Example usage for weka.core Instances Instances

List of usage examples for weka.core Instances Instances

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Introduction

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

Prototype

public Instances(String name, ArrayList<Attribute> attInfo, int capacity)

Source Link

Document

Creates an empty set of instances.

Usage

From source file:at.tuflowgraphy.semanticapps.semdroid.DalvikBaseAnalyzer.java

License:Apache License

protected Instances generateInstancesHeader(DActivationPatternPackage dActivationPatternPackage) {
    DActivationPattern activationPatternTemp = dActivationPatternPackage.getActivationPatterns().get(0);
    FastVector fvWekaAttributes = new FastVector(activationPatternTemp.getRawPattern().length);
    for (int j = 0; j < activationPatternTemp.getRawPattern().length; j++) {
        Attribute attribute = new Attribute(j + "");
        fvWekaAttributes.addElement(attribute);
    }/*  w  w  w . j av  a2 s .c o m*/

    Set<String> labelSet = getLabelSet(dActivationPatternPackage);

    FastVector classValues = new FastVector(labelSet.size());
    for (String label : labelSet) {
        classValues.addElement(label);
    }

    Attribute classAttribute = new Attribute("Class", classValues);
    fvWekaAttributes.addElement(classAttribute);

    Instances instances = new Instances(mAnalysisConfig.getApplicationAnalysisName(), fvWekaAttributes,
            dActivationPatternPackage.getActivationPatterns().size());
    instances.setClassIndex(instances.numAttributes() - 1);
    return instances;
}

From source file:at.tuflowgraphy.semanticapps.semdroid.utils.ArffHelper.java

License:Apache License

public Instances getWekaInstances(AnalysisChain analysisChain, String name) {
    Instances instances = null;/* w w  w  .ja va  2 s .  c  om*/
    List<DActivationPatternPackage> dActivationPatternPackages = analysisChain.getFinalLayers().get(0)
            .getResultAnalysisPackage().getActivationPatternPackages();
    int counter = 0;
    for (DActivationPatternPackage dActivationPatternPackage : dActivationPatternPackages) {
        if (counter > 0) {
            //                String resultFileName = arffFile.getName();
            //                String newName = resultFileName.split("_")[0];
            //                int index = resultFileName.indexOf("_");
            //                newName += "-MISSING-" + counter + "-"
            //                        + resultFileName.substring(index);
            //                arffFileToWriteTo = new File(arffFile.getParentFile(), newName);
            System.err.println("ERROR: Multiple activation pattern packages found! Should not happen...");
        }
        counter++;
        DActivationPattern activationPatternTemp = dActivationPatternPackage.getActivationPatterns().get(0);
        FastVector fvWekaAttributes = new FastVector(activationPatternTemp.getRawPattern().length);
        for (int j = 0; j < activationPatternTemp.getRawPattern().length; j++) {
            Attribute attribute = new Attribute(j + "");
            fvWekaAttributes.addElement(attribute);
        }

        Set<String> labelSet = getLabelSet(dActivationPatternPackage);

        FastVector classValues = new FastVector(labelSet.size());
        for (String label : labelSet) {
            classValues.addElement(label);
        }

        Attribute classAttribute = new Attribute("Class", classValues);
        fvWekaAttributes.addElement(classAttribute);

        instances = new Instances(name, fvWekaAttributes,
                dActivationPatternPackage.getActivationPatterns().size());
        instances.setClassIndex(instances.numAttributes() - 1);

        for (int i = 0; i < dActivationPatternPackage.getActivationPatterns().size(); i++) {
            DActivationPattern activationPattern = dActivationPatternPackage.getActivationPatterns().get(i);
            Instance instance = new Instance(fvWekaAttributes.size());

            for (int j = 0; j < activationPattern.getRawPattern().length; j++) {
                instance.setValue((Attribute) fvWekaAttributes.elementAt(j),
                        activationPattern.getRawPattern()[j]);
            }

            instance.setDataset(instances);

            DSimpleStringMetaData metadata = (DSimpleStringMetaData) activationPattern.getMetaData();
            List<String> keys = metadata.getMetaDataKeys();
            for (int k = 0; k < keys.size(); k++) {
                if (keys.get(k).equals(DalvikInputPlugin.TAG_LABEL)) {
                    String label = metadata.getMetaDataEntries().get(k);

                    // TODO: dynamically add new labels to instances so that getLabelSet for-loop is not required

                    // System.out.println(label);
                    // if(!labelSet.contains(label)) {
                    // labelSet.add(label);
                    // // classValues.addElement(label);
                    // classAttribute.addStringValue(label);
                    // instances.attribute(instances.classIndex()).addValue(label);
                    // System.out.println("ADDED " + label);
                    // }
                    instance.setClassValue(label);
                    // TODO: only first class value used
                    break;
                }
            }
            instances.add(instance);
        }

