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/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library 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
Library General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#include <public.h>
#include "mymaths.h"
#include "dynamicalGPR.h"
void DynamicalGPR::Train(std::vector< std::vector<fvec> > trajectories, ivec labels)
{
if(!trajectories.size()) return;
int count = trajectories[0].size();
if(!count) return;
dim = trajectories[0][0].size()/2;
// we forget about time and just push in everything
vector<fvec> samples;
FOR(i, trajectories.size())
{
FOR(j, trajectories[i].size())
{
samples.push_back(trajectories[i][j]);
}
}
if(!samples.size()) return;
Matrix inputs(dim, samples.size());
Matrix outputs(dim, samples.size());
FOR(n, samples.size())
{
FOR(d, dim) inputs(d+1,n+1) = samples[n][d];
//outputs(n+1,1) = samples[n][dim];
FOR(d, dim) outputs(d+1,n+1) = samples[n][dim+d];
}
if(sogp) delete sogp;
if(kernelType == kerPOL)
{
if(!degree) degree = 1;
RowVector deg(degree);
for (int i=0; i<degree; i++) deg(i+1) = param1*(1+i*0.3f);
POLKernel kern(deg);
SOGPParams params(&kern);
params.s20=param2;
params.capacity = capacity;
sogp = new SOGP(params);
}
else
{
RBFKernel kern(param1);
SOGPParams params(&kern);
params.s20=param2;
params.capacity = capacity;
sogp = new SOGP(params);
}
sogp->addM(inputs,outputs);
bTrained = true;
}
std::vector<fvec> DynamicalGPR::Test( const fvec &sample, const int count)
{
fvec start = sample;
dim = sample.size();
std::vector<fvec> res;
res.resize(count);
FOR(i, count) res[i].resize(dim,0);
if(!sogp) return res;
Matrix _testout;
ColumnVector _testin(dim);
fvec velocity; velocity.resize(dim,0);
FOR(i, count)
{
res[i] = start;
start += velocity*dT;
FOR(i,dim) _testin(1+i) = start[i];
_testout = sogp->predict(_testin);
FOR(d, dim) velocity[d] = _testout(d+1,1);
}
return res;
}
fvec DynamicalGPR::Test( const fvec &sample )
{
fvec res(dim);
if(!sogp) return res;
if(sample.size() < dim) return res;
Matrix _testout;
ColumnVector _testin(dim);
FOR(i,dim)
{
_testin(1+i) = sample[i];
}
_testout = sogp->predict(_testin);
FOR(d,dim) res[d] = _testout(d+1,1);
return res;
}
fVec DynamicalGPR::Test( const fVec &sample )
{
fVec res;
if(!sogp) return res;
Matrix _testout;
ColumnVector _testin(dim);
FOR(i,dim)
{
_testin(1+i) = sample._[i];
}
_testout = sogp->predict(_testin);
res[0] = _testout(1,1);
res[1] = _testout(2,1);
return res;
}
float DynamicalGPR::GetLikelihood(float mean, float sigma, float point)
{
const float sqrpi = 1.f/sqrtf(2.f*PIf);
const float divider = sqrpi/sigma;
const float exponent = -powf((point-mean)/sigma,2.f)*0.5;
return expf(exponent)*divider;
}
void DynamicalGPR::Clear()
{
bTrained = false;
delete sogp;
sogp = 0;
}
int DynamicalGPR::GetBasisCount()
{
return sogp ? sogp->size() : 0;
}
fvec DynamicalGPR::GetBasisVector( int index )
{
if(!sogp) return fvec();
if(index > sogp->size()) return fvec();
fvec res;
res.resize(4,0);
res[0] = sogp->BVloc(index, 0);
res[1] = sogp->BVloc(index, 1);
res[2] = sogp->alpha_acc(index, 0);
res[3] = sogp->alpha_acc(index, 1);
return res;
}
const char *DynamicalGPR::GetInfoString()
{
char *text = new char[2048];
sprintf(text, "Sparse Optimized Gaussian Processes\n");
sprintf(text, "%sKernel: ", text);
switch(kernelType)
{
case 0:
sprintf(text, "%s rbf (gamma: %f)\n", text, param1);
break;
case 1:
sprintf(text, "%s polynomial (deg: %d width: %f)\n", text, degree, param1);
break;
case 2:
sprintf(text, "%s rbf (gamma: %f)\n", text, param1);
break;
}
return text;
sprintf(text, "%sNoise: %.3f\n", text, param2);
sprintf(text, "%sBasis Functions: %d\n", text, GetBasisCount());
return text;
}
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