Example usage for weka.core Instance setValue

Introduction

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

Prototype

public void setValue(Attribute att, String value);

Source Link

Document

Sets a value of an nominal or string attribute to the given value.

Usage

From source file:boa.aggregators.LinearRegressionAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override/*www  .j  a  v  a 2 s.  co 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);
}

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From source file:boa.aggregators.NaiveBayesAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override/*from w  w w . j a  v  a  2s. c o m*/
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/* www. j a v a 2  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("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//from ww  w.  ja va 2s.  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:br.fapesp.myutils.MyUtils.java

License:Open Source License

/**
 * Generates a Gaussian data set with K clusters and m dimensions
 * /*from  w w w .  ja  v  a 2s  .  co m*/
 * @param centers
 *            K x m matrix
 * @param sigmas
 *            K x m matrix
 * @param pointsPerCluster
 *            number of points per cluster
 * @param seed
 *            for the RNG
 * @param randomize
 *            should the order of the instances be randomized?
 * @param supervised
 *            should class label be present? if true, the class is the m+1
 *            attribute
 * 
 * @return
 */
public static Instances genGaussianDataset(double[][] centers, double[][] sigmas, int pointsPerCluster,
        long seed, boolean randomize, boolean supervised) {
    Random r = new Random(seed);

    int K = centers.length; // number of clusters
    int m = centers[0].length; // number of dimensions

    FastVector atts = new FastVector(m);
    for (int i = 0; i < m; i++)
        atts.addElement(new Attribute("at" + i));

    if (supervised) {
        FastVector cls = new FastVector(K);
        for (int i = 0; i < K; i++)
            cls.addElement("Gauss-" + i);
        atts.addElement(new Attribute("Class", cls));
    }

    Instances data;
    if (supervised)
        data = new Instances(K + "-Gaussians-supervised", atts, K * pointsPerCluster);
    else
        data = new Instances(K + "-Gaussians", atts, K * pointsPerCluster);

    if (supervised)
        data.setClassIndex(m);

    Instance ith;

    for (int i = 0; i < K; i++) {
        for (int j = 0; j < pointsPerCluster; j++) {
            if (!supervised)
                ith = new DenseInstance(m);
            else
                ith = new DenseInstance(m + 1);
            ith.setDataset(data);
            for (int k = 0; k < m; k++)
                ith.setValue(k, centers[i][k] + (r.nextGaussian() * sigmas[i][k]));
            if (supervised)
                ith.setValue(m, "Gauss-" + i);
            data.add(ith);
        }
    }

    // run randomization filter if desired
    if (randomize)
        data.randomize(r);

    return data;
}

---

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

License:Apache License

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

    try {// www .  j a  v  a  2  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 {/*  ww  w  .j  a  va 2 s .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.puc_rio.ele.lvc.interimage.datamining.udf.BayesClassifier.java

License:Apache License

@Override
public String exec(Tuple input) throws IOException {
    if (input == null)
        return null;

    if (_trainData == null) {

        //Reads train data
        try {/*w w  w .ja v  a  2s . c o  m*/

            if (!_trainUrl.isEmpty()) {

                URL url = new URL(_trainUrl);
                URLConnection urlConn = url.openConnection();
                urlConn.connect();
                InputStreamReader inStream = new InputStreamReader(urlConn.getInputStream());
                BufferedReader buff = new BufferedReader(inStream);

                _trainData = _dataParser.parseData(buff);

            }
        } catch (Exception e) {
            throw new IOException("Caught exception reading training data file ", e);
        }

    }

    try {
        Integer numFeatures = input.size();

        double[] testData;
        testData = new double[numFeatures];

        for (int i = 0; i < numFeatures; i++)
            testData[i] = DataType.toDouble(input.get(i));

        Classifier csfr = null;
        csfr = (Classifier) Class.forName("weka.classifiers.bayes.NaiveBayes").newInstance();
        csfr.buildClassifier(_trainData);
        double classification = 0;

        Instance myinstance = _trainData.instance(0);
        for (int i = 0; i < numFeatures; i++)
            myinstance.setValue(i, testData[i]);
        classification = csfr.classifyInstance(myinstance);

        return myinstance.attribute(_trainData.classIndex()).value((int) classification);

    } catch (Exception e) {
        throw new IOException("Caught exception processing input row ", e);
    }
}

---

From source file:br.ufpe.cin.mpos.offload.DynamicDecisionSystem.java

License:Apache License

public synchronized boolean isRemoteAdvantage(int InputSize, Remotable.Classifier classifierRemotable) {
    boolean resp = false;
    try {/*from  ww w .ja  va 2 s  .  co m*/
        if ((!(this.classifierModel.equals(classifierRemotable.toString()))) || this.classifier == null) {
            Log.d("classificacao", "classificador=" + classifierRemotable.toString());
            this.classifierModel = classifierRemotable.toString();
            loadClassifier(classifierRemotable);
        }
        Cursor c = dc.getData();
        int colunas = c.getColumnCount();
        Instance instance = new DenseInstance(colunas - 2);
        ArrayList<String> values = new ArrayList<String>();
        ArrayList<Attribute> atts = new ArrayList<Attribute>();
        if (c.moveToFirst()) {
            for (int i = 1; i <= colunas - 2; i++) {
                String feature = c.getColumnName(i);
                String value = c.getString(i);
                Attribute attribute;
                if (feature.equals(DatabaseManager.InputSize)) {
                    values.add("" + InputSize);
                    attribute = new Attribute(DatabaseManager.InputSize);
                } else {
                    String[] strings = populateAttributes(i);
                    ArrayList<String> attValues = new ArrayList<String>(Arrays.asList(strings));
                    attribute = new Attribute(feature, attValues);
                    if (value != null) {
                        values.add(value);
                    }
                }
                atts.add(attribute);
            }
            Instances instances = new Instances("header", atts, atts.size());
            instances.setClassIndex(instances.numAttributes() - 1);
            instance.setDataset(instances);
            for (int i = 0; i < atts.size(); i++) {
                if (i == 9) {
                    instance.setMissing(atts.get(9));
                } else if (atts.get(i).name().equals(DatabaseManager.InputSize)) {
                    instance.setValue(atts.get(i), InputSize);
                } else {
                    instance.setValue(atts.get(i), values.get(i));
                }
            }
            double value = -1;
            value = classifier.distributionForInstance(instance)[0];
            Log.d("classificacao", instance.toString() + " classifiquei com o seguinte valor" + value);
            resp = (0.7 <= value);
            if (resp) {
                Log.d("classificacao", "sim");
                Log.d("Finalizado", "classifiquei " + instance.toString() + " com sim");
            } else {
                Log.d("classificacao", "nao");
                Log.d("Finalizado", "classifiquei " + instance.toString() + " com nao");
            }
        }
    } catch (Exception e) {
        e.printStackTrace();
        Log.e("sqlLite", e.getMessage());
        Log.e("sqlLite", "Causa: " + e.getCause());
    }
    return resp;
}

---

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 {// ww w. ja  va 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;

}

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