    }
    return instances;
}

From source file:boa.aggregators.DecisionTreeAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override// w  w w. ja  v a2  s  . co  m
public void finish() throws IOException, InterruptedException {
    int NumOfAttributes = this.getVectorSize();
    List<Attribute> attributes = new ArrayList<Attribute>();
    FastVector fvAttributes = new FastVector(NumOfAttributes);

    for (int i = 0; i < NumOfAttributes; i++) {
        attributes.add(new Attribute("Attribute" + i));
        fvAttributes.addElement(attributes.get(i));
    }

    Instances trainingSet = new Instances("DecisionTree", fvAttributes, 1);
    trainingSet.setClassIndex(NumOfAttributes - 1);

    for (List<Double> vector : this.vectors.values()) {
        Instance instance = new Instance(NumOfAttributes);
        for (int i = 0; i < vector.size(); i++) {
            instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
        }
        trainingSet.add(instance);
    }

    try {
        this.model = new J48();
        this.model.setOptions(options);
        this.model.buildClassifier(trainingSet);
    } catch (Exception ex) {
    }

    this.saveModel(this.model);
}

From source file:boa.aggregators.LinearRegressionAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override/*ww w .  ja  va2 s .  c  o  m*/
public void finish() throws IOException, InterruptedException {
    int NumOfAttributes = this.getVectorSize();
    List<Attribute> attribute = new ArrayList<Attribute>();
    FastVector fvAttributes = new FastVector(NumOfAttributes);

    for (int i = 0; i < NumOfAttributes; i++) {
        attribute.add(new Attribute("Attribute" + i));
        fvAttributes.addElement(attribute.get(i));
    }

    Instances trainingSet = new Instances("LinearRegression", fvAttributes, 1);
    trainingSet.setClassIndex(NumOfAttributes - 1);

    for (List<Double> vector : this.vectors.values()) {
        Instance instance = new Instance(NumOfAttributes);
        for (int i = 0; i < vector.size(); i++) {
            instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
        }
        trainingSet.add(instance);
    }

    try {
        this.model = new LinearRegression();
        this.model.setOptions(options);
        this.model.buildClassifier(trainingSet);
    } catch (Exception ex) {
    }

    this.saveModel(this.model);
}

From source file:boa.aggregators.NaiveBayesAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override//from w ww . j av a  2  s . c  om
public void finish() throws IOException, InterruptedException {

    Instances trainingSet = new Instances("NaiveBayes", fvAttributes, 1);
    trainingSet.setClassIndex(NumOfAttributes - 1);

    for (List<Double> vector : this.vectors.values()) {
        Instance instance = new Instance(NumOfAttributes);
        for (int i = 0; i < vector.size(); i++) {
            instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
        }
        trainingSet.add(instance);
    }

    try {
        this.model = new NaiveBayes();
        this.model.setOptions(options);
        this.model.buildClassifier(trainingSet);
    } catch (Exception ex) {
    }

    this.saveModel(this.model);
}

From source file:boa.aggregators.RandomForestAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override/*w  w  w . j ava2 s . c  om*/
public void finish() throws IOException, InterruptedException {
    int NumOfAttributes = this.getVectorSize();
    List<Attribute> attributes = new ArrayList<Attribute>();
    FastVector fvAttributes = new FastVector(NumOfAttributes);

    for (int i = 0; i < NumOfAttributes; i++) {
        attributes.add(new Attribute("Attribute" + i));
        fvAttributes.addElement(attributes.get(i));
    }

    Instances trainingSet = new Instances("RandomForest", fvAttributes, 1);
    trainingSet.setClassIndex(NumOfAttributes - 1);

    for (List<Double> vector : this.vectors.values()) {
        Instance instance = new Instance(NumOfAttributes);
        for (int i = 0; i < vector.size(); i++) {
            instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
        }
        trainingSet.add(instance);
    }

    try {
        this.model = new RandomForest();
        this.model.setOptions(options);
        this.model.buildClassifier(trainingSet);
    } catch (Exception ex) {
    }

    this.saveModel(this.model);
}

From source file:boa.functions.BoaIntrinsics.java

License:Apache License

/**
 * Classify instances for given ML model
 *
 * @param Take Model Type//  www. j  av  a 2  s.  c o  m
 * @return Predicted value for a instance
 */
@FunctionSpec(name = "classify", returnType = "float", formalParameters = { "Model", "array of float" })
public static double classify(final Object model, final double[] vector) throws Exception {
    List<Attribute> attribute = new ArrayList<Attribute>();
    int size = vector.length;
    int NumOfAttributes = size + 1;

