void MultilayerPerceptron::initMLP(int ninputs, int noutputs, const vector<int> &hiddenLayerSizes, const TransferFunctionType &tf)
{
tres = new MLPTrainingResult();
nInputs = ninputs;
setAlfa(1);
setLayerSizes(hiddenLayerSizes);
setInputSize(ninputs);
setOutputSize(noutputs);
setTransferFunctionType(tf);
// setOutputType(Continuous);
randomizeWeights();
connect(this, SIGNAL(finished()), SLOT(finished()));
}
OneLayerPerceptron::OneLayerPerceptron(const float pEta, const int pInputPerceptrons, const int pHiddenPerceptrons, const int pOutputPerceptrons)
: m_eta{pEta}, m_hidPerceptrons{pHiddenPerceptrons}, m_inpPerceptrons{pInputPerceptrons},
m_outPerceptrons{pOutputPerceptrons}, m_hidWeights{new float*[m_inpPerceptrons+1]},
m_outWeights{new float*[m_hidPerceptrons+1]}
{
//+1 due to constant coefficient, i.e. bias
for (int i=0;i<m_inpPerceptrons+1;i++)
{
m_hidWeights[i] = new float[m_hidPerceptrons];
}
for (int i=0;i<m_hidPerceptrons+1;i++)
{
m_outWeights[i] = new float[m_outPerceptrons];
}
randomizeWeights();
}
