double operator() (const vectord &x)
{
ProbabilityDistribution* d = mProc->prediction(x);
double mu = d->getMean();
double sigma2 = d->getStd() * d->getStd();
return mu + mSqAlpha * (sqrt(sigma2+mGamma) - sqrt(mGamma));
};
void update(const vectord &x)
{
ProbabilityDistribution* d = mProc->prediction(x);
double mu = d->getMean();
double sigma2 = d->getStd() * d->getStd();
mGamma += sigma2;
}
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);
}
};
