BootstrapTModel, TInput, TOutput Class

Bootstrap method for generalization performance measurements.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearning.PerformanceBaseSplitSetValidationBootstrapResultTModel, TInput, TOutput, TModel, ISupervisedLearningTModel, TInput, TOutput, TInput, TOutput
      Accord.MachineLearning.PerformanceBaseSplitSetValidationBootstrapResultTModel, TInput, TOutput, TModel, TInput, TOutput
        Accord.MachineLearning.PerformanceBootstrapTModel, TInput, TOutput
          Accord.MachineLearning.PerformanceBootstrapTModel, TInput

Namespace: Accord.MachineLearning.Performance
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

Copy

public class Bootstrap<TModel, TInput, TOutput> : BaseSplitSetValidation<BootstrapResult<TModel, TInput, TOutput>, TModel, TInput, TOutput>
where TModel : class, Object, ITransform<TInput, TOutput>

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

Request Example View Source

Type Parameters

TModel: The type of the machine learning model.
TInput: The type of the input data.
TOutput: The type of the output data or labels.

The BootstrapTModel, TInput, TOutput type exposes the following members.

Constructors

	Name	Description
	BootstrapTModel, TInput, TOutput	Initializes a new instance of the Bootstrap class.

Top

Properties

	Name	Description
	B	Gets or sets the number B of bootstrap samplings to be drawn from the population dataset.
	DefaultValue	Gets or sets a value to be used as the Loss in case the model throws an exception during learning. Default is null (exceptions will not be ignored). (Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
	Fit	Gets or sets a LearnNewModelTLearner, TInput, TOutput, TModel function that can be used to create new machine learning models using the current learning algorithm. (Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
	Learner	Gets or sets a CreateLearnerFromSubsetTLearner, TInput, TOutput function that can be used to create a TModel from a subset of the learning dataset. (Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
	Loss	Gets or sets a ComputeLossTOutput, TInfo function that can be used to measure how far the actual model predictions were from the expected ground-truth. (Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
	NumberOfSubsamples	Gets or sets the number of samples to be drawn in each subsample. If set to zero, all samples in the entire dataset will be selected.
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	SubSampleIndices	Gets the bootstrap samples drawn from the population dataset as indices.
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from ParallelLearningBase.)

Top

Methods

	Name	Description
	CreateSubSampleIndices	Draws the bootstrap samples from the population.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetSubsample	Gets a subset of the training and testing sets.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Learn	Learns a model that can map the given inputs to the given outputs. (Overrides BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutputLearn(TInput, TOutput, Double).)
	LearnSubset	Learns and evaluates a model in a single subset of the data. (Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Top

Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

Top

Examples

Copy

// 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;

Reference

Accord.MachineLearning.Performance Namespace

Accord.MachineLearning.PerformanceCrossValidationTModel, TInput

Accord.MachineLearning.PerformanceSplitSetValidationTModel, TInput

BootstrapAddLanguageSpecificTextSet("LST4C7FC585_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModel, TInput, TOutputAddLanguageSpecificTextSet("LST4C7FC585_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BootstrapTModel, TInput, TOutput Class