Example usage for weka.classifiers.functions SMO setC

List of usage examples for weka.classifiers.functions SMO setC

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Introduction

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

Prototype

public void setC(double v)

Source Link

Document

Set the value of C.

Usage

From source file:com.edwardraff.WekaMNIST.java

License:Open Source License

public static void main(String[] args) throws IOException, Exception {
    String folder = args[0];//from  w  w w .j  a  v  a  2 s. c  om
    String trainPath = folder + "MNISTtrain.arff";
    String testPath = folder + "MNISTtest.arff";

    System.out.println("Weka Timings");
    Instances mnistTrainWeka = new Instances(new BufferedReader(new FileReader(new File(trainPath))));
    mnistTrainWeka.setClassIndex(mnistTrainWeka.numAttributes() - 1);
    Instances mnistTestWeka = new Instances(new BufferedReader(new FileReader(new File(testPath))));
    mnistTestWeka.setClassIndex(mnistTestWeka.numAttributes() - 1);

    //normalize range like into [0, 1]
    Normalize normalizeFilter = new Normalize();
    normalizeFilter.setInputFormat(mnistTrainWeka);

    mnistTestWeka = Normalize.useFilter(mnistTestWeka, normalizeFilter);
    mnistTrainWeka = Normalize.useFilter(mnistTrainWeka, normalizeFilter);

    long start, end;

    System.out.println("RBF SVM (Full Cache)");
    SMO smo = new SMO();
    smo.setKernel(new RBFKernel(mnistTrainWeka, 0/*0 causes Weka to cache the whole matrix...*/, 0.015625));
    smo.setC(8.0);
    smo.setBuildLogisticModels(false);
    evalModel(smo, mnistTrainWeka, mnistTestWeka);

    System.out.println("RBF SVM (No Cache)");
    smo = new SMO();
    smo.setKernel(new RBFKernel(mnistTrainWeka, 1, 0.015625));
    smo.setC(8.0);
    smo.setBuildLogisticModels(false);
    evalModel(smo, mnistTrainWeka, mnistTestWeka);

    System.out.println("Decision Tree C45");
    J48 wekaC45 = new J48();
    wekaC45.setUseLaplace(false);
    wekaC45.setCollapseTree(false);
    wekaC45.setUnpruned(true);
    wekaC45.setMinNumObj(2);
    wekaC45.setUseMDLcorrection(true);

    evalModel(wekaC45, mnistTrainWeka, mnistTestWeka);

    System.out.println("Random Forest 50 trees");
    int featuresToUse = (int) Math.sqrt(28 * 28);//Weka uses different defaults, so lets make sure they both use the published way

    RandomForest wekaRF = new RandomForest();
    wekaRF.setNumExecutionSlots(1);
    wekaRF.setMaxDepth(0/*0 for unlimited*/);
    wekaRF.setNumFeatures(featuresToUse);
    wekaRF.setNumTrees(50);

    evalModel(wekaRF, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (brute)");
    IBk wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new LinearNNSearch());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (Ball Tree)");
    wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new BallTree());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (Cover Tree)");
    wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new CoverTree());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("Logistic Regression LBFGS lambda = 1e-4");
    Logistic logisticLBFGS = new Logistic();
    logisticLBFGS.setRidge(1e-4);
    logisticLBFGS.setMaxIts(500);

    evalModel(logisticLBFGS, mnistTrainWeka, mnistTestWeka);

    System.out.println("k-means (Loyd)");
    int origClassIndex = mnistTrainWeka.classIndex();
    mnistTrainWeka.setClassIndex(-1);
    mnistTrainWeka.deleteAttributeAt(origClassIndex);
    {
        long totalTime = 0;
        for (int i = 0; i < 10; i++) {
            SimpleKMeans wekaKMeans = new SimpleKMeans();
            wekaKMeans.setNumClusters(10);
            wekaKMeans.setNumExecutionSlots(1);
            wekaKMeans.setFastDistanceCalc(true);

            start = System.currentTimeMillis();
            wekaKMeans.buildClusterer(mnistTrainWeka);
            end = System.currentTimeMillis();
            totalTime += (end - start);
        }
        System.out.println("\tClustering took: " + (totalTime / 10.0) / 1000.0 + " on average");
    }
}

From source file:etc.aloe.oilspill2010.TrainingImpl.java

@Override
public WekaModel train(ExampleSet examples) {
    //These settings aren't terrible
    SMO smo = new SMO();
    RBFKernel rbf = new RBFKernel();
    rbf.setGamma(0.5);//from www.  j a v a  2  s .  c o  m
    smo.setKernel(rbf);
    smo.setC(1.5);

    //These also work pretty ok
    Logistic log = new Logistic();
    log.setRidge(100);

    Classifier classifier = log;

    try {
        System.out.print("Training on " + examples.size() + " examples... ");
        classifier.buildClassifier(examples.getInstances());
        System.out.println("done.");

        WekaModel model = new WekaModel(classifier);
        return model;
    } catch (Exception ex) {
        System.err.println("Unable to train classifier.");
        System.err.println("\t" + ex.getMessage());
        return null;
    }
}

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  ww  w.  j  a  va 2  s.  com*/
 *
 * @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;
}

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  .  ja  v a2 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_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;
}

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 w w .  j a v  a 2 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;
}