From: Francois Fleuret Date: Tue, 16 Oct 2007 11:58:02 +0000 (+0200) Subject: Initial commit X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=cmim.git;a=commitdiff_plain;h=43c0c6462e47b39c9355a35ce8a3eeb51f669253 Initial commit --- 43c0c6462e47b39c9355a35ce8a3eeb51f669253 diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..7e09205 --- /dev/null +++ b/Makefile @@ -0,0 +1,56 @@ + +################################################################################ +# This program is free software: you can redistribute it and#or modify # +# it under the terms of the version 3 of the GNU General Public License # +# as published by the Free Software Foundation. # +# # +# 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 . # +# # +# Written by Francois Fleuret # +# (C) Ecole Polytechnique Federale de Lausanne # +# Contact for comments & bug reports # +################################################################################ + +ifeq ($(CXX),) + CXX = g++-2.95 +endif + +ifeq ($(DEBUG),yes) + LDFLAGS = -lm + CXXFLAGS = -Wall -g -Wall -DDEBUG -DVERBOSE +else + LDFLAGS = -lm + CXXFLAGS = -ffast-math -fcaller-saves -finline-functions -funroll-all-loops -Wall -O3 +endif + +all: cmim create_samples Makefile.depend + +test: all + ./test.sh + +cmim: misc.o fastentropy.o classifier.o cmim.o + $(CXX) $(CXXFLAGS) -o $@ $^ $(LDFLAGS) + +create_samples: create_samples.o + $(CXX) $(CXXFLAGS) -o $@ $^ $(LDFLAGS) + +clean: + \rm -f *.o cmim + +Makefile.depend: *.h *.cc Makefile + $(CC) -M *.cc > Makefile.depend + +ARCHIVE_NAME = cmim-1.1 +DIRNAME = cmim-1.1 + +archive: + cd ..; tar zcvf ${ARCHIVE_NAME}.tgz \ + ${DIRNAME}/*.{cc,h,sh,txt} ${DIRNAME}/Makefile ${DIRNAME}/README + +-include Makefile.depend diff --git a/README b/README new file mode 100644 index 0000000..f156ead --- /dev/null +++ b/README @@ -0,0 +1,104 @@ +-*- mode: text -*- + ++----------------------------------------------------------------------+ +| This archive contains a simple implementation of the Conditional | +| Mutual Information Maximization for feature selection. | ++----------------------------------------------------------------------+ +| Written by François Fleuret | +| Contact for comments & bug reports | +| Copyright (C) 2004 EPFL | ++----------------------------------------------------------------------+ + +$Id: README,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +0/ INTRODUCTION + + The CMIM feature selection scheme is designed to select a small + number of binary features among a very large set in a context of two + class classification. It consists in picking features one after + another to maximize the conditional mutual information between the + selected feature and the class to predict given any one of the + features already picked. Such a criterion picks features which are + both individually informative yet pairwise weakly dependent. CMIM + stands for Conditional Mutual Information Maximization. See + + Fast Binary Feature Selection with Conditional Mutual Information + Francois Fleuret + JMLR 5 (Nov): 1531--1555, 2004 + http://www.jmlr.org/papers/volume5/fleuret04a/fleuret04a.pdf + +1/ INSTALLATION + + To compile and test, just type 'make test' + + This small test consists in generating a sample set for a toy + problem and testing CMIM, MIM and a random feature selection with + the naive Bayesian learner. The two populations of the toy problem + lives in the [0,1]^2 square. The positive population is in x^2+y^2 < + 1/4 and the negative population is everything else. Look at + create_samples.cc for more details. The features are responses of + linear classifiers generated at random. + +2/ DATA FILE FORMAT + + Each data file, either for training or testing, starts with the + number of samples and the number of features. Then follow for every + single sample two lines, one with the value of the features (0/1) + and one with the value of the class to predict (0/1). Check the + train.dat and test.dat generated by create_samples to get an + example. + + The test file has the same format, and the real class is used to + estimate the error rates. During test, the response of the naive + bayse before thresholding is saved in a result file (3rd parametre + of the --test option) + +3/ OPTIONS + + --silent + + Switch off all the outputs to stdout + + --feature-selection + + Selects the feature selection method + + --classifier + + Selects the classifier type + + --error + + Choses which error to minimize during bias estimation for the CMIM + + naive Bayesian. + + standard = P(f(X) = 0, Y = 1) + P(f(X) = 1, Y = 0) + + ber = (P(f(X) = 0 | Y = 1) + P(f(X) = 1 | Y = 0))/2 + + --nb-features + + Selects the number of selected features + + --cross-validation + + Do cross-validation + + --train + + Build and save to disk a classifier + + --test + + Load a