public override void Predict()
{
//Normalise Input
Normalise(X, Xmean, Xstd, Y);
//Process Gating Network 1
for (int i = 0; i < ControlNeurons.Length; i++)
{
BX.SetValue(i, 0, Y.GetValue(ControlNeurons[i], 0));
}
ELU(Layer(BX, BW0_1, Bb0_1, BY));
ELU(Layer(BY, BW1_1, Bb1_1, BY));
SoftMax(Layer(BY, BW2_1, Bb2_1, BY));
W0.SetZero(); b0.SetZero();
for (int i = 0; i < YDimBlend; i++)
{
float weight = BY.GetValue(i, 0);
Blend(W0, CW[6 * i + 0], weight);
Blend(b0, CW[6 * i + 1], weight);
}
//Process Gating Network 2
for (int i = 0; i < ControlNeurons.Length; i++)
{
BX.SetValue(i, 0, Y.GetValue(ControlNeurons[i], 0));
}
ELU(Layer(BX, BW0_2, Bb0_2, BY));
ELU(Layer(BY, BW1_2, Bb1_2, BY));
SoftMax(Layer(BY, BW2_2, Bb2_2, BY));
W1.SetZero(); b1.SetZero();
for (int i = 0; i < YDimBlend; i++)
{
float weight = BY.GetValue(i, 0);
Blend(W1, CW[6 * i + 2], weight);
Blend(b1, CW[6 * i + 3], weight);
}
//Process Gating Network 3
for (int i = 0; i < ControlNeurons.Length; i++)
{
BX.SetValue(i, 0, Y.GetValue(ControlNeurons[i], 0));
}
ELU(Layer(BX, BW0_3, Bb0_3, BY));
ELU(Layer(BY, BW1_3, Bb1_3, BY));
SoftMax(Layer(BY, BW2_3, Bb2_3, BY));
W2.SetZero(); b2.SetZero();
for (int i = 0; i < YDimBlend; i++)
{
float weight = BY.GetValue(i, 0);
Blend(W2, CW[6 * i + 4], weight);
Blend(b2, CW[6 * i + 5], weight);
}
//Process Motion-Prediction Network
ELU(Layer(Y, W0, b0, Y));
ELU(Layer(Y, W1, b1, Y));
Layer(Y, W2, b2, Y);
//Renormalise Output
Renormalise(Y, Ymean, Ystd, Y);
}
