Example usage for weka.core Instance setValue

List of usage examples for weka.core Instance setValue

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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:adams.opt.optimise.genetic.fitnessfunctions.AttributeSelection.java

License:Open Source License

public double evaluate(OptData opd) {
    init();/*from   www .j  a  v a2 s .  c  o  m*/
    int cnt = 0;
    int[] weights = getWeights(opd);
    Instances newInstances = new Instances(getInstances());
    for (int i = 0; i < getInstances().numInstances(); i++) {
        Instance in = newInstances.instance(i);
        cnt = 0;
        for (int a = 0; a < getInstances().numAttributes(); a++) {
            if (a == getInstances().classIndex())
                continue;
            if (weights[cnt++] == 0) {
                in.setValue(a, 0);
            } else {
                in.setValue(a, in.value(a));
            }
        }
    }
    Classifier newClassifier = null;

    try {
        newClassifier = (Classifier) OptionUtils.shallowCopy(getClassifier());
        // evaluate classifier on data
        Evaluation evaluation = new Evaluation(newInstances);
        evaluation.crossValidateModel(newClassifier, newInstances, getFolds(),
                new Random(getCrossValidationSeed()));

        // obtain measure
        double measure = 0;
        if (getMeasure() == Measure.ACC)
            measure = evaluation.pctCorrect();
        else if (getMeasure() == Measure.CC)
            measure = evaluation.correlationCoefficient();
        else if (getMeasure() == Measure.MAE)
            measure = evaluation.meanAbsoluteError();
        else if (getMeasure() == Measure.RAE)
            measure = evaluation.relativeAbsoluteError();
        else if (getMeasure() == Measure.RMSE)
            measure = evaluation.rootMeanSquaredError();
        else if (getMeasure() == Measure.RRSE)
            measure = evaluation.rootRelativeSquaredError();
        else
            throw new IllegalStateException("Unhandled measure '" + getMeasure() + "'!");
        measure = getMeasure().adjust(measure);

        return (measure);
        // process fitness

    } catch (Exception e) {
        getLogger().log(Level.SEVERE, "Error evaluating", e);
    }

    return 0;
}

From source file:adams.opt.optimise.genetic.fitnessfunctions.AttributeSelection.java

License:Open Source License

/**
 * Callback for best measure so far//from  w w w. j  ava  2 s .  c o m
 */
@Override
public void newBest(double val, OptData opd) {
    int cnt = 0;
    int[] weights = getWeights(opd);
    Instances newInstances = new Instances(getInstances());
    for (int i = 0; i < getInstances().numInstances(); i++) {
        Instance in = newInstances.instance(i);
        cnt = 0;
        for (int a = 0; a < getInstances().numAttributes(); a++) {
            if (a == getInstances().classIndex())
                continue;
            if (weights[cnt++] == 0) {
                in.setValue(a, 0);
            } else {
                in.setValue(a, in.value(a));
            }
        }
    }
    try {
        File file = new File(getOutputDirectory().getAbsolutePath() + File.separator
                + Double.toString(getMeasure().adjust(val)) + ".arff");
        file.createNewFile();
        Writer writer = new BufferedWriter(new FileWriter(file));
        Instances header = new Instances(newInstances, 0);

        // remove filter setup
        Remove remove = new Remove();
        remove.setAttributeIndices(getRemoveAsString(weights));
        remove.setInvertSelection(true);

        header.setRelationName(OptionUtils.getCommandLine(remove));

        writer.write(header.toString());
        writer.write("\n");
        for (int i = 0; i < newInstances.numInstances(); i++) {
            writer.write(newInstances.instance(i).toString());
            writer.write("\n");
        }
        writer.flush();
        writer.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:anndl.Anndl.java

private void ClassifyInstance(String fileinput) throws Exception {
    System.out.println("Membaca File Model ..");
    Classifier cls = (Classifier) weka.core.SerializationHelper.read(fileinput);

    System.out.println("Masukan Data yang ingin diklasifikasikan, pisahkan dengan spasi :");
    String testset = scanner.nextLine();
    testset = "1 1 0";
    String[] exploded = testset.split(" ");

    Instance newinstance = new Instance(exploded.length);
    for (int i = 0; i < exploded.length; i++) {
        String exploded1 = exploded[i];
        System.out.println(exploded[i] + ";");

        newinstance.setValue(i, Integer.valueOf(exploded[i]));
    }/*from   w  ww . jav  a 2  s  .c o m*/

    System.out.println("Melakukan klasifikasi ... ");
    double result = cls.classifyInstance(newinstance);
    System.out.println("Data ini:");
    System.out.println(testset);
    System.out.println("memiliki label :");
    System.out.println(newinstance.value(newinstance.classIndex()));
}

From source file:ant.Game.java

private char keyFromWeka(int classifier) {

    char key = W;

