private void Train()
{
for (int i = 0; i < dataSet.Count; i++)
{
//Layer 0
List <NeuralNetwork> hiddenLayer1 = neuralNetwork.FindAll(x => x.LayerNumber == 0);
double[] weightedSum1 = new double[32];
for (int j = 0; j < hiddenLayer1.Count; j++)
{
weightedSum1[j] = perceptron.Execute(hiddenLayer1[j].Weights, dataSet[i].PublicAddressDouble, hiddenLayer1[j].Bias);
}
for (int k = 0; k < weightedSum1.Length; k++)
{
weightedSum1[k] = activationFunctions.LeakyReLU(weightedSum1[k]);
}
//Layer 1
List <NeuralNetwork> hiddenLayer2 = neuralNetwork.FindAll(x => x.LayerNumber == 1);
double[] weightedSum2 = new double[64];
for (int j = 0; j < hiddenLayer2.Count; j++)
{
weightedSum2[j] = perceptron.Execute(hiddenLayer2[j].Weights, weightedSum1, hiddenLayer2[j].Bias);
}
for (int k = 0; k < weightedSum2.Length; k++)
{
weightedSum2[k] = activationFunctions.LeakyReLU(weightedSum2[k]);
}
//Layer 2
List <NeuralNetwork> hiddenLayer3 = neuralNetwork.FindAll(x => x.LayerNumber == 2);
double[] weightedSum3 = new double[128];
for (int j = 0; j < hiddenLayer3.Count; j++)
{
weightedSum3[j] = perceptron.Execute(hiddenLayer3[j].Weights, weightedSum2, hiddenLayer3[j].Bias);
}
for (int k = 0; k < weightedSum3.Length; k++)
{
weightedSum3[k] = activationFunctions.LeakyReLU(weightedSum3[k]);
}
//Output Layer
List <NeuralNetwork> outputLayer = neuralNetwork.FindAll(x => x.LayerNumber == 3);
double[] weightedSum4 = new double[256];
for (int j = 0; j < outputLayer.Count; j++)
{
weightedSum4[j] = perceptron.Execute(outputLayer[j].Weights, weightedSum3, outputLayer[j].Bias);
}
for (int k = 0; k < weightedSum4.Length; k++)
{
weightedSum4[k] = activationFunctions.BinaryStep(weightedSum4[k]);
}
Assess(weightedSum4, i);
}
}
