Evaluation eval(const Evaluation& x, const Evaluation& y) const
{
typedef MathToolbox<Evaluation> Toolbox;
#ifndef NDEBUG
if (!applies(x,y))
{
OPM_THROW(NumericalProblem,
"Attempt to get tabulated value for ("
<< x << ", " << y
<< ") on a table of extend "
<< xMin() << " to " << xMax() << " times "
<< yMin() << " to " << yMax());
};
#endif
Evaluation alpha = xToI(x);
Evaluation beta = yToJ(y);
unsigned i = std::max(0U, std::min(static_cast<unsigned>(numX()) - 2,
static_cast<unsigned>(Toolbox::value(alpha))));
unsigned j = std::max(0U, std::min(static_cast<unsigned>(numY()) - 2,
static_cast<unsigned>(Toolbox::value(beta))));
alpha -= i;
beta -= j;
// bi-linear interpolation
const Evaluation& s1 = getSamplePoint(i, j)*(1.0 - alpha) + getSamplePoint(i + 1, j)*alpha;
const Evaluation& s2 = getSamplePoint(i, j + 1)*(1.0 - alpha) + getSamplePoint(i + 1, j + 1)*alpha;
return s1*(1.0 - beta) + s2*beta;
}
void itsHybridEstimationOfDistribution::simplex()
{
// if no given NMS evaluations number
if( getSimplexEvaluations() == -1 ) {
//setSimplexEvaluations( (int)floor( pow( (this->problem->getDimension() + 1 ), 2.0 ) ) );
setSimplexEvaluations( this->getProblem()->getDimension() + 10 );
}
for( unsigned int i=0; i < getSampleSizeCurrent(); i++ ) {
itsNelderMead nms;
// problem optimized
nms.setProblem( this->getProblem() );
// no ending stopping criteria
nms.setValueMin(0.0);
nms.setIterationsMaxNumber( this->getIterationsMaxNumber() );
// used stopping criterion
nms.setEvaluationsMaxNumber( this->simplexEvaluations );
// edges from sample hypercube
vector<double> edges =
multiply(
substraction(
this->getProblem()->boundsMaxima(),
this->getProblem()->boundsMinima()
),
1 / ( pow( (double)getSampleSizeCurrent(), (double)1.0/this->getProblem()->getDimension() ) - 1 )
);
// init on current point
nms.initSimplexFromBasePoint( getSamplePoint(i), edges );
// silent launch
nms.startSilent();
// change the point to the new local optimum
setSamplePoint( i, nms.getOptimum() );
}
}
