public void LearnByRandom(double maxError)
{
int weightCount = Weight.Count();
double GlobalError = double.MaxValue;
while (GlobalError > maxError)
{
GlobalError = 0;
GenerateWeight();
for (int x = 0; x < Input.Count; x++)
{
NeuronLayers[0] = Input[x];
GetError();
GlobalError += AbsError(Output[x]);
}
Console.WriteLine(GlobalError);
}
Console.WriteLine(GlobalError);
foreach (var item in Weight)
{
Console.WriteLine(item);
}
}
public void calculate()
{
List <Val> answer = new List <Val>();
Val[] wp = new Val[Weight.Count()];
for (int i = 0; i < Weight.Count(); i++)
{
wp[i] = new Val(i, Price[i] / Weight[i]);
}
Array.Sort(wp);
bool fim = false;
while (fim == false)
{
int i = 0;
if (Weight[wp[i].position] < Size - GreaterWeight)
{
answer.Add(wp[i]);
GreaterPrice += Price[wp[i].position];
GreaterWeight += Weight[wp[i].position];
i++;
}
else
{
ListSize = answer.Count();
fim = true;
}
}
printValues();
}
public double LearnByBackPropagation(int Epoch, double Lambda)
{
GenerateWeight();
double GlobalError = 0;
do
{
for (int x = 0; x < Input.Count; x++)
{
NeuronLayers[0] = Input[x];
GetError();
double[] Etotal = PowError(Output[x]);
int weighIndex = Weight.Count() - 1;
int Last = NeuronLayers.Count() - 1;
double[] increment = new double[weighIndex + 1];
#region First Layer
for (int i = NeuronLayers[Last].Count() - 1; i > -1; i--)
{
double curentWeight = 1;
double err = NeuronLayers.Last()[i] - Output[x][i];
double log = NeuronLayers.Last()[i] * (1 - NeuronLayers.Last()[i]);
double total = err * log * curentWeight;
increment[weighIndex--] = total;
for (int j = NeuronLayers[Last - 1].Count() - 1; j > -1; j--)
{
double curentWeight1 = NeuronLayers[NeuronLayers.Count() - 2][i];
double err1 = NeuronLayers.Last()[i] - Output[x][i];
double log1 = NeuronLayers.Last()[i] * (1 - NeuronLayers.Last()[i]);
double total1 = err1 * log1 * curentWeight1;
increment[weighIndex--] = total1;
}
}
#endregion
#region ErrorOutput
int backIndex = weighIndex - 1;
List <double> sumsError = new List <double>();
for (int w = 0; w < NeuronLayers[NeuronLayers.Count() - 2].Count(); w++)
{
List <double> er = new List <double>();
for (int i = 0; i < Output[x].Count(); i++)
{
double err = NeuronLayers.Last()[i] - Output[x][i];
double log = NeuronLayers.Last()[i] * (1 - NeuronLayers.Last()[i]);
double ww = (Weight[backIndex]);
double y = err * log * ww;
er.Add(y);
backIndex += Output[x].Count() + 1;
}
sumsError.Add(er.Sum());
backIndex = weighIndex;
}
#endregion
#region Other Layers
for (int yy = NeuronLayers.Count() - 2; yy > 0; yy--)
{
for (int i = NeuronLayers[yy].Count() - 1; i > -1; i--)
{
double curentWeight = 1;
double err = sumsError[i];
double log = NeuronLayers[yy][i] * (1 - NeuronLayers[yy][i]);
double total = err * log * curentWeight;
increment[weighIndex--] = total;
for (int j = NeuronLayers[yy - 1].Count() - 1; j > -1; j--)
{
double curentWeight1 = NeuronLayers[yy - 1][j];
double err1 = sumsError[i];
double log1 = NeuronLayers[yy][i] * (1 - NeuronLayers[yy][i]);
double total1 = err1 * log1 * curentWeight1;
increment[weighIndex--] = total1;
}
}
#region Error Hidden Layers
backIndex = weighIndex;
List <double> NewSumError = new List <double>();
for (int w = 0; w < NeuronLayers[yy - 1].Count(); w++)
{
List <double> er = new List <double>();
for (int k = 0; k < NeuronLayers[yy].Count() - 1; k++)
{
double y = (sumsError[k]) * (Weight[++backIndex]);
er.Add(y);
}
NewSumError.Add(er.Sum());
}
sumsError.Clear(); sumsError.AddRange(NewSumError);
#endregion
}
#endregion
for (int i = 0; i < Weight.Count(); i++)
{
Weight[i] -= increment[i] * Lambda;
}
}
//GlobalError = 0;
//for (int x = 0; x < Input.Count; x++)
//{
// NeuronLayers[0] = Input[x];
// GetError();
// GlobalError += AbsError(Output[x]);
//}
//Console.WriteLine("Epoch :{0} Global_Error:{1}", Epoch, GlobalError);
} while (Epoch-- > 0);
for (int x = 0; x < Input.Count; x++)
{
NeuronLayers[0] = Input[x];
GetError();
GlobalError += AbsError(Output[x]);
}
Console.WriteLine(" Global_Error:{0}", GlobalError);
return(GlobalError);
}