public void RunTest() { var dataset = SequentialMinimalOptimizationTest.yinyang; double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged(); int[] labels = dataset.GetColumn(2).ToInt32(); Accord.Math.Random.Generator.Seed = 0; var svm = new SupportVectorMachine(inputs: 2); var teacher = new ProbabilisticDualCoordinateDescent(svm, inputs, labels); teacher.Tolerance = 1e-10; teacher.UseComplexityHeuristic = true; Assert.IsFalse(svm.IsProbabilistic); double error = teacher.Run(); Assert.IsTrue(svm.IsProbabilistic); double[] weights = svm.ToWeights(); Assert.AreEqual(0.13, error); Assert.AreEqual(3, weights.Length); Assert.AreEqual(-0.52913278486359605, weights[0], 1e-4); Assert.AreEqual(-1.6426069611746976, weights[1], 1e-4); Assert.AreEqual(-0.77766953652287762, weights[2], 1e-4); Assert.AreEqual(svm.Threshold, weights[0]); }
public static void train_one(Problem prob, Parameters param, out double[] w, double Cp, double Cn) { double[][] inputs = prob.Inputs; int[] labels = prob.Outputs.Apply(x => x >= 0 ? 1 : -1); double eps = param.Tolerance; int pos = 0; for (int i = 0; i < labels.Length; i++) if (labels[i] >= 0) pos++; int neg = prob.Outputs.Length - pos; double primal_solver_tol = eps * Math.Max(Math.Min(pos, neg), 1.0) / prob.Inputs.Length; SupportVectorMachine svm = new SupportVectorMachine(prob.Dimensions); ISupportVectorMachineLearning teacher = null; switch (param.Solver) { case LibSvmSolverType.L2RegularizedLogisticRegression: // l2r_lr_fun teacher = new ProbabilisticNewtonMethod(svm, inputs, labels) { PositiveWeight = Cp, NegativeWeight = Cn, Tolerance = primal_solver_tol }; break; case LibSvmSolverType.L2RegularizedL2LossSvc: // fun_obj=new l2r_l2_svc_fun(prob, C); teacher = new LinearNewtonMethod(svm, inputs, labels) { PositiveWeight = Cp, NegativeWeight = Cn, Tolerance = primal_solver_tol }; break; case LibSvmSolverType.L2RegularizedL2LossSvcDual: // solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L2LOSS_SVC_DUAL); teacher = new LinearCoordinateDescent(svm, inputs, labels) { Loss = Loss.L2, PositiveWeight = Cp, NegativeWeight = Cn, }; break; case LibSvmSolverType.L2RegularizedL1LossSvcDual: // solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L1LOSS_SVC_DUAL); teacher = new LinearCoordinateDescent(svm, inputs, labels) { Loss = Loss.L1, PositiveWeight = Cp, NegativeWeight = Cn, }; break; case LibSvmSolverType.L1RegularizedLogisticRegression: // solve_l1r_lr(&prob_col, w, primal_solver_tol, Cp, Cn); teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels) { PositiveWeight = Cp, NegativeWeight = Cn, Tolerance = primal_solver_tol }; break; case LibSvmSolverType.L2RegularizedLogisticRegressionDual: // solve_l2r_lr_dual(prob, w, eps, Cp, Cn); teacher = new ProbabilisticDualCoordinateDescent(svm, inputs, labels) { PositiveWeight = Cp, NegativeWeight = Cn, Tolerance = primal_solver_tol, }; break; } Trace.WriteLine("Training " + param.Solver); // run the learning algorithm var sw = Stopwatch.StartNew(); double error = teacher.Run(); sw.Stop(); // save the solution w = svm.ToWeights(); Trace.WriteLine(String.Format("Finished {0}: {1} in {2}", param.Solver, error, sw.Elapsed)); }