public void AddNeuron(int index, Neuron n)
{
this.neurons[index] = n;
}
protected Neuron(Neuron neuron)
{
this.activationFunction = neuron.activationFunction;
this.learningAlpha = neuron.learningAlpha;
}
//public double Bias { get; set; }
public Connection(Neuron from, Neuron to)
{
toNeuron = to;
fromNeuron = from;
Weight = RandomProvider.random.NextDouble() * 0.5;
}
public List <List <List <double> > > BackPropogate(DataSet datain, double learninnRate, double momentum = 0)
{
if (PrevDW == null)
{
PrevDW = new List <List <List <double> > >();
deltaArr = new List <List <double> >();
for (int i = 0; i < NumLayers; i++)
{
deltaArr.Add(new List <double>());
PrevDW.Add(new List <List <double> >());
for (int n = 0; n < Layers[i].NumNeurons; n++)
{
deltaArr[i].Add(0);
PrevDW[i].Add(new List <double>());
IEnumerable <Connection> outConn = Layers[i].Neurons[n].Connections.Where(r => r.toNeuron == Layers[i].Neurons[n]);
int d = 0;
foreach (Connection c in outConn)
{
PrevDW[i][n].Add(0);
d++;
}
}
}
}
ApplyInput(datain.Inputs);
CalculateOutput();
Layer currentLayer = Layers[OutputIndex];
while (currentLayer != Layers[InputIndex])
{
Parallel.ForEach(currentLayer.Neurons, new Action <Neuron>((n) =>
{
double error = 0;
if (currentLayer == Layers[OutputIndex])
{
error = datain.Outputs[n.Index] - n.Value;
}
else
{
foreach (Connection c in n.Connections.Where(r => r.fromNeuron == n))
{
error += c.Weight * deltaArr[c.toNeuron.SelfLayer.Index][c.toNeuron.Index];
}
}
error = error * n.Value * (1 - n.Value);
deltaArr[currentLayer.Index][n.Index] = error;
}));
currentLayer = Layers[currentLayer.Index - 1];
}
currentLayer = Layers[OutputIndex];
while (currentLayer != Layers[InputIndex])
{
for (int i = 0; i < currentLayer.NumNeurons; i++)
{
Neuron n = currentLayer.Neurons[i];
foreach (Connection c in n.Connections.Where(r => r.toNeuron == n))
{
double dw = (deltaArr[c.toNeuron.SelfLayer.Index][c.toNeuron.Index] * learninnRate * c.fromNeuron.Value) + (momentum * PrevDW[currentLayer.Index][i][n.Connections.IndexOf(c)]);
c.Weight += dw;
PrevDW[currentLayer.Index][i][n.Connections.IndexOf(c)] = dw;
}
n.Bias += deltaArr[currentLayer.Index][i] * learninnRate;
}
currentLayer = Layers[currentLayer.Index - 1];
}
return(PrevDW);
}
public void AddConnection(Neuron neuron, bool to, double weight = 0/*, double bias = 0*/)
{
Connection c;
if (to)
c = new Connection(this, neuron, weight);
else
c = new Connection(neuron, this, weight);
this.Connections.Add(c);
neuron.Connections.Add(c);
}
public Neuron(int index, Layer initLayer, Neuron.ActivationType actType = ActivationType.SIGMOID, double bias = 0)
{
Index = index;
Connections = new List<Connection>();
SelfLayer = initLayer;
ActType = actType;
Bias = bias;
}
public Layer(int index, int numNeurons, Neuron.ActivationType actTye = Neuron.ActivationType.SIGMOID, IEnumerable<double> initBias = null)
{
Index = index;
NumNeurons = numNeurons;
ActType = actTye;
Neurons = new Neuron[NumNeurons];
if (initBias != null)
{
for (int i = 0; i < NumNeurons; i++)
{
Neurons[i] = new Neuron(i, this, ActType, initBias.ElementAt(i));
}
}
else
{
for (int i = 0; i < NumNeurons; i++)
{
Neurons[i] = new Neuron(i, this, ActType);
}
}
}
public Connection(Neuron from, Neuron to, double weight)
{
toNeuron = to;
fromNeuron = from;
Weight = weight;
//Bias = bias;
}
//public double Bias { get; set; }
public Connection(Neuron from, Neuron to)
{
toNeuron = to;
fromNeuron = from;
Weight = RandomProvider.random.NextDouble() * 0.5;
}