 gs 1.000 0.000 0.000 rg 296.00 171.71 m 325.33 201.77 l 325.33 199.78 l 296.00 168.99 l h f /GS311 gs 1.000 0.000 0.000 rg 178.67 77.57 m 208.00 104.27 l 208.00 109.15 l 178.67 83.17 l h f /GS312 gs 1.000 0.000 0.000 rg 208.00 104.27 m 237.33 130.97 l 237.33 135.13 l 208.00 109.15 l h f /GS313 gs 1.000 0.000 0.000 rg 237.33 130.97 m 266.67 157.67 l 266.67 161.11 l 237.33 135.13 l h f /GS314 gs 1.000 0.000 0.000 rg 266.67 157.67 m 296.00 184.37 l 296.00 187.08 l 266.67 161.11 l h f /GS315 gs 1.000 0.000 0.000 rg 296.00 184.37 m 325.33 211.06 l 325.33 213.06 l 296.00 187.08 l h f /GS285 gs 1.000 0.000 0.000 rg 325.33 201.77 m 354.67 205.36 l 354.67 203.47 l 325.33 199.78 l h f /GS299 gs 1.000 0.000 0.000 rg 354.67 205.36 m 384.00 208.94 l 384.00 207.16 l 354.67 203.47 l h f /GS316 gs 1.000 0.000 0.000 rg 384.00 208.94 m 413.33 212.53 l 413.33 210.85 l 384.00 207.16 l h f /GS317 gs 1.000 0.000 0.000 rg 413.33 212.53 m 442.67 216.11 l 442.67 214.54 l 413.33 210.85 l h f /GS272 gs 1.000 0.000 0.000 rg 442.67 216.11 m 472.00 219.70 l 472.00 218.23 l 442.67 214.54 l h f /GS285 gs 1.000 0.000 0.000 rg 325.33 211.06 m 354.67 214.16 l 354.67 216.05 l 325.33 213.06 l h f /GS299 gs 1.000 0.000 0.000 rg 354.67 214.16 m 384.00 217.25 l 384.00 219.04 l 354.67 216.05 l h f /GS316 gs 1.000 0.000 0.000 rg 384.00 217.25 m 413.33 220.35 l 413.33 222.03 l 384.00 219.04 l h f /GS317 gs 1.000 0.000 0.000 rg 413.33 220.35 m 442.67 223.44 l 442.67 225.02 l 413.33 222.03 l h f /GS272 gs 1.000 0.000 0.000 rg 442.67 223.44 m 472.00 226.54 l 472.00 228.01 l 442.67 225.02 l h f /GS1 gs 0.000 1.000 0.000 RG 0.08 w [] 0 d 32.00 398.71 m 178.67 77.22 l 325.33 202.60 l 472.00 222.40 l S /GS1 gs 0.000 0.000 0.000 RG 0.08 w [ 3.00 5.00] 0 d 32.00 398.71 m 178.67 77.22 l 325.33 202.60 l 472.00 222.40 l S /GS318 gs 0.000 1.000 0.000 rg 32.00 398.71 m 61.33 334.41 l 61.33 330.98 l 32.00 395.08 l h f /GS307 gs 0.000 1.000 0.000 rg 61.33 334.41 m 90.67 270.11 l 90.67 266.89 l 61.33 330.98 l h f /GS297 gs 0.000 1.000 0.000 rg 90.67 270.11 m 120.00 205.81 l 120.00 202.79 l 90.67 266.89 l h f /GS295 gs 0.000 1.000 0.000 rg 120.00 205.81 m 149.33 141.51 l 149.33 138.70 l 120.00 202.79 l h f /GS319 gs 0.000 1.000 0.000 rg 149.33 141.51 m 178.67 77.22 l 178.67 74.60 l 149.33 138.70 l h f /GS318 gs 0.000 1.000 0.000 rg 32.00 398.71 m 61.33 334.41 l 61.33 337.84 l 32.00 402.34 l h f /GS307 gs 0.000 1.000 0.000 rg 61.33 334.41 m 90.67 270.11 l 90.67 273.33 l 61.33 337.84 l h f /GS297 gs 0.000 1.000 0.000 rg 90.67 270.11 m 120.00 205.81 l 120.00 208.83 l 90.67 273.33 l h f /GS295 gs 0.000 1.000 0.000 rg 120.00 205.81 m 149.33 141.51 l 149.33 144.33 l 120.00 208.83 l h f /GS319 gs 0.000 1.000 0.000 rg 149.33 141.51 m 178.67 77.22 l 178.67 79.83 l 149.33 144.33 l h f /GS320 gs 0.000 1.000 0.000 rg 178.67 77.22 m 208.00 102.29 l 208.00 99.88 l 178.67 74.60 l h f /GS321 gs 0.000 1.000 0.000 rg 208.00 102.29 m 237.33 127.37 l 237.33 125.17 l 208.00 99.88 l h f /GS322 gs 0.000 1.000 0.000 rg 237.33 127.37 m 266.67 152.45 l 266.67 150.45 l 237.33 125.17 l h f /GS259 gs 0.000 1.000 0.000 rg 266.67 152.45 m 296.00 177.53 l 296.00 175.73 l 266.67 150.45 l h f /GS316 gs 0.000 1.000 0.000 rg 296.00 177.53 m 325.33 202.60 l 325.33 201.02 l 296.00 175.73 l h f /GS320 gs 0.000 1.000 0.000 rg 178.67 77.22 m 208.00 102.29 l 208.00 104.70 l 178.67 79.83 l h f /GS321 gs 0.000 1.000 0.000 rg 208.00 102.29 m 237.33 127.37 l 237.33 129.58 l 208.00 104.70 l h f /GS322 gs 0.000 1.000 0.000 rg 237.33 127.37 m 266.67 152.45 l 266.67 154.45 l 237.33 129.58 l h f /GS259 gs 0.000 1.000 0.000 rg 266.67 152.45 m 296.00 177.53 l 296.00 179.32 l 266.67 154.45 l h f /GS316 gs 0.000 1.000 0.000 rg 296.00 177.53 m 325.33 202.60 l 325.33 204.19 l 296.00 179.32 l h f /GS279 gs 0.000 1.000 0.000 rg 325.33 202.60 m 354.67 206.56 l 354.67 205.14 l 325.33 201.02 l h f /GS323 gs 0.000 1.000 0.000 rg 354.67 206.56 m 384.00 210.52 l 384.00 209.26 l 354.67 205.14 l h f /GS324 gs 0.000 1.000 0.000 rg 384.00 210.52 m 413.33 214.48 l 413.33 213.38 l 384.00 209.26 l h f /GS325 gs 0.000 1.000 0.000 rg 413.33 214.48 m 442.67 218.44 l 442.67 217.50 l 413.33 213.38 l h f /GS326 gs 0.000 1.000 0.000 rg 442.67 218.44 m 472.00 222.40 l 472.00 221.62 l 442.67 217.50 l h f /GS279 gs 0.000 1.000 0.000 rg 325.33 202.60 m 354.67 206.56 l 354.67 207.99 l 325.33 204.19 l h f /GS323 gs 0.000 1.000 0.000 rg 354.67 206.56 m 384.00 210.52 l 384.00 211.78 l 354.67 207.99 l h f /GS324 gs 0.000 1.000 0.000 rg 384.00 210.52 m 413.33 214.48 l 413.33 215.58 l 384.00 211.78 l h f /GS325 gs 0.000 1.000 0.000 rg 413.33 214.48 m 442.67 218.44 l 442.67 219.38 l 413.33 215.58 l h f /GS326 gs 0.000 1.000 0.000 rg 442.67 218.44 m 472.00 