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:org.conqat.engine.commons.machine_learning.BaseWekaClassifier.java

License:Apache License

/**
 * Returns a new classifier based on the given algorithm.
 *///from  ww w . j av a  2  s.  com
protected weka.classifiers.Classifier getClassifier(EClassificationAlgorithm algorithm) {
    switch (algorithm) {
    case DECISION_TREE_REP:
        return new REPTree();
    case SUPPORT_VECTOR_MACHINE_SMO:
        return new SMO();
    case COST_SENSITIVE_CLASSIFIER:
        return new CostSensitiveClassifier();
    case DECISION_TREE_J48:
        return new J48();
    default:
        throw new AssertionError("Cannot create a classifier without a specified algorithm.");
    }

}

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From source file:org.dkpro.similarity.algorithms.ml.ClassifierSimilarityMeasure.java

License:Open Source License

public static Classifier getClassifier(WekaClassifier classifier) throws IllegalArgumentException {
    try {//from  w ww.  ja v a 2 s .com
        switch (classifier) {
        case NAIVE_BAYES:
            return new NaiveBayes();
        case J48:
            J48 j48 = new J48();
            j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
            return j48;
        case 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\""));
            return smo;
        case LOGISTIC:
            Logistic logistic = new Logistic();
            logistic.setOptions(Utils.splitOptions("-R 1.0E-8 -M -1"));
            return logistic;
        default:
            throw new IllegalArgumentException("Classifier " + classifier + " not found!");
        }
    } catch (Exception e) {
        throw new IllegalArgumentException(e);
    }

}

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From source file:org.opentox.qsar.processors.trainers.classification.SVCTrainer.java

License:Open Source License

public QSARModel train(Instances data) throws QSARException {

    // GET A UUID AND DEFINE THE TEMPORARY FILE WHERE THE TRAINING DATA
    // ARE STORED IN ARFF FORMAT PRIOR TO TRAINING.
    final String rand = java.util.UUID.randomUUID().toString();
    final String temporaryFilePath = ServerFolders.temp + "/" + rand + ".arff";
    final File tempFile = new File(temporaryFilePath);

    // SAVE THE DATA IN THE TEMPORARY FILE
    try {/*from  www  .  j a  v a  2  s. c o m*/
        ArffSaver dataSaver = new ArffSaver();
        dataSaver.setInstances(data);
        dataSaver.setDestination(new FileOutputStream(tempFile));
        dataSaver.writeBatch();
        if (!tempFile.exists()) {
            throw new IOException("Temporary File was not created");
        }
    } catch (final IOException ex) {/*
                                    * The content of the dataset cannot be
                                    * written to the destination file due to
                                    * some communication issue.
                                    */
        tempFile.delete();
        throw new RuntimeException(
                "Unexpected condition while trying to save the " + "dataset in a temporary ARFF file", ex);
    }

    // INITIALIZE THE CLASSIFIER
    SMO classifier = new SMO();
    classifier.setEpsilon(0.1);
    classifier.setToleranceParameter(tolerance);

    // CONSTRUCT A KERNEL ACCORDING TO THE POSTED PARAMETERS
    // SUPPORTED KERNELS ARE {rbf, linear, polynomial}
    Kernel svc_kernel = null;
    if (this.kernel.equalsIgnoreCase("rbf")) {
        RBFKernel rbf_kernel = new RBFKernel();
        rbf_kernel.setGamma(gamma);
        rbf_kernel.setCacheSize(cacheSize);
        svc_kernel = rbf_kernel;
    } else if (this.kernel.equalsIgnoreCase("polynomial")) {
        PolyKernel poly_kernel = new PolyKernel();
        poly_kernel.setExponent(degree);
        poly_kernel.setCacheSize(cacheSize);
        poly_kernel.setUseLowerOrder(true);
        svc_kernel = poly_kernel;
    } else if (this.kernel.equalsIgnoreCase("linear")) {
        PolyKernel linear_kernel = new PolyKernel();
        linear_kernel.setExponent((double) 1.0);
        linear_kernel.setCacheSize(cacheSize);
        linear_kernel.setUseLowerOrder(true);
        svc_kernel = linear_kernel;
    }
    classifier.setKernel(svc_kernel);

    String modelFilePath = ServerFolders.models_weka + "/" + uuid.toString();
    String[] generalOptions = { "-c", Integer.toString(data.classIndex() + 1), "-t", temporaryFilePath,
            /// Save the model in the following directory
            "-d", modelFilePath };

    // AFTER ALL, BUILD THE CLASSIFICATION MODEL AND SAVE IT AS A SERIALIZED
    // WEKA FILE IN THE CORRESPONDING DIRECTORY.
    try {
        Evaluation.evaluateModel(classifier, generalOptions);
    } catch (final Exception ex) {
        tempFile.delete();
        throw new QSARException(Cause.XQReg350, "Unexpected condition while trying to train "
                + "a support vector classification model. Possible explanation : {" + ex.getMessage() + "}",
                ex);
    }

    ArrayList<Feature> independentFeatures = new ArrayList<Feature>();
    for (int i = 0; i < data.numAttributes(); i++) {
        Feature f = new Feature(data.attribute(i).name());
        if (data.classIndex() != i) {
            independentFeatures.add(f);
        }
    }

    Feature dependentFeature = new Feature(data.classAttribute().name());
    Feature predictedFeature = dependentFeature;

