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Bootstrap Class

Bootstrap method for generalization performance measurements.
Inheritance Hierarchy
SystemObject
  Accord.MachineLearningBootstrap

Namespace:  Accord.MachineLearning
Assembly:  Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.5.0
Syntax
[SerializableAttribute]
public class Bootstrap
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The Bootstrap type exposes the following members.

Constructors
Properties
  NameDescription
Public propertyB
Gets the number B of bootstrap samplings to be drawn from the population dataset.
Public propertyFitting
Gets or sets the model fitting function.
Public propertyRunInParallel
Gets or sets a value indicating whether to use parallel processing through the use of multiple threads or not. Default is true.
Public propertySamples
Gets the total number of samples in the population dataset.
Public propertySubsamples
Gets the bootstrap samples drawn from the population dataset as indices.
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Methods
  NameDescription
Public methodCompute
Computes the cross validation algorithm.
Public methodCreatePartitions
Gets the indices for the training and validation sets for the specified validation fold index.
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetPartitionSize
Gets the number of instances in training and validation sets for the specified validation fold index.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodStatic memberSamplings
Draws the bootstrap samples from the population.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodToTOverloaded.
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.)
Public Extension MethodToTOverloaded.
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 Matrix.)
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Examples
// This is a sample code on how to use Bootstrap estimate
// 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 set size and the number of resamplings
var bootstrap = new Bootstrap(size: data.Length, subsamples: 50);

// Define a fitting function using Support Vector Machines. The objective of this
// function is to learn a SVM in the subset of the data indicated by the bootstrap.

bootstrap.Fitting = delegate(int[] indicesTrain, int[] indicesValidation)
{
    // The fitting function is passing the indices of the original set which
    // should be considered training data and the indices of the original set
    // which should be considered validation data.

    // Lets now grab the training data:
    var trainingInputs = data.Submatrix(indicesTrain);
    var trainingOutputs = xor.Submatrix(indicesTrain);

    // And now the validation data:
    var validationInputs = data.Submatrix(indicesValidation);
    var validationOutputs = xor.Submatrix(indicesValidation);


    // Create a Kernel Support Vector Machine to operate on the set
    var svm = new KernelSupportVectorMachine(new Polynomial(2), 2);

    // Create a training algorithm and learn the training data
    var smo = new SequentialMinimalOptimization(svm, trainingInputs, trainingOutputs);

    double trainingError = smo.Run();

    // Now we can compute the validation error on the validation data:
    double validationError = smo.ComputeError(validationInputs, validationOutputs);

    // Return a new information structure containing the model and the errors achieved.
    return new BootstrapValues(trainingError, validationError);
};


// Compute the bootstrap estimate
var result = bootstrap.Compute();

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

// And compute the 0.632 estimate
double estimate = result.Estimate;
See Also