Example usage for weka.core Instances add

List of usage examples for weka.core Instances add

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Introduction

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

Prototype

@Override
public boolean add(Instance instance)

Source Link

Document

Adds one instance to the end of the set.

Usage

From source file:br.puc_rio.ele.lvc.interimage.datamining.DataParser.java

License:Apache License

@SuppressWarnings({ "unchecked", "rawtypes" })
public Instances parseData(Object objData) {

    try {/* w ww.  j  a  va2  s.  co m*/
        Instances dataInstance;
        DataBag values = (DataBag) objData;
        int numAttributes = values.iterator().next().size(); // N_Features + 1 Class
        int bagSize = 0; // To set the number of train samples

        // To find the number of samples (instances in a bag)
        for (Iterator<Tuple> it = values.iterator(); it.hasNext();) {
            it.next();
            bagSize = bagSize + 1;
        }

        // Code for find the different classes names in the input 
        String[] inputClass = new String[bagSize]; // String vector with the samples class's names
        int index = 0;
        for (Iterator<Tuple> it = values.iterator(); it.hasNext();) {
            Tuple tuple = it.next();
            inputClass[index] = DataType.toString(tuple.get(numAttributes - 1));
            index = index + 1;
        }

        HashSet classSet = new HashSet(Arrays.asList(inputClass));

        String[] classValue = (String[]) classSet.toArray(new String[0]);
        // To set the classes names in the attribute for the instance

        FastVector classNames = new FastVector();
        for (int i = 0; i < classValue.length; i++)
            classNames.addElement(classValue[i]);

        // Creating the instance model N_Features + 1_ClassNames

        FastVector atts = new FastVector();
        for (int i = 0; i < numAttributes - 1; i++)
            atts.addElement(new Attribute("att" + i));
        dataInstance = new Instances("MyRelation", atts, numAttributes);
        dataInstance.insertAttributeAt(new Attribute("ClassNames", classNames), numAttributes - 1);

        // To set the instance values for the dataInstance model created 
        Instance tmpData = new DenseInstance(numAttributes);
        index = 0;
        for (Iterator<Tuple> it = values.iterator(); it.hasNext();) {
            Tuple tuple = it.next();
            for (int i = 0; i < numAttributes - 1; i++)
                tmpData.setValue((weka.core.Attribute) atts.elementAt(i), DataType.toDouble(tuple.get(i)));
            //tmpData.setValue((weka.core.Attribute) atts.elementAt(numAttributes-1), DataType.toString(tuple.get(numAttributes-1)));
            dataInstance.add(tmpData);
            dataInstance.instance(index).setValue(numAttributes - 1,
                    DataType.toString(tuple.get(numAttributes - 1)));
            index = index + 1;
        }

        // Setting the class index
        dataInstance.setClassIndex(dataInstance.numAttributes() - 1);

        return dataInstance;
    } catch (Exception e) {
        System.err.println("Failed to process input; error - " + e.getMessage());
        return null;
    }
}

From source file:br.puc_rio.ele.lvc.interimage.datamining.DataParser.java

License:Apache License

@SuppressWarnings({ "unchecked", "rawtypes" })
public Instances parseData(BufferedReader buff) {

    try {// w  ww .ja va2s.  c  o  m
        Instances dataInstance;
        //DataBag values = (DataBag)objData;

        int numAttributes = 0; // N_Features + 1 Class

        List<String> inputClass = new ArrayList<String>();

        List<String[]> dataset = new ArrayList<String[]>();

        // To find the number of samples (instances in a bag)
        String line;
        while ((line = buff.readLine()) != null) {
            if (!line.isEmpty()) {
                String[] data = line.split(",");
                if (numAttributes == 0)
                    numAttributes = data.length;
                inputClass.add(data[data.length - 1]);
                dataset.add(data);
            }
        }

        HashSet classSet = new HashSet(inputClass);

        String[] classValue = (String[]) classSet.toArray(new String[0]);
        // To set the classes names in the attribute for the instance

        FastVector classNames = new FastVector();
        for (int i = 0; i < classValue.length; i++)
            classNames.addElement(classValue[i]);

        // Creating the instance model N_Features + 1_ClassNames

        FastVector atts = new FastVector();
        for (int i = 0; i < numAttributes - 1; i++)
            atts.addElement(new Attribute("att" + i));
        dataInstance = new Instances("MyRelation", atts, numAttributes);
        dataInstance.insertAttributeAt(new Attribute("ClassNames", classNames), numAttributes - 1);