222.40 l 472.00 223.17 l 442.67 219.38 l h f /GS327 gs 0.000 1.000 0.000 rg 32.00 395.08 m 61.33 330.98 l 61.33 327.39 l 32.00 391.28 l h f /GS318 gs 0.000 1.000 0.000 rg 61.33 330.98 m 90.67 266.89 l 90.67 263.51 l 61.33 327.39 l h f /GS328 gs 0.000 1.000 0.000 rg 90.67 266.89 m 120.00 202.79 l 120.00 199.63 l 90.67 263.51 l h f /GS329 gs 0.000 1.000 0.000 rg 120.00 202.79 m 149.33 138.70 l 149.33 135.74 l 120.00 199.63 l h f /GS295 gs 0.000 1.000 0.000 rg 149.33 138.70 m 178.67 74.60 l 178.67 71.86 l 149.33 135.74 l h f /GS327 gs 0.000 1.000 0.000 rg 32.00 402.34 m 61.33 337.84 l 61.33 341.42 l 32.00 406.14 l h f /GS318 gs 0.000 1.000 0.000 rg 61.33 337.84 m 90.67 273.33 l 90.67 276.71 l 61.33 341.42 l h f /GS328 gs 0.000 1.000 0.000 rg 90.67 273.33 m 120.00 208.83 l 120.00 212.00 l 90.67 276.71 l h f /GS329 gs 0.000 1.000 0.000 rg 120.00 208.83 m 149.33 144.33 l 149.33 147.28 l 120.00 212.00 l h f /GS295 gs 0.000 1.000 0.000 rg 149.33 144.33 m 178.67 79.83 l 178.67 82.57 l 149.33 147.28 l h f /GS298 gs 0.000 1.000 0.000 rg 178.67 74.60 m 208.00 99.88 l 208.00 97.36 l 178.67 71.86 l h f /GS315 gs 0.000 1.000 0.000 rg 208.00 99.88 m 237.33 125.17 l 237.33 122.86 l 208.00 97.36 l h f /GS289 gs 0.000 1.000 0.000 rg 237.33 125.17 m 266.67 150.45 l 266.67 148.36 l 237.33 122.86 l h f /GS274 gs 0.000 1.000 0.000 rg 266.67 150.45 m 296.00 175.73 l 296.00 173.86 l 266.67 148.36 l h f /GS263 gs 0.000 1.000 0.000 rg 296.00 175.73 m 325.33 201.02 l 325.33 199.36 l 296.00 173.86 l h f /GS298 gs 0.000 1.000 0.000 rg 178.67 79.83 m 208.00 104.70 l 208.00 107.23 l 178.67 82.57 l h f /GS315 gs 0.000 1.000 0.000 rg 208.00 104.70 m 237.33 129.58 l 237.33 131.88 l 208.00 107.23 l h f /GS289 gs 0.000 1.000 0.000 rg 237.33 129.58 m 266.67 154.45 l 266.67 156.54 l 237.33 131.88 l h f /GS274 gs 0.000 1.000 0.000 rg 266.67 154.45 m 296.00 179.32 l 296.00 181.19 l 266.67 156.54 l h f /GS263 gs 0.000 1.000 0.000 rg 296.00 179.32 m 325.33 204.19 l 325.33 205.85 l 296.00 181.19 l h f /GS330 gs 0.000 1.000 0.000 rg 325.33 201.02 m 354.67 205.14 l 354.67 203.65 l 325.33 199.36 l h f /GS331 gs 0.000 1.000 0.000 rg 354.67 205.14 m 384.00 209.26 l 384.00 207.94 l 354.67 203.65 l h f /GS332 gs 0.000 1.000 0.000 rg 384.00 209.26 m 413.33 213.38 l 413.33 212.23 l 384.00 207.94 l h f /GS333 gs 0.000 1.000 0.000 rg 413.33 213.38 m 442.67 217.50 l 442.67 216.52 l 413.33 212.23 l h f /GS334 gs 0.000 1.000 0.000 rg 442.67 217.50 m 472.00 221.62 l 472.00 220.81 l 442.67 216.52 l h f /GS330 gs 0.000 1.000 0.000 rg 325.33 204.19 m 354.67 207.99 l 354.67 209.48 l 325.33 205.85 l h f /GS331 gs 0.000 1.000 0.000 rg 354.67 207.99 m 384.00 211.78 l 384.00 213.10 l 354.67 209.48 l h f /GS332 gs 0.000 1.000 0.000 rg 384.00 211.78 m 413.33 215.58 l 413.33 216.73 l 384.00 213.10 l h f /GS333 gs 0.000 1.000 0.000 rg 413.33 215.58 m 442.67 219.38 l 442.67 220.36 l 413.33 216.73 l h f /GS334 gs 0.000 1.000 0.000 rg 442.67 219.38 m 472.00 223.17 l 472.00 223.98 l 442.67 220.36 l h f /GS335 gs 0.000 1.000 0.000 rg 32.00 391.28 m 61.33 327.39 l 61.33 323.42 l 32.00 387.07 l h f /GS336 gs 0.000 1.000 0.000 rg 61.33 327.39 m 90.67 263.51 l 90.67 259.78 l 61.33 323.42 l h f /GS312 gs 0.000 1.000 0.000 rg 90.67 263.51 m 120.00 199.63 l 120.00 196.13 l 90.67 259.78 l h f /GS296 gs 0.000 1.000 0.000 rg 120.00 199.63 m 149.33 135.74 l 149.33 132.48 l 120.00 196.13 l h f /GS328 gs 0.000 1.000 0.000 rg 149.33 135.74 m 178.67 71.86 l 178.67 68.83 l 149.33 132.48 l h f /GS335 gs 0.000 1.000 0.000 rg 32.00 406.14 m 61.33 341.42 l 61.33 345.39 l 32.00 410.34 l h f /GS336 gs 0.000 1.000 0.000 rg 61.33 341.42 m 90.67 276.71 l 90.67 280.45 l 61.33 345.39 l h f /GS312 gs 0.000 1.000 0.000 rg 90.67 276.71 m 120.00 212.00 l 120.00 215.50 l 90.67 280.45 l h f /GS296 gs 0.000 1.000 0.000 rg 120.00 212.00 m 149.33 147.28 l 149.33 150.55 l 120.00 215.50 l h f /GS328 gs 0.000 1.000 0.000 rg 149.33 147.28 m 178.67 82.57 l 178.67 85.60 l 149.33 150.55 l h f /GS319 gs 0.000 1.000 0.000 rg 178.67 71.86 m 208.00 97.36 l 208.00 94.57 l 178.67 68.83 l h f /GS275 gs 0.000 1.000 0.000 rg 208.00 97.36 m 237.33 122.86 l 237.33 120.31 l 208.00 94.57 l h f /GS276 gs 0.000 1.000 0.000 rg 237.33 122.86 m 266.67 148.36 l 266.67 146.04 l 237.33 120.31 l h f /GS286 gs 0.000 1.000 0.000 rg 266.67 148.36 m 296.00 173.86 l 296.00 171.78 l 266.67 146.04 l h f /GS337 gs 0.000 1.000 0.000 rg 296.00 173.86 m 325.33 199.36 l 325.33 197.52 l 296.00 171.78 l h f /GS319 gs 0.000 1.000 0.000 rg 178.67 82.57 m 208.00 107.23 l 208.00 110.02 l 178.67 85.60 l h f /GS275 gs 0.000 1.000 0.000 rg 208.00 107.23 m 237.33 131.88 l 237.33 134.44 l 208.00 110.02 l h f /GS276 gs 0.000 1.000 0.000 rg 237.33 131.88 m 266.67 