public FlatMatrix(FlatMatrix <T> matrix)
{
Width = matrix.Width;
Height = matrix.Height;
Values = new T[matrix.Width * matrix.Height];
matrix.Values.CopyTo(Values, 0);
}
public Matrix(FlatMatrix flatMatrix)
{
rows = flatMatrix.rows;
colums = flatMatrix.colums;
matrix = new float[rows][];
for (int y = 0; y < rows; y++)
{
matrix[y] = new float[colums];
for (int x = 0; x < colums; x++)
{
matrix[y][x] = flatMatrix.matrix[x + y * colums];
}
}
}
public void SetNetworkVariables(NeuralNetwork_Matrix neuralNetwork)
{
nodeCounts = new int[]
{
neuralNetwork.getInputNodeCount,
neuralNetwork.getHiddenNodeCount,
neuralNetwork.getOutputNodeCount,
};
weightsInputToHidden = new FlatMatrix(neuralNetwork.getWeightsInputToHidden);
weightsHiddenToOutput = new FlatMatrix(neuralNetwork.getWeightsHiddenToOutput);
biasHidden = neuralNetwork.getBiasHidden;
biasOutput = neuralNetwork.getBiasOutput;
learningRate = neuralNetwork.getLearningRate;
activationFunc = neuralNetwork.getActivationFunction;
networkSet = true;
}
