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:NaiveBayes.NaiveBayes.java

public String classify(Instances init) {
    int nAttributes = init.numAttributes();
    Instance ins = new DenseInstance(nAttributes);
    Instances newData = init;/*from   w  ww .  j  a va  2s  .c o  m*/
    Scanner s = new Scanner(System.in);
    Double in;
    System.out.println("Jumlah Atribut : " + (nAttributes - 1));

    for (int i = 1; i <= nAttributes - 1; i++) {
        //Attribute a = train.attribute(i - 1);
        System.out.print("Attribute " + i + " : ");
        in = s.nextDouble();
        ins.setValue(i, in);
        //newIns[i] = in;
    }
    newData.add(ins);
    double nomorKelas = classifyInstance(newData.lastInstance());
    return init.attribute(init.numAttributes() - 1).value((int) nomorKelas);
}

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From source file:net.sf.bddbddb.OrderClassifier.java

License:LGPL

public double importance(weka.core.Attribute attribute, String attrValue) {//, String classValue){

    int count = 0;
    int goodCount = 0, badCount = 0;
    List newInstances = new LinkedList();

    for (Iterator it = orders.iterator(); it.hasNext();) {
        Instance instance = (Instance) it.next();
        if (//!instance.stringValue(instance.classIndex()).equals(classValue) ||
        !instance.stringValue(attribute).equals(attrValue))
            continue;

        if (goodClusters.contains(instance.stringValue(instance.classIndex())))
            ++goodCount;//from  w  w  w .  j a va 2  s.c om
        else
            ++badCount;

        Instance newInstance = new Instance(instance);
        newInstance.setDataset(instance.dataset());
        newInstances.add(newInstance);
    }
    goodCount *= attrOptions.size() - 1;
    badCount *= attrOptions.size() - 1;
    for (Iterator it = newInstances.iterator(); it.hasNext();) {
        Instance instance = (Instance) it.next();
        /*      if(//!instance.stringValue(instance.classIndex()).equals(classValue) || 
         !instance.stringValue(attribute).equals(attrValue)) continue;
         */

        String classValue = instance.stringValue(instance.classIndex());
        FastVector newOptions = new FastVector();
        newOptions.appendElements(attrOptions);
        newOptions.removeElementAt(newOptions.indexOf(instance.stringValue(attribute)));
        //int index = Math.abs(LearnedOrder.randomNumGen.nextInt()) % newOptions.size();
        int index = 0;
        while (index < newOptions.size()) {
            instance.setValue(attribute, attrOptions.indexOf(newOptions.elementAt(index)));
            String value = classify(instance);
            if (goodClusters.contains(classValue)) {
                if (goodClusters.contains(value))
                    --goodCount;
            } else if (!goodClusters.contains(classValue)) {
                if (!goodClusters.contains(value))
                    --badCount;
            }
            ++index;
        }
        //if(value.equals(classValue)) --count;
    }

    count = goodCount - badCount;
    count /= attrOptions.size() - 1;

    double importance = ((double) count) / newInstances.size();
    if (Double.isNaN(importance))
        return 0;
    return importance;
}

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From source file:net.sf.bddbddb.OrderClassifier.java

License:LGPL

public void load(BufferedReader in) {
    try {/*from w  ww . ja v a 2 s. c  o m*/
        String strClusts = in.readLine();
        setNumClasses(Integer.parseInt(strClusts));

        for (;;) {
            String line = in.readLine();
            if (line == null)
                break;
            Instance instance = new Instance(attributes.size());
            StringTokenizer st = new StringTokenizer(line, ",");
            int i = 0;
            while (st.hasMoreTokens()) {
                String str = st.nextToken();
                //   System.out.println("attr " + attributes.elementAt(i) + " val: " + str);
                instance.setValue((weka.core.Attribute) attributes.elementAt(i), str);
                ++i;
            }
            line = in.readLine();
            instance = new MyInstance(instance, Long.parseLong(line));
            orders.add(instance);
        }
        buildClassifier();
    } catch (IOException e) {
        e.printStackTrace();
        Assert.UNREACHABLE("Could not load instances");
    } catch (Exception e) {
        e.printStackTrace();
        Assert.UNREACHABLE("Could not build classifier");
    }
}