classifier and test it on a dataset + +4/ LICENCE + + This program is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License version 3 as + published by the Free Software Foundation. + + 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. diff --git a/classifier.cc b/classifier.cc new file mode 100644 index 0000000..23950a4 --- /dev/null +++ b/classifier.cc @@ -0,0 +1,349 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: classifier.cc,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +#include +#include + +#include "classifier.h" + +DataSet::DataSet(const DataSet &ds) { + copy(ds); +} + +DataSet::DataSet(int ns, int nf) { + nb_samples = ns; + nb_features = nf; + + size = (nb_samples + 31)/32; + + raw = new RawData; + + raw->nrefs = 1; + raw->x = new uint32_t[nb_features * size]; + raw->y = new uint32_t[size]; + + y_va = raw->y; + x_va = new uint32_t *[nb_features]; + for(int j = 0; j < nb_features; j++) x_va[j] = raw->x + size*j; +} + +DataSet::DataSet(istream &is) { + is >> nb_samples >> nb_features; + size = (nb_samples + 31)/32; + + raw = new RawData; + + raw->nrefs = 1; + raw->x = new uint32_t[nb_features * size]; + raw->y = new uint32_t[size]; + + y_va = raw->y; + x_va = new uint32_t *[nb_features]; + for(int j = 0; j < nb_features; j++) x_va[j] = raw->x + size*j; + + int v; + + for(int s = 0; s < nb_samples; s++) { + // cerr << "s=" << s << "\n"; cerr.flush(); + for(int f = 0; f < nb_features; f++) { + is >> v; + fe_set_bit(s, x_va[f], v > 0); + } + + is >> v; + fe_set_bit(s, y_va, v > 0); + + if(is.eof()) { + cerr << "Error: missing data!\n"; + exit(1); + } + } +} + +DataSet::DataSet(const DataSet &ds, const FeatureSelector &fs) { + nb_samples = ds.nb_samples; + nb_features = fs.nb_selected_features; + size = (nb_samples + 31)/32; + + raw = ds.raw; + + raw->nrefs++; + + y_va = raw->y; + x_va = new uint32_t *[nb_features]; + for(int j = 0; j < nb_features; j++) x_va[j] = raw->x + size*fs.selected_index[j]; +} + +DataSet::DataSet(const DataSet &ds, bool *selected_samples) { + nb_samples = 0; + for(int i = 0; i < ds.nb_samples; i++) if(selected_samples[i]) nb_samples++; + nb_features = ds.nb_features; + size = (nb_samples + 31)/32; + raw = new RawData; + + raw->nrefs = 1; + raw->x = new uint32_t[nb_features * size]; + raw->y = new uint32_t[size]; + + y_va = raw->y; + x_va = new uint32_t *[nb_features]; + for(int j = 0; j < nb_features; j++) x_va[j] = raw->x + size*j; + + int k = 0; + for(int s = 0; s < ds.nb_samples; s++) if(selected_samples[s]) { + fe_set_bit(k, y_va, fe_get_bit(s, ds.y_va)); + for(int j = 0; j < nb_features; j++) fe_set_bit(k, x_va[j], fe_get_bit(s, ds.x_va[j])); + k++; + } +} + +DataSet &DataSet::operator = (const DataSet &ds) { + if(this != &ds) { + delete[] x_va; + raw->nrefs--; + if(raw->nrefs == 0) { + delete[] raw->x; + delete[] raw->y; + delete raw; + } + copy(ds); + } + return *this; +} + +DataSet::~DataSet() { + delete[] x_va; + raw->nrefs--; + if(raw->nrefs == 0) { + delete[] raw->x; + delete[] raw->y; + delete raw; + } +} + +void DataSet::copy(const DataSet &ds) { + nb_samples = ds.nb_samples; + nb_features = ds.nb_features; + size = (nb_samples + 31)/32; + raw = ds.raw; + raw->nrefs++; + y_va = raw->y; + x_va = new uint32_t *[nb_features]; + for(int j = 0; j < nb_features; j++) x_va[j] = ds.x_va[j]; +} + +void DataSet::save_ascii(ostream &os) { + os << nb_samples << " " << nb_features << "\n"; + for(int s = 0; s < nb_samples; s++) { + for(int f = 0; f < nb_features; f++) { + if(fe_get_bit(s, x_va[f])) os << "1"; else os << "0"; + if(f < nb_features-1) os << " "; else os << "\n"; + } + if(fe_get_bit(s, y_va)) os << "1\n"; else os << "0\n"; + } +} + +Classifier *Classifier::load(istream &is) { + Classifier *result; + + int scheme; + is >> scheme; + + switch(scheme) { + case Classifier::ID_LINEAR: + result = new LinearClassifier(); + break; + + default: + result = 0; + cerr << "Unknown classifier type!\n"; + exit(1); + } + + result->inner_load(is); + + return result; +} + +Classifier::~Classifier() {} + +FeatureSelector::FeatureSelector(int nb) : nb_selected_features(nb), + selected_index(new int[nb_selected_features]), + weights(new float[nb_selected_features]) { } + +FeatureSelector::~FeatureSelector() { + delete[] weights; + delete[] selected_index; +} + +LinearClassifier::LinearClassifier(int nf) : nb_features(nf), + weights(new float[nb_features]), + bias(0) { } + +LinearClassifier::LinearClassifier() : nb_features(0), + weights(0), + bias(0) { } + +LinearClassifier::~LinearClassifier() { + delete[] weights; +} + +void LinearClassifier::compute_bayesian_weights(int nb_samples, uint32_t *y_va, uint32_t **x_va) { + for(int nf = 0; nf < nb_features; nf++) { + int n11 = fe_count_and(nb_samples, y_va, x_va[nf]); + int n10 = fe_count_and_not(nb_samples, y_va, x_va[nf]); + int n01 = fe_count_and_not(nb_samples, x_va[nf], y_va); + int n00 = fe_count_and_not_not(nb_samples, y_va, x_va[nf]); + if(n00 == 0) n00 = 1; // This is sort of a dirty way to emulate +/- infty + if(n01 == 0) n01 = 1; + if(n10 == 0) n10 = 1; + if(n11 == 0) n11 = 1; + weights[nf] = log(float(n11 * n00) / float(n10 * n01)); + } +} + +void LinearClassifier::compute_bias(int nb_samples, uint32_t *y_va, uint32_t **x_va, bool balanced_error) { + Couple tmp[nb_samples]; + + int n00 = 0, n01 = 0, n10 = 0, n11 = 0; + for(int s = 0; s < nb_samples; s++) { + float r = 0; + for(int nf = 0; nf < nb_features; nf++) if(fe_get_bit(s, x_va[nf])) r += weights[nf]; + tmp[s].index = s; + tmp[s].value = r; + if(fe_get_bit(s, y_va)) n11++; else n01++; + } + + qsort(tmp, nb_samples, sizeof(Couple), compare_couple); + + float error, best_error = 2.0; + bias = 0; + for(int t = 0; t < nb_samples-1; t++) { + if(fe_get_bit(tmp[t].index, y_va)) { n10++; n11--; } else { n01--; n00++; } + + if(balanced_error) + error = (float(n01) / float(n00 + n01) + float(n10) / float(n10 + n11)) / 2.0; + else + error = float(n01+n10) / float(n00 + n01 + n10 + n11); + + if(error < best_error) { + best_error = error; + bias = - (tmp[t].value + tmp[t+1].value)/2.0; + } + } +} + +void LinearClassifier::learn_bayesian(const DataSet &ds, bool balanced_error) { + compute_bayesian_weights(ds.nb_samples, ds.y_va, ds.x_va); + compute_bias(ds.nb_samples, ds.y_va, ds.x_va, balanced_error); +} + +void LinearClassifier::learn_perceptron(const DataSet &ds, bool balanced_error) { + for(int i = 0; i < nb_features; i++) weights[i] = 0.0; + + int n_loop_max = 5000; + + for(int i = 0; i < n_loop_max * ds.nb_samples; i++) { + int ns = i % ds.nb_samples; + float r = 0; + for(int f = 0; f < nb_features; f++) + if(fe_get_bit(ns, ds.x_va[f])) r += weights[f]; else r -= weights[f]; + float correct; + if(fe_get_bit(ns, ds.y_va)) correct = 1.0; else correct = -1.0; + if((r < 0 && correct >= 0) || (r >= 0 && correct < 0)) { + for(int f = 0; f < nb_features; f++) + if(fe_get_bit(ns, ds.x_va[f])) weights[f] += correct; else weights[f] += -correct; + } + } + + compute_bias(ds.nb_samples, ds.y_va, ds.x_va, balanced_error); +} + +void FeatureSelector::cmim(const DataSet &ds) { + fe_selection_cmim(ds.nb_samples, ds.nb_features, ds.x_va, ds.y_va, nb_selected_features, selected_index); +} + +void FeatureSelector::mim(const DataSet &ds) { + fe_selection_mim(ds.nb_samples, ds.nb_features, ds.x_va, ds.y_va, nb_selected_features, selected_index); +} + +void FeatureSelector::random(const DataSet &ds) { + bool used[ds.nb_features]; + for(int i = 0; i < ds.nb_features; i++) used[i] = false; + int f; + for(int nf = 0; nf < nb_selected_features; nf++) { + do { f = int(drand48() * ds.nb_features); } while(used[f]); + used[f] = true; + selected_index[nf] = f; + } +} + +FeatureSelector::FeatureSelector(istream &is) { + is >> nb_selected_features; + weights = new float[nb_selected_features]; + selected_index = new int[nb_selected_features]; + for(int i = 0; i < nb_selected_features; i++) is >> selected_index[i]; +} + +void FeatureSelector::save(ostream &os) { + os << nb_selected_features << "\n"; + for(int i = 0; i < nb_selected_features; i++) os << selected_index[i] << ((i < nb_selected_features-1) ? " " : "\n"); +} + +void LinearClassifier::inner_load(istream &is) { + is >> nb_features; + delete[] weights; + weights = new float[nb_features]; + for(int i = 0; i < nb_features; i++) is >> weights[i]; + is >> bias; +} + +void LinearClassifier::save(ostream &os) { + os << ID_LINEAR << "\n"; + os << nb_features << "\n"; + for(int i = 0; i < nb_features; i++) os << weights[i] << ((i < nb_features-1) ? " " : "\n"); + os << bias << "\n"; +} + +void LinearClassifier::predict(const DataSet &ds, float *result) { + for(int s = 0; s < ds.nb_samples; s++) { + float r = bias; + for(int nf = 0; nf < nb_features; nf++) if(fe_get_bit(s, ds.x_va[nf])) r += weights[nf]; + result[s] = r; + } +} + +void compute_error_rates(FeatureSelector *selector, Classifier *classifier, + const DataSet &testing_set, int &n00, int &n01, int &n10, int &n11, float *result) { + + DataSet reduced_test_set(testing_set, *selector); + + classifier->predict(reduced_test_set, result); + + n00 = 0; n01 = 0; n10 = 0; n11 = 0; + for(int s = 0; s < testing_set.nb_samples; s++) { + if(fe_get_bit(s, testing_set.y_va)) { + if(result[s] >= 0) n11++; else n10++; + } else { + if(result[s] >= 0) n01++; else n00++; + } + } +} diff --git a/classifier.h b/classifier.h new file mode 100644 index 0000000..8b4ce15 --- /dev/null +++ b/classifier.h @@ -0,0 +1,119 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: classifier.h,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +#ifndef CLASSIFIER_H +#define CLASSIFIER_H + +using namespace std; + +#include +#include + +#include "misc.h" +#include "fastentropy.h" + +class FeatureSelector; + +class DataSet { + // This keeps track of the number of references + struct RawData { + int nrefs; + uint32_t *x, *y; + }; +public: + int nb_samples, nb_features; + int size; + RawData *raw; + uint32_t *y_va, **x_va; + DataSet(istream &is); + DataSet(int nb_samples, int nb_features); + DataSet(const DataSet &ds); + DataSet(const DataSet &ds, const FeatureSelector &fs); + DataSet(const DataSet &ds, bool *selected_samples); + DataSet &operator = (const DataSet &ds); + ~DataSet(); + void copy(const DataSet &ds); + void save_ascii(ostream &os); +}; + +////////////////////////////////////////////////////////////////////// +// The classifier //////////////////////////////////////////////////// +////////////////////////////////////////////////////////////////////// + +class Classifier { +public: + enum { ID_LINEAR }; + static Classifier *load(istream &is); + virtual ~Classifier(); + virtual void predict(const DataSet &ds, float *result) = 0; + virtual void save(ostream &out) = 0; + virtual void inner_load(istream &in) = 0; +}; + +class FeatureSelector { +public: + int nb_selected_features; + int *selected_index; + + // Those remains from the feature selection process. They can be + // used as-is in the case of adaboost + float *weights; + + FeatureSelector(istream &is); + FeatureSelector(int nb_selected_features); + ~FeatureSelector(); + + // Selects features according to the Conditional Mutual Information Maximisation + void cmim(const DataSet &ds); + + // Selects features according to the Mutual Information Maximisation + void mim(const DataSet &ds); + + // Selects random features + void random(const DataSet &ds); + + void save(ostream &os); +}; + +class LinearClassifier : public Classifier { + int nb_features; + float *weights; + float bias; +public: + LinearClassifier(); + LinearClassifier(int nb_features); + virtual ~LinearClassifier(); + + void compute_bayesian_weights(int nb_samples, uint32_t *y_va, uint32_t **x_va); + void compute_bias(int nb_samples, uint32_t *y_va, uint32_t **x_va, bool balanced_error); + + void learn_bayesian(const DataSet &ds, bool balanced_error); + void learn_perceptron(const DataSet &ds, bool balanced_error); + + virtual void predict(const DataSet &ds, float *result); + virtual void save(ostream &out); + virtual void inner_load(istream &is); +}; + +void compute_error_rates(FeatureSelector *selector, Classifier *classifier, + const DataSet &testing_set, int &n00, int &n01, int &n10, int &n11, float *result); + +#endif diff --git a/cmim.cc b/cmim.cc new file mode 100644 index 0000000..0aa3b8d --- /dev/null +++ b/cmim.cc @@ -0,0 +1,382 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: cmim.cc,v 1.4 2007-08-23 08:36:50 fleuret Exp $ + +// This software was developped on GNU/Linux systems with many GPL +// tools including emacs, gcc, gdb, and bash (see http://www.fsf.org). + +/* + +To test + +./cmim --feature-selection cmim --classifier bayesian --error ber --train ./train.dat ./classifier.nb 100 +./cmim --test ./test.dat ./classifier.nb ./result.dat + +*/ + +using namespace std; + +#include +#include +#include +#include +#include +#include +#include + +#include "classifier.h" + +#define BUFFER_SIZE 256 + +FeatureSelector *selector; +Classifier *classifier; +char classifier_type[BUFFER_SIZE] = "bayesian"; +char feature_selection_type[BUFFER_SIZE] = "cmim"; +float reg_param = 0.0; +bool verbose = true; +bool balanced_error = false; +int nb_selected_features = 100; + +void check_opt(int argc, char **argv, int n_opt, int n, char *help) { + if(n_opt+n >= argc) { + cerr << "Missing argument for " << argv[n_opt] << ".\n"; + cerr << "Expecting " << help << ".\n"; + exit(1); + } +} + +void train(const DataSet &training_set) { + timeval tv_start, tv_end; + fe_init_tables(); + + if(verbose) { + cout << "Selecting features with " << feature_selection_type; + cout.flush(); + gettimeofday(&tv_start, 0); + } + + cout.flush(); + + selector = new FeatureSelector(nb_selected_features); + + if(strcmp(feature_selection_type, "cmim") == 0) selector->cmim(training_set); + else if(strcmp(feature_selection_type, "mim") == 0) selector->mim(training_set); + else if(strcmp(feature_selection_type, "random") == 0) selector->random(training_set); + else { + cerr << "Unknown feature selection type " << feature_selection_type << "\n"; + exit(1); + } + + if(verbose) { + gettimeofday(&tv_end, 0); + cout << " (" + << (float(tv_end.tv_sec - tv_start.tv_sec) * 1000 + float(tv_end.tv_usec - tv_start.tv_usec)/1000) + << "ms).