/// <summary>
/// This method calculates the results of the network at the output layer
/// </summary>
/// <param name="input">input layer data</param>
/// <param name="numOfFeatures">number of input neurons</param>
/// <param name="output">output parameter to store outputs at the output layer</param>
/// <param name="outputSum">output sum of the last layer.</param>
private void CalculateResultatOutputlayer(double[] input, int numOfFeatures, bool softmax, out double[] output, out double[] outputSum)
{
output = new double[m_OutputLayerNeurons];
outputSum = new double[m_OutputLayerNeurons];
int numOfHiddenNeuronsInLastHiddenLayer = m_HiddenLayerNeurons[m_HiddenLayerNeurons.Length - 1];
for (int j = 0; j < m_OutputLayerNeurons; j++)
{
outputSum[j] = 0.0;
for (int i = 0; i < numOfHiddenNeuronsInLastHiddenLayer; i++)
{
outputSum[j] += m_Weights[m_HiddenLayerNeurons.Length][j, i] * input[i];
}
outputSum[j] += m_Biases[m_HiddenLayerNeurons.Length][j];
if (softmax == false)
{
output[j] = ActivationFunctions.Sigmoid(outputSum[j]);
}
else
{
// Do nothing
}
}
if (softmax == true)
{
output = ActivationFunctions.SoftMaxClassifier(outputSum);
}
}