---

From source file:net.sf.bddbddb.OrderClassifier.java

License:LGPL

public Instance makeInstance(String varOrder) {
    Instance instance = new MyInstance(attributes.size());
    for (int i = 0; i < attributes.size() - 1; i++) {
        instance.setValue((weka.core.Attribute) attributes.elementAt(i), ">");
    }/*from w w  w.  j  a  v a2s  .  c  om*/
    StringTokenizer st = new StringTokenizer(varOrder, "_");
    List lefts = new LinkedList();
    while (st.hasMoreTokens()) {
        String pdomain = st.nextToken();
        // System.out.println("pdomain: " + pdomain);
        StringTokenizer st2 = new StringTokenizer(pdomain, "x");
        if (st2.countTokens() > 1) {
            List interLefts = new LinkedList();
            while (st2.hasMoreTokens()) {
                String idomain = st2.nextToken();
                //   System.out.println("idomain: " + idomain);

                BDDDomain d = rule.solver.getBDDDomain(idomain);
                if (!domainSet.contains(d))
                    continue;
                Collection pairs = domainToAttrPairs.getValues(d);
                //  System.out.println("interlefts: " + interLefts);
                //  System.out.println("pairs: " + pairs);

                pair(instance, interLefts, pairs, "=", true);
                interLefts.addAll(pairs);

                pair(instance, lefts, pairs, "<", false);
                lefts.addAll(pairs);
            }

            continue;
        }
        BDDDomain d = rule.solver.getBDDDomain(pdomain);
        if (!domainSet.contains(d))
            continue;
        Collection pairs = domainToAttrPairs.getValues(d);
        pair(instance, lefts, pairs, "<", false);
        lefts.addAll(pairs);

    }
    // System.out.print("attributes:");
    /*   for(int i = 0; i < attributes.size(); i++){
     System.out.print(" " + attributes.elementAt(i));
     }
     */
    //  System.out.println();
    // System.out.println("instance: " + instance);
    return instance;
}

---

From source file:net.sf.bddbddb.OrderClassifier.java

License:LGPL

private void pair(Instance instance, List lefts, Collection pairs, String value, boolean reverse) {
    for (Iterator it = lefts.iterator(); it.hasNext();) {
        Pair left = (Pair) it.next();// w w  w .  j a v  a2 s .co m
        for (Iterator jt = pairs.iterator(); jt.hasNext();) {
            Pair right = (Pair) jt.next();
            if (right.equals(left))
                continue;
            Pair p = new Pair(left, right);
            weka.core.Attribute a = new MyAttribute(p, attrOptions);
            int index = attributes.indexOf(a);
            if (index == -1) {
                if (reverse) {
                    Pair r = new Pair(right, left);
                    a = new MyAttribute(r, attrOptions);
                    index = attributes.indexOf(a);
                } else
                    continue;
            }

            Assert._assert(index != -1, " no attribute for " + p + " found. attributes: " + attributes);
            //  System.out.println("Setting " + a.toString() + " to " + value);
            instance.setValue((weka.core.Attribute) attributes.elementAt(index), value);
        }
    }
}

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From source file:net.sf.jclal.activelearning.oracle.ConsoleHumanOracle.java

License:Open Source License

/**
 * Method for the specific case of a multi-label dataset.
 *
 * @param queryStrategy The query strategy to use
 *//*w  w  w.  j av a 2 s .c om*/
private void labelMultiLabelInstances(IQueryStrategy queryStrategy) {

    //Object to read from the console
    Scanner scanner = new Scanner(new BufferedInputStream(System.in));

