public BackPropagationNetwork(int[] layerSizes, TransferFunction[] transferFunctions)
{
// Validate the input data
if (transferFunctions.Length != layerSizes.Length || transferFunctions[0] != TransferFunction.None)
{
throw new ArgumentException("Cannot construct a network with these parameters.");
}
// Initialize network layers
layerCount = layerSizes.Length - 1;
inputSize = layerSizes[0];
layerSize = new int[layerCount];
for (int i = 0; i < layerCount; i++)
{
layerSize[i] = layerSizes[i + 1];
}
transferFunction = new TransferFunction[layerCount];
for (int i = 0; i < layerCount; i++)
{
transferFunction[i] = transferFunctions[i + 1];
}
// Start dimensioning arrays
bias = new double[layerCount][];
previousBiasDelta = new double[layerCount][];
delta = new double[layerCount][];
layerOutput = new double[layerCount][];
layerInput = new double[layerCount][];
weight = new double[layerCount][][];
previousWeightDelta = new double[layerCount][][];
// Fill 2 dimensional arrays
for (int l = 0; l < layerCount; l++)
{
bias[l] = new double[layerSize[l]];
previousBiasDelta[l] = new double[layerSize[l]];
delta[l] = new double[layerSize[l]];
layerOutput[l] = new double[layerSize[l]];
layerInput[l] = new double[layerSize[l]];
weight[l] = new double[l == 0 ? inputSize : layerSize[l - 1]][];
previousWeightDelta[l] = new double[l == 0 ? inputSize : layerSize[l - 1]][];
for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++)
{
weight[l][i] = new double[layerSize[l]];
previousWeightDelta[l][i] = new double[layerSize[l]];
}
}
// Initialize the weights
for (int l = 0; l < layerCount; l++)
{
for (int j = 0; j < layerSize[l]; j++)
{
bias[l][j] = Gaussian.GetRandomGaussian();
previousBiasDelta[l][j] = 0.0;
layerOutput[l][j] = 0.0;
layerInput[l][j] = 0.0;
delta[l][j] = 0.0;
}
for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++)
{
for (int j = 0; j < layerSize[l]; j++)
{
weight[l][i][j] = Gaussian.GetRandomGaussian();
previousWeightDelta[l][i][j] = 0.0;
}
}
}
}
public BoltzmanMachine(int[] layerSizes, TransferFunction[] TransferFunctions)
{
// Перевірка вхідних даних
if (TransferFunctions.Length != layerSizes.Length || TransferFunctions[0] != TransferFunction.None)
{
throw new ArgumentException("The network cannot be created with these parameters");
}
// Ініціалізація шарів мережі
layerCount = layerSizes.Length - 1;
inputSize = layerSizes[0];
layerSize = new int[layerCount];
transferFunction = new TransferFunction[layerCount];
for (int i = 0; i < layerCount; i++)
{
layerSize[i] = layerSizes[i + 1];
}
for (int i = 0; i < layerCount; i++)
{
transferFunction[i] = TransferFunctions[i + 1];
}
// Визначення вимірів масивів
bias = new double[layerCount][];
previosBiasDelta = new double[layerCount][];
delta = new double[layerCount][];
layerOtput = new double[layerCount][];
layerInput = new double[layerCount][];
weight = new double[layerCount][][];
previousWeightDelta = new double[layerCount][][];
// Заповнення двовимірних масивів
for (int l = 0; l < layerCount; l++)
{
bias[l] = new double[layerSize[l]];
previosBiasDelta[l] = new double[layerSize[l]];
delta[l] = new double[layerSize[l]];
layerOtput[l] = new double[layerSize[l]];
layerInput[l] = new double[layerSize[l]];
weight[l] = new double[l == 0 ? inputSize : layerSize[l - 1]][];
previousWeightDelta[l] = new double[l == 0 ? inputSize : layerSize[l - 1]][];
for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++)
{
weight[l][i] = new double[layerSize[l]];
previousWeightDelta[l][i] = new double[layerSize[l]];
}
}
// Ініціалізація ваг
for (int l = 0; l < layerCount; l++)
{
for (int i = 0; i < layerSize[l]; i++)
{
bias[l][i] = Gaussian.GetRandomGaussian();
previosBiasDelta[l][i] = 0.0;
layerInput[l][i] = 0.0;
layerOtput[l][i] = 0.0;
delta[l][i] = 0.0;
}
for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++)
{
for (int j = 0; j < layerSize[l]; j++)
{
weight[l][i][j] = Gaussian.GetRandomGaussian();
previousWeightDelta[l][i][j] = 0.0;
}
}
}
}
