void MatrixErrorValueCalculator::calculateParallelErrorValue(vector< ModelTuningParameters > & paramList) {
vector< double > errorValues(paramList.size());
vector< ModelResults > results = model->runParallelModel(paramList);
for (unsigned int i = 0; i < paramList.size(); i++) {
showMessage("Model VdVdtMatrices\n",5,fixedParams);
for (int nTrace = 0; nTrace < results[i].getLength(); nTrace++) {
modelVdVdtMatrix->readFrom(results[i][nTrace]);
errorValues[i] += results[i][nTrace].getWeight() * expVdVdtMatrices[nTrace]->compare(*modelVdVdtMatrix);
showMessage(modelVdVdtMatrix->toString() + "\n",5,fixedParams);
}
numberOfEvaluations++;
errorValueHistory.push_back(pair< ModelTuningParameters, double >(paramList[i],errorValues[i]));
showMessage("Eval: " + str(numberOfEvaluations) + " Generation: " + str(numberOfGenerations) + " Calculated error value of: " + paramList[i].toString() + ": " + str(errorValues[i]) + "\n",3,fixedParams);
if (exportFileStream.is_open()) {
exportFileStream << numberOfGenerations << " "<< numberOfEvaluations << " " << errorValues[i] << " ";
for (int j = 0; j < paramList[i].getLength(); j++) {
exportFileStream << (paramList[i][j]) << " ";
}
exportFileStream << endl;
}
paramList[i].setErrorValue(errorValues[i]);
}
numberOfGenerations++;
}
bool LMA::step()
{
OPENANN_CHECK(opt);
if(iteration < 0)
initialize();
OPENANN_CHECK(n > 0);
try
{
while(alglib_impl::minlmiteration(state.c_ptr(), &envState))
{
if(state.needfi)
{
for(unsigned i = 0; i < n; i++)
parameters(i) = state.x[i];
opt->setParameters(parameters);
for(unsigned i = 0; i < opt->examples(); i++)
state.fi[i] = opt->error(i);
if(iteration != state.c_ptr()->repiterationscount)
{
iteration = state.c_ptr()->repiterationscount;
opt->finishedIteration();
return true;
}
continue;
}
if(state.needfij)
{
for(unsigned i = 0; i < n; i++)
parameters(i) = state.x[i];
opt->setParameters(parameters);
for(int ex = 0; ex < opt->examples(); ex++)
{
opt->errorGradient(ex, errorValues(ex), gradient);
state.fi[ex] = errorValues(ex);
for(unsigned d = 0; d < opt->dimension(); d++)
state.j[ex][d] = gradient(d);
}
if(iteration != state.c_ptr()->repiterationscount)
{
iteration = state.c_ptr()->repiterationscount;
opt->finishedIteration();
return true;
}
continue;
}
if(state.xupdated)
continue;
throw alglib::ap_error("ALGLIB: error in 'minlmoptimize' (some "
"derivatives were not provided?)");
}
alglib_impl::ae_state_clear(&envState);
}
catch(alglib_impl::ae_error_type)
{
throw OpenANNException(envState.error_msg);
}
reset();
return false;
}
