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// Copyright (C) 2002 Samy Bengio (bengio@idiap.ch)
// and Bison Ravi (francois.belisle@idiap.ch)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch 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 2 of the License, or
// (at your option) any later version.
//
// Torch 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 Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#include "BayesClassifier.h"
#include "log_add.h"
namespace Torch {
BayesClassifier::BayesClassifier(BayesClassifierMachine* machine_,DataSet* data_)
: Trainer(machine_,data_)
{
bayesmachine = (BayesClassifierMachine*)machine;
n_classes = bayesmachine->n_trainers;
classes = (int**) xalloc(n_classes * sizeof(int*));
for(int i = 0;i < n_classes ;i++)
classes[i] = (int*) xalloc(data->n_examples * sizeof(int));
classes_n = (int*)xalloc(n_classes * sizeof(int));
}
BayesClassifier::~BayesClassifier()
{
for(int i = 0;i < n_classes;i++)
free(classes[i]);
free(classes);
free(classes_n);
}
void BayesClassifier::train(List* measurers)
{
message("BayesClassifier: Training");
// attribute the classes
for (int i=0;i<n_classes;i++)
classes_n[i] = 0;
for (int i=0;i<data->n_examples;i++) {
data->setExample(i);
int c = bayesmachine->class_format->getTargetClass(data->targets);
classes[c][classes_n[c]++] = i;
}
// eventually compute prior given training set
if (bayesmachine->allocated_log_priors) {
for (int i=0;i<n_classes;i++)
if (classes_n[i] > 0)
bayesmachine->log_priors[i] = - log((real)classes_n[i]);
else
bayesmachine->log_priors[i] = LOG_ZERO;
}
for(int c = 0;c < n_classes;c++) {
data->pushSubset(classes[c],classes_n[c]);
bayesmachine->trainers[c]->machine->reset();
if (bayesmachine->trainers_measurers)
bayesmachine->trainers[c]->train(bayesmachine->trainers_measurers[c]);
else
bayesmachine->trainers[c]->train(NULL);
data->popSubset();
}
if (measurers) {
test(measurers);
}
}
}
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