156.54 l 266.67 158.85 l 237.33 134.44 l h f /GS286 gs 0.000 1.000 0.000 rg 266.67 156.54 m 296.00 181.19 l 296.00 183.27 l 266.67 158.85 l h f /GS337 gs 0.000 1.000 0.000 rg 296.00 181.19 m 325.33 205.85 l 325.33 207.69 l 296.00 183.27 l h f /GS338 gs 0.000 1.000 0.000 rg 325.33 199.36 m 354.67 203.65 l 354.67 202.00 l 325.33 197.52 l h f /GS339 gs 0.000 1.000 0.000 rg 354.67 203.65 m 384.00 207.94 l 384.00 206.48 l 354.67 202.00 l h f /GS340 gs 0.000 1.000 0.000 rg 384.00 207.94 m 413.33 212.23 l 413.33 210.96 l 384.00 206.48 l h f /GS305 gs 0.000 1.000 0.000 rg 413.33 212.23 m 442.67 216.52 l 442.67 215.44 l 413.33 210.96 l h f /GS341 gs 0.000 1.000 0.000 rg 442.67 216.52 m 472.00 220.81 l 472.00 219.91 l 442.67 215.44 l h f /GS338 gs 0.000 1.000 0.000 rg 325.33 205.85 m 354.67 209.48 l 354.67 211.13 l 325.33 207.69 l h f /GS339 gs 0.000 1.000 0.000 rg 354.67 209.48 m 384.00 213.10 l 384.00 214.56 l 354.67 211.13 l h f /GS340 gs 0.000 1.000 0.000 rg 384.00 213.10 m 413.33 216.73 l 413.33 218.00 l 384.00 214.56 l h f /GS305 gs 0.000 1.000 0.000 rg 413.33 216.73 m 442.67 220.36 l 442.67 221.44 l 413.33 218.00 l h f /GS341 gs 0.000 1.000 0.000 rg 442.67 220.36 m 472.00 223.98 l 472.00 224.88 l 442.67 221.44 l h f /GS310 gs 0.000 1.000 0.000 rg 32.00 387.07 m 61.33 323.42 l 61.33 318.65 l 32.00 382.02 l h f /GS342 gs 0.000 1.000 0.000 rg 61.33 323.42 m 90.67 259.78 l 90.67 255.29 l 61.33 318.65 l h f /GS343 gs 0.000 1.000 0.000 rg 90.67 259.78 m 120.00 196.13 l 120.00 191.92 l 90.67 255.29 l h f /GS344 gs 0.000 1.000 0.000 rg 120.00 196.13 m 149.33 132.48 l 149.33 128.55 l 120.00 191.92 l h f /GS345 gs 0.000 1.000 0.000 rg 149.33 132.48 m 178.67 68.83 l 178.67 65.19 l 149.33 128.55 l h f /GS310 gs 0.000 1.000 0.000 rg 32.00 410.34 m 61.33 345.39 l 61.33 350.16 l 32.00 415.39 l h f /GS342 gs 0.000 1.000 0.000 rg 61.33 345.39 m 90.67 280.45 l 90.67 284.93 l 61.33 350.16 l h f /GS343 gs 0.000 1.000 0.000 rg 90.67 280.45 m 120.00 215.50 l 120.00 219.70 l 90.67 284.93 l h f /GS344 gs 0.000 1.000 0.000 rg 120.00 215.50 m 149.33 150.55 l 149.33 154.47 l 120.00 219.70 l h f /GS345 gs 0.000 1.000 0.000 rg 149.33 150.55 m 178.67 85.60 l 178.67 89.24 l 149.33 154.47 l h f /GS346 gs 0.000 1.000 0.000 rg 178.67 68.83 m 208.00 94.57 l 208.00 91.21 l 178.67 65.19 l h f /GS329 gs 0.000 1.000 0.000 rg 208.00 94.57 m 237.33 120.31 l 237.33 117.24 l 208.00 91.21 l h f /GS319 gs 0.000 1.000 0.000 rg 237.33 120.31 m 266.67 146.04 l 266.67 143.26 l 237.33 117.24 l h f /GS288 gs 0.000 1.000 0.000 rg 266.67 146.04 m 296.00 171.78 l 296.00 169.29 l 266.67 143.26 l h f /GS320 gs 0.000 1.000 0.000 rg 296.00 171.78 m 325.33 197.52 l 325.33 195.31 l 296.00 169.29 l h f /GS346 gs 0.000 1.000 0.000 rg 178.67 85.60 m 208.00 110.02 l 208.00 113.37 l 178.67 89.24 l h f /GS329 gs 0.000 1.000 0.000 rg 208.00 110.02 m 237.33 134.44 l 237.33 137.50 l 208.00 113.37 l h f /GS319 gs 0.000 1.000 0.000 rg 237.33 134.44 m 266.67 158.85 l 266.67 161.63 l 237.33 137.50 l h f /GS288 gs 0.000 1.000 0.000 rg 266.67 158.85 m 296.00 183.27 l 296.00 185.76 l 266.67 161.63 l h f /GS320 gs 0.000 1.000 0.000 rg 296.00 183.27 m 325.33 207.69 l 325.33 209.89 l 296.00 185.76 l h f /GS347 gs 0.000 1.000 0.000 rg 325.33 197.52 m 354.67 202.00 l 354.67 200.02 l 325.33 195.31 l h f /GS258 gs 0.000 1.000 0.000 rg 354.67 202.00 m 384.00 206.48 l 384.00 204.72 l 354.67 200.02 l h f /GS348 gs 0.000 1.000 0.000 rg 384.00 206.48 m 413.33 210.96 l 413.33 209.43 l 384.00 204.72 l h f /GS349 gs 0.000 1.000 0.000 rg 413.33 210.96 m 442.67 215.44 l 442.67 214.13 l 413.33 209.43 l h f /GS323 gs 0.000 1.000 0.000 rg 442.67 215.44 m 472.00 219.91 l 472.00 218.84 l 442.67 214.13 l h f /GS347 gs 0.000 1.000 0.000 rg 325.33 207.69 m 354.67 211.13 l 354.67 213.11 l 325.33 209.89 l h f /GS258 gs 0.000 1.000 0.000 rg 354.67 211.13 m 384.00 214.56 l 384.00 216.32 l 354.67 213.11 l h f /GS348 gs 0.000 1.000 0.000 rg 384.00 214.56 m 413.33 218.00 l 413.33 219.53 l 384.00 216.32 l h f /GS349 gs 0.000 1.000 0.000 rg 413.33 218.00 m 442.67 221.44 l 442.67 222.75 l 413.33 219.53 l h f /GS323 gs 0.000 1.000 0.000 rg 442.67 221.44 m 472.00 224.88 l 472.00 225.96 l 442.67 222.75 l h f /GS350 gs 0.000 1.000 0.000 rg 32.00 382.02 m 61.33 318.65 l 61.33 311.88 l 32.00 374.85 l h f /GS351 gs 0.000 1.000 0.000 rg 61.33 318.65 m 90.67 255.29 l 90.67 248.92 l 61.33 311.88 l h f /GS352 gs 0.000 1.000 0.000 rg 90.67 255.29 m 120.00 191.92 l 120.00 185.95 l 90.67 248.92 l h f /GS353 gs 0.000 1.000 0.000 rg 120.00 191.92 m 149.33 128.55 l 149.33 122.99 l 120.00 185.95 l h f /GS354 gs 0.000 1.000 0.000 rg 149.33 128.55 m 178.67 65.19 l 178.67 60.02 l 149.33 122.99 l h f /GS350 gs 0.000 1.000 0.000 rg 32.00 415.39 