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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 "LogRBF.h"
namespace Torch {
LogRBF::LogRBF(int n_inputs_, int n_outputs_, EMTrainer* kmeans_trainer)
{
n_inputs = n_inputs_;
n_outputs = n_outputs_;
initial_kmeans_trainer = kmeans_trainer;
}
int LogRBF::numberOfParams()
{
return(2*n_inputs*n_outputs);
}
void LogRBF::init()
{
GradientMachine::init();
reset();
}
void LogRBF::reset()
{
if (initial_kmeans_trainer) {
initial_kmeans_trainer->machine->reset();
initial_kmeans_trainer->train(NULL);
// transform variances into gamma
real* kmeans_params =
(real*)initial_kmeans_trainer->distribution->params->ptr;
real* kmeans_var = kmeans_params + n_outputs + n_inputs;
real* kmeans_mu = kmeans_params + n_outputs;
real* mu = (real*)params->ptr;
real* gamma = (real*)params->ptr + n_inputs;
for (int i=0;i<n_outputs;i++) {
for (int j=0;j<n_inputs;j++,gamma++,kmeans_var++,kmeans_mu++,mu++) {
*gamma = 1./sqrt(*kmeans_var);
*mu = *kmeans_mu;
}
kmeans_var += n_inputs;
gamma += n_inputs;
kmeans_mu += n_inputs;
mu += n_inputs;
}
} else {
// think more about this...
real *params_ = (real *)params->ptr;
real bound = 1./sqrt((real)n_inputs);
for(int i = 0; i < n_params; i++)
params_[i] = bounded_uniform(-bound, bound);
}
}
void LogRBF::forward(List *inputs)
{
real *ptr_params = (real *)params->ptr;
real *ptr_outputs = (real *)outputs->ptr;
real *mu = ptr_params;
real *gamma = mu + n_inputs;
for(int s = 0; s < n_outputs; s++)
{
real out = 0;
List *inputs_ = inputs;
while(inputs_)
{
real *x = (real *)inputs_->ptr;
for(int j = 0; j < inputs_->n; j++) {
real diff = (*x++ - *mu++) * *gamma++;
out += diff*diff;
}
inputs_ = inputs_->next;
}
out *= -0.5;
*ptr_outputs++ = out;
mu += n_inputs;
gamma += n_inputs;
}
}
void LogRBF::backward(List *inputs, real *alpha)
{
real *beta_ptr = beta;
for(int k = 0; k < n_inputs; k++)
*beta_ptr++ = 0;
real *alpha_ptr = alpha;
real *params_ptr = (real *)params->ptr;
real *der_params_ptr = (real *)der_params->ptr;
real *mu = params_ptr;
real *gamma = mu + n_inputs;
real *der_mu = der_params_ptr;
real *der_gamma = der_mu + n_inputs;
for(int j = 0; j < n_outputs; j++, alpha_ptr++)
{
List *inputs_ = inputs;
beta_ptr = beta;
while(inputs_)
{
real *x = (real *)inputs_->ptr;
for(int k = 0; k < inputs_->n; k++) {
real diff = (*x++ - *mu++);
real g2 = (*gamma * *gamma);
real dd = *alpha_ptr * 2. * diff * g2;
*der_mu++ = dd;
*beta_ptr++ += dd;
*der_gamma++ = *alpha_ptr * 2. * diff*diff * *gamma++;
}
inputs_ = inputs_->next;
}
mu += n_inputs;
gamma += n_inputs;
der_mu += n_inputs;
der_gamma += n_inputs;
}
}
LogRBF::~LogRBF()
{
}
}
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