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displaygp.cpp
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displaygp.cpp
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/*
-------------------------------------------------------------------------
This file is part of BayesOpt, an efficient C++ library for
Bayesian optimization.
Copyright (C) 2011-2015 Ruben Martinez-Cantin <rmcantin@unizar.es>
BayesOpt is free software: you can redistribute it and/or modify it
under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
BayesOpt 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with BayesOpt. If not, see <http://www.gnu.org/licenses/>.
------------------------------------------------------------------------
*/
#include "displaygp.hpp"
#include "dataset.hpp"
#include "prob_distribution.hpp"
namespace bayesopt
{
namespace utils
{
DisplayProblem1D::DisplayProblem1D(): MatPlot()
{
status = NOT_READY;
}
void DisplayProblem1D::init(BayesOptBase* bopt, size_t dim)
{
if (dim != 1)
{
throw std::invalid_argument("Display only works for 1D problems");
}
bopt_model = bopt;
bopt->initializeOptimization();
size_t n_points = bopt->getData()->getNSamples();
for (size_t i = 0; i<n_points;++i)
{
const double res = bopt->getData()->getSampleY(i);
const vectord last = bopt->getData()->getSampleX(i);
ly.push_back(res);
lx.push_back(last(0));
}
state_ii = 0;
status = STOP;
};
void DisplayProblem1D::setSTEP()
{
if (status != NOT_READY)
{
status = STEP;
}
};
void DisplayProblem1D::toogleRUN()
{
if (status != NOT_READY)
{
if(status != RUN)
{
status = RUN;
}
else
{
status = STOP;
}
}
}
void DisplayProblem1D::DISPLAY()
{
if (status != NOT_READY)
{
size_t nruns = bopt_model->getParameters()->n_iterations;
if ((status != STOP) && (state_ii < nruns))
{
// We are moving. Next iteration
++state_ii;
bopt_model->stepOptimization();
const double res = bopt_model->getData()->getLastSampleY();
const vectord last = bopt_model->getData()->getLastSampleX();
ly.push_back(res);
lx.push_back(last(0));
if (status == STEP) { status = STOP; }
}
// We compute the prediction, true value and criteria at 1000 points
int n=1000;
std::vector<double> x,y,z,su,sl,c;
x = linspace(0,1,n);
y = x; z = x; su = x; sl = x; c = x;
// Query functions at the 1000 points
vectord q(1);
for(size_t i=0; i<n; ++i)
{
q(0) = x[i]; // Query
ProbabilityDistribution* pd = bopt_model->getPrediction(q);
y[i] = pd->getMean(); //Expected value
su[i] = y[i] + 2*pd->getStd(); //Upper bound (95 %)
sl[i] = y[i] - 2*pd->getStd(); //Lower bound (95 %)
c[i] = -bopt_model->evaluateCriteria(q); //Criteria value
z[i] = bopt_model->evaluateSample(q); //Target function true value
}
//GP subplot
subplot(2,1,1);
title("Press r to run and stop, s to run a step and q to quit.");
plot(x,y); set(3); // Expected value in default color (blue)
plot(lx,ly);set("k");set("o");set(4); // Data points as black star
plot(x,su);set("g"); set(2); // Uncertainty as green lines
plot(x,sl);set("g"); set(2);
plot(x,z);set("r"); set(3); // True function as red line
//Criterion subplot
subplot(2,1,2);
plot(x,c); set(3);
}
};
DisplayProblem2D::DisplayProblem2D():
MatPlot(), cx(1), cy(1), c_points(100), cX(c_points),
cY(c_points), cZ(c_points,std::vector<double>(c_points))
{
status = NOT_READY;
}
void DisplayProblem2D::setSolution(vectord sol)
{
solx.push_back(sol(0));
soly.push_back(sol(1));
}
void DisplayProblem2D::prepareContourPlot()
{
cX=linspace(0,1,c_points);
cY=linspace(0,1,c_points);
for(int i=0;i<c_points;++i)
{
for(int j=0;j<c_points;++j)
{
vectord q(2);
q(0) = cX[j]; q(1) = cY[i];
cZ[i][j]= bopt_model->evaluateSample(q);
}
}
}
void DisplayProblem2D::init(BayesOptBase* bopt, size_t dim)
{
if (dim != 2)
{
throw std::invalid_argument("This display only works "
"for 2D problems");
}
bopt_model = bopt;
prepareContourPlot();
bopt->initializeOptimization();
size_t n_points = bopt->getData()->getNSamples();
for (size_t i = 0; i<n_points;++i)
{
const vectord last = bopt->getData()->getSampleX(i);
lx.push_back(last(0));
ly.push_back(last(1));
}
state_ii = 0;
status = STOP;
};
void DisplayProblem2D::setSTEP()
{
if (status != NOT_READY)
{
status = STEP;
}
};
void DisplayProblem2D::toogleRUN()
{
if (status != NOT_READY)
{
if(status != RUN)
{
status = RUN;
}
else
{
status = STOP;
}
}
}
void DisplayProblem2D::DISPLAY()
{
if (status != NOT_READY)
{
size_t nruns = bopt_model->getParameters()->n_iterations;
title("Press r to run and stop, s to run a step and q to quit.");
contour(cX,cY,cZ,50); // Contour plot (50 lines)
plot(cx,cy);set("g");set("o");set(4); // Data points as black star
plot(solx,soly);set("r"); set("o");set(4); // Solutions as red points
if ((status != STOP) && (state_ii < nruns))
{
// We are moving. Next iteration
++state_ii;
bopt_model->stepOptimization();
const vectord last = bopt_model->getData()->getLastSampleX();
//GP subplot
cx[0] = last(0);
cy[0] = last(1);
if (!lx.empty())
{
plot(lx,ly);set("k");set("o");set(4); // Data points as black star
}
lx.push_back(last(0));
ly.push_back(last(1));
if (status == STEP) { status = STOP; }
}
else
{
plot(lx,ly);set("k");set("o");set(4); // Data points as black star
}
}
};
} //namespace utils
} //namespace bayesopt