/// <summary>
/// calculates the value of a single layer's neuron values
/// </summary>
/// <param name="preLayer"></param>
/// <param name="curLayer"></param>
public void calcLayer(Layer preLayer, Layer curLayer)
{
double[] temp = NetworkMath.multiply(curLayer.weight, preLayer.neuron); //multiplies the weights and the value of the previous layers neurons
curLayer.z = NetworkMath.add(curLayer.bias, temp); //adds the appropriate biases to the product of the weights and neurons
curLayer.neuron = NetworkMath.activationFunction(curLayer.z); //applies an activation function on the previous sum
}
/// <summary>
/// calculates all neuron values in the network
/// </summary>
/// <param name="input"></param>
public void calcNetwork(double[] input)
{
layers[0].neuron = input; //sets the correct values of the nodes of the input layer
for (int i = 1; i < (layers.Count); i++) //loops through each layer (starting with the first hidden layer)
{
//calcLayer(layers[i - 1], layers[i]);
double[] temp = NetworkMath.multiply(layers[i].weight, layers[i - 1].neuron); //multiplies the weights and the value of the previous layers neurons
layers[i].z = NetworkMath.add(layers[i].bias, temp); //adds the appropriate biases to the product of the weights and neurons
layers[i].neuron = NetworkMath.activationFunction(layers[i].z); //applies an activation function on the previous sum
}
}
