Example usage for weka.classifiers.functions SMO sparseIndices

Introduction

In this page you can find the example usage for weka.classifiers.functions SMO sparseIndices.

Prototype

public int[][][] sparseIndices() 

Source Link

Document

Returns the indices in sparse format.

Usage

From source file:etc.aloe.cscw2013.SMOFeatureWeighting.java

License:Open Source License

@Override
public List<Entry<String, Double>> getFeatureWeights(ExampleSet trainingExamples, Model model) {
    WekaModel wekaModel = (WekaModel) model;
    Classifier classifier = wekaModel.getClassifier();
    Instances dataFormat = trainingExamples.getInstances();

    SMO smo = getSMO(classifier);

    double[] sparseWeights = smo.sparseWeights()[0][1];
    int[] sparseIndices = smo.sparseIndices()[0][1];

    Map<String, Double> weights = new HashMap<String, Double>();
    for (int i = 0; i < sparseWeights.length; i++) {
        int index = sparseIndices[i];
        double weight = sparseWeights[i];
        String name = dataFormat.attribute(index).name();
        weights.put(name, weight);//from w  ww .  jav a  2  s  .c om
    }

    List<Map.Entry<String, Double>> entries = new ArrayList<Map.Entry<String, Double>>(weights.entrySet());

    Collections.sort(entries, new Comparator<Map.Entry<String, Double>>() {
        @Override
        public int compare(Map.Entry<String, Double> o1, Map.Entry<String, Double> o2) {
            return o1.getKey().compareTo(o2.getKey());
        }
    });

    return entries;
}

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From source file:KFST.featureSelection.embedded.SVMBasedMethods.MSVM_RFE.java

License:Open Source License

/**
 * generates binary classifiers (SVM by applying k-fold cross validation
 * resampling strategy) using input data and based on selected feature
 * subset.//from w w  w.ja  v  a  2  s  . c  o m
 *
 * @param selectedFeature an array of indices of the selected feature subset
 *
 * @return an array of the weights of features
 */
protected double[][] buildSVM_KFoldCrossValidation(int[] selectedFeature) {
    double[][] weights = new double[numRun * kFoldValue][selectedFeature.length];
    int classifier = 0;

    for (int i = 0; i < numRun; i++) {
        double[][] copyTrainSet = ArraysFunc.copyDoubleArray2D(trainSet);

        //shuffles the train set
        MathFunc.randomize(copyTrainSet);

        int numSampleInFold = copyTrainSet.length / kFoldValue;
        int remainder = copyTrainSet.length % kFoldValue;
        int indexStart = 0;
        for (int k = 0; k < kFoldValue; k++) {
            int indexEnd = indexStart + numSampleInFold;
            if (k < remainder) {
                indexEnd++;
            }
            double[][] subTrainSet = ArraysFunc.copyDoubleArray2D(copyTrainSet, indexStart, indexEnd);

            String nameDataCSV = TEMP_PATH + "dataCSV[" + i + "-" + k + "].csv";
            String nameDataARFF = TEMP_PATH + "dataARFF[" + i + "-" + k + "].arff";

            FileFunc.createCSVFile(subTrainSet, selectedFeature, nameDataCSV, nameFeatures, classLabel);
            FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures,
                    nameFeatures, numClass, classLabel);

            try {
                BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF));
                Instances dataTrain = new Instances(readerTrain);
                readerTrain.close();
                dataTrain.setClassIndex(dataTrain.numAttributes() - 1);

                SMO svm = new SMO();
                svm.setC(parameterC);
                svm.setKernel(WekaSVMKernel.parse(kernelType));
                svm.buildClassifier(dataTrain);

                double[] weightsSparse = svm.sparseWeights()[0][1];
                int[] indicesSparse = svm.sparseIndices()[0][1];
                for (int m = 0; m < weightsSparse.length; m++) {
                    weights[classifier][indicesSparse[m]] = weightsSparse[m];
                }
            } catch (Exception ex) {
                Logger.getLogger(MSVM_RFE.class.getName()).log(Level.SEVERE, null, ex);
            }

            indexStart = indexEnd;
            classifier++;
        }
    }

    return weights;
}

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From source file:KFST.featureSelection.embedded.SVMBasedMethods.SVMBasedMethods.java

