public Vector <double> Conduct(NeuralNetworkFlash flash, int layer)
{
var signal = flash.SignalsSum[layer];
signal = (signal - signal.Maximum()).PointwiseExp();
return(signal / signal.Sum());
}
public Vector <double> ConductDerivative(NeuralNetworkFlash flash, int layer)
{
// count of this layer
layer = flash.InputSignals[layer + 1].Count;
// return existing vector
if (vector_one.ContainsKey(layer))
{
return(vector_one[layer]);
}
// build a new vector
var one_list = Vector <double> .Build.DenseOfArray(new double[layer]) + 1;
vector_one.Add(layer, one_list);
return(one_list);
}
public Vector <double> ConductDerivative(NeuralNetworkFlash flash, int layer)
{
var signal = flash.InputSignals[layer + 1];
var derivaite = Matrix <double> .Build.DenseOfArray(new double[signal.Count, signal.Count]);
for (int i = signal.Count - 1; i >= 0; i--)
{
for (int j = i; j >= 0; j--)
{
if (i == j)
{
derivaite[i, i] = signal[i] * (1 - signal[i]);
}
else
{
derivaite[i, j] = derivaite[j, i] = -signal[j] * signal[i];
}
}
}
return(derivaite * signal);
}
public Vector <double> ConductDerivative(NeuralNetworkFlash flash, int layer)
{
return(flash.SignalsExtra[EXP][layer] / flash.SignalsExtra[EXP_1][layer]);
}
public Vector <double> Conduct(NeuralNetworkFlash flash, int layer)
{
flash.SignalsExtra[EXP][layer] = flash.SignalsSum[layer].PointwiseMinimum(700).PointwiseExp();
flash.SignalsExtra[EXP_1][layer] = flash.SignalsExtra[EXP][layer] + 1;
return(flash.SignalsExtra[EXP_1][layer].PointwiseLog());
}
public double Accuracy(NeuralNetworkFlash flash, double[] _)
{
// actually 'TotalError' is avrage of errors
// becaue in 'NegativeErrorDerivative' function output is divided by count
return(1 - flash.TotalError);
}
public Vector <double> ConductDerivative(NeuralNetworkFlash flash, int layer)
{
return(flash.InputSignals[layer + 1]
.PointwiseMultiply(1 - flash.InputSignals[layer + 1]));
}
public Vector <double> Conduct(NeuralNetworkFlash flash, int layer)
{
return(1 / (1 + (flash.SignalsSum[layer] * -1).PointwiseExp()));
}
public Vector <double> Conduct(NeuralNetworkFlash flash, int layer)
{
return(flash.SignalsSum[layer].PointwiseMaximum(0));
}
public double Accuracy(NeuralNetworkFlash flash, double[] values)
{
return(values[flash.InputSignals[^ 1].MaximumIndex()]);
public void FillTotalError(NeuralNetworkFlash flash, double[] values)
{
chista_nets[index].FillTotalError(flash, values);
}
public Vector <double> Conduct(NeuralNetworkFlash flash, int layer)
{
return(flash.SignalsSum[layer]);
}
public double Accuracy(NeuralNetworkFlash flash, double[] values)
{
return(1 - flash.ErrorAverage);
//return values[flash.InputSignals[^1].MaximumIndex()];
}
