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// Copyright (C) 2002 Ronan Collobert (collober@iro.umontreal.ca)
//
//
// 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 "WeightedMseCriterion.h"
namespace Torch {
WeightedMseCriterion::WeightedMseCriterion(DataSet *data_)
{
data = data_;
n_inputs = data->n_targets;
weights = NULL;
weights_is_allocated = false;
}
WeightedMseCriterion::WeightedMseCriterion(DataSet *data_, real *weights_)
{
data = data_;
n_inputs = data->n_targets;
weights = weights_;
weights_is_allocated = false;
}
void WeightedMseCriterion::setDataSet(DataSet *data_)
{
if(weights_is_allocated)
{
if(data_->n_real_examples != data->n_real_examples)
error("WeightedMseCriterion: trying to use a wrong DataSet");
}
data = data_;
}
void WeightedMseCriterion::allocateMemory()
{
GradientMachine::allocateMemory();
if(!weights)
{
weights = (real *)xalloc(sizeof(real)*data->n_real_examples);
for(int i = 0; i < data->n_real_examples; i++)
weights[i] = 1;
weights_is_allocated = true;
}
}
void WeightedMseCriterion::freeMemory()
{
if(is_free)
return;
GradientMachine::freeMemory();
if(weights_is_allocated)
free(weights);
is_free = true;
}
void WeightedMseCriterion::forward(List *inputs)
{
real *desired = (real *)data->targets;
real err = 0;
while(inputs)
{
real *x = (real *)inputs->ptr;
for(int j = 0; j < inputs->n; j++)
{
real z = *desired++ - *x++;
err += z*z;
}
inputs = inputs->next;
}
real *ptr_out = (real *)outputs->ptr;
ptr_out[0] = err*weights[data->current_example];
// printf("%g %g\n", data->targets[0], weights[data->current_example]);
}
void WeightedMseCriterion::backward(List *inputs, real *alpha)
{
real *desired = (real *)data->targets;
real *ptr_beta = beta;
real z = weights[data->current_example];
while(inputs)
{
real *x = (real *)inputs->ptr;
for(int j = 0; j < inputs->n; j++)
*ptr_beta++ = 2.*z*(*x++ - *desired++);
inputs = inputs->next;
}
// printf("%g %g\n", data->targets[0], weights[data->current_example]);
}
WeightedMseCriterion::~WeightedMseCriterion()
{
freeMemory();
}
}
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