Example usage for weka.core.neighboursearch CoverTree CoverTree

Introduction

In this page you can find the example usage for weka.core.neighboursearch CoverTree CoverTree.

Prototype

public CoverTree() 

Source Link

Document

default constructor.

Usage

From source file:au.edu.usyd.it.yangpy.snp.Ensemble.java

License:Open Source License

public void ensemble(String mode) throws Exception {

    numInstances = test.numInstances();//from  w  ww. j a  v  a2s  .c  o  m
    numClasses = test.numClasses();
    givenValue = new double[numInstances];
    predictDistribution = new double[numClassifiers][numInstances][numClasses];
    predictValue = new double[numClassifiers][numInstances];
    voteValue = new double[numInstances][numClasses];

    // Setting the given class values of the test instances.
    for (int i = 0; i < numInstances; i++) {
        givenValue[i] = test.instance(i).classValue();
    }

    // Calculating the predicted class values using each classifier respectively.
    // J48 coverTree1NN KStar coverTree3NN coverTree5NN

    J48 tree = new J48();
    tree.setUnpruned(true);
    aucClassifiers[0] = classify(tree, 0);

    KStar kstar = new KStar();
    aucClassifiers[1] = classify(kstar, 1);

    IBk ctnn1 = new IBk(1);
    CoverTree search = new CoverTree();
    ctnn1.setNearestNeighbourSearchAlgorithm(search);
    aucClassifiers[2] = classify(ctnn1, 2);

    IBk ctnn3 = new IBk(3);
    ctnn3.setNearestNeighbourSearchAlgorithm(search);
    aucClassifiers[3] = classify(ctnn3, 3);

    IBk ctnn5 = new IBk(5);
    ctnn5.setNearestNeighbourSearchAlgorithm(search);
    aucClassifiers[4] = classify(ctnn5, 4);

    // Print the classification results if in print mode.
    if (mode.equals("v")) {
        System.out.println("J48   AUC: " + aucClassifiers[0]);
        System.out.println("KStar AUC: " + aucClassifiers[1]);
        System.out.println("CTNN1 AUC: " + aucClassifiers[2]);
        System.out.println("CTNN3 AUC: " + aucClassifiers[3]);
        System.out.println("CTNN5 AUC: " + aucClassifiers[4]);
        System.out.println("   -         -   ");
    }
}

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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   www  . jav a  2 s .  c o m*/
    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");
    }
}

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