void registerMapRules( Map& i_map, const std::string& i_inputLayer, const std::string& i_outputLayer, Automap& io_automap )
{
if(!i_map.isLayerExist(i_inputLayer))
{
AutomapLog::report("No input layer in map", AutomapLog::Type::Warning);
return;
}
if(!i_map.isLayerExist(i_outputLayer))
{
AutomapLog::report("No output layer in map", AutomapLog::Type::Warning);
return;
}
Layer& inputLayer = i_map.getLayer(i_inputLayer);
Layer& outputLayer = i_map.getLayer(i_outputLayer);
RuleSimpleChange rulemakerSimple;
RuleCopyAndChange rulemaker;
for( size_t y = 0; y<i_map.getHeight(); ++y)
{
for( size_t x = 0; x<i_map.getWidth(); ++x)
{
if(inputLayer(x,y).info().path.empty())
continue;
if(outputLayer(x,y).info().path.empty())
{
auto* rule = rulemakerSimple.makeRule();
rule->setInputLayerName(i_inputLayer);
rule->assignIndex(0, i_inputLayer);
rule->assignIndex(1, inputLayer(x,y).info());
rule->assignIndex(2, inputLayer(x,y).info());
io_automap.registerRule(rule);
}
else
{
auto* rule = rulemaker.makeRule();
rule->setInputLayerName(i_inputLayer);
rule->assignIndex(0, i_inputLayer);
rule->assignIndex(1, i_outputLayer);
rule->assignIndex(1, inputLayer(x,y).info());
rule->assignIndex(2, outputLayer(x,y).info());
io_automap.registerRule(rule);
}
}
}
}
CNeuralNet::CNeuralNet(uint inputLayerSize, uint hiddenLayerSize, uint outputLayerSize, double lRate, double mse_cutoff)
: m_inputLayerSize(inputLayerSize), m_hiddenLayerSize(hiddenLayerSize), m_outputLayerSize(outputLayerSize), m_lRate(lRate), m_mse_cutoff(mse_cutoff) //intializer list
{
//initialize vectors for output layer values
std::vector<double> _outputActivation(m_outputLayerSize);
//initialize the two neuron layers
SNeuronLayer hiddenLayer(m_hiddenLayerSize, m_inputLayerSize);
SNeuronLayer outputLayer(m_outputLayerSize, m_hiddenLayerSize);
//push the layers onto a vector
m_vecLayer.push_back(hiddenLayer);
m_vecLayer.push_back(outputLayer);
//initialize random weights for neurons
initWeights();
//std::cout << "Constructor complete" << std::endl;
}