    QSARModel model = new QSARModel();
    model.setCode(uuid.toString());
    model.setAlgorithm(YaqpAlgorithms.SVC);
    model.setPredictionFeature(predictedFeature);
    model.setDependentFeature(dependentFeature);
    model.setIndependentFeatures(independentFeatures);
    model.setDataset(datasetUri);
    model.setParams(getParameters());
    model.setModelStatus(ModelStatus.UNDER_DEVELOPMENT);

    tempFile.delete();
    return model;
}

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From source file:org.uclab.mm.icl.llc.config.RecognizerType.java

License:Apache License

/**
 * Returns the corresponding recognizer//from w  w w .  j av  a2s  . co m
 * @param rec recognizer type to return
 * @param userID user ID to set
 * @return instance of the corresponding recognizer
 */
public LLCRecognizer getRecognizer(long userID) {

    RecognizerType rec = this.values()[value];
    switch (rec) {
    case SER:
        String[] labels = { "Anger", "Happiness", "Sadness" };
        String path = FileUtil.getRootPath() + "/training/modeldataV2.7.txt";
        SMO svm = new SMO(); // Define Classifier with Weka
        try {
            svm.setOptions(weka.core.Utils.splitOptions(
                    "-C 1.0 -L 0.0010 -P 1.0E-12 -N 1 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.RBFKernel -C 250007 -G 0.01\""));
            svm.setFilterType(new SelectedTag(SMO.FILTER_STANDARDIZE, SMO.TAGS_FILTER));
        } catch (Exception e) {
            e.printStackTrace();
        }
        ExtClassification classifier = new ExtClassification(path, 78 * 2, labels, svm);
        AudioEmotionRecognizer aer = new AudioEmotionRecognizer(classifier, path, userID);

        return aer;
    case ER:
        return new AudioEmotionRecognizerV(userID);
    case IAR:
        return new InertialActivityRecognizer(userID);
    case VAR:
        return new VideoActivityRecognizer(userID);
    case LR:
        //get user loc coord / label with userID by restful service
        return new GPSLocationRecognizer(userID);
    case FR:
        return new FoodRecognizer(userID);
    }
    return null;
}

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From source file:qa.experiment.ProcessFeatureVector.java

public void evaluate(Instances trainingData) throws Exception {
    Classifier c1 = new SMO();
    Evaluation eval = new Evaluation(trainingData);
    eval.crossValidateModel(c1, trainingData, 10, new Random(1));
    System.out.println("Estimated Accuracy: " + Double.toString(eval.pctCorrect()));
}

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From source file:sentinets.TrainModel.java

License:Open Source License

public void runExps() {
    Classifier c1 = new SMO();
    Classifier c2 = new J48();
    Classifier c3 = new NaiveBayes();
    trainModel(c1, "SVM");
    trainModel(c2, "J48");
    trainModel(c3, "Naive Bayes");

}

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From source file:statistics.BinaryStatisticsEvaluator.java

@Override
public double[][] getConfusionMatrix(Instances Training_Instances, Instances Testing_Instances,
        String classifier) {//from www.  j a v a  2s.c  o m

    Classifier cModel = null;
    if ("NB".equals(classifier)) {
        cModel = (Classifier) new NaiveBayes();
        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    } else if ("DT".equals(classifier)) {
        cModel = (Classifier) new J48();
        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    } else if ("SVM".equals(classifier)) {
        cModel = (Classifier) new SMO();

        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    } else if ("KNN".equals(classifier)) {
        cModel = (Classifier) new IBk();
        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    //Test the model
    Evaluation eTest;
    try {
        eTest = new Evaluation(Training_Instances);
        eTest.evaluateModel(cModel, Testing_Instances);
        //Print the result
        String strSummary = eTest.toSummaryString();
        System.out.println(strSummary);
        String strSummary1 = eTest.toMatrixString();
        System.out.println(strSummary1);
        String strSummary2 = eTest.toClassDetailsString();
        System.out.println(strSummary2);

        //Get the confusion matrix
        double[][] cmMatrix = eTest.confusionMatrix();
        return cmMatrix;
    } catch (Exception ex) {
        Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
    }
    return null;
}

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From source file:tutorials.tools.TutorialWekaClassifier.java

License:Open Source License

public static void main(String[] args) throws Exception {
    /* Load data */
    Dataset data = FileHandler.loadDataset(new File("devtools/data/iris.data"), 4, ",");
    /* Create Weka classifier */
    SMO smo = new SMO();
    /* Wrap Weka classifier in bridge */
    Classifier javamlsmo = new WekaClassifier(smo);
    /* Initialize cross-validation */
    CrossValidation cv = new CrossValidation(javamlsmo);
    /* Perform cross-validation */
    Map<Object, PerformanceMeasure> pm = cv.crossValidation(data);
    /* Output results */
    System.out.println(pm);/*from   w  w w  .jav a2 s  .  c o  m*/
}

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From source file:wedt.project.SvmClassifier.java

SvmClassifier() {
    cls = new SMO();
    try {//from   www. j a v  a  2  s . c o  m
        ((SMO) cls).setOptions(weka.core.Utils.splitOptions("-M"));
        ((SMO) cls).setBuildLogisticModels(true);
    } catch (Exception e) {
    }
}

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From source file:wekimini.learning.SVMModelBuilder.java

public SVMModelBuilder() {
    classifier = new SMO();
    kernelType = KernelType.POLYNOMIAL;/*w  w  w. j av a 2s.c om*/
    PolyKernel k = new PolyKernel();
    k.setExponent(polyExponent);
    ((SMO) classifier).setKernel(k);
}

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