bool CVarianceDecomposition::trainGP()
{
bool conv = false;
// initGP if is not init
if (this->is_init==0) this->initGP();
//train GP
conv = this->opt->opt();
//check convergence
VectorXd scales;
this->agetScales(&scales);
conv &= (scales.unaryExpr(std::bind2nd( std::ptr_fun<double,double,double>(pow), 2) ).maxCoeff()<(mfloat_t)10.0);
return conv;
}
VectorXd LayeredFeedForwardNeuralNet::FireSingleLayer(const VectorXd& inputActivations, long layerIndex) const
{
// get layer input weights (also checks valid layerIndex)
const MatrixXd& layerInputWeights = GetLayerInputWeights(layerIndex);
if (layerInputWeights.cols() - 1 != inputActivations.size())
{
// input is invalid for this neural net topology
throw NeuralNetTopologyMismatch("activation input must match number of units in neural network layer");
}
// get the activation function
auto expressionParser = UnaryExpressionParserFactory::CreateParser();
UnaryFunction activationFunction = expressionParser->GetFunctionForExpression(m_activationFunction);
// bias activation
VectorXd bias(1);
bias << -1.0;
// calculate layer net inputs
VectorXd inputPlusBias(layerInputWeights.cols());
inputPlusBias << inputActivations, bias;
//std::cout << "layer " << layerIndex << " input activations +bias : " << std::endl << inputPlusBias << std::endl << std::endl;
//std::cout << "layer " << layerIndex << " input weights : " << std::endl << layerInputWeights << std::endl << std::endl;
VectorXd layerNetInputs = layerInputWeights * inputPlusBias;
//std::cout << "layer " << layerIndex << " net inputs : " << std::endl << layerNetInputs << std::endl << std::endl;
// calculate layer activations
VectorXd layerActivations = layerNetInputs.unaryExpr(activationFunction);
//std::cout << "layer " << layerIndex << " output activations : " << layerActivations << std::endl << std::endl;
return layerActivations;
}
VectorXd D_P_ANN_Controller::sgm(VectorXd x){
VectorXd W_in;
W_in=x.unaryExpr(std::ptr_fun(sigmoid));
if(thresholdNo) W_in(0)=1;
return W_in;
}