m 61.33 350.16 l 61.33 356.93 l 32.00 422.56 l h f /GS351 gs 0.000 1.000 0.000 rg 61.33 350.16 m 90.67 284.93 l 90.67 291.30 l 61.33 356.93 l h f /GS352 gs 0.000 1.000 0.000 rg 90.67 284.93 m 120.00 219.70 l 120.00 225.67 l 90.67 291.30 l h f /GS353 gs 0.000 1.000 0.000 rg 120.00 219.70 m 149.33 154.47 l 149.33 160.04 l 120.00 225.67 l h f /GS354 gs 0.000 1.000 0.000 rg 149.33 154.47 m 178.67 89.24 l 178.67 94.41 l 149.33 160.04 l h f /GS355 gs 0.000 1.000 0.000 rg 178.67 65.19 m 208.00 91.21 l 208.00 86.45 l 178.67 60.02 l h f /GS311 gs 0.000 1.000 0.000 rg 208.00 91.21 m 237.33 117.24 l 237.33 112.88 l 208.00 86.45 l h f /GS335 gs 0.000 1.000 0.000 rg 237.33 117.24 m 266.67 143.26 l 266.67 139.32 l 237.33 112.88 l h f /GS308 gs 0.000 1.000 0.000 rg 266.67 143.26 m 296.00 169.29 l 296.00 165.75 l 266.67 139.32 l h f /GS318 gs 0.000 1.000 0.000 rg 296.00 169.29 m 325.33 195.31 l 325.33 192.18 l 296.00 165.75 l h f /GS355 gs 0.000 1.000 0.000 rg 178.67 89.24 m 208.00 113.37 l 208.00 118.13 l 178.67 94.41 l h f /GS311 gs 0.000 1.000 0.000 rg 208.00 113.37 m 237.33 137.50 l 237.33 141.86 l 208.00 118.13 l h f /GS335 gs 0.000 1.000 0.000 rg 237.33 137.50 m 266.67 161.63 l 266.67 165.58 l 237.33 141.86 l h f /GS308 gs 0.000 1.000 0.000 rg 266.67 161.63 m 296.00 185.76 l 296.00 189.30 l 266.67 165.58 l h f /GS318 gs 0.000 1.000 0.000 rg 296.00 185.76 m 325.33 209.89 l 325.33 213.03 l 296.00 189.30 l h f /GS356 gs 0.000 1.000 0.000 rg 325.33 195.31 m 354.67 200.02 l 354.67 197.21 l 325.33 192.18 l h f /GS357 gs 0.000 1.000 0.000 rg 354.67 200.02 m 384.00 204.72 l 384.00 202.23 l 354.67 197.21 l h f /GS289 gs 0.000 1.000 0.000 rg 384.00 204.72 m 413.33 209.43 l 413.33 207.26 l 384.00 202.23 l h f /GS277 gs 0.000 1.000 0.000 rg 413.33 209.43 m 442.67 214.13 l 442.67 212.28 l 413.33 207.26 l h f /GS358 gs 0.000 1.000 0.000 rg 442.67 214.13 m 472.00 218.84 l 472.00 217.31 l 442.67 212.28 l h f /GS356 gs 0.000 1.000 0.000 rg 325.33 209.89 m 354.67 213.11 l 354.67 215.92 l 325.33 213.03 l h f /GS357 gs 0.000 1.000 0.000 rg 354.67 213.11 m 384.00 216.32 l 384.00 218.81 l 354.67 215.92 l h f /GS289 gs 0.000 1.000 0.000 rg 384.00 216.32 m 413.33 219.53 l 413.33 221.70 l 384.00 218.81 l h f /GS277 gs 0.000 1.000 0.000 rg 413.33 219.53 m 442.67 222.75 l 442.67 224.60 l 413.33 221.70 l h f /GS358 gs 0.000 1.000 0.000 rg 442.67 222.75 m 472.00 225.96 l 472.00 227.49 l 442.67 224.60 l h f /GS1 gs 0.000 0.000 1.000 RG 0.08 w [] 0 d 32.00 444.29 m 178.67 68.59 l 325.33 209.07 l 472.00 223.80 l S /GS1 gs 0.000 0.000 0.000 RG 0.08 w [ 3.00 5.00] 0 d 32.00 444.29 m 178.67 68.59 l 325.33 209.07 l 472.00 223.80 l S /GS335 gs 0.000 0.000 1.000 rg 32.00 444.29 m 61.33 369.15 l 61.33 365.12 l 32.00 439.99 l h f /GS336 gs 0.000 0.000 1.000 rg 61.33 369.15 m 90.67 294.01 l 90.67 290.25 l 61.33 365.12 l h f /GS312 gs 0.000 0.000 1.000 rg 90.67 294.01 m 120.00 218.87 l 120.00 215.39 l 90.67 290.25 l h f /GS346 gs 0.000 0.000 1.000 rg 120.00 218.87 m 149.33 143.73 l 149.33 140.52 l 120.00 215.39 l h f /GS359 gs 0.000 0.000 1.000 rg 149.33 143.73 m 178.67 68.59 l 178.67 65.65 l 149.33 140.52 l h f /GS335 gs 0.000 0.000 1.000 rg 32.00 444.29 m 61.33 369.15 l 61.33 373.17 l 32.00 448.58 l h f /GS336 gs 0.000 0.000 1.000 rg 61.33 369.15 m 90.67 294.01 l 90.67 297.76 l 61.33 373.17 l h f /GS312 gs 0.000 0.000 1.000 rg 90.67 294.01 m 120.00 218.87 l 120.00 222.35 l 90.67 297.76 l h f /GS346 gs 0.000 0.000 1.000 rg 120.00 218.87 m 149.33 143.73 l 149.33 146.94 l 120.00 222.35 l h f /GS359 gs 0.000 0.000 1.000 rg 149.33 143.73 m 178.67 68.59 l 178.67 71.52 l 149.33 146.94 l h f /GS319 gs 0.000 0.000 1.000 rg 178.67 68.59 m 208.00 96.68 l 208.00 93.92 l 178.67 65.65 l h f /GS314 gs 0.000 0.000 1.000 rg 208.00 96.68 m 237.33 124.78 l 237.33 122.19 l 208.00 93.92 l h f /GS360 gs 0.000 0.000 1.000 rg 237.33 124.78 m 266.67 152.88 l 266.67 150.46 l 237.33 122.19 l h f /GS361 gs 0.000 0.000 1.000 rg 266.67 152.88 m 296.00 180.98 l 296.00 178.73 l 266.67 150.46 l h f /GS262 gs 0.000 0.000 1.000 rg 296.00 180.98 m 325.33 209.07 l 325.33 207.00 l 296.00 178.73 l h f /GS319 gs 0.000 0.000 1.000 rg 178.67 68.59 m 208.00 96.68 l 208.00 99.45 l 178.67 71.52 l h f /GS314 gs 0.000 0.000 1.000 rg 208.00 96.68 m 237.33 124.78 l 237.33 127.37 l 208.00 99.45 l h f /GS360 gs 0.000 0.000 1.000 rg 237.33 124.78 m 266.67 152.88 l 266.67 155.30 l 237.33 127.37 l h f /GS361 gs 0.000 0.000 1.000 rg 266.67 152.88 m 296.00 180.98 l 296.00 183.23 l 266.67 155.30 l h f /GS262 gs 0.000 0.000 1.000 rg 296.00 180.98 m 325.33 209.07 l 325.33 211.15 l 296.00 183.23 l h f /GS277 gs 0.000 0.000 1.000 rg 325.33 209.07 m 354.67 212.02 l 354.67 210.11 l 325.33 207.00 l h f /GS358 gs 