/// <summary>
/// Return a clone of 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 this.layers)
{
FeedforwardLayer clonedLayer = new FeedforwardLayer(layer.NeuronCount);
result.AddLayer(clonedLayer);
}
return(result);
}
/// <summary>
/// Prune one of the neurons from this layer. Remove all entries in this
/// weight matrix and other layers.
/// </summary>
/// <param name="neuron">The neuron to prune. Zero specifies the first neuron.</param>
public void Prune(int neuron)
{
// delete a row on this matrix
if (this.matrix != null)
{
this.LayerMatrix = (MatrixMath.DeleteRow(this.matrix, neuron));
}
// delete a column on the previous
FeedforwardLayer previous = this.Previous;
if (previous != null)
{
if (previous.LayerMatrix != null)
{
previous.LayerMatrix = (MatrixMath.DeleteCol(previous.LayerMatrix,
neuron));
}
}
}
/// <summary>
/// Add a layer to the neural network. The first layer added is the input
/// layer, the last layer added is the output layer.
/// </summary>
/// <param name="layer">The layer to be added.</param>
public void AddLayer(FeedforwardLayer layer)
{
// setup the forward and back pointer
if (this.outputLayer != null)
{
layer.Previous = this.outputLayer;
this.outputLayer.Next = layer;
}
// update the inputLayer and outputLayer variables
if (this.layers.Count == 0)
{
this.inputLayer = this.outputLayer = layer;
}
else
{
this.outputLayer = layer;
}
// add the new layer to the list
this.layers.Add(layer);
}
/// <summary>
/// Compare the two neural networks. For them to be equal they must be of the
/// same structure, and have the same matrix values.
/// </summary>
/// <param name="other">The other neural network.</param>
/// <returns>True if the two networks are equal.</returns>
public bool Equals(FeedforwardNetwork other)
{
int i = 0;
foreach (FeedforwardLayer layer in this.Layers)
{
FeedforwardLayer otherLayer = other.Layers[i++];
if (layer.NeuronCount != otherLayer.NeuronCount)
{
return(false);
}
// make sure they either both have or do not have
// a weight matrix.
if ((layer.LayerMatrix == null) && (otherLayer.LayerMatrix != null))
{
return(false);
}
if ((layer.LayerMatrix != null) && (otherLayer.LayerMatrix == null))
{
return(false);
}
// if they both have a matrix, then compare the matrices
if ((layer.LayerMatrix != null) && (otherLayer.LayerMatrix != null))
{
if (!layer.LayerMatrix.Equals(otherLayer.LayerMatrix))
{
return(false);
}
}
}
return(true);
}
/// <summary>
/// Return a clone of 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 this.layers)
{
FeedforwardLayer clonedLayer = new FeedforwardLayer(layer.NeuronCount);
result.AddLayer(clonedLayer);
}
return result;
}
/// <summary>
/// Add a layer to the neural network. The first layer added is the input
/// layer, the last layer added is the output layer.
/// </summary>
/// <param name="layer">The layer to be added.</param>
public void AddLayer(FeedforwardLayer layer)
{
// setup the forward and back pointer
if (this.outputLayer != null)
{
layer.Previous = this.outputLayer;
this.outputLayer.Next = layer;
}
// update the inputLayer and outputLayer variables
if (this.layers.Count == 0)
{
this.inputLayer = this.outputLayer = layer;
}
else
{
this.outputLayer = layer;
}
// add the new layer to the list
this.layers.Add(layer);
}