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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 "BayesClassifierMachine.h"
namespace Torch {
BayesClassifierMachine::BayesClassifierMachine(Trainer** trainers_, int n_trainers_, List** trainers_measurers_, ClassFormat* class_format_,real* log_priors_)
{
allocated_log_priors = false;
trainers = trainers_;
n_trainers = n_trainers_;
trainers_measurers = trainers_measurers_;
class_format = class_format_;
log_posteriors = (real*) xalloc (n_trainers * sizeof(real));
list_log_posteriors = NULL;
addToList(&list_log_posteriors, n_trainers, log_posteriors);
//if we are not given any log_prior class probabilities,
//then we will assume training set proportions.
if(log_priors_ != NULL)
log_priors = log_priors_;
else {
allocated_log_priors = true;
log_priors = (real*) xalloc (n_trainers * sizeof(real));
//as a first approximation
for(int i = 0;i < n_trainers;i++)
log_priors[i] = -log((real)n_trainers);
}
n_outputs = class_format->getOutputSize();
addToList(&outputs, n_outputs, (real*) xalloc(n_outputs * sizeof(real)));
}
BayesClassifierMachine::~BayesClassifierMachine()
{
free(log_posteriors);
if(allocated_log_priors)
free(log_priors);
free(outputs->ptr);
freeList(&outputs);
freeList(&list_log_posteriors);
}
void BayesClassifierMachine::forward(List* inputs)
{
for(int trainer = 0;trainer < n_trainers;trainer++) {
trainers[trainer]->machine->forward(inputs);
real* trainer_output = (real*) trainers[trainer]->machine->outputs->ptr;
// *trainer_output contains the negative log probabililty
log_posteriors[trainer] = log_priors[trainer] - *trainer_output;
}
//transform the output from one_hot representation to class_format
class_format->fromOneHot(outputs,list_log_posteriors);
}
void BayesClassifierMachine::reset()
{
/* I think this is not necessary as it is done in the train method...
for(int i = 0;i < n_trainers;i++)
trainers[i]->machine->reset();
*/
}
void BayesClassifierMachine::loadFILE(FILE* file)
{
for(int i = 0;i < n_trainers;i++)
trainers[i]->loadFILE(file);
}
void BayesClassifierMachine::saveFILE(FILE* file)
{
for(int i = 0;i < n_trainers;i++)
trainers[i]->saveFILE(file);
}
}
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