    FastVector fvAttributes = new FastVector(NumOfAttributes);
    for (int i = 0; i < NumOfAttributes; i++) {
        attribute.add(new Attribute("Attribute" + i));
        fvAttributes.addElement(attribute.get(i));
    }

    Instances testingSet = new Instances("Classifier", fvAttributes, 1);
    testingSet.setClassIndex(NumOfAttributes - 1);

    Instance instance = new Instance(NumOfAttributes);
    for (int i = 0; i < size; i++) {
        instance.setValue((Attribute) fvAttributes.elementAt(i), vector[i]);
    }

    Classifier classifier = (Classifier) model;
    double predval = classifier.classifyInstance(instance);

    return predval;
}

From source file:boostingPL.boosting.InstancesHelper.java

License:Open Source License

/**
 * create instances header from metadata,
 * the metadata like this:/*from   ww w .j  a v a 2  s  .  c  om*/
 * 
 *   <br/>
 *   <p>attributesNum:100</p>
 *   <p>classes:+1,-1</p>
 *   <br/>
 * 
 * @param in
 * @return
 * @throws IOException
 */
public static Instances createInstancesFromMetadata(LineReader in) throws IOException {
    int attributesNum = 0;
    ArrayList<Attribute> attInfo = null;
    List<String> classItems = null;

    Text line = new Text();
    while (in.readLine(line) > 0) {
        String sline = line.toString();
        if (sline.startsWith("attributesNum:")) {
            attributesNum = Integer.parseInt(sline.substring(14));
            attInfo = new ArrayList<Attribute>(attributesNum + 1);
            for (int i = 0; i < attributesNum; i++) {
                attInfo.add(new Attribute("attr" + i));
            }

            System.out.println("AttributeNum:" + attributesNum);
        } else if (sline.startsWith("classes:")) {
            String classes = sline.substring(8);
            String[] citems = classes.split(",");
            classItems = new ArrayList<String>(citems.length);
            for (String s : citems) {
                classItems.add(s);
            }

            System.out.println("classes:" + classes);
        }
    }

    attInfo.add(new Attribute("class", classItems));
    Instances insts = new Instances("BoostingPL-dataset", attInfo, 0);
    insts.setClassIndex(insts.numAttributes() - 1);

    return insts;
}

From source file:boostingPL.boosting.InstancesHelper.java

License:Open Source License

/**
 * create instances header from a instance
 * /*  w w  w  . j  ava  2s.co m*/
 * @param instance
 * @return instances
 */
public static Instances createInstances(String text) {
    String[] items = text.split(" ");

    ArrayList<Attribute> attInfo = new ArrayList<Attribute>();
    for (int i = 0; i < items.length - 1; i++) {
        attInfo.add(new Attribute("attr" + i));
    }

    List<String> classItems = new ArrayList<String>(2);
    classItems.add("1");
    classItems.add("-1");
    attInfo.add(new Attribute("class", classItems));
    Instances insts = new Instances("BoostingPL-dataset", attInfo, 0);
    insts.setClassIndex(insts.numAttributes() - 1);

    return insts;
}

From source file:br.com.edu.arff.ConstructArff.java

public static void main(String[] args) throws Exception {
    FastVector atts;/*from  w w w. ja  v a  2 s.c om*/
    FastVector attsRel;
    FastVector attVals;
    FastVector attValsRel;
    Instances data;
    Instances dataRel;
    double[] vals;
    double[] valsRel;
    int i, j, cont;

    ConnectionLAK conexao = new ConnectionLAK();
    ArrayList<String> uris;
    ArrayList<String> todos_topicos;
    ArrayList<Topicos> valores;

    uris = conexao.buscaURIS();
    todos_topicos = conexao.BuscarTodosTopicos();

    atts = new FastVector();
    for (String topicos : todos_topicos) {
        atts.addElement(new Attribute(topicos));
    }

    data = new Instances("Relacao", atts, 0);

    for (String uri : uris) {
        i = 0;
        vals = new double[data.numAttributes()];
        valores = conexao.BuscarTopicos(uri);
        for (int t = 0; t < data.numAttributes(); t++) {
            for (Topicos topic : valores) {
                String comparacao = topic.getTopico();
                String atributo = data.attribute(t).name();
                if (comparacao.equals(atributo)) {
                    vals[t] = topic.getValor();
                    break;
                }
            }
        }

        data.add(new Instance(1.0, vals));
    }

    System.out.println(data);
}