Example usage for weka.core Instances classAttribute

Introduction

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

Prototype

publicAttribute classAttribute() 

Source Link

Document

Returns the class attribute.

Usage

From source file:moa.classifiers.macros.TACNB.java

License:Open Source License

public void initHeader(Instances dataset) {
    int numLabels = this.numOldLabelsOption.getValue();
    Attribute target = dataset.classAttribute();

    List<String> possibleValues = new ArrayList<String>();
    int n = target.numValues();
    for (int i = 0; i < n; i++) {
        possibleValues.add(target.value(i));
    }//from  ww w .  j  a  v  a2s .  co m

    ArrayList<Attribute> attrs = new ArrayList<Attribute>(numLabels + dataset.numAttributes());
    for (int i = 0; i < numLabels; i++) {
        attrs.add(new Attribute(target.name() + "_" + i, possibleValues));
    }
    for (int i = 0; i < dataset.numAttributes(); i++) {
        attrs.add((Attribute) dataset.attribute(i).copy());
    }
    this.header = new Instances("extended_" + dataset.relationName(), attrs, 0);
    this.header.setClassIndex(numLabels + dataset.classIndex());
}

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From source file:moa.clusterer.outliers.Sieve.java

License:Apache License

/**
 * Todo - move to utils package/*  ww w  .  ja v a 2s .co m*/
 * @param v
 * @param h 
 */
public final static void safeNormalize(double[] v, Instances h) {
    int outlierIdx = h.classAttribute().indexOfValue(AbstractNovelClassClassifier.OUTLIER_LABEL_STR);
    if (outlierIdx < 0) {
        outlierIdx = h.numAttributes();
    }
    double sum = 0;
    for (int i = 0; i < v.length && i < outlierIdx; ++i) {
        sum += v[i];
    }
    if (sum != 0) {
        for (int i = 0; i < v.length && i < outlierIdx; ++i) {
            v[i] /= sum;
        }
    }
}

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From source file:moa.clusterer.outliers.Sieve.java

License:Apache License

/**
 * Todo - move to utils package/*from   ww  w  .ja  va  2s. c  o m*/
 * @param v
 * @param h
 * @return 
 */
public final static int maxNonOutlier(double[] v, Instances h) {
    int outlierIdx = h.classAttribute().indexOfValue(AbstractNovelClassClassifier.OUTLIER_LABEL_STR);
    if (outlierIdx < 0) {
        outlierIdx = h.numAttributes();
    }
    int maxIdx = 0;
    double maxVal = 0;
    for (int i = 0; i < v.length && i < outlierIdx; ++i) {
        if (v[i] > maxVal) {
            maxIdx = i;
            maxVal = v[i];
        }
    }
    return maxIdx;
}

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From source file:moa.streams.ConceptDriftRealStream.java

License:Open Source License

@Override
public void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) {

    this.inputStream = (InstanceStream) getPreparedClassOption(this.streamOption);
    this.driftStream = (InstanceStream) getPreparedClassOption(this.driftstreamOption);
    this.random = new Random(this.randomSeedOption.getValue());
    numberInstanceStream = 0;//from w w w  .  j av  a  2  s.  c  o  m
    if (this.alphaOption.getValue() != 0.0) {
        this.widthOption.setValue((int) (1 / Math.tan(this.alphaOption.getValue() * Math.PI / 180)));
    }

    // generate header
    Instances first = this.inputStream.getHeader();
    Instances second = this.driftStream.getHeader();
    FastVector newAttributes = new FastVector();
    for (int i = 0; i < first.numAttributes() - 1; i++) {
        newAttributes.addElement(first.attribute(i));
    }
    for (int i = 0; i < second.numAttributes() - 1; i++) {
        newAttributes.addElement(second.attribute(i));
    }

    Attribute classLabels;
    if (first.numClasses() < second.numClasses()) {
        classLabels = second.classAttribute();
    } else {
        classLabels = first.classAttribute();
    }
    newAttributes.addElement(classLabels);

    this.streamHeader = new InstancesHeader(
            new Instances(getCLICreationString(InstanceStream.class), newAttributes, 0));
    this.streamHeader.setClassIndex(this.streamHeader.numAttributes() - 1);
    restart();

}

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From source file:myclassifier.MyC45.java

/**
 * Method building ID3 tree.//from  ww  w.j  a  v a  2 s.c  om
 *
 * @param data the training data
 * @exception Exception if decision tree can't be built successfully
 */
private void makeTree(Instances data) throws Exception {

