Accord.Tests.MachineLearning.BootstrapTest.BootstrapConstructorTest3 C# (CSharp) Method

        public void BootstrapConstructorTest3()
        {

            Accord.Math.Tools.SetupGenerator(0);

            // This is a sample code on how to use 0.632 Bootstrap
            // to assess the performance of Support Vector Machines.

            // Consider the example binary data. We will be trying
            // to learn a XOR problem and see how well does SVMs
            // perform on this data.

            double[][] data =
            {
                new double[] { -1, -1 }, new double[] {  1, -1 },
                new double[] { -1,  1 }, new double[] {  1,  1 },
                new double[] { -1, -1 }, new double[] {  1, -1 },
                new double[] { -1,  1 }, new double[] {  1,  1 },
                new double[] { -1, -1 }, new double[] {  1, -1 },
                new double[] { -1,  1 }, new double[] {  1,  1 },
                new double[] { -1, -1 }, new double[] {  1, -1 },
                new double[] { -1,  1 }, new double[] {  1,  1 },
            };

            int[] xor = // result of xor for the sample input data
            {
                -1,       1,
                 1,      -1,
                -1,       1,
                 1,      -1,
                -1,       1,
                 1,      -1,
                -1,       1,
                 1,      -1,
            };


            // Create a new Bootstrap algorithm passing the set size and the number of resamplings
            var bootstrap = new Bootstrap(size: data.Length, subsamples: 50);

            // Define a fitting function using Support Vector Machines. The objective of this
            // function is to learn a SVM in the subset of the data indicated by the bootstrap.

            bootstrap.Fitting = delegate(int[] indicesTrain, int[] indicesValidation)
            {
                // The fitting function is passing the indices of the original set which
                // should be considered training data and the indices of the original set
                // which should be considered validation data.

                // Lets now grab the training data:
                var trainingInputs = data.Submatrix(indicesTrain);
                var trainingOutputs = xor.Submatrix(indicesTrain);

                // And now the validation data:
                var validationInputs = data.Submatrix(indicesValidation);
                var validationOutputs = xor.Submatrix(indicesValidation);


                // Create a Kernel Support Vector Machine to operate on the set
                var svm = new KernelSupportVectorMachine(new Polynomial(2), 2);

                // Create a training algorithm and learn the training data
                var smo = new SequentialMinimalOptimization(svm, trainingInputs, trainingOutputs);

                double trainingError = smo.Run();

                // Now we can compute the validation error on the validation data:
                double validationError = smo.ComputeError(validationInputs, validationOutputs);

                // Return a new information structure containing the model and the errors achieved.
                return new BootstrapValues(trainingError, validationError);
            };


            // Compute the bootstrap estimate
            var result = bootstrap.Compute();

            // Finally, access the measured performance.
            double trainingErrors = result.Training.Mean;
            double validationErrors = result.Validation.Mean;

            // And compute the 0.632 estimate
            double estimate = result.Estimate;

            Assert.AreEqual(50, bootstrap.B);
            Assert.AreEqual(0, trainingErrors);
            Assert.AreEqual(0.021428571428571429, validationErrors);

            Assert.AreEqual(50, bootstrap.Subsamples.Length);
            Assert.AreEqual(0.013542857142857143, estimate);
        }