Example usage for weka.classifiers.functions SMO SMO

Introduction

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

Prototype

SMO

Source Link

Usage

From source file:cs.man.ac.uk.predict.Predictor.java

License:Open Source License

public static void makePredictionsEnsembleStream(String trainPath, String testPath, String resultPath) {
    System.out.println("Training set: " + trainPath);
    System.out.println("Test set: " + testPath);

    /**//from   w w w.  j a v a2s .co  m
     * The ensemble classifiers. This is a heterogeneous ensemble.
     */
    J48 learner1 = new J48();
    SMO learner2 = new SMO();
    NaiveBayes learner3 = new NaiveBayes();
    MultilayerPerceptron learner5 = new MultilayerPerceptron();

    System.out.println("Training Ensemble.");
    long startTime = System.nanoTime();
    try {
        BufferedReader reader = new BufferedReader(new FileReader(trainPath));
        Instances data = new Instances(reader);
        data.setClassIndex(data.numAttributes() - 1);
        System.out.println("Training data length: " + data.numInstances());

        learner1.buildClassifier(data);
        learner2.buildClassifier(data);
        learner3.buildClassifier(data);
        learner5.buildClassifier(data);

        long endTime = System.nanoTime();
        long nanoseconds = endTime - startTime;
        double seconds = (double) nanoseconds / 1000000000.0;
        System.out.println("Training Ensemble completed in " + nanoseconds + " (ns) or " + seconds + " (s).");
    } catch (IOException e) {
        System.out.println("Could not train Ensemble classifier IOException on training data file.");
    } catch (Exception e) {
        System.out.println("Could not train Ensemble classifier Exception building model.");
    }

    try {
        // A different ARFF loader used here (compared to above) as
        // the ARFF file may be extremely large. In which case the whole
        // file cannot be read in. Instead it is read in incrementally.
        ArffLoader loader = new ArffLoader();
        loader.setFile(new File(testPath));

        Instances data = loader.getStructure();
        data.setClassIndex(data.numAttributes() - 1);

        System.out.println("Ensemble Classifier is ready.");
        System.out.println("Testing on all instances avaialable.");

        startTime = System.nanoTime();

        int instanceNumber = 0;

        // label instances
        Instance current;

        while ((current = loader.getNextInstance(data)) != null) {
            instanceNumber += 1;

            double classification1 = learner1.classifyInstance(current);
            double classification2 = learner2.classifyInstance(current);
            double classification3 = learner3.classifyInstance(current);
            double classification5 = learner5.classifyInstance(current);

            // All classifiers must agree. This is a very primitive ensemble strategy!
            if (classification1 == 1 && classification2 == 1 && classification3 == 1 && classification5 == 1) {
                Writer.append(resultPath, instanceNumber + "\n");
            }
        }

        System.out.println("Test set instances: " + instanceNumber);

        long endTime = System.nanoTime();
        long duration = endTime - startTime;
        double seconds = (double) duration / 1000000000.0;

        System.out.println("Testing Ensemble completed in " + duration + " (ns) or " + seconds + " (s).");
    } catch (Exception e) {
        System.out.println("Could not test Ensemble classifier due to an error.");
    }
}

---

From source file:de.tudarmstadt.ukp.dkpro.spelling.experiments.hoo2012.featureextraction.AllFeaturesExtractor.java

License:Apache License

private Classifier getClassifier() throws Exception {
    Classifier cl = null;/*from  www . j a va 2 s  . c  om*/
    // Build and evaluate classifier
    // The options given correspond to the default settings in the WEKA GUI
    if (classifier.equals("smo")) {
        SMO smo = new SMO();
        smo.setOptions(Utils.splitOptions(
                "-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
        cl = smo;
    } else if (classifier.equals("j48")) {
        J48 j48 = new J48();
        j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
        cl = j48;
    } else if (classifier.equals("naivebayes")) {
        cl = new NaiveBayes();
    } else if (classifier.equals("randomforest")) {
        RandomForest rf = new RandomForest();
        rf.setOptions(Utils.splitOptions("-I 10 -K 0 -S 1"));
        cl = rf;
    }
    return cl;
}

