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()); }