private void CalculateMatrixTrain()
{
trainY = MatrixCal.MatrixCreate(nTrain, 1);
for (int i = 0; i < nTrain; i++)
{
trainA[i] = new double[nFeature + 1];
trainA[i][0] = 1;
for (int j = 0; j < 8; j++)
{
trainA[i][j + 1] = trainData[i][j];
}
trainY[i][0] = trainData[i][8];
}
trainAT = MatrixCal.MatrixTranspose(trainA);
trainATA = MatrixCal.MatrixProduct(trainAT, trainA);
}
private void CalculateResult()
{
CalculateRidgeRegression(lamdaVal[nMinIndex]);
testY = MatrixCal.MatrixCreate(nTest, 1);
labelResult.Text = "";
for (int i = 0; i < nTest; i++)
{
double[][] testAi = new double[1][];
testAi[0] = new double[nFeature + 1];
for (int j = 0; j < nFeature + 1; j++)
{
testAi[0][j] = testA[i][j];
}
double[][] testAiw = MatrixCal.MatrixProduct(testAi, matrixWeight);
testY[i][0] = testAiw[0][0];
labelResult.Text += testY[i][0].ToString() + "\n";
}
}
private void CalculateLoss(double ld)
{
lamdaVal.Add(ld);
//train loss
double trainLoss = 0;
for (int i = 0; i < nTrain; i++)
{
double[][] trainAi = new double[1][];
trainAi[0] = new double[nFeature + 1];
for (int j = 0; j < nFeature + 1; j++)
{
trainAi[0][j] = trainA[i][j];
}
double[][] trainAiw = MatrixCal.MatrixProduct(trainAi, matrixWeight);
trainLoss += Math.Pow(trainY[i][0] - trainAiw[0][0], 2);
}
trainLossVal.Add(trainLoss / (double)nTrain);
//valid loss
double validLoss = 0;
for (int i = 0; i < nVal; i++)
{
double[][] valAi = new double[1][];
valAi[0] = new double[nFeature + 1];
for (int j = 0; j < nFeature + 1; j++)
{
valAi[0][j] = validA[i][j];
}
double[][] valAiw = MatrixCal.MatrixProduct(valAi, matrixWeight);
validLoss += Math.Pow(validY[i][0] - valAiw[0][0], 2);
}
valLossVal.Add(validLoss / (double)nVal);
}