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

Namespace:  Accord.MachineLearning.Performance
Assembly:  Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0
public static GridSearch<TModel, TLearner, TInput, TOutput> Create<TInput, TOutput, TModel, TLearner>(
	GridSearchRange[] ranges,
	CreateLearnerFromParameter<TLearner, GridSearchParameterCollection> 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>
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Type: Accord.MachineLearningGridSearchRange
The range of parameters to consider during search.
Type: Accord.MachineLearning.PerformanceCreateLearnerFromParameterTLearner, GridSearchParameterCollection
A function that can create a TModel given training parameters.
Type: Accord.MachineLearning.PerformanceComputeLossTOutput, TModel
A function that can measure how far model predictions are from the expected ground-truth.
Type: Accord.MachineLearning.PerformanceLearnNewModelTLearner, TInput, TOutput, TModel
A function that specifies how to create a new model using the teacher learning algorirhm.
Type: TInput
The input data to be used during training.
Type: TOutput
The output data to be used during training.

Type Parameters

The type of the input data. Default is double[].
The type of the output data. Default is int.
The type of the machine learning model whose parameters should be searched.
The type of the learning algorithm used to learn TModel.

Return Value

Type: GridSearchTModel, TLearner, TInput, TOutput
A grid-search algorithm that has been configured with the given parameters.
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;

// Example binary data
double[][] inputs =
    new double[] { -1, -1 },
    new double[] { -1,  1 },
    new double[] {  1, -1 },
    new double[] {  1,  1 }

int[] xor = // xor labels
    -1, 1, 1, -1

// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = GridSearch<double[], int>.Create(

    ranges: new GridSearchRange[]
        new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 0.50 } ),
        new GridSearchRange("degree",     new double[] { 1, 10, 2, 3, 4, 5 } ),
        new GridSearchRange("constant",   new double[] { 0, 1, 2 } )

    learner: (p) => new SequentialMinimalOptimization<Polynomial>
        Complexity = p["complexity"],
        Kernel = new Polynomial((int)p["degree"].Value, p["constant"])

    // 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 generated during the search
SupportVectorMachine<Polynomial> svm = result.BestModel;

// Get an estimate for its error:
double bestError = result.BestModelError;

// Get the best values for its parameters:
double bestC = result.BestParameters["complexity"].Value;
double bestDegree = result.BestParameters["degree"].Value;
double bestConstant = result.BestParameters["constant"].Value;
See Also