/*public vNeuron(main.Network inNet, List<List<int>> inInputNs, int inLayer, int inDim)
* {
* this.inputNeurons = inInputNs;
* this.layer = inLayer;
* this.dimension = inDim;
*
* this.weights = new List<List<double>>();
* for(int i = 0; i < inNet.NUM_DEPTH; i++)
* {
* this.weights.Add(Enumerable.Repeat(DEFAULT_WEIGHT, inDim).ToList());
* }
* }*/
//Input Neuron Constructor
public vNeuron(List <double> output)
{
this.layer = 0;
this.actFunc = actFuncType.identity;
this.dimension = output.Count;
this.inputs.Add(new List <double>());
for (int i = 0; i < this.dimension; i++)
{
this.inputs[0].Add(output[i]);
this.beta.Add(0d);
}
this.weights.Add(MyMath.makeIdentityMatrix(this.dimension));
}
//Hidden and Output Neuron Constructor
public vNeuron(int layer, actFuncType func, List <List <int> > inputNeu, double weightVar, /*List<List<double>> weights, */ List <double> beta)
{
this.layer = layer;
this.actFunc = func;
this.dimension = beta.Count;
foreach (List <int> vec in inputNeu)
{
List <int> curVec = new List <int>();
foreach (int i in vec)
{
curVec.Add(i);
}
this.inputNeurons.Add(curVec);
}
Random rng = main.rng;
for (int k = 0; k < inputNeu.Count; k++)
{
this.weights.Add(new List <List <double> >());
for (int i = 0; i < this.dimension; i++)
{
this.weights[k].Add(new List <double>());
for (int j = 0; j < this.dimension; j++)
{
this.weights[k][i].Add(weightVar * rng.NextDouble());
}
}
}
foreach (double x in beta)
{
this.beta.Add(x);
}
for (int j = 0; j < inputNeu.Count; j++)
{
//this.handedDownDelta.Add(new List<double>());
for (int i = 0; i < beta.Count; i++)
{
//this.handedDownDelta[j].Add(0d);
}
}
}
//Custom Hidden and Output Neurons
public vNeuron(int layer, actFuncType func, List <List <int> > inputNeu, List <List <List <double> > > weights, List <double> beta)
{
this.layer = layer;
this.actFunc = func;
this.dimension = beta.Count;
foreach (List <int> vec in inputNeu)
{
List <int> curVec = new List <int>();
foreach (int i in vec)
{
curVec.Add(i);
}
this.inputNeurons.Add(curVec);
}
//Random rng = new Random();
for (int k = 0; k < weights.Count; k++)
{
this.weights.Add(new List <List <double> >());
for (int i = 0; i < weights[0].Count; i++)
{
this.weights[k].Add(new List <double>());
for (int j = 0; j < weights[0][0].Count; j++)
{
this.weights[k][i].Add(weights[k][i][j]);
}
}
}
foreach (double x in beta)
{
this.beta.Add(x);
}
for (int j = 0; j < inputNeu.Count; j++)
{
//this.handedDownDelta.Add(new List<double>());
for (int i = 0; i < beta.Count; i++)
{
//this.handedDownDelta[j].Add(0d);
}
}
}
//Input Neuron Constructor
public mNeuron(List <List <double> > output)
{
this.layer = 0;
this.actFunc = actFuncType.identity;
int d = 0;
this.inputs.Add(new List <List <double> >());
for (int i = 0; i < output.Count; i++)
{
d++;
this.inputs[0].Add(new List <double>());
this.beta.Add(new List <double>());
for (int j = 0; j < output[0].Count; j++)
{
this.inputs[0][i].Add(output[i][j]);
this.beta[i].Add(0d);
}
}
this.weights.Add(MyMath.makeIdentityMatrix(d));
}
//Hidden and Output Neuron Constructor
public mNeuron(int layer, int depth, actFuncType func, List <List <int> > inputNeu, double weightVar, double betaVar, int mCount, int nCount)
{
this.layer = layer;
this.depth = depth;
this.actFunc = func;
foreach (List <int> vec in inputNeu)
{
List <int> curVec = new List <int>();
foreach (int i in vec)
{
curVec.Add(i);
}
this.inputNeurons.Add(curVec);
}
Random rng = main.rng;
for (int k = 0; k < inputNeu.Count; k++)
{
this.weights.Add(new List <List <double> >());
for (int i = 0; i < mCount; i++)
{
this.weights[k].Add(new List <double>());
for (int j = 0; j < mCount; j++)
{
this.weights[k][i].Add(weightVar * rng.NextDouble());
}
}
}
for (int i = 0; i < mCount; i++)
{
this.beta.Add(new List <double>());
for (int j = 0; j < nCount; j++)
{
this.beta[i].Add(betaVar);
}
}
}
//Hidden and Output Neuron Constructor
public Neuron(int layer, actFuncType func, List <List <int> > inputNeu, List <double> weights, double beta)
{
this.layer = layer;
this.actFunc = func;
//this.inputNeurons = inputNeu;
foreach (List <int> vec in inputNeu)
{
List <int> curVec = new List <int>();
foreach (int i in vec)
{
curVec.Add(i);
}
this.inputNeurons.Add(curVec);
}
//this.weights = weights;
foreach (double w in weights)
{
this.weights.Add(w);
}
this.beta = beta;
}
public void setActFunc(actFuncType x)
{
this.actFunc = x;
}
