SplitSetValidationTModel, TInput, TOutput Class

public class SplitSetValidation<TModel, TInput, TOutput> : BaseSplitSetValidation<SplitResult<TModel, TInput, TOutput>, TModel, TInput, TOutput>
where TModel : class, Object, ITransform<TInput, TOutput>

Public Class SplitSetValidation(Of TModel As {Class, Object, ITransform(Of TInput, TOutput)}, TInput, TOutput)
	Inherits BaseSplitSetValidation(Of SplitResult(Of TModel, TInput, TOutput), TModel, TInput, TOutput)

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

// This is a sample code on how to use Train-Val validation (split-set)
// 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 Cross-validation algorithm passing the data set size and the number of folds
var splitset = new SplitSetValidation<SupportVectorMachine<Linear, double[]>, double[]>()
{
    Learner = (s) => new SequentialMinimalOptimization<Linear, double[]>()
    {
        Complexity = 1000
    },

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

    Stratify = false,
};

splitset.ParallelOptions.MaxDegreeOfParallelism = 1;

// Compute the cross-validation
var result = splitset.Learn(data, xor);

// Finally, access the measured performance.
double trainingErrors = result.Training.Value; // should be 0.53846153846153844 (+/- var. 0)
double validationErrors = result.Validation.Value; // should be 0.33333333333333331 (+/- var. 0)