        // To set the instance values for the dataInstance model created 
        Instance tmpData = new DenseInstance(numAttributes);
        int index = 0;
        for (int k = 0; k < dataset.size(); k++) {

            for (int i = 0; i < numAttributes - 1; i++)
                tmpData.setValue((weka.core.Attribute) atts.elementAt(i), DataType.toDouble(dataset.get(k)[i]));
            //tmpData.setValue((weka.core.Attribute) atts.elementAt(numAttributes-1), DataType.toString(tuple.get(numAttributes-1)));
            dataInstance.add(tmpData);
            dataInstance.instance(index).setValue(numAttributes - 1,
                    DataType.toString(dataset.get(k)[numAttributes - 1]));
            index = index + 1;
        }

        // Setting the class index
        dataInstance.setClassIndex(dataInstance.numAttributes() - 1);

        return dataInstance;
    } catch (Exception e) {
        System.err.println("Failed to process input; error - " + e.getMessage());
        return null;
    }
}

From source file:br.unicamp.ic.recod.gpsi.gp.gpsiJGAPRoiFitnessFunction.java

@Override
protected double evaluate(IGPProgram igpp) {

    double mean_accuracy = 0.0;
    Object[] noargs = new Object[0];

    gpsiRoiBandCombiner roiBandCombinator = new gpsiRoiBandCombiner(new gpsiJGAPVoxelCombiner(super.b, igpp));
    // TODO: The ROI descriptors must combine the images first
    //roiBandCombinator.combineEntity(this.dataset.getTrainingEntities());

    gpsiMLDataset mlDataset = new gpsiMLDataset(this.descriptor);
    try {//from w ww  .ja  v a 2 s  .  c  o m
        mlDataset.loadWholeDataset(this.dataset, true);
    } catch (Exception ex) {
        Logger.getLogger(gpsiJGAPRoiFitnessFunction.class.getName()).log(Level.SEVERE, null, ex);
    }

    int dimensionality = mlDataset.getDimensionality();
    int n_classes = mlDataset.getTrainingEntities().keySet().size();
    int n_entities = mlDataset.getNumberOfTrainingEntities();
    ArrayList<Byte> listOfClasses = new ArrayList<>(mlDataset.getTrainingEntities().keySet());

    Attribute[] attributes = new Attribute[dimensionality];
    FastVector fvClassVal = new FastVector(n_classes);

    int i, j;
    for (i = 0; i < dimensionality; i++)
        attributes[i] = new Attribute("f" + Integer.toString(i));
    for (i = 0; i < n_classes; i++)
        fvClassVal.addElement(Integer.toString(listOfClasses.get(i)));

    Attribute classes = new Attribute("class", fvClassVal);

    FastVector fvWekaAttributes = new FastVector(dimensionality + 1);

    for (i = 0; i < dimensionality; i++)
        fvWekaAttributes.addElement(attributes[i]);
    fvWekaAttributes.addElement(classes);

    Instances instances = new Instances("Rel", fvWekaAttributes, n_entities);
    instances.setClassIndex(dimensionality);

    Instance iExample;
    for (byte label : mlDataset.getTrainingEntities().keySet()) {
        for (double[] featureVector : mlDataset.getTrainingEntities().get(label)) {
            iExample = new Instance(dimensionality + 1);
            for (j = 0; j < dimensionality; j++)
                iExample.setValue(i, featureVector[i]);
            iExample.setValue(dimensionality, label);
            instances.add(iExample);
        }
    }

    int folds = 5;
    Random rand = new Random();
    Instances randData = new Instances(instances);
    randData.randomize(rand);

    Instances trainingSet, testingSet;
    Classifier cModel;
    Evaluation eTest;
    try {
        for (i = 0; i < folds; i++) {
            cModel = (Classifier) new SimpleLogistic();
            trainingSet = randData.trainCV(folds, i);
            testingSet = randData.testCV(folds, i);

            cModel.buildClassifier(trainingSet);

            eTest = new Evaluation(trainingSet);
            eTest.evaluateModel(cModel, testingSet);

            mean_accuracy += eTest.pctCorrect();

        }
    } catch (Exception ex) {
        Logger.getLogger(gpsiJGAPRoiFitnessFunction.class.getName()).log(Level.SEVERE, null, ex);
    }

    mean_accuracy /= (folds * 100);

    return mean_accuracy;

