Example usage for weka.classifiers.functions Logistic setMaxIts

Introduction

In this page you can find the example usage for weka.classifiers.functions Logistic setMaxIts.

Prototype

public void setMaxIts(int newMaxIts) 

Source Link

Document

Set the value of MaxIts.

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 2s  .  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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