NeuralNetworkSimpleInternal::NeuralNetworkSimpleInternal(IntArray const& numNodes,
const float _learnRate,
const float _actFuncOffset) throw()
: learnRate(_learnRate),
actFuncOffset(_actFuncOffset),
layers((numNodes.size() < 2 ? 2 : numNodes.size())-1)
{
const int numLayers = numNodes.size();
if(getNumLayersIncludingInput() == numLayers)
{
const int size = getNumLayersExcludingInput();
for(int i = 0; i < size; i++)
{
int numLayerInputs = numNodes.atUnchecked(i);
int numLayerOutputs = numNodes.atUnchecked(i+1);
if(numLayerInputs < 1) numLayerInputs = 1;
if(numLayerOutputs < 1) numLayerOutputs = 1;
layers[i] = NeuralLayer(numLayerOutputs, numLayerInputs);
}
numInputs = numNodes.atUnchecked(0);
numOutputs = numNodes.atUnchecked(numLayers-1);
}
else if(numLayers == 1)
{
int num = numNodes.atUnchecked(0);
if(num < 1) num = 1;
layers[0] = NeuralLayer(num, num);
layers[1] = NeuralLayer(num, num);
numInputs = num;
numOutputs = num;
}
else
{
layers[0] = NeuralLayer(1, 1);
layers[1] = NeuralLayer(1, 1);
numInputs = 1;
numOutputs = 1;
}
errorVector = NumericalArray<float>::withSize(numOutputs);
}
NeuralNetwork::NeuralNetwork(std::vector<int> NumNeurons, // = {3,3,3} Размерность слоев
NetworkType Nt // = BackPropagation Тип нейронной сети
){
#ifdef LOG
std::cout<<NumNeurons.size()<<"size \n";
#endif
rnd = new Random;
for(int i=1;i<NumNeurons.size();++i)
Layers.push_back(NeuralLayer(NumNeurons[i-1],NumNeurons[i],i,rnd,false));
Layers.push_back(NeuralLayer(NumNeurons.back(),0,NumNeurons.size(),rnd,true)); // Последний слой - заполняется отдельно
#ifdef LOG
print();
#endif
}; // NeuralNetwork::NeuralNetwork