}

From source file:categorization.SpectralWEKA.java

License:Open Source License

public void buildClusterer(ArrayList<String> seqDB, double[][] sm) {
    seqList = seqDB;//from w w w. j  a  v a 2 s  .  c o m

    this.setSimMatrix(sm);

    Attribute seqString = new Attribute("sequence", (FastVector) null);
    FastVector attrInfo = new FastVector();
    attrInfo.addElement(seqString);
    Instances data = new Instances("data", attrInfo, 0);

    for (int i = 0; i < seqList.size(); i++) {
        Instance currentInst = new Instance(1);
        currentInst.setDataset(data);
        currentInst.setValue(0, seqList.get(i));
        data.add(currentInst);
    }

    try {
        buildClusterer(data);
    } catch (Exception e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
}

From source file:CEP.CEPListener.java

Instances SetDuration(Instances in) throws InterruptedException {
    Instances out = HeaderManager.GetEmptyStructure();
    for (Instance inst : in) {

        double time = inst.toDoubleArray()[5];
        if (time > 20) {
            inst.setValue(5, time - windowSize);
            out.add(inst);
        }//from  w w w . jav  a2s.com
    }
    return out;

}

From source file:cezeri.utils.FactoryInstance.java

public static Instances generateInstances(String relationName, int nCols) {
    CMatrix cm = CMatrix.getInstance().zeros(1, nCols);
    FastVector att = new FastVector();
    for (int i = 0; i < cm.getColumnNumber(); i++) {
        att.addElement(new Attribute("f" + (i + 1)));
    }//from  w  w w  .j a  v a 2  s .  com
    Instances ret = new Instances(relationName, att, cm.getRowNumber());
    for (int i = 0; i < cm.getRowNumber(); i++) {
        Instance ins = new Instance(cm.getColumnNumber());
        for (int j = 0; j < cm.getColumnNumber(); j++) {
            ins.setValue(j, cm.get2DArrayDouble()[i][j]);
        }
        ret.add(ins);
    }
    ret.setClassIndex(ret.numAttributes() - 1);
    return ret;
}

From source file:cezeri.utils.FactoryInstance.java

public static Instances generateInstances(String relationName, CMatrix cm) {
    FastVector att = new FastVector();
    for (int i = 0; i < cm.getColumnNumber(); i++) {
        att.addElement(new Attribute("f" + (i + 1)));
    }//from  w ww .j  a v  a  2 s  . com
    Instances ret = new Instances(relationName, att, cm.getRowNumber());
    for (int i = 0; i < cm.getRowNumber(); i++) {
        Instance ins = new Instance(cm.getColumnNumber());
        for (int j = 0; j < cm.getColumnNumber(); j++) {
            ins.setValue(j, cm.get2DArrayDouble()[i][j]);
        }
        ret.add(ins);
    }
    ret.setClassIndex(ret.numAttributes() - 1);
    return ret;
}

From source file:cezeri.utils.FactoryInstance.java

public static Instances generateInstances(String relationName, double[][] m) {
    FastVector att = new FastVector();
    for (int i = 0; i < m[0].length; i++) {
        att.addElement(new Attribute("f" + (i + 1)));
    }/*from  w ww  . j a va 2s .  co  m*/
    Instances ret = new Instances(relationName, att, m.length);
    for (int i = 0; i < m.length; i++) {
        Instance ins = new Instance(m[0].length);
        for (int j = 0; j < m[0].length; j++) {
            ins.setValue(j, m[i][j]);
        }
        ret.add(ins);
    }
    ret.setClassIndex(ret.numAttributes() - 1);
    return ret;
}

From source file:cezeri.utils.FactoryInstance.java

public static Instances getSubsetData(Instances data, String[] attList) {
    Instances temp = new Instances(data);
    for (int i = 0; i < data.numAttributes(); i++) {
        if (!temp.attribute(0).equals(temp.classAttribute())) {
            temp.deleteAttributeAt(0);/*from  w ww.j ava 2  s  .  c  o m*/
        }
    }
    double[][] m = new double[attList.length + 1][data.numInstances()];
    for (int i = 0; i < attList.length; i++) {
        int n = attList.length - 1 - i;
        String str = attList[n];
        Attribute t = data.attribute(str);
        double[] d = data.attributeToDoubleArray(t.index());
        m[n] = d;
        temp.insertAttributeAt(t, 0);
    }
    m[attList.length] = data.attributeToDoubleArray(data.classIndex());
    m = CMatrix.getInstance(m).transpose().get2DArrayDouble();