0.000 0.000 1.000 rg 354.67 212.02 m 384.00 214.96 l 384.00 213.23 l 354.67 210.11 l h f /GS362 gs 0.000 0.000 1.000 rg 384.00 214.96 m 413.33 217.91 l 413.33 216.35 l 384.00 213.23 l h f /GS330 gs 0.000 0.000 1.000 rg 413.33 217.91 m 442.67 220.85 l 442.67 219.46 l 413.33 216.35 l h f /GS291 gs 0.000 0.000 1.000 rg 442.67 220.85 m 472.00 223.80 l 472.00 222.58 l 442.67 219.46 l h f /GS277 gs 0.000 0.000 1.000 rg 325.33 209.07 m 354.67 212.02 l 354.67 213.92 l 325.33 211.15 l h f /GS358 gs 0.000 0.000 1.000 rg 354.67 212.02 m 384.00 214.96 l 384.00 216.70 l 354.67 213.92 l h f /GS362 gs 0.000 0.000 1.000 rg 384.00 214.96 m 413.33 217.91 l 413.33 219.47 l 384.00 216.70 l h f /GS330 gs 0.000 0.000 1.000 rg 413.33 217.91 m 442.67 220.85 l 442.67 222.24 l 413.33 219.47 l h f /GS291 gs 0.000 0.000 1.000 rg 442.67 220.85 m 472.00 223.80 l 472.00 225.02 l 442.67 222.24 l h f /GS343 gs 0.000 0.000 1.000 rg 32.00 439.99 m 61.33 365.12 l 61.33 360.91 l 32.00 435.49 l h f /GS363 gs 0.000 0.000 1.000 rg 61.33 365.12 m 90.67 290.25 l 90.67 286.32 l 61.33 360.91 l h f /GS308 gs 0.000 0.000 1.000 rg 90.67 290.25 m 120.00 215.39 l 120.00 211.74 l 90.67 286.32 l h f /GS364 gs 0.000 0.000 1.000 rg 120.00 215.39 m 149.33 140.52 l 149.33 137.16 l 120.00 211.74 l h f /GS346 gs 0.000 0.000 1.000 rg 149.33 140.52 m 178.67 65.65 l 178.67 62.57 l 149.33 137.16 l h f /GS343 gs 0.000 0.000 1.000 rg 32.00 448.58 m 61.33 373.17 l 61.33 377.39 l 32.00 453.08 l h f /GS363 gs 0.000 0.000 1.000 rg 61.33 373.17 m 90.67 297.76 l 90.67 301.69 l 61.33 377.39 l h f /GS308 gs 0.000 0.000 1.000 rg 90.67 297.76 m 120.00 222.35 l 120.00 225.99 l 90.67 301.69 l h f /GS364 gs 0.000 0.000 1.000 rg 120.00 222.35 m 149.33 146.94 l 149.33 150.29 l 120.00 225.99 l h f /GS346 gs 0.000 0.000 1.000 rg 149.33 146.94 m 178.67 71.52 l 178.67 74.60 l 149.33 150.29 l h f /GS295 gs 0.000 0.000 1.000 rg 178.67 65.65 m 208.00 93.92 l 208.00 91.02 l 178.67 62.57 l h f /GS365 gs 0.000 0.000 1.000 rg 208.00 93.92 m 237.33 122.19 l 237.33 119.47 l 208.00 91.02 l h f /GS314 gs 0.000 0.000 1.000 rg 237.33 122.19 m 266.67 150.46 l 266.67 147.92 l 237.33 119.47 l h f /GS261 gs 0.000 0.000 1.000 rg 266.67 150.46 m 296.00 178.73 l 296.00 176.37 l 266.67 147.92 l h f /GS276 gs 0.000 0.000 1.000 rg 296.00 178.73 m 325.33 207.00 l 325.33 204.82 l 296.00 176.37 l h f /GS295 gs 0.000 0.000 1.000 rg 178.67 71.52 m 208.00 99.45 l 208.00 102.34 l 178.67 74.60 l h f /GS365 gs 0.000 0.000 1.000 rg 208.00 99.45 m 237.33 127.37 l 237.33 130.09 l 208.00 102.34 l h f /GS314 gs 0.000 0.000 1.000 rg 237.33 127.37 m 266.67 155.30 l 266.67 157.83 l 237.33 130.09 l h f /GS261 gs 0.000 0.000 1.000 rg 266.67 155.30 m 296.00 183.23 l 296.00 185.58 l 266.67 157.83 l h f /GS276 gs 0.000 0.000 1.000 rg 296.00 183.23 m 325.33 211.15 l 325.33 213.33 l 296.00 185.58 l h f /GS366 gs 0.000 0.000 1.000 rg 325.33 207.00 m 354.67 210.11 l 354.67 208.12 l 325.33 204.82 l h f /GS367 gs 0.000 0.000 1.000 rg 354.67 210.11 m 384.00 213.23 l 384.00 211.42 l 354.67 208.12 l h f /GS368 gs 0.000 0.000 1.000 rg 384.00 213.23 m 413.33 216.35 l 413.33 214.71 l 384.00 211.42 l h f /GS257 gs 0.000 0.000 1.000 rg 413.33 216.35 m 442.67 219.46 l 442.67 218.01 l 413.33 214.71 l h f /GS369 gs 0.000 0.000 1.000 rg 442.67 219.46 m 472.00 222.58 l 472.00 221.30 l 442.67 218.01 l h f /GS366 gs 0.000 0.000 1.000 rg 325.33 211.15 m 354.67 213.92 l 354.67 215.92 l 325.33 213.33 l h f /GS367 gs 0.000 0.000 1.000 rg 354.67 213.92 m 384.00 216.70 l 384.00 218.51 l 354.67 215.92 l h f /GS368 gs 0.000 0.000 1.000 rg 384.00 216.70 m 413.33 219.47 l 413.33 221.11 l 384.00 218.51 l h f /GS257 gs 0.000 0.000 1.000 rg 413.33 219.47 m 442.67 222.24 l 442.67 223.70 l 413.33 221.11 l h f /GS369 gs 0.000 0.000 1.000 rg 442.67 222.24 m 472.00 225.02 l 472.00 226.29 l 442.67 223.70 l h 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THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR’S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR’S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR’s publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user. Description: The C-package CARP is a convenient and easy tool for evaluating performance of clustering algorithms. The underlying methodology is based on first simulating Gaussian mixture models according to prespecified levels of average and maximum pairwise overlaps. The concept of overlap is defined as the sum of two misclassification probabilities (Maitra and Melnykov, 2010). Datasets are then simulated from the realized Gaussian mixtures. The software implementing this phase can be invoked standalone. This concludes the first phase of the procedure. In the second phase, the clustering