public void TestGroupByDoubleKeyDoubleValueColumn()
{
float[,] tss = { { 0, 0, 0, 2, 2 }, { 2, 2, 2, 4, 4 }, { 0, 1, 2, 3, 4 }, { 1, 1, 1, 1, 1 } };
using (KhivaArray arr = KhivaArray.Create(tss), groupBy = Regularization.GroupBy(arr, 0, 2, 2))
{
float[,] expected = { { 1, 3.5F }, { 1, 1 } };
var result = groupBy.GetData2D <float>();
Assert.AreEqual(expected, result);
}
}
public void TestGroupBySingleColumn()
{
int[,] tss = { { 0, 1, 1, 2, 2, 3 }, { 0, 3, 3, 1, 1, 2 } };
using (KhivaArray arr = KhivaArray.Create(tss), groupBy = Regularization.GroupBy(arr, 0))
{
int[] expected = { 0, 3, 1, 2 };
var result = groupBy.GetData1D <int>();
Assert.AreEqual(expected, result);
}
}
public void Linear_Regression_Test_CostFunction_Regularized()
{
Vector theta = new Vector(new double[] { 1, 1 });
Matrix X = new[, ] {
{ 1, -15.9368 },
{ 1, -29.1530 },
{ 1, 36.1895 },
{ 1, 37.4922 },
{ 1, -48.0588 },
{ 1, -8.9415 },
{ 1, 15.3078 },
{ 1, -34.7063 },
{ 1, 1.3892 },
{ 1, -44.3838 },
{ 1, 7.0135 },
{ 1, 22.7627 }
};
Vector y = new Vector(new double[] {
2.1343,
1.1733,
34.3591,
36.8380,
2.8090,
2.1211,
14.7103,
2.6142,
3.7402,
3.7317,
7.6277,
22.7524
});
ICostFunction costFunction = new Functions.CostFunctions.LinearCostFunction();
IRegularizer regulariser = new Regularization();
double cost = costFunction.ComputeCost(theta, X, y, 1, regulariser);
Vector grad = costFunction.ComputeGradient(theta, X, y, 1, regulariser);
Assert.AreEqual(303.99, System.Math.Round(cost, 2));
Assert.AreEqual(new double[] { -15.3, 598.3 }, grad.Select(s => System.Math.Round(s, 1)).ToArray());
}