License:Open Source License

/**
 * generates binary classifiers (SVM) using input data and based on selected
 * feature subset, and finally returns the weights of features.
 * One-Versus-One strategy is used to construct classifiers in multiclass
 * classification.//from w  w  w. jav  a  2s .co m
 *
 * @param selectedFeature an array of indices of the selected feature subset
 *
 * @return an array of the weights of features
 */
protected double[][][] buildSVM_OneAgainstOne(int[] selectedFeature) {
    String nameDataCSV = TEMP_PATH + "dataCSV.csv";
    String nameDataARFF = TEMP_PATH + "dataARFF.arff";
    double[][][] weights = new double[numClass][numClass][selectedFeature.length];

    FileFunc.createCSVFile(trainSet, selectedFeature, nameDataCSV, nameFeatures, classLabel);
    FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures,
            nameFeatures, numClass, classLabel);

    try {
        BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF));
        Instances dataTrain = new Instances(readerTrain);
        readerTrain.close();
        dataTrain.setClassIndex(dataTrain.numAttributes() - 1);

        SMO svm = new SMO();
        svm.setC(parameterC);
        svm.setKernel(WekaSVMKernel.parse(kernelType));
        svm.buildClassifier(dataTrain);

        for (int i = 0; i < numClass; i++) {
            for (int j = i + 1; j < numClass; j++) {
                double[] weightsSparse = svm.sparseWeights()[i][j];
                int[] indicesSparse = svm.sparseIndices()[i][j];
                for (int k = 0; k < weightsSparse.length; k++) {
                    weights[i][j][indicesSparse[k]] = weightsSparse[k];
                }
            }
        }
    } catch (Exception ex) {
        Logger.getLogger(SVMBasedMethods.class.getName()).log(Level.SEVERE, null, ex);
    }

    return weights;
}

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From source file:KFST.featureSelection.embedded.SVMBasedMethods.SVMBasedMethods.java

License:Open Source License

/**
 * generates binary classifiers (SVM) using input data and based on selected
 * feature subset, and finally returns the weights of features.
 * One-Versus-All strategy is used to construct classifiers in multiclass
 * classification./*from   w  ww .j  a v a2  s  .com*/
 *
 * @param selectedFeature an array of indices of the selected feature subset
 *
 * @return an array of the weights of features
 */
protected double[][] buildSVM_OneAgainstRest(int[] selectedFeature) {
    double[][] weights = new double[numClass][selectedFeature.length];
    String[] tempClassLabel = new String[] { "c1", "c2" };

    for (int indexClass = 0; indexClass < numClass; indexClass++) {
        double[][] copyTrainSet = ArraysFunc.copyDoubleArray2D(trainSet);
        String nameDataCSV = TEMP_PATH + "dataCSV" + indexClass + ".csv";
        String nameDataARFF = TEMP_PATH + "dataARFF" + indexClass + ".arff";

        for (double[] dataRow : copyTrainSet) {
            if (dataRow[numFeatures] == classLabelInTrainSet[indexClass]) {
                dataRow[numFeatures] = 0;
            } else {
                dataRow[numFeatures] = 1;
            }
        }

        FileFunc.createCSVFile(copyTrainSet, selectedFeature, nameDataCSV, nameFeatures, tempClassLabel);
        FileFunc.convertCSVtoARFF(nameDataCSV, nameDataARFF, TEMP_PATH, selectedFeature.length, numFeatures,
                nameFeatures, tempClassLabel.length, tempClassLabel);

        try {
            BufferedReader readerTrain = new BufferedReader(new FileReader(nameDataARFF));
            Instances dataTrain = new Instances(readerTrain);
            readerTrain.close();
            dataTrain.setClassIndex(dataTrain.numAttributes() - 1);

            SMO svm = new SMO();
            svm.setC(parameterC);
            svm.setKernel(WekaSVMKernel.parse(kernelType));
            svm.buildClassifier(dataTrain);

            double[] weightsSparse = svm.sparseWeights()[0][1];
            int[] indicesSparse = svm.sparseIndices()[0][1];
            for (int k = 0; k < weightsSparse.length; k++) {
                weights[indexClass][indicesSparse[k]] = weightsSparse[k];
            }
        } catch (Exception ex) {
            Logger.getLogger(SVMBasedMethods.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    return weights;
}

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