GridSearchCreateTInput, TOutput, TRange, TModel, TLearner Method (TRange, CreateLearnerFromParameterTLearner, TRange, ComputeLossTOutput, TModel, LearnNewModelTLearner, TInput, TOutput, TModel, TInput, TOutput)

public static GridSearch<TModel, TRange, TLearner, TInput, TOutput> Create<TInput, TOutput, TRange, TModel, TLearner>(
	TRange ranges,
	CreateLearnerFromParameter<TLearner, TRange> learner,
	ComputeLoss<TOutput, TModel> loss,
	LearnNewModel<TLearner, TInput, TOutput, TModel> fit,
	TInput[] x,
	TOutput[] y
)
where TModel : class, Object, ITransform<TInput, TOutput>
where TLearner : Object, ISupervisedLearning<TModel, TInput, TOutput>

Public Shared Function Create(Of TInput, TOutput, TRange, TModel As {Class, Object, ITransform(Of TInput, TOutput)}, TLearner As {Object, ISupervisedLearning(Of TModel, TInput, TOutput)}) ( 
	ranges As TRange,
	learner As CreateLearnerFromParameter(Of TLearner, TRange),
	loss As ComputeLoss(Of TOutput, TModel),
	fit As LearnNewModel(Of TLearner, TInput, TOutput, TModel),
	x As TInput(),
	y As TOutput()
) As GridSearch(Of TModel, TRange, TLearner, TInput, TOutput)

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

// This is a sample code showing how to use Grid-Search in combination with 
// 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[][] inputs =
{
    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 Grid-Search with Cross-Validation algorithm. Even though the
// generic, strongly-typed approach used accross the framework is most of the
// time easier to handle, meta-algorithms such as grid-search can be a bit hard
// to setup. For this reason. the framework offers a specialized method for it:
var gridsearch = GridSearch<double[], int>.Create(

    // Here we can specify the range of the parameters to be included in the search
    ranges: new
    {
        Kernel = GridSearch.Values<IKernel>(new Linear(), new ChiSquare(), new Gaussian(), new Sigmoid()),
        Complexity = GridSearch.Values(0.00000001, 5.20, 0.30, 0.50),
        Tolerance = GridSearch.Range(1e-10, 1.0, stepSize: 0.05)
    },

    // Indicate how learning algorithms for the models should be created
    learner: (p) => new SequentialMinimalOptimization<IKernel>
    {
        Complexity = p.Complexity,
        Kernel = p.Kernel.Value,
        Tolerance = p.Tolerance
    },

    // Define how the model should be learned, if needed
    fit: (teacher, x, y, w) => teacher.Learn(x, y, w),

    // Define how the performance of the models should be measured
    loss: (actual, expected, m) => new ZeroOneLoss(expected).Loss(actual)
);

// If needed, control the degree of CPU parallelization
gridsearch.ParallelOptions.MaxDegreeOfParallelism = 1;

// Search for the best model parameters
var result = gridsearch.Learn(inputs, xor);

// Get the best SVM:
SupportVectorMachine<IKernel> svm = result.BestModel;

// Estimate its error:
double bestError = result.BestModelError;

// Get the best values for the parameters:
double bestC = result.BestParameters.Complexity;
double bestTolerance = result.BestParameters.Tolerance;
IKernel bestKernel = result.BestParameters.Kernel.Value;