public void AddOutputLayer(int[] Offsets, ScalarFunction Activator)
{
if (this._phase != 0)
throw new InvalidOperationException("Cannot alter the state of the network after it has been constructed");
this._terminis = new OutputLinearLayer(Offsets, Activator);
}
public NeuralLinkLinear(NeuralLayer ForwardLayer, NeuralLayer BackwardLayer)
: base(ForwardLayer, BackwardLayer)
{
}
// Construction Methods //
public void AddDataLayer(int[] Offsets, bool Bias)
{
if (this._phase != 0)
throw new InvalidOperationException("Cannot alter the state of the network after it has been constructed");
this._origin = new DataLayer(Offsets, Bias);
}
public NeuralLink(NeuralLayer ForwardLayer, NeuralLayer BackwardLayer)
{
// Set this size //
this.BACKWARD_SIZE = BackwardLayer.SIZE;
this.FORWARD_SIZE = ForwardLayer.SIZE;
// Set the counts //
this.ROW_COUNT = BackwardLayer.SIZE;
this.COL_COUNT = (ForwardLayer.BIAS ? ForwardLayer.SIZE - 1 : ForwardLayer.SIZE);
// Bind nodes to this layer //
this.BACKWARD_LAYER = BackwardLayer;
this.FORWARD_LAYER = ForwardLayer;
// Bind the layers to this node //
this.FORWARD_LAYER.BACKWARD_LINK = this;
this.BACKWARD_LAYER.FORWARD_LINK = this;
this.GRADIENT = new double[this.ROW_COUNT, this.COL_COUNT];
this.LAG_GRADIENT = new double[this.ROW_COUNT, this.COL_COUNT];
this.WORK_GRADIENT = new double[this.ROW_COUNT, this.COL_COUNT];
this.WEIGHT = new double[this.ROW_COUNT, this.COL_COUNT];
this.LAG_WEIGHT = new double[this.ROW_COUNT, this.COL_COUNT];
this.DELTA = new double[this.ROW_COUNT, this.COL_COUNT];
this.LAG_DELTA = new double[this.ROW_COUNT, this.COL_COUNT];
}