    // Check if no instances have reached this node.
    if (data.numInstances() == 0) {
        m_Attribute = null;
        m_ClassValue = -1; //Instance.missingValue();
        m_Distribution = new double[data.numClasses()];
        return;
    }

    // Compute attribute with maximum information gain.
    double[] gainRatios = new double[data.numAttributes()];
    Enumeration attEnum = data.enumerateAttributes();
    while (attEnum.hasMoreElements()) {
        Attribute att = (Attribute) attEnum.nextElement();
        gainRatios[att.index()] = computeGainRatio(data, att);
    }
    m_Attribute = data.attribute(Utils.maxIndex(gainRatios));

    // Make leaf if information gain is zero. 
    // Otherwise create successors.

    if (Utils.eq(gainRatios[m_Attribute.index()], 0)) {
        m_Attribute = null;
        m_Distribution = new double[data.numClasses()];
        Enumeration instEnum = data.enumerateInstances();
        while (instEnum.hasMoreElements()) {
            Instance inst = (Instance) instEnum.nextElement();
            m_Distribution[(int) inst.classValue()]++;
        }
        Utils.normalize(m_Distribution);
        m_ClassValue = Utils.maxIndex(m_Distribution);
        m_ClassAttribute = data.classAttribute();
    } else {
        Instances[] splitData = splitData(data, m_Attribute);
        m_Successors = new MyC45[m_Attribute.numValues()];
        for (int j = 0; j < m_Attribute.numValues(); j++) {
            m_Successors[j] = new MyC45();
            m_Successors[j].makeTree(splitData[j]);
        }
    }
}

---

From source file:myclassifier.naiveBayes.java

public void Klasifikasi(String filename) throws Exception {
    // load unlabeled data and set class attribute
    Instances unlabeled = ConverterUtils.DataSource.read("unlabeled_" + filename);
    unlabeled.setClassIndex(unlabeled.numAttributes() - 1);
    // create copy
    Instances labeled = new Instances(unlabeled);
    // label instances
    for (int i = 0; i < unlabeled.numInstances(); i++) {
        double clsLabel = NBClassifier.classifyInstance(labeled.instance(i));
        labeled.instance(i).setClassValue(clsLabel);
    }//www.j a  v  a2  s  . com
    // save newly labeled data
    ConverterUtils.DataSink.write("labeled_" + filename, labeled);

    //print hasil
    System.out.println("Classification Result");
    System.out.println("# - actual - predicted - distribution");
    for (int i = 0; i < labeled.numInstances(); i++) {

        double pred = NBClassifier.classifyInstance(labeled.instance(i));
        double[] dist = NBClassifier.distributionForInstance(labeled.instance(i));
        System.out.print((i + 1) + " - ");
        System.out.print(labeled.instance(i).toString(labeled.classIndex()) + " - ");
        System.out.print(labeled.classAttribute().value((int) pred) + " - ");
        System.out.println(Utils.arrayToString(dist));
    }
}

---

From source file:myclassifier.wekaCode.java

public static void classifyUnseenData(String[] attributes, Classifier classifiers, Instances data)
        throws Exception {
    Instance newInstance = new Instance(data.numAttributes());
    newInstance.setDataset(data);//from w w  w . j  ava2 s .c  o m
    for (int i = 0; i < data.numAttributes() - 1; i++) {
        if (Attribute.NUMERIC == data.attribute(i).type()) {
            Double value = Double.valueOf(attributes[i]);
            newInstance.setValue(i, value);
        } else {
            newInstance.setValue(i, attributes[i]);
        }
    }

    double clsLabel = classifiers.classifyInstance(newInstance);
    newInstance.setClassValue(clsLabel);

    String result = data.classAttribute().value((int) clsLabel);

    System.out.println("Hasil Classify Unseen Data Adalah: " + result);
}

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From source file:myclusterer.WekaCode.java

public static void classifyUnseenData(String[] attributes, Clusterer clusterer, Instances data)
        throws Exception {
    Instance newInstance = new Instance(data.numAttributes());
    newInstance.setDataset(data);/*from   w  w w.  java 2  s.co  m*/
    for (int i = 0; i < data.numAttributes() - 1; i++) {
        if (Attribute.NUMERIC == data.attribute(i).type()) {
            Double value = Double.valueOf(attributes[i]);
            newInstance.setValue(i, value);
        } else {
            newInstance.setValue(i, attributes[i]);
        }
    }

    double clsLabel = clusterer.clusterInstance(newInstance);
    newInstance.setClassValue(clsLabel);