---

From source file:development.CrossValidateShapelets.java

public static ArrayList<Classifier> setSingleClassifiers(ArrayList<String> names) {
    ArrayList<Classifier> sc = new ArrayList<>();
    kNN n = new kNN(50);
    n.setCrossValidate(true);/*from w  w  w.  j a v a 2s . co m*/
    sc.add(n);
    names.add("kNN");
    sc.add(new J48());
    names.add("C45");
    sc.add(new NaiveBayes());
    names.add("NB");
    BayesNet bn = new BayesNet();
    sc.add(bn);
    names.add("BayesNet");
    RandomForest rf = new RandomForest();
    rf.setNumTrees(200);
    sc.add(rf);
    names.add("RandForest");
    RotationForest rot = new RotationForest();
    rot.setNumIterations(30);
    sc.add(rf);
    names.add("RotForest");
    SMO svmL = new SMO();
    PolyKernel kernel = new PolyKernel();
    kernel.setExponent(1);
    svmL.setKernel(kernel);
    sc.add(svmL);
    names.add("SVML");
    kernel = new PolyKernel();
    kernel.setExponent(2);
    SMO svmQ = new SMO();
    svmQ.setKernel(kernel);
    sc.add(svmQ);
    names.add("SVMQ");
    return sc;
}

---

From source file:DiversifyQuery.DivTopK.java

public static void table4_5() throws Exception {

    // Initialise classifiers required for this experiment
    classifiers = new Classifier[8];
    classifiers[0] = new ShapeletTreeClassifier("infoTree.txt");
    classifiers[1] = new J48();
    classifiers[2] = new IB1();
    classifiers[3] = new NaiveBayes();
    classifiers[4] = new BayesNet();
    classifiers[5] = new RandomForest();
    classifiers[6] = new RotationForest();
    classifiers[7] = new SMO();

    // Set up names for the classifiers - only used for output
    classifierNames = new String[8];
    classifierNames[0] = "ShapeletTree";
    classifierNames[1] = "C4.5";
    classifierNames[2] = "1NN";
    classifierNames[3] = "Naive Bayes";
    classifierNames[4] = "Bayesian Network";
    classifierNames[5] = "Random Forest";
    classifierNames[6] = "Rotation Forest";
    classifierNames[7] = "SVM (linear)";

    //        if ((classifierToProcessIndex < 1 || classifierToProcessIndex > classifiers.length) && classifierToProcessIndex != -1) {
    //            throw new IOException("Invalid classifier identifier.");
    //        } else {
    //            if (classifierToProcessIndex != -1) {
    //                classifierToProcessIndex--;
    //            }
    //        }//  w  w w. j  ava  2  s.  c  o m

    // Compute classifier accuracies for each classifier
    double accuracies[] = new double[classifiers.length];

    for (int i = 1; i < classifiers.length; i++) {

        //if (i == classifierToProcessIndex || classifierToProcessIndex == -1) {
        accuracies[i] = classifierAccuracy(i, true, true);

    }

    // Write experiment output to file 
    writeFileContent(accuracies);
}

---

From source file:es.upm.dit.gsi.barmas.launcher.WekaClassifiersValidator.java

License:Open Source License

/**
 * @return a list of all WEKA classifiers
 *//*from  w w  w .  j  a va  2s  . c  om*/
public List<Classifier> getNewClassifiers() {
    Classifier classifier;
    List<Classifier> classifiers = new ArrayList<Classifier>();

    // NBTree
    classifier = new NBTree();
    classifiers.add(classifier);

    // PART
    classifier = new PART();
    classifiers.add(classifier);

    // J48
    classifier = new J48();
    ((J48) classifier).setUnpruned(true);
    classifiers.add(classifier);

    // // J48Graft
    // classifier = new J48graft();
    // ((J48graft) classifier).setUnpruned(true);
    // classifiers.add(classifier);

    // // OneR
    // classifier = new OneR();
    // classifiers.add(classifier);

    // LADTree
    classifier = new LADTree();
    classifiers.add(classifier);

    // // REPTree
    // classifier = new REPTree();
    // classifiers.add(classifier);

    // // SimpleLogistic
    // classifier = new SimpleLogistic();
    // classifiers.add(classifier);

    // // Logistic
    // classifier = new Logistic();
    // classifiers.add(classifier);

    // // MultiLayerPerceptron
    // classifier = new MultilayerPerceptron();
    // classifiers.add(classifier);

    // // DecisionStump
    // classifier = new DecisionStump();
    // classifiers.add(classifier);

    // // LMT
    // classifier = new LMT();
    // classifiers.add(classifier);

    // // SimpleCart
    // classifier = new SimpleCart();
    // classifiers.add(classifier);

    // // BFTree
    // classifier = new BFTree();
    // classifiers.add(classifier);

    // // RBFNetwork
    // classifier = new RBFNetwork();
    // classifiers.add(classifier);

    // // DTNB
    // classifier = new DTNB();
    // classifiers.add(classifier);

    // // Jrip
    // classifier = new JRip();
    // classifiers.add(classifier);

