BootstrapTModel, TInput Class

public class Bootstrap<TModel, TInput> : Bootstrap<TModel, TInput, int>
where TModel : class, Object, IClassifier<TInput, int>

Public Class Bootstrap(Of TModel As {Class, Object, IClassifier(Of TInput, Integer)}, TInput)
	Inherits Bootstrap(Of TModel, TInput, Integer)

// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;

// This is a sample code on how to use Cross-Validation
// 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 data set size and the number of folds
var bootstrap = new Bootstrap<SupportVectorMachine<Linear, double[]>, double[]>()
{
    B = 1000, // Use 1000 resamplings when doing bootstrap

    Learner = (s) => new SequentialMinimalOptimization<Linear, double[]>()
    {
        Complexity = 100
    },

    Loss = (expected, actual, p) => new ZeroOneLoss(expected).Loss(actual),

    Stratify = false, // do not use stratification

    DefaultValue = 1.0, // value to use as error if the algorithm throws an exception
};

bootstrap.ParallelOptions.MaxDegreeOfParallelism = 1;

// Compute the bootstrap
var result = bootstrap.Learn(data, xor);

// Finally, access the measured performance.
double trainingErrors = result.Training.Mean;
double validationErrors = result.Validation.Mean;
double estimate632  = result.Estimate;