Examples of weka.classifiers.functions.SPegasos

weka.classifiers.functions.SPegasos

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. For more information, see

S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

BibTeX:

 @inproceedings{Shalev-Shwartz2007, author = {S. Shalev-Shwartz and Y. Singer and N. Srebro}, booktitle = {24th International Conference on MachineLearning}, pages = {807-814}, title = {Pegasos: Primal Estimated sub-GrAdient SOlver for SVM}, year = {2007} }

Valid options are:

 -L <double> The lambda regularization constant (default = 0.0001)

 -E <integer> The number of epochs to perform (batch learning only, default = 500)

 -N Don't normalize the data

 -M Don't replace missing values

@author Mark Hall (mhall{[at]}pentaho{[dot]}com) @version $Revision: 6580 $


    public Evaluation crossValidate(ArrayList<LinkedHashMap<Integer, Double>> trainingSamples, ArrayList<Integer> labels, int numFolds, int numFeats) {
        Instances trainingSet = fillDataSet(trainingSamples, labels, numFeats);
        Instances initial = trainingSet.stringFreeStructure();
        Evaluation eTest = null;
        SPegasos cModel = new SPegasos();
        try {
            cModel.buildClassifier(initial);
            for (int i = 0; i < trainingSet.numInstances(); i++) {
                cModel.updateClassifier(trainingSet.instance(i));
            }
            eTest = new Evaluation(trainingSet);
            if (numFolds > trainingSet.numInstances()) {
                numFolds = trainingSet.numInstances();
            }

Examples of weka.classifiers.functions.SPegasos

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