\n"; + gettimeofday(&tv_start, 0); + cout << "Learning with " << classifier_type; + cout.flush(); + } + + cout.flush(); + + DataSet reduced_training_set(training_set, *selector); + + if(strcmp(classifier_type, "bayesian") == 0) { + LinearClassifier *tmp = new LinearClassifier(nb_selected_features); + tmp->learn_bayesian(reduced_training_set, balanced_error); + classifier = tmp; + } + + else if(strcmp(classifier_type, "perceptron") == 0) { + LinearClassifier *tmp = new LinearClassifier(nb_selected_features); + tmp->learn_perceptron(reduced_training_set, balanced_error); + classifier = tmp; + } + + else { + cerr << "Unknown learning method type " << classifier_type << "\n"; + exit(1); + } + + if(verbose) { + gettimeofday(&tv_end, 0); + cout << " (" + << (float(tv_end.tv_sec - tv_start.tv_sec) * 1000 + float(tv_end.tv_usec - tv_start.tv_usec)/1000) + << "ms).\n"; + } + + cout.flush(); +} + +int main(int argc, char **argv) { + bool arg_error = false; + + int i = 1; + while(i < argc && !arg_error) { + + ////////////////////////////////////////////////////////////////////// + // Parameters //////////////////////////////////////////////////////// + ////////////////////////////////////////////////////////////////////// + + if(strcmp(argv[i], "--silent") == 0) { + verbose = false; + i++; + } + + else if(strcmp(argv[i], "--feature-selection") == 0) { + check_opt(argc, argv, i, 1, ""); + strncpy(feature_selection_type, argv[i+1], BUFFER_SIZE); + i += 2; + } + + else if(strcmp(argv[i], "--classifier") == 0) { + check_opt(argc, argv, i, 1, ""); + strncpy(classifier_type, argv[i+1], BUFFER_SIZE); + i += 2; + } + + else if(strcmp(argv[i], "--error") == 0) { + check_opt(argc, argv, i, 1, ""); + if(strcmp(argv[i+1], "standard") == 0) balanced_error = false; + else if(strcmp(argv[i+1], "ber") == 0) balanced_error = true; + else { + cerr << "Unknown error type " << argv[i+1] << "!\n"; + exit(1); + } + i += 2; + } + + else if(strcmp(argv[i], "--nb-features") == 0) { + check_opt(argc, argv, i, 1, ""); + nb_selected_features = atoi(argv[i+1]); + if(nb_selected_features <= 0) { + cerr << "Unconsistent number of selected features (" << nb_selected_features << ").\n"; + exit(1); + } + i += 2; + } + + ////////////////////////////////////////////////////////////////////// + // Training ////////////////////////////////////////////////////////// + ////////////////////////////////////////////////////////////////////// + + else if(strcmp(argv[i], "--cross-validation") == 0) { + check_opt(argc, argv, i, 3, " "); + if(verbose) { + cout << "Loading data.\n"; + cout.flush(); + } + + ifstream complete_data(argv[i+1]); + if(complete_data.fail()) { + cerr << "Can not open " << argv[i+1] << " for reading!\n"; + exit(1); + } + + int nb_for_test = atoi(argv[i+2]); + if(nb_for_test <= 0) { + cerr << "Unconsistent number of samples for test (" << nb_selected_features << ").\n"; + exit(1); + } + + int nb_cv_loops = atoi(argv[i+3]); + if(nb_cv_loops <= 0) { + cerr << "Unconsistent number of cross-validation loops (" << nb_cv_loops << ").\n"; + exit(1); + } + + DataSet complete_set(complete_data); + + int n00_test = 0, n01_test = 0, n10_test = 0, n11_test = 0; + int n00_train = 0, n01_train = 0, n10_train = 0, n11_train = 0; + + for(int ncv = 0; ncv < nb_cv_loops; ncv++) { + bool for_test[complete_set.nb_samples]; + + for(int s = 0; s < complete_set.nb_samples; s++) for_test[s] = false; + for(int i = 0; i < nb_for_test; i++) { + int s; + do { + s = int(drand48() * complete_set.nb_samples); + } while(for_test[s]); + for_test[s] = true; + } + + DataSet testing_set(complete_set, for_test); + for(int s = 0; s < complete_set.nb_samples; s++) for_test[s] = !for_test[s]; + DataSet training_set(complete_set, for_test); + + train(training_set); + + int n00, n01, n10, n11; + + { + float result[training_set.nb_samples]; + compute_error_rates(selector, classifier, training_set, n00, n01, n10, n11, result); + n00_train += n00; n01_train += n01; n10_train += n10; n11_train += n11; + } + + { + float result[testing_set.nb_samples]; + compute_error_rates(selector, classifier, testing_set, n00, n01, n10, n11, result); + n00_test += n00; n01_test += n01; n10_test += n10; n11_test += n11; + } + + delete classifier; + delete selector; + } + + if(balanced_error) { + cout << "BER [" << nb_cv_loops << " loops] " + << " train " << 0.5 * (float(n01_train)/float(n00_train + n01_train) + float(n10_train)/float(n10_train + n11_train)) + << " test " << 0.5 * (float(n01_test)/float(n00_test + n01_test) + float(n10_test)/float(n10_test + n11_test)) << "\n"; + } else { + cout << "Error [" << nb_cv_loops << " loops] " + << " train " << float(n01_train + n10_train)/float(n00_train + n01_train + n10_train + n11_train) + << " test " << float(n01_test + n10_test)/float(n00_test + n01_test + n10_test + n11_test) << "\n"; + } + + i += 4; + } + + ////////////////////////////////////////////////////////////////////// + + else if(strcmp(argv[i], "--train") == 0) { + check_opt(argc, argv, i, 2, " "); + + if(verbose) { + cout << "Loading data.