algorithm being evaluated is run on the generated datasets. We provide an example here using an agglomerative hierarchical clustering algorithm hierclust which is included. The third phase compares obtained and true groupings. By default, the comparison measure is the adjusted Rand index of Hubert and Arabie (1985) but the user can also provide some other measure in executable form. Upon conclusion, CARP provides a distribution of the desired performance measure for the clustering method being evaluated at the preferred setting. This provides for a detailed understanding of the performance of the clustering algorithm being evaluated. CARP is released under the GNU GPL license. Project homepage: http://www.public.iastate.edu/~maitra/Software/CARP.html Dependencies: gcc compiler, glibc library, other programming language if needed for an external clustering algorithm or clustering metrics Installation: CARP can be installed in two easy steps: - extract files from "CARP_v3.1.tar.gz": > tar -xzvf CARP_v3.1.tar.gz - compile files running the makefile: > make CARP To check the integrity of the package, run the command: > make check To remove the installed files, use the command: > make clean Usage: > ./CARP Parameters: Parameters: -b : average overlap (no default value) -m : maximum overlap (no default value) -p : number of dimensions (2 by default) -G : number of mixing components at simulation stage (2 by default) -s : spherical covariance matrix structure (non-spherical by default if option is unspecified) -H : homogeneous covariance matrices (non-homogeneous by default if option is unspecified) -e : maximum eccentricity (0.90 by default) -z : smallest mixing proportion (equal mixing proportions 1/K by default) -k : minimum number of mixing components (no default value) -K : maximum number of mixing components (no default value) -L : lower bound for Uniform(, ) distribution from which mean vectors are generated -U : upper bound for Uniform(, ) distribution from which mean vectors are generated -r : maximum number of resimulations (100 by default) -a : accuracy of estimation (1e-06 by default) -l : maximum number of integration terms (1e06 by default) -P : file containing mixing proportions ("Pi.dat" by default) -M : file containing mean vectors ("Mu.dat" by default) -S : file containing covariance matrices in triangular form ("LTSigma.dat" by default) -D : working directory ("DATA" by default) -N : file containing cluster sizes ("Nk.dat" by default) -I : file containing true classifications ("idTrue.dat" by default) -i : file containing estimated classifications ("idEst.dat" by default) -X : file containing simulated datasets ("x.dat" by default) -W : file containing maps of pairwise overlaps ("overMap.dat" by default) -O : file containing characteristics of simulated mixtures ("overBarMax.dat" by default) -R : file containing index values (Adjusted Rand index and "AR.dat" by default) -n : number of observations generated from every mixture (0 by default) -# : number of simulated mixtures (1 by default) -V : name of the file (if file does not exist random transformation is applied) -w : number of noise variables (0 by default) -o : number of outliers for generated datasets (0 by default) -A : name of clustering program (if this option is not specified, only the first phase generating the datasets is run) -c : activate the command line interface with option -A (FALSE by default) -E : name of partition analyzing program (Adjusted Rand index is used by default; program "AdjRand") -C : activate the command line interface with option -E (FALSE by default) -h : help Details: Upon launching CARP, three stages of the program are processed. The first stage is reponsible for the simulation of Gaussian mixtures with prespecified level of complexity expressed in terms of pairwise overlap and for the generation of datasets from these mixtures. The following options are related to the first stage: -b, -m, -p, -G, -s, -H, -e, -z, -L, -U, -r, -a, -l, -P, -M, -S, -D, -N, -I, -X, -W, -O, -n, -#, -V, -w, -o. The option -h provides help. The output will be directed to a working folder specified by the option -D. The obtained parameters - mixing proportions, mean vectors and covariance matrices - will be saved to the files specified by options -P, -M, and -S correspondingly while samples drawn from the mixtures will be saved into the file specified by the option -X. The file provided with the option -I contains true classifications for all observations while option -N provides the name for the file with cluster sizes. In addition, the map of misclassification probabilities will be saved into the file specified by the option -W while average and maximum overlaps as well as the row and column numbers of the components that produced maximum overlap are stored in the file given by the option -C. The element with the index (i, j) in the misclassification map represents the probability that X simulated from the ith component is classified to the jth component. At the second stage, a user-specified clustering method has to be run for the simulated datasets. For illustration, several examples involving algorithms such as hierarchical clustering with Ward's linkage (Ward, 1963) (hierclust), expectation-maximization (Dempster et al., 1977; Fraley and Raftery, 2006) (Mclust), k-means (Forgy, 1965; MacQueen, 1967) (Kmeans), and partitioning around medoids (Kaufman and Rousseuw, 1990) (PAM) are provided. Other methods can be run with the option -A. User's clustering program has to be able to accept the following options: -p, -n, -#, -D, -i, -X, -K, and also -k if the best clustering solution has to be searched between the minimum and maximum numbers of clusters specified by options -k and -K respectively. The program will obtain partitionings and write them into the file specified by the option -i. Two options are provided, one for the expert user and the other for those that are not so comfortable with writing code. For the expert user, it is assumed that (s)he is comfortable writing a small piece of code responsible for accepting the above parameters coming from the first stage. For the less experienced user not comfortable with this writing a few lines of C code, the command-line interface provides a ready alternative; this is readily done with the option -c inluded into the call of CARP. In this case, CARP will not attempt to pass the parameters between stages. The user is then reponsible for specifying these parameters within a clustering program or command line. At the third stage, classifications derived by the clustering algorithm under investigation are compared with the true to assess performance. The adjusted Rand index (AdjRand) is incorporated as a default measure of similarity between the two classifications. An user, however, can specify other program for comparing the obtained and true partitionings. Examples of other methods that are included with this manual are through call to R packages which calculate Rand index (Rand, 1971) (Rand), bilogical homogeneity index (Datta and Datta, 2006) (BHI), and the proprotion of correctly classified observations (Diag). If an option -E is specified, the program should comply with the following options: -n, -#, -D, -I, -i, -R. Calculated values of the provided similarity measure have to be written to the file specified by the option -R. Alternatively, less experienced users can specify an option -C with similar meaning as for the option -c from the second stage: a user can provide a command line invoking a partition analyzing program. If both options -b and -m are specified, CARP produces a mixture satisfying both characteristics, average and maximum overlap. If one option, either -b or -m is specified, a mixture satisfying the prespecified value is generated. Simple Examples: % simulate a 3-dimensional 4-component mixture with spherical covariance matrices, equal mixing proportions, maximum overlap 0.1 and average overlap 0.05; generate a sample of size 100 and analyze using hierclust > ./CARP -m0.1 -b0.05 -p3 -G4 -s -n100 -Ahierclust % simulate a 2-dimensional 4-component mixture with homogeneous covariance matrices, maximum overlap 0.1 and mixing proportions greater or equal than 0.10; generate a sample of size 100 > ./CARP -m0.1 -p2 -G4 -H -z0.10 -n100 % compute the overlap for the 2-dimensional 4-component mixture with parameters specified in DATA/Pi.dat, DATA/Mu.dat and DATA/LTSigma.dat; generate a sample of size 100 > ./CARP -p2 -G4 -n100 % get help > ./CARP -h An illustration with corresponding output is provided below. See the manual for other comprehensive examples. Example: Evaluating clustering algorithms with spherical dispersion structures and equal representation This example illustrates the simulation of three 2-dimensional 4-component mixtures with spherical covariance matrices and equal mixing proportions according to pre-specified levels of maximum and average overlap, drawing samples of 100 observations from simulated mixtures, running the clustering program hierclust, and computing the adjusted Rand index. Consider the following command: ./CARP -m0.1 -b0.05 -p2 -G4 -s -n100 -\#3 -DDATA -Xdata.dat -IIDtrue.dat -iIDest.dat -RAdjRand.dat -Ahierclust -r1000 CARP runs the following three steps: 1. Mixture simulation Three 2-dimensional 4-component mixtures with spherical covariance matrices, equal mixing proportions, maximum overlap 0.1, and average overlap 0.05 are simulated. 