/// <summary>
/// Compare this neural network with another.
/// To be equal it must have the same structure and matrix values.
/// </summary>
/// <param name="other">The other neural network.</param>
/// <returns>True if the neural networks are equal.</returns>
public bool Equals(FeedforwardNetwork other)
{
int i = 0;
foreach (FeedforwardLayer layer in _layers)
{
FeedforwardLayer otherLayer = other.Layers[i++];
if (layer.NeuronCount != otherLayer.NeuronCount)
{
return(false);
}
if ((layer.LayerMatrix == null) && (otherLayer.LayerMatrix != null))
{
return(false);
}
if ((layer.LayerMatrix != null) && (otherLayer.LayerMatrix == null))
{
return(false);
}
if ((layer.LayerMatrix != null) && (otherLayer.LayerMatrix != null))
{
if (!layer.LayerMatrix.Equals(otherLayer.LayerMatrix))
{
return(false);
}
}
}
return(true);
}
/// <summary>
/// Returns a clone of this neural network. Including weight, threshold and structure.
/// </summary>
/// <returns>A cloned copy of this neural network.</returns>
public object Clone()
{
FeedforwardNetwork result = CloneStructure();
double[] copy = MatrixCODEC.NetworkToArray(this);
MatrixCODEC.ArrayToNetwork(copy, result);
return(result);
}
/// <summary>
/// Clone the structure of this neural network.
/// </summary>
/// <returns>A cloned copy of the structure of the neural network.</returns>
public FeedforwardNetwork CloneStructure()
{
FeedforwardNetwork result = new FeedforwardNetwork();
foreach (FeedforwardLayer layer in _layers)
{
FeedforwardLayer cloned = new FeedforwardLayer(layer.NeuronCount);
result.AddLayer(cloned);
}
return(result);
}