\n"; + cout.flush(); + } + + ifstream training_data(argv[i+1]); + if(training_data.fail()) { + cerr << "Can not open " << argv[i+1] << " for reading!\n"; + exit(1); + } + + DataSet training_set(training_data); + + ////////////////////////////////////////////////////////////////////// + // Learning with CMIM + naive Bayesian /////////////////////////////// + ////////////////////////////////////////////////////////////////////// + + train(training_set); + + ////////////////////////////////////////////////////////////////////// + // Finishing and saving ////////////////////////////////////////////// + ////////////////////////////////////////////////////////////////////// + + if(verbose) cout << "Saving the classifier in [" << argv[i+2] << "].\n"; + ofstream classifier_out(argv[i+2]); + if(classifier_out.fail()) { + cerr << "Can not open " << argv[i+2] << " for writing!\n"; + exit(1); + } + + selector->save(classifier_out); + classifier->save(classifier_out); + + delete classifier; + delete selector; + + i += 3; + } + + ////////////////////////////////////////////////////////////////////// + // Test ////////////////////////////////////////////////////////////// + ////////////////////////////////////////////////////////////////////// + + else if(strcmp(argv[i], "--test") == 0) { + check_opt(argc, argv, i, 3, " "); + + // Load the classifier + + if(verbose) cout << "Loading the classifier from [" << argv[i+1] << "].\n"; + + ifstream classifier_in(argv[i+1]); + if(classifier_in.fail()) { + cerr << "Can not open " << argv[i+1] << " for reading!\n"; + exit(1); + } + + selector = new FeatureSelector(classifier_in); + classifier = Classifier::load(classifier_in); + + // Load the testing data + + ifstream testing_data(argv[i+2]); + if(testing_data.fail()) { + cerr << "Can not open " << argv[i+2] << " for reading!\n"; + exit(1); + } + + ofstream result_out(argv[i+3]); + if(result_out.fail()) { + cerr << "Can not open " << argv[i+3] << " for writing!\n"; + exit(1); + } + + DataSet testing_set(testing_data); + + // Compute the predicted responses + + int n00, n01, n10, n11; + float result[testing_set.nb_samples]; + compute_error_rates(selector, classifier, testing_set, n00, n01, n10, n11, result); + + for(int s = 0; s < testing_set.nb_samples; s++) + result_out << result[s] << "\n"; + + cout << "ERROR " << float(n01 + n10)/float(n00 + n01 + n10 + n11) << "\n"; + cout << "BER " << 0.5 * (float(n01)/float(n00 + n01) + float(n10)/float(n10 + n11)) << "\n"; + cout << "FN " << float(n10)/float(n10+n11) << "\n"; + cout << "FP " << float(n01)/float(n01+n00) << "\n"; + cout << "real_0_predicted_0 " << n00 << "\n"; + cout << "real_0_predicted_1 " << n01 << "\n"; + cout << "real_1_predicted_0 " << n10 << "\n"; + cout << "real_1_predicted_1 " << n11 << "\n"; + + delete classifier; + delete selector; + + i += 4; + } + + else arg_error = true; + } + + if(arg_error) { + cerr << "Conditional Mutual Information Maximization\n"; + cerr << "Written by François Fleuret (c) EPFL 2004\n"; + cerr << "Comments and bug reports to \n"; + cerr << "\n"; + cerr << "Usage: " << argv[0] << "\n"; + cerr << "--silent\n"; + cerr << "--feature-selection \n"; + cerr << "--classifier \n"; + cerr << "--error \n"; + cerr << "--nb-features \n"; + cerr << "--cross-validation \n"; + cerr << "--train \n"; + cerr << "--test \n"; + exit(1); + } +} diff --git a/create_samples.cc b/create_samples.cc new file mode 100644 index 0000000..7a61edf --- /dev/null +++ b/create_samples.cc @@ -0,0 +1,68 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: create_samples.cc,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +using namespace std; + +#include +#include +#include +#include + +// This defines the positive population + +bool in_region(double x, double y) { + return x*x + y*y <= 0.25; +} + +int main(int argc, char **argv) { + + const int nb_samples = 1000; + + const int nb_features = 1000; + double vxf[nb_features], vyf[nb_features], kf[nb_features]; + for(int j = 0; j < nb_features; j++) { + double alpha = drand48() * 2 * M_PI; + vxf[j] = sin(alpha); + vyf[j] = cos(alpha); + kf[j] = - drand48() * vxf[j] - drand48() * vyf[j]; + } + + cout << "Saving the training set.\n"; + ofstream training("train.dat"); + training << nb_samples << " " << nb_features << "\n"; + for(int k = 0; k < nb_samples; k++) { + double x = drand48(), y = drand48(); + for(int j = 0; j < nb_features; j++) + training << ((x*vxf[j] + y*vyf[j] + kf[j] >= 0) ? 1 : 0) << ((j < nb_features-1) ? " " : "\n"); + training << (in_region(x, y) ? 1 : 0) << "\n"; + } + + int delta = 50; + cout << "Saving the test set.\n"; + ofstream test("test.dat"); + test << delta*delta << " " << nb_features << "\n"; + for(int xx = 0; xx < delta; xx++) for(int yy = 0; yy < delta; yy++) { + double x = double(xx)/double(delta-1), y = double(yy)/double(delta-1); + for(int j = 0; j < nb_features; j++) + test << ((x*vxf[j] + y*vyf[j] + kf[j] >= 0) ? 