public void ComputeTest1() { double[][] inputs = { new double[] { 1, 4, 2, 0, 1 }, new double[] { 1, 3, 2, 0, 1 }, new double[] { 3, 0, 1, 1, 1 }, new double[] { 3, 0, 1, 0, 1 }, new double[] { 0, 5, 5, 5, 5 }, new double[] { 1, 5, 5, 5, 5 }, new double[] { 1, 0, 0, 0, 0 }, new double[] { 1, 0, 0, 0, 0 }, }; int[] outputs = { 0, 0, 1, 1, 2, 2, 3, 3, }; IKernel kernel = new Polynomial(2); var msvm = new MultilabelSupportVectorMachine(5, kernel, 4); var smo = new MultilabelSupportVectorLearning(msvm, inputs, outputs); smo.Algorithm = (svm, classInputs, classOutputs, i, j) => new SequentialMinimalOptimization(svm, classInputs, classOutputs) { Complexity = 1 }; Assert.AreEqual(0, msvm.GetLastKernelEvaluations()); double error = smo.Run(); Assert.AreEqual(0, error); int[] evals = new int[inputs.Length]; for (int i = 0; i < inputs.Length; i++) { double expected = outputs[i]; double[] responses; msvm.Compute(inputs[i], out responses); int actual; responses.Max(out actual); Assert.AreEqual(expected, actual); evals[i] = msvm.GetLastKernelEvaluations(); } for (int i = 0; i < evals.Length; i++) { Assert.AreEqual(msvm.SupportVectorUniqueCount, evals[i]); } }
/// <summary> /// Compute the error ratio. /// </summary> /// public double ComputeError(double[][] inputs, int[][] expectedOutputs) { // Compute errors int count = 0; for (int i = 0; i < inputs.Length; i++) { int[] actual = msvm.Compute(inputs[i]); int[] expected = expectedOutputs[i]; for (int j = 0; j < actual.Length; j++) { if (actual[j] != expected[j]) { Interlocked.Increment(ref count); } } } // Return misclassification error ratio return(count / (double)(inputs.Length * msvm.Classes)); }
public void SerializeTest1() { double[][] inputs = { new double[] { 1, 4, 2, 0, 1 }, new double[] { 1, 3, 2, 0, 1 }, new double[] { 3, 0, 1, 1, 1 }, new double[] { 3, 0, 1, 0, 1 }, new double[] { 0, 5, 5, 5, 5 }, new double[] { 1, 5, 5, 5, 5 }, new double[] { 1, 0, 0, 0, 0 }, new double[] { 1, 0, 0, 0, 0 }, }; int[] outputs = { 0, 0, 1, 1, 2, 2, 3, 3, }; IKernel kernel = new Linear(); var msvm = new MultilabelSupportVectorMachine(5, kernel, 4); var smo = new MultilabelSupportVectorLearning(msvm, inputs, outputs); smo.Algorithm = (svm, classInputs, classOutputs, i, j) => new SequentialMinimalOptimization(svm, classInputs, classOutputs) { Complexity = 1 }; double error = smo.Run(); Assert.AreEqual(0, error); int count = 0; // Compute errors for (int i = 0; i < inputs.Length; i++) { double[] responses; msvm.Compute(inputs[i], out responses); int y; responses.Max(out y); if (y != outputs[i]) { count++; } } double expected = (double)count / inputs.Length; Assert.AreEqual(msvm.Inputs, 5); Assert.AreEqual(msvm.Classes, 4); Assert.AreEqual(4, msvm.Machines.Length); MemoryStream stream = new MemoryStream(); // Save the machines msvm.Save(stream); // Rewind stream.Seek(0, SeekOrigin.Begin); // Reload the machines var target = MultilabelSupportVectorMachine.Load(stream); double actual; count = 0; // Compute errors for (int i = 0; i < inputs.Length; i++) { double[] responses; target.Compute(inputs[i], out responses); int y; responses.Max(out y); if (y != outputs[i]) { count++; } } actual = (double)count / inputs.Length; Assert.AreEqual(expected, actual); Assert.AreEqual(msvm.Inputs, target.Inputs); Assert.AreEqual(msvm.Classes, target.Classes); for (int i = 0; i < msvm.Machines.Length; i++) { var a = msvm[i]; var b = target[i]; Assert.IsTrue(a.SupportVectors.IsEqual(b.SupportVectors)); } }
public void LinearComputeTest1() { double[][] inputs = { new double[] { 1, 4, 2, 0, 1 }, new double[] { 1, 3, 2, 0, 1 }, new double[] { 3, 0, 1, 1, 1 }, new double[] { 3, 0, 1, 0, 1 }, new double[] { 0, 5, 5, 5, 5 }, new double[] { 1, 5, 5, 5, 5 }, new double[] { 1, 0, 0, 0, 0 }, new double[] { 1, 0, 0, 0, 0 }, }; int[] outputs = { 0, 0, 1, 1, 2, 2, 3, 3, }; var msvm = new MultilabelSupportVectorMachine(5, 4); var smo = new MultilabelSupportVectorLearning(msvm, inputs, outputs); smo.Algorithm = (svm, classInputs, classOutputs, i, j) => new LinearNewtonMethod(svm, classInputs, classOutputs) { Complexity = 1 }; Assert.AreEqual(0, msvm.GetLastKernelEvaluations()); #if DEBUG smo.ParallelOptions.MaxDegreeOfParallelism = 1; msvm.ParallelOptions.MaxDegreeOfParallelism = 1; #endif double error = smo.Run(); Assert.AreEqual(0.125, error); int[] evals = new int[inputs.Length]; int[] y = new int[inputs.Length]; for (int i = 0; i < inputs.Length; i++) { double expected = outputs[i]; double[] responses; msvm.Compute(inputs[i], out responses); int actual; responses.Max(out actual); y[i] = actual; if (i < 6) { Assert.AreEqual(expected, actual); evals[i] = msvm.GetLastKernelEvaluations(); } else { Assert.AreNotEqual(expected, actual); evals[i] = msvm.GetLastKernelEvaluations(); } } for (int i = 0; i < evals.Length; i++) { Assert.AreEqual(0, evals[i]); } for (int i = 0; i < inputs.Length; i++) { int actual; msvm.Scores(inputs[i], out actual); Assert.AreEqual(y[i], actual); } }