1000 resimulations are allowed for reaching the desired level of overlap. 100 observations will be generated from every mixture. The working directory is DATA. Simulated samples will be stored into the file data.dat while the classifications will be stored into the file IDtrue.dat. Files with mixture parameters keep their default names specified in the options description. The corresponding output is provided below. Output: MIXTURE MODEL #1: The desired overlap has been met... Map of misclassification probabilities: [0][0] : 1.000000 [0][1] : 0.015200 [0][2] : 0.003266 [0][3] : 0.033826 [1][0] : 0.012737 [1][1] : 1.000000 [1][2] : 0.000054 [1][3] : 0.035891 [2][0] : 0.003856 [2][1] : 0.000074 [2][2] : 1.000000 [2][3] : 0.036315 [3][0] : 0.049968 [3][1] : 0.064109 [3][2] : 0.044703 [3][3] : 1.000000 Average Overlap: 0.050000 Maximum Overlap: 0.100000 (components: 1 and 3) Mixture parameters: Pi: 0.250000 0.250000 0.250000 0.250000 Mu: [0] : 0.471083 0.675651 [1] : 0.335962 0.337816 [2] : 0.980450 0.580291 [3] : 0.652717 0.362861 Sigma: [0] : 0.007961 0.000000 0.000000 0.007961 [1] : 0.005868 0.000000 0.000000 0.005868 [2] : 0.010726 0.000000 0.000000 0.010726 [3] : 0.015040 0.000000 0.000000 0.015040 Dataset with cluster sizes Nk = 28 21 29 22 has been generated... MIXTURE MODEL #2: The desired overlap has been met... Map of misclassification probabilities: [0][0] : 1.000000 [0][1] : 0.030979 [0][2] : 0.029165 [0][3] : 0.001868 [1][0] : 0.013597 [1][1] : 1.000000 [1][2] : 0.010535 [1][3] : 0.006902 [2][0] : 0.048703 [2][1] : 0.036413 [2][2] : 1.000000 [2][3] : 0.056377 [3][0] : 0.002374 [3][1] : 0.019464 [3][2] : 0.043623 [3][3] : 1.000000 Average Overlap: 0.050000 Maximum Overlap: 0.100000 (components: 2 and 3) Mixture parameters: Pi: 0.250000 0.250000 0.250000 0.250000 Mu: [0] : 0.203039 0.189259 [1] : 0.485454 0.285278 [2] : 0.338648 0.623985 [3] : 0.795138 0.499927 Sigma: [0] : 0.010917 0.000000 0.000000 0.010917 [1] : 0.002621 0.000000 0.000000 0.002621 [2] : 0.025417 0.000000 0.000000 0.025417 [3] : 0.016861 0.000000 0.000000 0.016861 Dataset with cluster sizes Nk = 26 22 21 31 has been generated... MIXTURE MODEL #3: The desired overlap has been met... Map of misclassification probabilities: [0][0] : 1.000000 [0][1] : 0.000000 [0][2] : 0.021907 [0][3] : 0.020430 [1][0] : 0.000000 [1][1] : 1.000000 [1][2] : 0.013279 [1][3] : 0.000000 [2][0] : 0.078093 [2][1] : 0.051412 [2][2] : 1.000000 [2][3] : 0.041697 [3][0] : 0.049043 [3][1] : 0.000000 [3][2] : 0.024139 [3][3] : 1.000000 Average Overlap: 0.050000 Maximum Overlap: 0.100000 (components: 0 and 2) Mixture parameters: Pi: 0.250000 0.250000 0.250000 0.250000 Mu: [0] : 0.694165 0.531804 [1] : 0.166167 0.386339 [2] : 0.466549 0.391484 [3] : 0.927160 0.438973 Sigma: [0] : 0.002244 0.000000 0.000000 0.002244 [1] : 0.001815 0.000000 0.000000 0.001815 [2] : 0.024865 0.000000 0.000000 0.024865 [3] : 0.009984 0.000000 0.000000 0.009984 Dataset with cluster sizes Nk = 26 24 29 21 has been generated... 2. Running clustering algorithm ./hierclust -p2 -K4 -n100 -#3 -DDATA -Xdata.dat -iIDest.dat At this stage, the program hierclust performing hierarchical clustering is run. The estimated classification will be written into the file IDest.dat. The output contains the estimated classification vectors. The value given with the option -K is the number of clusters specified by the option -G. Output: OBTAINED CLASSIFICATIONS: Dataset #1: 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 1 0 1 2 1 2 2 0 3 1 3 1 2 1 3 2 3 1 1 0 Dataset #2: 0 0 0 0 3 0 0 0 0 3 0 0 0 0 0 0 3 0 0 0 0 3 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 0 2 3 2 3 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Dataset #3: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 0 0 1 3 3 3 3 1 0 0 3 3 3 1 3 3 0 1 3 0 3 0 1 3 3 0 1 3 2 2 2 2 2 2 2 0 2 0 2 2 2 2 2 2 2 2 2 2 2 3. Computing adjusted Rand index ./AdjRand -#3 -DDATA -IIDtrue.dat -iIDest.dat -RAdjRand.dat -n100 This command computes three adjusted Rand index values and saves them into the file AdjRand.dat. The following is the ouptut from the program. Output: ADJUSTED RAND VALUES: Dataset #1: Ktrue = 4 Kest = 4 : 0.722711 Dataset #2: Ktrue = 4 Kest = 4 : 0.753868 Dataset #3: Ktrue = 4 Kest = 4 : 0.622881 References: L. Hubert and P. Arabie. Comparing partitions. Journal of Classification, 2:193–218, 1985. R. Maitra and V. Melnykov. Simulating data to study performance of finite mixture modeling and clustering algorithms. Journal of Computational and Graphical Statistics, 19(2):354–376, 2010. doi: 10.1198/jcgs.2009.08054.