    String result = data.classAttribute().value((int) clsLabel);

    System.out.println("Hasil Classify Unseen Data Adalah: " + result);
}

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From source file:myID3.MyId3.java

/**
 * Construct the tree using the given instance
 * Find the highest attribute value which best at dividing the data
 * @param data Instance/*from   w ww. ja  va 2  s .c o  m*/
 */
public void buildTree(Instances data) {
    if (data.numInstances() > 0) {
        // Lets find the highest Information Gain!
        // First compute each information gain attribute
        double IG[] = new double[data.numAttributes()];
        Enumeration enumAttribute = data.enumerateAttributes();
        while (enumAttribute.hasMoreElements()) {
            Attribute attribute = (Attribute) enumAttribute.nextElement();
            IG[attribute.index()] = informationGain(data, attribute);
            // System.out.println(attribute.toString() + ": " + IG[attribute.index()]);
        }
        // Assign it as the tree attribute!
        currentAttribute = data.attribute(maxIndex(IG));
        //System.out.println(Arrays.toString(IG) + IG[currentAttribute.index()]);

        // IG = 0 then current node = leaf!
        if (Utils.eq(IG[currentAttribute.index()], 0)) {
            // Set the class value as the highest frequency of the class
            currentAttribute = null;
            classDistribution = new double[data.numClasses()];
            Enumeration enumInstance = data.enumerateInstances();
            while (enumInstance.hasMoreElements()) {
                Instance temp = (Instance) enumInstance.nextElement();
                classDistribution[(int) temp.classValue()]++;
            }
            Utils.normalize(classDistribution);
            classValue = Utils.maxIndex(classDistribution);
            classAttribute = data.classAttribute();
        } else {
            // Create another node from the current tree
            Instances[] splitData = splitDataByAttribute(data, currentAttribute);
            nodes = new MyId3[currentAttribute.numValues()];

            for (int i = 0; i < currentAttribute.numValues(); i++) {
                nodes[i] = new MyId3();
                nodes[i].buildTree(splitData[i]);
            }
        }
    } else {
        classAttribute = null;
        classValue = Utils.missingValue();
        classDistribution = new double[data.numClasses()];
    }
}

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From source file:myid3andc45classifier.Model.MyC45.java

public void makeMyC45Tree(Instances data) throws Exception {
    if (data.numInstances() == 0) {
        attribute = null;/*from   w  w w  . jav  a  2  s  .c o m*/
        label = Instance.missingValue();
        return;
    }
    //System.out.println("NEW");
    double[] infoGainRatios = new double[data.numAttributes()];
    Enumeration attEnum = data.enumerateAttributes();
    while (attEnum.hasMoreElements()) {
        Attribute att = (Attribute) attEnum.nextElement();
        if (!att.isNumeric())
            infoGainRatios[att.index()] = computeInfoGainRatio(data, att);
        else
            infoGainRatios[att.index()] = Double.NEGATIVE_INFINITY;
        //System.out.println(att.name() + " " + infoGainRatios[att.index()]);
    }

    // TODO: build the tree
    attribute = data.attribute(maxIndex(infoGainRatios));
    //System.out.println(infoGainRatios[maxIndex(infoGainRatios)]);
    // Make leaf if information gain is zero. 
    // Otherwise create successors.
    if (infoGainRatios[maxIndex(infoGainRatios)] <= epsilon
            || Double.isNaN(infoGainRatios[maxIndex(infoGainRatios)])) {
        attribute = null;
        double[] numClasses = new double[data.numClasses()];

        Enumeration instEnum = data.enumerateInstances();
        while (instEnum.hasMoreElements()) {
            Instance inst = (Instance) instEnum.nextElement();
            numClasses[(int) inst.classValue()]++;
        }

        label = maxIndex(numClasses);
        classAttribute = data.classAttribute();
    } else {
        classAttribute = data.classAttribute();
        Instances[] splitData = splitInstancesByAttribute(data, attribute);
        Instances[] distrData = splitInstancesByAttribute(data, data.classAttribute());
        distribution = new double[distrData.length];
        for (int j = 0; j < distribution.length; j++) {
            distribution[j] = distrData[j].numInstances();
        }
        successors = new MyC45[attribute.numValues()];
        for (int j = 0; j < attribute.numValues(); j++) {
            successors[j] = new MyC45();
            successors[j].buildClassifier(splitData[j]);
        }
    }
    // TODO: prune
    //pruneTree(data);
}

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