//Back Propagate for Output Neuron
public void BackPropag(vNeuron[,] net, List <double> expOut, double learnRate, double bLearnRate)
{
List <double> error = Enumerable.Repeat(0d, this.postActOutput.Count).ToList();
for (int i = 0; i < expOut.Count; i++)
{
error[i] = this.postActOutput[i] - expOut[i];
}
//List<double> error = MyMath.colSub(this.postActOutput, expOut);
List <double> fPrime = new List <double>();
if (this.actFunc == actFuncType.logistic)
{
for (int i = 0; i < this.output.Count; i++)
{
fPrime.Add(MyMath.logisticPrimeFunc(this.output[i]));
}
}
else if (this.actFunc == actFuncType.identity)
{
for (int i = 0; i < this.output.Count; i++)
{
fPrime.Add(1d);
}
}
List <List <double> > result = MyMath.pointwiseMatMulti(MyMath.makeHorizonColMat(error), MyMath.makeHorizonColMat(fPrime));
this.delta = result[0];
this.beta = MyMath.matSub(MyMath.makeHorizonColMat(this.beta), MyMath.scalarProd(bLearnRate, MyMath.makeHorizonColMat(this.delta)))[0];
for (int i = 0; i < this.inputNeurons.Count; i++)
{
int m = inputNeurons[i][0];
int n = inputNeurons[i][1];
if (net[m, n].handedDownDelta.Count > 0)
{
net[m, n].handedDownDelta[0] /*.Add(this.delta);*/ = MyMath.colAdd(net[m, n].handedDownDelta[0], this.delta);
}
else
{
net[m, n].handedDownDelta.Add(Enumerable.Repeat(0d, delta.Count).ToList());
net[m, n].handedDownDelta[0] /*.Add(this.delta);*/ = MyMath.colAdd(net[m, n].handedDownDelta[0], this.delta);
}
List <List <double> > matrix = this.weights[i];
this.newWeights.Add(MyMath.matSub(matrix, MyMath.scalarProd(learnRate, MyMath.matMulti(MyMath.makeVertiColMat(this.delta), MyMath.makeHorizonColMat(this.inputs[i])))));
}
}
//Back Propagate for Hidden Neuron
public void BackPropag(mNeuron[,] net, double learnRate, double bLearnRate)
{
List <List <double> > fPrime = new List <List <double> >();
if (this.actFunc == actFuncType.logistic)
{
for (int i = 0; i < this.output.Count; i++)
{
fPrime.Add(new List <double>());
for (int j = 0; j < this.output[0].Count; j++)
{
fPrime[i].Add(MyMath.logisticPrimeFunc(this.output[i][j]));
}
}
}
else if (this.actFunc == actFuncType.identity)
{
for (int i = 0; i < this.output.Count; i++)
{
fPrime.Add(new List <double>());
for (int j = 0; j < this.output[0].Count; j++)
{
fPrime[i].Add(1d);
}
}
}
for (int i = 0; i < this.inputNeurons.Count; i++)
{
List <List <double> > error = MyMath.matMulti(MyMath.transposeMat(this.weights[i]), this.handedDownDelta);
List <List <double> > result = MyMath.pointwiseMatMulti(error, fPrime);
this.delta = result;
this.beta = MyMath.matSub(this.beta, MyMath.scalarProd(bLearnRate, this.delta));
int m = inputNeurons[i][0];
int n = inputNeurons[i][1];
net[m, n].handedDownDelta = MyMath.matAdd(net[m, n].handedDownDelta, this.delta);
List <List <double> > matrix = this.weights[i];
List <List <double> > gradient = MyMath.matMulti(this.delta, MyMath.transposeMat(this.inputs[i]));
this.newWeights.Add(MyMath.matSub(matrix, MyMath.scalarProd(learnRate, gradient)));
}
}