1 : 0) << ((j < nb_features-1) ? " " : "\n"); + test << (in_region(x, y) ? 1 : 0) << "\n"; + } +} diff --git a/fastentropy.cc b/fastentropy.cc new file mode 100644 index 0000000..d23a7cf --- /dev/null +++ b/fastentropy.cc @@ -0,0 +1,180 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: fastentropy.cc,v 1.4 2007-08-23 08:36:50 fleuret Exp $ + +using namespace std; + +#include +#include +#include +#include + +#include "misc.h" +#include "fastentropy.h" + +int fe_nb_bits[65536]; +double fe_logn[65536], fe_nlogn[65536]; + +#ifdef DEBUG +int fe_was_initialized = 0; +#endif + +inline void fe_and(int n, uint32_t *a, uint32_t *b, uint32_t *ab) { + for(int k = 0; k < (n+31)/32; k++) *ab++ = *a++ & *b++; +} + +inline void fe_and_not(int n, uint32_t *a, uint32_t *b, uint32_t *ab) { + for(int k = 0; k < (n+31)/32; k++) *ab++ = *a++ & ~*b++; +} + +inline void fe_and_not_not(int n, uint32_t *a, uint32_t *b, uint32_t *ab) { + for(int k = 0; k < (n+31)/32; k++) *ab++ = ~*a++ & ~*b++; +} + +inline int fe_count(int n, uint32_t *a) { + uint16_t *aa = (uint16_t *) a; + int t = 0; + for(int k = 0; k < n/16; k++) t += fe_nb_bits[*aa++]; + if(n%16 > 0) t += fe_nb_bits[(65535 >> (16-(n%16))) & *aa]; + return t; +} + +inline double fe_entropy(int n, uint32_t *a) { + int n1 = fe_count(n, a); + return fe_logn[n] - (fe_nlogn[n1] + fe_nlogn[n-n1])/double(n); +} + +inline double fe_entropy_couple(int n, uint32_t *a, uint32_t *b) { + int n11 = fe_count_and(n, a, b); + int n10 = fe_count_and_not(n, a, b); + int n01 = fe_count_and_not(n, b, a); + int n00 = fe_count_and_not_not(n, a, b); + + return fe_logn[n] - (fe_nlogn[n00] + fe_nlogn[n01] + fe_nlogn[n10] + fe_nlogn[n11])/double(n); +} + +void fe_init_tables() { +#ifdef DEBUG + fe_was_initialized = 1; +#endif + for(int i = 0; i < 65536; i++) { + if(i == 0) { fe_logn[i] = 0.0; fe_nlogn[i] = 0.0; } + else { fe_logn[i] = log(double(i)); fe_nlogn[i] = double(i) * log(double(i)); } + int n = 0; + for(int j = 0; j < 16; j++) if(i & (1 << j)) n++; + fe_nb_bits[i] = n; + } +} + +void fe_selection_cmim(int nb_samples, + int nb_tests, uint32_t **x, uint32_t *y, + int nb_selected, int *selected) { + +#ifdef DEBUG + if(!fe_was_initialized) { + cerr << "fe_init_tables() was not called!\n"; + abort(); + } +#endif + + if(nb_samples > 65535) { + cerr << "Too many pictures, the nlogn table is too small.\n"; + exit(1); + } + + double s[nb_tests]; + double ch[nb_tests]; + int m[nb_tests]; + + double h = fe_entropy(nb_samples, y); + + for(int i = 0; i < nb_tests; i++) { + ch[i] = fe_entropy_couple(nb_samples, y, x[i]) - fe_entropy(nb_samples, x[i]); + s[i] = h - ch[i]; + m[i] = 0; + } + + for(int n = 0; n < nb_selected; n++) { + double best_s = 0; + for(int i = 0; i < nb_tests; i++) { + if(s[i] > best_s) { + int nb_uint32 = (nb_samples+31)/32; + if(m[i] < n) { + uint32_t z00[nb_uint32], z01[nb_uint32], z10[nb_uint32], z11[nb_uint32]; + fe_and(nb_samples, x[i], y, z11); + fe_and_not(nb_samples, x[i], y, z10); + fe_and_not(nb_samples, y, x[i], z01); + fe_and_not_not(nb_samples, x[i], y, z00); + while(s[i] > best_s && m[i] < n) { + double h_y_xi_xmi = fe_logn[nb_samples] - + ( fe_nlogn[fe_count_and (nb_samples, z11, x[selected[m[i]]])] + + fe_nlogn[fe_count_and (nb_samples, z10, x[selected[m[i]]])] + + fe_nlogn[fe_count_and (nb_samples, z01, x[selected[m[i]]])] + + fe_nlogn[fe_count_and (nb_samples, z00, x[selected[m[i]]])] + + fe_nlogn[fe_count_and_not(nb_samples, z11, x[selected[m[i]]])] + + fe_nlogn[fe_count_and_not(nb_samples, z10, x[selected[m[i]]])] + + fe_nlogn[fe_count_and_not(nb_samples, z01, x[selected[m[i]]])] + + fe_nlogn[fe_count_and_not(nb_samples, z00, x[selected[m[i]]])])/double(nb_samples); + double h_xi_xmi = fe_entropy_couple(nb_samples, x[i], x[selected[m[i]]]); + double ss = ch[selected[m[i]]] - (h_y_xi_xmi - h_xi_xmi); + if(ss < s[i]) s[i] = ss; + m[i]++; + } + } + if(s[i] > best_s) { + best_s = s[i]; + selected[n] = i; + } + } + } + } +} + +void fe_selection_mim(int nb_samples, + int nb_tests, uint32_t **x, uint32_t *y, + int nb_selected, int *selected) { + +#ifdef DEBUG + if(!fe_was_initialized) { + cerr << "fe_init_tables() was not called!\n"; + abort(); + } +#endif + + if(nb_samples > 65535) { + cerr << "Too many pictures, the nlogn table is too small.