public void TestMultyThreadedNormalizationData()
{
BaseNormalizer normlize = new MultiThreadedNormalization();
var mtrx = GetDataForSingleThreadedNormalization();
var normalizedMatrix = normlize.Normalize(mtrx);
Assert.Equal(normalizedMatrix[0, 0], -0.5);
Assert.Equal(normalizedMatrix[1, 1], -0.25);
Assert.Equal(normalizedMatrix[3, 2], 0);
Assert.Equal(normalizedMatrix[4, 3], 0.25);
Assert.Equal(normalizedMatrix[4, 4], 0.5);
}
public void TestLinearRegression()
{
double [][] inputs = new double[3][];
inputs[0] = new double[3];
inputs[1] = new double[3];
inputs[2] = new double[3];
/*
* system of equations:
* x1 + x2 + x3 = 35
* x1 + x2 - x3 = -13
* x1 - x2 + x3 = 19
*
* x1 should be 3, x2 should be 8, x3 should be 24
*
*/
inputs[0][0] = 1;
inputs[0][1] = 1;
inputs[0][2] = 1;
inputs[1][0] = 1;
inputs[1][1] = 1;
inputs[1][2] = -1;
inputs[2][0] = 1;
inputs[2][1] = -1;
inputs[2][2] = 1;
double [] outputs = new double[3];
outputs[0] = 35;
outputs[1] = -13;
outputs[2] = 19;
var normalizer = new MultiThreadedNormalization();
var regressor = new LinearRegressor(normalizer, epsilon: 0.0000001);
var matrix = new Matrix(inputs);
var weights = regressor.Fit(matrix, outputs);
var inputs2 = new double[1][];
inputs2[0] = new double[3];
inputs2[0][0] = 2;
inputs2[0][1] = 2;
inputs2[0][2] = 2;
var inputMatrix = new Matrix(inputs2);
var result = regressor.Predict(inputMatrix);
}
public void TestMultyThreadedNormalization()
{
BaseNormalizer normlize = new MultiThreadedNormalization();
var mtrx = GetDataForSingleThreadedNormalization();
var normalizedMatrix = normlize.Normalize(mtrx);
BaseNormalizer normlize1 = new SingleThreadedNormalization();
var mtrx1 = GetDataForSingleThreadedNormalization();
var normalizedMatrix1 = normlize1.Normalize(mtrx1);
for (int i = 0; i < normalizedMatrix.LinesNumber; i++)
{
for (int j = 0; j < normalizedMatrix.ColumnsNumber; j++)
{
Assert.Equal(normalizedMatrix[i, j], normalizedMatrix1[i, j]);
}
}
}
