Documentation of 'jsat.classifiers.svm.PegasosK' Java class
PegasosK
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jsat.classifiers.svm

Class PegasosK

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, BinaryScoreClassifier, Classifier, Parameterized

    public class PegasosK
extends SupportVectorLearner
implements BinaryScoreClassifier, Parameterized
    Implements the kernelized version of the Pegasos algorithm for SVMs. Unlike standard SVM algorithms, this one relies on randomness and has no guarantee to reach the optimal solution, however it is very fast to train. Each iteration of the algorithm randomly selects one datapoint to potentially update the coefficient of.
    The resulting set of support vectors may be more or less sparse than a normal SVM implementation.
    Because the Pegasos algorithm is stochastic and the kernelized updates on errors given regularization, the kernelized version may have more difficulty with noisy or overlapping class distributions.

    See: Shalev-Shwartz, S., Singer, Y.,&Srebro, N. (2007). Pegasos : Primal Estimated sub-GrAdient SOlver for SVM. 24th international conference on Machine learning (pp. 807–814). New York, NY: ACM. doi:10.1145/1273496.1273598
    See Also:
    Serialized Form

    • Constructor Detail

      • PegasosK

        public PegasosK(double regularization,
                int iterations,
                KernelTrick kernel)
        Creates a new kernelized Pegasos SVM solver
        Parameters:
        regularization - the amount of regularization to apply, normally a very small positive value
        iterations - the number of update iterations to perform
        kernel - the kernel to use

      • PegasosK

        public PegasosK(double regularization,
                int iterations,
                KernelTrick kernel,
                SupportVectorLearner.CacheMode cacheMode)
        Creates a new kernelized Pegasos SVM solver
        Parameters:
        regularization - the amount of regularization to apply, normally a very small positive value
        iterations - the number of update iterations to perform
        kernel - the kernel to use
        cacheMode - what type of kernel caching to use

    • Method Detail

      • setIterations

        public void setIterations(int iterations)
        Sets the number of iterations of the algorithm to perform. Each iteration may or may not update a single coefficient for a specific data point.
        Parameters:
        iterations - the number of learning iterations to perform

      • getIterations

        public int getIterations()
        Returns the number of iterations used during training
        Returns:
        the number of iterations used in training

      • setRegularization

        public void setRegularization(double regularization)
        Sets the amount of regularization to apply. The regularization must be a positive value
        Parameters:
        regularization - the amount of regularization to apply

      • getRegularization

        public double getRegularization()
        Returns the amount of regularization used
        Returns:
        the amount of regularization used

      • classify

        public CategoricalResults classify(DataPoint data)
        Description copied from interface: Classifier
        Performs classification on the given data point.
        Specified by:
        classify in interface Classifier
        Parameters:
        data - the data point to classify
        Returns:
        the results of the classification.

      • getScore

        public double getScore(DataPoint dp)
        Description copied from interface: BinaryScoreClassifier
        Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
        Specified by:
        getScore in interface BinaryScoreClassifier
        Parameters:
        dp - the data point to predict the class label of
        Returns:
        the score for the given data point

      • train

        public void train(ClassificationDataSet dataSet,
                  boolean parallel)
        Description copied from interface: Classifier
        Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
        Specified by:
        train in interface Classifier
        Parameters:
        dataSet - the data set to train on
        parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

      • supportsWeightedData

        public boolean supportsWeightedData()
        Description copied from interface: Classifier
        Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
        Specified by:
        supportsWeightedData in interface Classifier
        Returns:
        true if the model supports weighted data, false otherwise
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