    AbstractMultiLabelQueryStrategy multiLabelQueryStrategy = (AbstractMultiLabelQueryStrategy) queryStrategy;

    ArrayList<Integer> selected = multiLabelQueryStrategy.getSelectedInstances();

    ArrayList<String> labels = new ArrayList<String>(
            multiLabelQueryStrategy.getLabelsMetaData().getLabelNames());

    //For each selected instance
    for (int i : selected) {

        //Ask to the oracle about the class of the instance
        Instance instance = multiLabelQueryStrategy.getUnlabelledData().instance(i);

        System.out.println("\nWhat are the labels of this multi-label instance?");

        System.out.println("Instance:" + instance.toString() + "\n");

        StringTokenizer line;

        do {

            System.out.println("IndexLabels: LabelName");

            int index = 0;

            for (String label : labels) {

                System.out.println((index++) + "-" + label);
            }

            System.out.println(
                    "\n Type the indexes of the labels that the instance belongs separated by a colon");

            line = new StringTokenizer(scanner.next(), ",");

        } while (line.countTokens() == 0);

        //Reset the labels
        for (int labelIndex = 0; labelIndex < multiLabelQueryStrategy.getNumLabels(); labelIndex++) {
            instance.setValue(multiLabelQueryStrategy.getLabelIndices()[labelIndex], 0);
        }

        while (line.hasMoreTokens()) {

            int labelIndex = Integer.valueOf(line.nextToken());

            Attribute att = instance.dataset().attribute(labels.get(labelIndex));

            instance.setValue(att, 1);

        }
        System.out.println();
    }

}

---

From source file:net.sf.markov4jmeter.behaviormodelextractor.extraction.transformation.clustering.AbstractClusteringStrategy.java

License:Apache License

/**
 * Returns a instance as vector based on a single BehaviorModelAbsolute.
 * //from ww w.j  a  v  a2  s  .com
 * @param behaviorModelsAbsolute
 * @return a instance, transitions of behaviorModelAbsolute as input vector
 */
protected Instance getInstance(BehaviorModelAbsolute behaviorModelAbsolute, Instances instances) {

    // create new instance with size n x (n + 1)
    int nrVertices = behaviorModelAbsolute.getVertices().size() - 1;
    Instance instance = new Instance((nrVertices * (nrVertices + 1)) + 1);
    final List<Vertex> vertices = behaviorModelAbsolute.getVertices();
    int indexOfAttribute = 0;
    for (final Vertex srcVertex : vertices) {
        if (srcVertex.getUseCase() != null) { // no final state
            // for each transition set the value of the instance vector
            for (final Vertex dstVertex : vertices) {
                final UseCase srcUseCase = srcVertex.getUseCase();
                final UseCase dstUseCase = dstVertex.getUseCase();
                final String srcUseCaseId = srcUseCase.getId();

                // if dstUseCase is null, its vertex denotes the final state
                // (no ID);
                final String dstUseCaseId = (dstUseCase != null) ? dstUseCase.getId() : null;
                Transition transition = this.findTransitionByUseCaseIDs(behaviorModelAbsolute, srcUseCaseId,
                        dstUseCaseId);

                if (transition != null) {
                    instance.setValue(indexOfAttribute, transition.getValue());
                } else {
                    instance.setValue(indexOfAttribute, 0);
                }
                indexOfAttribute++;
            }
        } else {
            continue; // skip final state ("$");
        }
    }

    instance.setDataset(instances);

    // set attribute for classification
    instance.setValue(indexOfAttribute, behaviorModelAbsolute.getTransactionType());
    return instance;
}

---

From source file:Neural_Network.NuralN.java

private static Instance toInstance(String[] instanceData) {
    try {// w ww  .  j a v  a2 s.com
        loadDataTemplate();
        Instance tempInstance = new Instance(temp.numAttributes());
        tempInstance.setDataset(temp);

        for (int index = 0; index < instanceData.length; index++) {
            tempInstance.setValue(index, instanceData[index]);
        }
        temp.add(tempInstance);
    } catch (Exception e) {
        System.err.println(e.toString());
    }
    return temp.lastInstance();
}

