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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 "FixedMachineDistribution.h"
namespace Torch {
FixedMachineDistribution::FixedMachineDistribution(Machine* machine_,int output_number_,real prior_) : Distribution()
{
machine = machine_;
output_number = output_number_;
prior = prior_;
n_observations = machine->n_inputs;
n_inputs = 0;
machine_inputs.next = NULL;
machine_inputs.n = n_observations;
}
int FixedMachineDistribution::numberOfParams()
{
return 0;
}
void FixedMachineDistribution::reset()
{
machine->reset();
}
real FixedMachineDistribution::frameLogProbability(real *observations, real *inputs, int t)
{
machine_inputs.ptr = observations;
// this is not very optimal as each state of the HMM will call this
// method... a better solution would be to modify the HMM code itself...
// but I have not found a clean way to do it yet.
machine->forward(&machine_inputs);
real res = 0;
int i = output_number;
List* out = machine->outputs;
while (out) {
if (i < out->n) {
res = ((real*)out->ptr)[i];
break;
}
i -= out->n;
out = out->next;
}
return log(res / prior);
}
FixedMachineDistribution::~FixedMachineDistribution()
{
}
}
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