void Toolbox::calculateGlobalMeanAndStd(DataSet &X,dVector& mean,dVector& stdDev)
{
calculateGlobalMean(X,mean);
int nbElements = 0;
//Calculate standard deviation
stdDev.set(0);
for(int i = 0;i < (int)X.size() ;i++)
{
double* pData = X.at(i)->getPrecomputedFeatures()->get();
int Width = X.at(i)->getPrecomputedFeatures()->getWidth();
int Height = X.at(i)->getPrecomputedFeatures()->getHeight();
for(int col=0; col < Width; col++)
{
double* pStdDev = stdDev.get();
double* pMean = mean.get();
for(int row = 0; row < Height;row++)
{
*pStdDev += (*pData-*pMean) * (*pData-*pMean);
pStdDev++;
pData++;
pMean++;
}
}
nbElements+=Width;
}
stdDev.multiply(1.0/(double)nbElements);
stdDev.eltSqrt();
}
//Compute gradient
void UnconstrainedOptimizer::G()
{
dVector dgrad(n);
memcpy(vecGradient.get(),x,n*sizeof(double));
currentModel->setWeights(vecGradient);
if(currentModel->getDebugLevel() >= 2)
std::cout << "Compute gradient..." << std::endl;
currentGradient->computeGradient(dgrad, currentModel,currentDataset);
memcpy(g,dgrad.get(),n*sizeof(double));
}
// Compute error function
void UnconstrainedOptimizer::F()
{
memcpy(vecGradient.get(),x,n*sizeof(double));
currentModel->setWeights(vecGradient);
if(currentModel->getDebugLevel() >= 2)
std::cout << "Compute error..." << std::endl;
f = currentEvaluator->computeError(currentDataset, currentModel);
if(currentModel->getDebugLevel() >= 3)
{
// printf(" Iteration # = %i Nb error eval = %i Nb gradient eval = %i\n\n", cnls, cnf, cng);
// printf("F = %-0.10lg\n",f);
std::cout << " Iteration # = " << cnls << " Nb error eval = " <<cnf << " Nb gradient eval = " << cng <<std::endl << std::endl;
std::cout << "F = " << f << std::endl;
std::cout.flush();
}
}
