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; }