--- /dev/null
+
+################################################################################
+# 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 <http://www.gnu.org/licenses/>. #
+# #
+# Written by Francois Fleuret #
+# (C) Ecole Polytechnique Federale de Lausanne #
+# Contact <francois.fleuret@epfl.ch> 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 ${DIRNAME}/README
+
+-include Makefile.depend
--- /dev/null
+-*- mode: text -*-
+
++----------------------------------------------------------------------+
+| This archive contains a simple implementation of the Conditional |
+| Mutual Information Maximization for feature selection. |
++----------------------------------------------------------------------+
+| Written by François Fleuret |
+| Contact <francois.fleuret@epfl.ch> for comments & bug reports |
+| Copyright (C) 2004 EPFL |
++----------------------------------------------------------------------+
+
+$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 <random|mim|cmim>
+
+ Selects the feature selection method
+
+ --classifier <bayesian|perceptron>
+
+ Selects the classifier type
+
+ --error <standard|ber>
+
+ 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 <int: nb of features>
+
+ Selects the number of selected features
+
+ --cross-validation <file: data set> <int: nb test samples> <int: nb loops>
+
+ Do cross-validation
+
+ --train <file: data set> <file: classifier>
+
+ Build and save to disk a classifier
+
+ --test <file: classifier> <file: data set> <file: result>
+
+ 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.
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> for comments & bug reports //
+//////////////////////////////////////////////////////////////////////////////////
+
+// $Id: classifier.cc,v 1.3 2007-08-23 08:36:50 fleuret Exp $
+
+#include <stdlib.h>
+#include <cmath>
+
+#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++;
+ }
+ }
+}
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> 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 <iostream>
+#include <fstream>
+
+#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
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> 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 <cmath>
+#include <iostream>
+#include <fstream>
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+#include <sys/time.h>
+
+#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, "<random|mim|cmim>");
+ strncpy(feature_selection_type, argv[i+1], BUFFER_SIZE);
+ i += 2;
+ }
+
+ else if(strcmp(argv[i], "--classifier") == 0) {
+ check_opt(argc, argv, i, 1, "<bayesian|perceptron>");
+ strncpy(classifier_type, argv[i+1], BUFFER_SIZE);
+ i += 2;
+ }
+
+ else if(strcmp(argv[i], "--error") == 0) {
+ check_opt(argc, argv, i, 1, "<standard|ber>");
+ 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, "<int: nb features>");
+ 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, "<file: data set> <int: nb test samples> <int: nb loops>");
+ 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, "<file: data set> <file: classifier>");
+
+ 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, "<file: classifier> <file: data set> <file: result>");
+
+ // 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 <francois.fleuret@epfl.ch>\n";
+ cerr << "\n";
+ cerr << "Usage: " << argv[0] << "\n";
+ cerr << "--silent\n";
+ cerr << "--feature-selection <random|mim|cmim>\n";
+ cerr << "--classifier <bayesian|perceptron>\n";
+ cerr << "--error <standard|ber>\n";
+ cerr << "--nb-features <int: nb of features>\n";
+ cerr << "--cross-validation <file: data set> <int: nb test samples> <int: nb loops>\n";
+ cerr << "--train <file: data set> <file: classifier>\n";
+ cerr << "--test <file: classifier> <file: data set> <file: result>\n";
+ exit(1);
+ }
+}
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> for comments & bug reports //
+//////////////////////////////////////////////////////////////////////////////////
+
+// $Id: create_samples.cc,v 1.3 2007-08-23 08:36:50 fleuret Exp $
+
+using namespace std;
+
+#include <cmath>
+#include <iostream>
+#include <fstream>
+#include <stdlib.h>
+
+// 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";
+ }
+}
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> for comments & bug reports //
+//////////////////////////////////////////////////////////////////////////////////
+
+// $Id: fastentropy.cc,v 1.4 2007-08-23 08:36:50 fleuret Exp $
+
+using namespace std;
+
+#include <iostream>
+#include <cmath>
+#include <stdio.h>
+#include <stdlib.h>
+
+#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;
+}
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> 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 <stdint.h>
+
+// 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
--- /dev/null
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+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+ 16. Limitation of Liability.
+
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
+
+ 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 <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ <program> Copyright (C) <year> <name of author>
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+ 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
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> 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;
+}
+
--- /dev/null
+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact <francois.fleuret@epfl.ch> 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<class T> T sq(T x) { return x*x; }
+
+struct Couple {
+ int index;
+ double value;
+};
+
+int compare_couple(const void *a, const void *b);
+
+#endif
--- /dev/null
+#!/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 <http://www.gnu.org/licenses/>. #
+# #
+# Written by Francois Fleuret #
+# (C) Ecole Polytechnique Federale de Lausanne #
+# Contact <francois.fleuret@epfl.ch> 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
projects / cmim.git / commitdiff