/// <summary>
/// Predict the value of each output node using trained weights of a neural network.
/// </summary>
/// <param name="input"> The input layer vector.</param>
/// <returns>A vector of size (number of nodes in the output layer), containing the results. </returns>
public matrix FeedForward(double[][] input)
{
matrix result = new matrix(input.Length, nr_of_nodes[nr_of_nodes.Length - 1]);
for (int i = 0; i < input.Length; i++)
{
matrix z_ = new matrix(input[i]);
a[0] = new matrix(input[i]);
for (int j = 0; j < this.nr_of_layers - 1; j++)
{
z_ = weights[j].Transpose * a[j] + bias[j].Transpose;
a[j + 1] = z_;
a[j + 1].ApplySigmoid();
}
//z[L]
//a[L]
for (int j = 0; j < result.columns; j++)
{
result.data[i][j] = a[nr_of_layers - 1].data[j][0];
}
}
return(result);
}
double CostFunction_L(int layer, int node, matrix target)
{
return(a[layer].data[node][0] * (1 - a[layer].data[node][0]) * (a[layer].data[node][0] - target.data[node][0]));
}
/// <summary>
/// Indicates if this Matrix has the same dimensions as another supplied Matrix.
/// </summary>
/// <param name="other">Another matrix object to compare this instance to.</param>
/// <returns>true if both matrices have the same dimensions. Otherwise, false.</returns>
public bool HaveSameDimensions(matrix other)
{
return((this.rows == other.rows) && (this.columns == other.columns));
}