private void LoadMatrixValidTest()
{
validY = MatrixCal.MatrixCreate(nVal, 1);
for (int i = 0; i < nVal; i++)
{
validA[i] = new double[nFeature + 1];
validA[i][0] = 1;
for (int j = 0; j < 8; j++)
{
validA[i][j + 1] = validData[i][j];
}
validY[i][0] = validData[i][8];
}
testY = MatrixCal.MatrixCreate(nTest, 1);
for (int i = 0; i < nTest; i++)
{
testA[i] = new double[nFeature + 1];
testA[i][0] = 1;
for (int j = 0; j < 8; j++)
{
testA[i][j + 1] = testData[i][j];
}
testY[i][0] = testData[i][8];
}
}
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";
}
}