\n"; + exit(1); + } + + Couple tmp[nb_tests]; + double h = fe_entropy(nb_samples, y); + + for(int i = 0; i < nb_tests; i++) { + tmp[i].index = i; + tmp[i].value = fe_entropy_couple(nb_samples, y, x[i]) - h - fe_entropy(nb_samples, x[i]); + } + + qsort(tmp, nb_tests, sizeof(Couple), compare_couple); + + // Here we have the features sorted according to their mutual + // information with the class to predict + + for(int n = 0; n < nb_selected; n++) selected[n] = tmp[n].index; +} diff --git a/fastentropy.h b/fastentropy.h new file mode 100644 index 0000000..af4914c --- /dev/null +++ b/fastentropy.h @@ -0,0 +1,83 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: fastentropy.h,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +#ifndef FASTENTROPY_H +#define FASTENTROPY_H + +#include + +// Lookup tables to speed up the training + +extern int fe_nb_bits[65536]; +extern double fe_logn[65536], fe_nlogn[65536]; + +void fe_init_tables(); + +inline void fe_set_bit(int k, uint32_t *a, int v) { + if(v) a[k/32] = a[k/32] | (1 << (k%32)); + else a[k/32] = a[k/32] & ~(1 << (k%32)); +} + +inline int fe_get_bit(int k, uint32_t *a) { + return a[k/32] & (1 << (k%32)); +} + +inline int fe_count_and(int n, uint32_t *a, uint32_t *b) { + uint16_t *aa = (uint16_t *) a, *bb = (uint16_t *) b; + int t = 0; + for(int k = 0; k < n/16; k++) t += fe_nb_bits[*aa++ & *bb++]; + if(n%16 > 0) t += fe_nb_bits[(65535 >> (16-(n%16))) & *aa & *bb]; + return t; +} + +inline int fe_count_and_not(int n, uint32_t *a, uint32_t *b) { + uint16_t *aa = (uint16_t *) a, *bb = (uint16_t *) b; + int t = 0; + for(int k = 0; k < n/16; k++) t += fe_nb_bits[*aa++ & ~*bb++]; + if(n%16 > 0) t += fe_nb_bits[(65535 >> (16-(n%16))) & *aa & ~*bb]; + return t; +} + +inline int fe_count_and_not_not(int n, uint32_t *a, uint32_t *b) { + uint16_t *aa = (uint16_t *) a, *bb = (uint16_t *) b; + int t = 0; + for(int k = 0; k < n/16; k++) t += fe_nb_bits[65535 & ~*aa++ & ~*bb++]; + if(n%16 > 0) t += fe_nb_bits[(65535 >> (16-(n%16))) & ~*aa & ~*bb]; + return t; +} + +// This selection maximises the conditional mutual information between +// the features and the class to predict, given any feature already +// picked + +void fe_selection_cmim(int nb_samples, + int nb_total_features, uint32_t **x, uint32_t *y, + int nb_selected, int *selected); + +// This selection maximises the mutual information between the +// features and the class to predict, without taking care of the +// redundancy + +void fe_selection_mim(int nb_samples, + int nb_total_features, uint32_t **x, uint32_t *y, + int nb_selected, int *selected); + +#endif diff --git a/gpl-3.0.txt b/gpl-3.0.txt new file mode 100644 index 0000000..94a9ed0 --- /dev/null +++ b/gpl-3.0.txt @@ -0,0 +1,674 @@ + 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. 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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 +. diff --git a/misc.cc b/misc.cc new file mode 100644 index 0000000..541503b --- /dev/null +++ b/misc.cc @@ -0,0 +1,29 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: misc.cc,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +#include "misc.h" + +int compare_couple(const void *a, const void *b) { + if(((Couple *) a)->value < ((Couple *) b)->value) return -1; + else if(((Couple *) a)->value > ((Couple *) b)->value) return 1; + else return 0; +} + diff --git a/misc.h b/misc.h new file mode 100644 index 0000000..f9297e9 --- /dev/null +++ b/misc.h @@ -0,0 +1,34 @@ + +////////////////////////////////////////////////////////////////////////////////// +// This program is free software: you can redistribute it and/or modify // +// it under the terms of the version 3 of the GNU General Public License // +// as published by the Free Software Foundation. // +// // +// 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 . // +// // +// Written by Francois Fleuret // +// (C) Ecole Polytechnique Federale de Lausanne // +// Contact for comments & bug reports // +////////////////////////////////////////////////////////////////////////////////// + +// $Id: misc.h,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +#ifndef MISC_H +#define MISC_H + +template T sq(T x) { return x*x; } + +struct Couple { + int index; + double value; +}; + +int compare_couple(const void *a, const void *b); + +#endif diff --git a/test.sh b/test.sh new file mode 100755 index 0000000..727defe --- /dev/null +++ b/test.sh @@ -0,0 +1,28 @@ +#!/bin/sh + +################################################################################ +# This program is free software: you can redistribute it and#or modify # +# it under the terms of the version 3 of the GNU General Public License # +# as published by the Free Software Foundation. # +# # +# 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 . # +# # +# Written by Francois Fleuret # +# (C) Ecole Polytechnique Federale de Lausanne # +# Contact for comments & bug reports # +################################################################################ + +# $Id: test.sh,v 1.3 2007-08-23 08:36:50 fleuret Exp $ + +./create_samples + +for fs in cmim mim random; do + ./cmim --nb-features 10 --feature-selection $fs --classifier bayesian --train ./train.dat /tmp/$fs.clf + ./cmim --silent --test /tmp/$fs.clf test.dat results.dat | grep ERROR +done