private static double CalculationZr(HiddenLayerNeuron neuron, double[] x, int r)
{
var result = 0D;
for (int j = 0; j < neuron.Q.GetLength(0); j++)
{
result += neuron.Q[j, r] * (x[j] - neuron.C[j]);
}
return(result);
}
public HiddenLayer(int k, int n, List <double[]> centers)
{
ActivationW = 0.2;// tools.MathHelper.Rnd.NextDouble();
K = k; N = n;
Neurons = new HiddenLayerNeuron[k];
for (int i = 0; i < k; i++)
{
Neurons[i] = new HiddenLayerNeuron(n, centers[i]);
}
}
private static double CalculationUi(HiddenLayerNeuron neuron, double[] x)
{
var result = 0D;
for (int j = 0; j < neuron.Q.GetLength(0); j++)
{
result += Math.Pow(CalculationZr(neuron, x, j), 2);
}
return(result);
}
public static double CalculateOut(this HiddenLayerNeuron neuron, double[] x)
{
if (neuron is HiddenLayerNeuron)
{
var v1 = neuron.Q.Dot(x.Sub(neuron.C));
return(Math.Exp(COEF * v1.Dot(v1.Transpose())[0]));
}
else
{
return(1);
}
}
public RecogAkshaysPictureNetwork()
{
Globals.LearningRateForNeurons = (decimal)0.00000000000000000043368086899420177360298112034798;
NeuralLayer rawInputLayer = new NeuralLayer();
for (int i = 0; i < 1024 * 1024; i++)
{
RawInputLayerNeuron riln = new RawInputLayerNeuron((decimal)0, (decimal)2097152);
rawInputLayer.AddNeuronToLayer(riln);
}
Layers.Add(rawInputLayer);
NeuralLayer hiddenLayer = new NeuralLayer();
for (int i = 0; i < 1024 * 1024; i++)
{
HiddenLayerNeuron hln = new HiddenLayerNeuron((decimal)2199023255552);
hiddenLayer.AddNeuronToLayer(hln);
}
Layers.Add(hiddenLayer);
foreach (HiddenLayerNeuron hln in hiddenLayer.LayerNeurons)
{
foreach (RawInputLayerNeuron riln in rawInputLayer.LayerNeurons)
{
NeuralConnection nc = new NeuralConnection(riln, hln, (decimal)2097152);
hln.Inputs.Add(nc);
riln.Outputs.Add(nc);
}
}
NeuralLayer outputLayer = new NeuralLayer();
RawOutputLayerNeuron roln = new RawOutputLayerNeuron((decimal)2305843009213693952);
foreach (HiddenLayerNeuron hln in hiddenLayer.LayerNeurons)
{
NeuralConnection nc = new NeuralConnection(hln, roln, (decimal)2199023255552);
hln.Outputs.Add(nc);
roln.Inputs.Add(nc);
}
}
public static double dEdQijr(this HiddenLayerNeuron neuron, double[] x, double error, int j, int r) => - dEdWi(neuron, x, error) * neuron.W * (x[j] - neuron.C[j]) * CalculationZr(neuron, x, r);
public static double dEdCij(this HiddenLayerNeuron neuron, double error, double[] x, int j) => - dEdWi(neuron, x, error) * neuron.W * neuron.Q.GetRow(j) .Select((t1, r) => t1 * CalculationZr(neuron, x, r)) .Sum();
public static double dEdWi(this HiddenLayerNeuron neuron, double[] x, double error) => Math.Exp(COEF * CalculationUi(neuron, x)) * error;
public void AddNeuronToLayer(HiddenLayerNeuron hln)
{
LayerNeurons.Add((object)hln);
}
