/// <summary>
/// Return a clone of this neural network. Including structure, weights and
/// threshold values.
/// </summary>
/// <returns>A cloned copy of the neural network.</returns>
public Object Clone()
{
FeedforwardNetwork result = CloneStructure();
Double[] copy = MatrixCODEC.NetworkToArray(this);
MatrixCODEC.ArrayToNetwork(copy, result);
return(result);
}
/// <summary>
/// Randomize the weights and thresholds. This function does most of the work
/// of the class. Each call to this class will randomize the data according
/// to the current temperature. The higher the temperature the more
/// randomness.
/// </summary>
override public void Randomize()
{
Random rand = new Random();
double[] array = MatrixCODEC.NetworkToArray(this.network);
for (int i = 0; i < array.Length; i++)
{
double add = 0.5 - (rand.NextDouble());
add /= this.StartTemperature;
add *= this.temperature;
array[i] = array[i] + add;
}
MatrixCODEC.ArrayToNetwork(array, this.network);
}
/// <summary>
/// Update the neural network from the gene values.
/// </summary>
public void UpdateNetwork()
{
MatrixCODEC.ArrayToNetwork(this.Genes, this.network);
}
/// <summary>
/// Update the gene values from the network values.
/// </summary>
public void UpdateGenes()
{
this.Genes = MatrixCODEC.NetworkToArray(this.network);
}
/// <summary>
/// Convert an array of doubles to the current best network.
/// </summary>
/// <param name="array">The array that contains the neural network.</param>
override public void PutArray(double[] array)
{
MatrixCODEC.ArrayToNetwork(array, this.network);
}
/// <summary>
/// Get the network as an array of doubles.
/// </summary>
/// <returns>The network as an array of doubles.</returns>
override public double[] GetArray()
{
return(MatrixCODEC.NetworkToArray(this.network));
}
