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// Copyright (C) 2002 Samy Bengio (bengio@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 "ParzenDistribution.h"
#include "log_add.h"
#include "random.h"
#include "SeqDataSet.h"
namespace Torch {
ParzenDistribution::ParzenDistribution(SeqDataSet* data_, real var_) : Distribution()
{
data = data_;
n_observations = data->n_observations;
n_inputs = data->n_inputs;
setVar(var_);
n_real_examples = 0;
real_examples = NULL;
}
void ParzenDistribution::setVar(real var_)
{
var = var_;
sum_log_var_plus_n_obs_log_2_pi = -0.5 * n_observations*(LOG_2_PI + log(var));
minus_half_over_var = -0.5 / var;
}
void ParzenDistribution::allocateMemory()
{
max_n_frames = 1;
n_params = numberOfParams();
addToList(&outputs,n_outputs,(real*)xalloc(sizeof(real)*n_outputs));
log_probabilities = (real*)xalloc(sizeof(real)*max_n_frames);
}
void ParzenDistribution::freeMemory()
{
freeList(&outputs,true);
free(log_probabilities);
}
int ParzenDistribution::numberOfParams()
{
return 0;
}
void ParzenDistribution::reset()
{
n_real_examples = data->n_examples;
real_examples = (int*)xrealloc(real_examples,n_real_examples*sizeof(int));
for (int i=0;i<data->n_examples;i++) {
real_examples[i] = data->selected_examples[i];
}
}
void ParzenDistribution::eMSequenceInitialize(List* inputs)
{
if (!inputs)
return;
SeqExample* ex = (SeqExample*)inputs->ptr;
if (ex->n_real_frames > max_n_frames) {
max_n_frames = ex->n_real_frames;
log_probabilities = (real*)xrealloc(log_probabilities,sizeof(real)*max_n_frames);
}
}
void ParzenDistribution::sequenceInitialize(List* inputs)
{
eMSequenceInitialize(inputs);
}
real ParzenDistribution::frameLogProbability(real *observations, real *inputs, int t)
{
// first keep the current pointers...
int current_example = data->current_example;
int current_frame = data->current_frame;
// then compute the likelihood...
real lp = LOG_ZERO;
tot_n_frames = 0;
int *i_ptr = real_examples;
for (int i=0;i<n_real_examples;i++) {
data->setRealExample(*i_ptr++);
tot_n_frames += data->n_frames;
for (int j=0;j<data->n_frames;j++) {
data->setFrame(j);
real lp_ij = frameLogProbabilityOneFrame(observations,data->examples[data->current_example].observations[data->current_frame]);
lp = log_add(lp, lp_ij);
}
}
lp -= log((real)tot_n_frames);
log_probabilities[t] = lp;
// restore the dataset status
data->setRealExample(current_example);
data->setFrame(current_frame);
return lp;
}
real ParzenDistribution::frameLogProbabilityOneFrame(real *observations, real *mean)
{
real sum_xmu = 0.;
real *x = observations;
for(int j = 0; j < n_observations; j++) {
real xmu = (*x++ - *mean++);
sum_xmu += xmu*xmu;
}
real lp = sum_xmu*minus_half_over_var + sum_log_var_plus_n_obs_log_2_pi;
return lp;
}
void ParzenDistribution::frameExpectation(real *observations, real *inputs, int t)
{
real *obs = observations;
for (int i=0;i<n_observations;i++) {
*obs++ = 0;
}
}
ParzenDistribution::~ParzenDistribution()
{
free(real_examples);
freeMemory();
}
}
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