    FastVector att = new FastVector();
    for (int i = 0; i < temp.numAttributes(); i++) {
        att.addElement(temp.attribute(i));
    }
    Instances ret = new Instances(temp.relationName(), att, m.length);
    for (int i = 0; i < m.length; i++) {
        Instance ins = new Instance(m[0].length);
        for (int j = 0; j < m[0].length; j++) {
            ins.setValue(j, m[i][j]);
        }
        ret.add(ins);
    }
    ret.setClassIndex(temp.classIndex());

    return ret;
}

From source file:clasificacion.Clasificacion.java

public String clasificar(String[] testCases) throws Exception {
    String ruta = "nursery_model.model";

    InputStream classModelStream;
    classModelStream = getClass().getResourceAsStream(ruta);
    //classModel = (Classifier)SerializationHelper.read(classModelStream);
    Classifier clasify = (Classifier) SerializationHelper.read(classModelStream);

    FastVector parents = new FastVector();
    parents.addElement("usual");
    parents.addElement("pretentious");
    parents.addElement("great_pret");
    Attribute _parent = new Attribute("parents", parents);

    FastVector nurs = new FastVector();
    nurs.addElement("proper");
    nurs.addElement("less_proper");
    nurs.addElement("improper");
    nurs.addElement("critical");
    nurs.addElement("very_crit");
    Attribute _has_nurs = new Attribute("has_nurs", nurs);

    FastVector form = new FastVector();
    form.addElement("complete");
    form.addElement("completed");
    form.addElement("incomplete");
    form.addElement("foster");
    Attribute _form = new Attribute("form", form);

    FastVector children = new FastVector();
    children.addElement("1");
    children.addElement("2");
    children.addElement("3");
    children.addElement("more");
    Attribute _children = new Attribute("children", children);

    FastVector housing = new FastVector();
    housing.addElement("convenient");
    housing.addElement("less_conv");
    housing.addElement("critical");
    Attribute _housing = new Attribute("housing", housing);

    FastVector finance = new FastVector();
    finance.addElement("convenient");
    finance.addElement("inconv");
    Attribute _finance = new Attribute("finance", finance);

    FastVector social = new FastVector();
    social.addElement("nonprob");
    social.addElement("slightly_prob");
    social.addElement("problematic");
    Attribute _social = new Attribute("social", social);

    FastVector health = new FastVector();
    health.addElement("recommended");
    health.addElement("priority");
    health.addElement("not_recom");
    Attribute _health = new Attribute("health", health);

    FastVector Class = new FastVector();
    Class.addElement("not_recom");
    Class.addElement("recommend");
    Class.addElement("very_recom");
    Class.addElement("priority");
    Class.addElement("spec_prior");
    Attribute _Class = new Attribute("class", Class);

    FastVector atributos = new FastVector(9);
    atributos.addElement(_parent);//from  w w  w  . ja v a  2  s.c o m
    atributos.addElement(_has_nurs);
    atributos.addElement(_form);
    atributos.addElement(_children);
    atributos.addElement(_housing);
    atributos.addElement(_finance);
    atributos.addElement(_social);
    atributos.addElement(_health);
    atributos.addElement(_Class);

    ArrayList<Attribute> atributs = new ArrayList<>();
    atributs.add(_parent);
    atributs.add(_has_nurs);
    atributs.add(_form);
    atributs.add(_children);
    atributs.add(_housing);
    atributs.add(_finance);
    atributs.add(_social);
    atributs.add(_health);
    atributs.add(_Class);

    //Aqu se crea la instacia, que tiene todos los atributos del modelo
    Instances dataTest = new Instances("TestCases", atributos, 1);
    dataTest.setClassIndex(8);

    Instance setPrueba = new Instance(9);

    int index = -1;
    for (int i = 0; i < 8; i++) {
        index = atributs.get(i).indexOfValue(testCases[i]);
        //System.out.println(i + " " + atributs.get(i)  + " " + index + " " + testCases[i]);
        setPrueba.setValue(atributs.get(i), index);
    }

    //Agregando el set que se desea evaluar.
    dataTest.add(setPrueba);

    //Realizando la Prediccin
    //La instancia es la 0 debido a que es la unica que se encuentra.
    double valorP = clasify.classifyInstance(dataTest.instance(0));
    //get the name of the class value
    String prediccion = dataTest.classAttribute().value((int) valorP);

    return prediccion;
}