---

From source file:newdtl.NewJ48.java

/**
 * Creates a J48 tree./*from   w  w w  .j  a v a2 s . c  o  m*/
 *
 * @param data the training data
 * @exception Exception if tree failed to build
 */
private void makeTree(Instances data) throws Exception {

    // Mengecek apakah tidak terdapat instance dalam node ini
    if (data.numInstances() == 0) {
        splitAttribute = null;
        label = DOUBLE_MISSING_VALUE;
        classDistributions = new double[data.numClasses()];
        isLeaf = true;
    } else {
        // Mencari Gain Ratio maksimum
        double[] gainRatios = new double[data.numAttributes()];
        double[] thresholds = new double[data.numAttributes()];

        Enumeration attEnum = data.enumerateAttributes();
        while (attEnum.hasMoreElements()) {
            Attribute att = (Attribute) attEnum.nextElement();
            double[] result = computeGainRatio(data, att);
            gainRatios[att.index()] = result[0];
            thresholds[att.index()] = result[1];
        }

        splitAttribute = data.attribute(maxIndex(gainRatios));

        if (splitAttribute.isNumeric()) {
            splitThreshold = thresholds[maxIndex(gainRatios)];
        } else {
            splitThreshold = Double.NaN;
        }

        classDistributions = new double[data.numClasses()];
        for (int i = 0; i < data.numInstances(); i++) {
            Instance inst = (Instance) data.instance(i);
            classDistributions[(int) inst.classValue()]++;
        }

        // Membuat daun jika Gain Ratio-nya 0
        if (Double.compare(gainRatios[splitAttribute.index()], 0) == 0) {
            splitAttribute = null;

            label = maxIndex(classDistributions);
            classAttribute = data.classAttribute();
            isLeaf = true;
        } else {
            // Mengecek jika ada missing value
            if (isMissing(data, splitAttribute)) {
                // cari modus
                int index = modusIndex(data, splitAttribute);

                // ubah data yang punya missing value
                Enumeration dataEnum = data.enumerateInstances();
                while (dataEnum.hasMoreElements()) {
                    Instance inst = (Instance) dataEnum.nextElement();
                    if (inst.isMissing(splitAttribute)) {
                        inst.setValue(splitAttribute, splitAttribute.value(index));
                    }
                }
            }

            // Membuat tree baru di bawah node ini
            Instances[] splitData;
            if (splitAttribute.isNumeric()) {
                splitData = splitData(data, splitAttribute, splitThreshold);
                children = new NewJ48[2];
                for (int j = 0; j < 2; j++) {
                    children[j] = new NewJ48();
                    children[j].makeTree(splitData[j]);
                }
            } else {
                splitData = splitData(data, splitAttribute);
                children = new NewJ48[splitAttribute.numValues()];
                for (int j = 0; j < splitAttribute.numValues(); j++) {
                    children[j] = new NewJ48();
                    children[j].makeTree(splitData[j]);
                }
            }
            isLeaf = false;
        }
    }
}

---

From source file:ocr.ARFFSymbolFilter.java

License:Apache License

public static Instance constructInstance(final Symbol s, final double alph) {
    final StringTokenizer st = new StringTokenizer(constructStringInstance(s, alph), " ");
    ARFFSymbolFilter.na = st.countTokens() / 2;
    if (ARFFSymbolFilter.na == 0) {
        return null;
    }/*from   w  w  w.ja  va2s  .  c  om*/
    final Instance instance = new Instance(ARFFSymbolFilter.na + 1);
    for (int i = 0; i < ARFFSymbolFilter.na; ++i) {
        st.nextToken();
        instance.setValue(i, Double.parseDouble(st.nextToken()));
    }
    return instance;
}

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