/// <summary>
/// Iteratively calls the forward ropagation method of each layer on the output of the previous layer
/// </summary>
/// <param name="input">The inputs to the neural network for the current problem</param>
/// <returns></returns>
public override Vector <double> Predict(Vector <double> input)
{
//Layers[0].LayerActivationsSumInputs = CreateVector.DenseOfVector(input);
Layers[0].LayerActivations = CreateVector.DenseOfVector(input);// Layers[0].LayerActivationsSumInputs;
/* if (Layers[0].Bias)
* {
* var d = Layers[0].LayerActivations.ToList<double>();
* d.Add(1);
* Layers[0].LayerActivations = CreateVector.Dense(d.ToArray());
* //Layers[0].LayerActivations = Layers[0].LayerActivationsSumInputs;
*
* }*/
for (int i = 1; i < Layers.Count; i++)
{
if (Debug)
{
Console.WriteLine("Input to Layer:{0}", i);
Console.WriteLine(input);
Console.WriteLine("WeightsMatrix:");
Console.WriteLine(Weights[i - 1]);
Console.WriteLine("Layer ActivationsSigmoid:{0}", i);
}
if (Math.Abs(Layers[i - 1].NumberOfNeurons - input.Count) < 1 && Layers[i - 1].Bias)
{
var d = input.ToList <double>();
d.Add(1);
input = CreateVector.Dense(d.ToArray());
}
input = Layers[i].ForwardPropagation(input, Weights[i - 1], Debug);
if (Debug)
{
Console.WriteLine("Output Of Layer:{0}", i);
Console.WriteLine(input);
}
}
return(input);
}
public static Vector <double> Response(Matrix <double>[] weights, Vector <double> input, int func)
{
if (input.Count != weights[0].ColumnCount)
{
throw new Exception("Dimension mismatch.");
}
Vector <double> temp = CreateVector.DenseOfVector(input);
var fx = Activators.dict[func];
for (int i = 0; i < weights.Length; i++)
{
temp = weights[i] * temp;
for (int j = 0; j < temp.Count; j++)
{
temp[j] = fx(temp[j]);
}
}
return(temp);
}