    // // Conjunction Rule
    // classifier = new ConjunctiveRule();
    // classifiers.add(classifier);

    // // ZeroR
    // classifier = new ZeroR();
    // classifiers.add(classifier);

    // SMO
    classifier = new SMO();
    classifiers.add(classifier);

    // // OneR
    // classifier = new OneR();
    // classifiers.add(classifier);

    // // RandomForest
    // classifier = new RandomForest();
    // classifiers.add(classifier);

    return classifiers;

}

---

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

License:Open Source License

@Override
public WekaModel train(ExampleSet examples) {
    System.out.println("SMO Options: " + SMO_OPTIONS);
    SMO smo = new SMO();
    try {//from   w w  w.  j a v a 2  s  .  c  o  m
        smo.setOptions(Utils.splitOptions(SMO_OPTIONS));
    } catch (Exception ex) {
        System.err.println("Unable to configure SMO.");
        System.err.println("\t" + ex.getMessage());
        return null;
    }

    //Build logistic models if desired
    smo.setBuildLogisticModels(isBuildLogisticModel());

    Classifier classifier = smo;

    if (useCostTraining) {
        CostSensitiveClassifier cost = new CostSensitiveClassifier();
        cost.setClassifier(smo);
        CostMatrix matrix = new CostMatrix(2);
        matrix.setElement(0, 0, 0);
        matrix.setElement(0, 1, falsePositiveCost);
        matrix.setElement(1, 0, falseNegativeCost);
        matrix.setElement(1, 1, 0);
        cost.setCostMatrix(matrix);

        classifier = cost;

        System.out.print("Wrapping SMO in CostSensitiveClassifier " + matrix.toMatlab());

        if (useReweighting) {
            cost.setMinimizeExpectedCost(false);
            System.out.println(" using re-weighting.");
        } else {
            cost.setMinimizeExpectedCost(true);
            System.out.println(" using min-cost criterion.");
        }
    }

    try {
        System.out.print("Training SMO 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 SMO.");
        System.err.println("\t" + ex.getMessage());
        return null;
    }
}

---

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   w w  w.  j a v a 2s.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:farm_ads.MyClassifier.java

public Classifier classifierSMO(Instances instances) throws Exception {
    SMO classifier = new SMO();
    classifier.setOptions(weka.core.Utils.splitOptions(
            "-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
    classifier.buildClassifier(instances);
    return classifier;
}

---

From source file:focusedCrawler.target.ClassifyBuilder.java

License:Open Source License

public String buildClassifier(String trainFile, String outputModel) throws Exception {
    double max = Double.MIN_NORMAL;
    double cValue = 0;
    for (double c = 1; c > 0.1; c = c - 0.2) {
        SMO classifier = new SMO();
        String[] argum = new String[] { "-t", trainFile, "-C", "" + c, "-v", "-d", outputModel + c };
        String output = Evaluation.evaluateModel(classifier, argum);
        int index = output.indexOf("Correctly Classified Instances");
        if (index >= 0) {
            int end = output.indexOf("%", index);
            String line = (output.substring(index, end)).trim();
            line = line.substring(line.lastIndexOf(" "));
            double accuracy = Double.parseDouble(line.trim());
            if (accuracy > max) {
                max = accuracy;//from   w w  w. j a  v a  2s. co  m
                cValue = c;
            }
        }
    }
    System.out.println("C:" + cValue);
    return outputModel + cValue;
}

---

From source file:focusedCrawler.target.ClassifyBuilder.java

License:Open Source License

public void testClassifier(String testFile, String outputModel) throws Exception {
    SMO classifier = new SMO();
    String[] argum = new String[] { "-T", testFile, "-l", outputModel, "-i" };
    String output = Evaluation.evaluateModel(classifier, argum);
    int index = output.indexOf("F-Measure");
    if (index >= 0) {
        index = output.indexOf("\n", index);
        int end = output.indexOf("\n", index + 1);
        String line = (output.substring(index, end)).trim();
        System.out.println(line);
        StringTokenizer tokenizer = new StringTokenizer(line, " ");
        int count = 0;
        while (tokenizer.hasMoreTokens()) {
            String word = tokenizer.nextToken();
            if (count == 2) {
                System.out.println("PRECISION:" + word);
            }//  w  w  w .  jav  a 2 s  . com
            if (count == 3) {
                System.out.println("RECALL:" + word);
            }
            if (count == 4) {
                System.out.println("F-MEASURE:" + word);
            }
            count++;
        }
    }
}

---

• «
• 1
• 2
• 3
• 4
• 5
• »