    Instance i = new Instance(m_ant.getNumAttributes() + 1);
    i.setDataset(m_data);//w  ww.  j av  a 2 s .c om
    for (int j = 0; j < i.numAttributes() - 1; ++j) {
        i.setValue(new Attribute(Integer.toString(j), j), see(j));
    }

    i.setClassValue(1);
    double p = -1;
    try {
        p = m_wrapper10x1.classifyInstance(i);
    } catch (Exception ex) {
        Logger.getLogger(Game.class.getName()).log(Level.SEVERE, null, ex);
    }

    switch ((int) p) {
    case 0:
        key = W;
        break;
    case 1:
        key = A;
        break;
    case 2:
        key = S;
        break;
    case 3:
        key = D;
        break;
    default:
        System.err.println("Unexpected classifier output!");
        break;
    }

    return key;
}

From source file:at.ac.tuwien.ifs.myluceneanalyzers.fa.algorithm.PersianDictionaryCountCompoundWord.java

/**
 * Same as {@link DictionaryCompoundWordTokenFilter#decompose()} except if it found a match, it moves the pointer to the end of it
 * @throws Exception //from ww w  .  j  a  v  a  2 s .c o m
 */
public List<String> decompose(String token) throws Exception {
    List<String> result = new ArrayList<String>();
    List<String> bestTokens = new ArrayList<String>();

    if (!dictionary.contains(token)) {
        if (!dictionary.contains(stem(token))) {
            final int len = token.length();
            int i = 0;
            List<ArrayList<String>> listMatchTokens = new ArrayList<ArrayList<String>>();
            for (int j = this.minSubwordSize; j <= len - this.minSubwordSize; ++j) {
                if (i + j > len) {
                    break;
                }
                if (dictionary.contains(token.substring(i, j))
                        && dictionary.contains(token.substring(j, len))) {
                    String matchToken1 = token.substring(i, j);
                    String matchToken2 = token.substring(j, len);
                    ArrayList<String> tokens = new ArrayList<String>();
                    tokens.add(matchToken1);
                    tokens.add(matchToken2);
                    listMatchTokens.add(tokens);
                }
            }

            Double maxcountavg = mapWordCount.get(token.toString());
            for (ArrayList<String> matchTokens : listMatchTokens) {
                List<Double> wordCounts = new ArrayList<Double>();
                for (String matchToken : matchTokens) {
                    Double wordCount = mapWordCount.get(matchToken);
                    if (wordCount == null)
                        wordCount = 0.0;

                    wordCounts.add(wordCount);
                }

                //mean
                double sum = 0.0;
                for (Double a : wordCounts)
                    sum += a;
                Double countmean = (sum / matchTokens.size());
                //var
                double temp = 0;
                for (Double a : wordCounts)
                    temp += (countmean - a) * (countmean - a);
                Double countvar = Math.sqrt((temp / matchTokens.size()));
                //harmmean
                Double countharmmean = 0.0;
                if ((wordCounts.get(0) != 0) && (wordCounts.get(1) != 0))
                    countharmmean = ((2 * wordCounts.get(0) * wordCounts.get(1))
                            / (wordCounts.get(0) + wordCounts.get(1)));

                //check with the max
                if (maxcountavg <= countmean) {
                    bestTokens.clear();
                    for (String matchToken : matchTokens) {
                        bestTokens.add(matchToken);
                    }
                    bestTokens.add(String.valueOf((int) Math.floor(countmean)));
                    bestTokens.add(String.valueOf((int) Math.floor(countvar)));
                    bestTokens.add(String.valueOf((int) Math.floor(countharmmean)));
                    maxcountavg = countmean;
                }
            }
        }
    }
    if (bestTokens.size() > 0) {
        //check the classifier
        //Evaluation eTest = new Evaluation(instances);
        //eTest.evaluateModel(cls, instances);
        //String strSummary = eTest.toSummaryString();
        //System.out.println(strSummary);

        Instance instance = new Instance(2);
        int harmmean = Integer.valueOf(bestTokens.get(bestTokens.size() - 1));
        instance.setValue(attributeHermmean, harmmean);
        instances.add(instance);

        double value = cls.classifyInstance(instances.instance(0));
        String prediction = instances.classAttribute().value((int) value);

        if (prediction.equals("1")) {
            result = bestTokens;
        }
    }

    return result;
}

From source file:at.aictopic1.sentimentanalysis.machinelearning.impl.TwitterClassifer.java

public Integer classify(Tweet[] tweets) {
    // TEST//from w  w w .  ja  v  a  2s  . c  om

    // Generate two tweet examples
    Tweet exOne = new Tweet("This is good and fantastic");
    exOne.setPreprocessedText("This is good and fantastic");
    Tweet exTwo = new Tweet("Horribly, terribly bad and more");
    exTwo.setPreprocessedText("Horribly, terribly bad and more");
    Tweet exThree = new Tweet(
            "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh.");
    exThree.setPreprocessedText(
            "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh.");
    Tweet exFour = new Tweet("bad hate worst sick");
    exFour.setPreprocessedText("bad hate worst sick");
    tweets = new Tweet[] { exOne, exTwo, exThree, exFour };
    // TEST

    // Load model
    //        loadModel();
    // Convert Tweet to Instance type 
    // Get String Data
    // Create attributes for the Instances set
    Attribute twitter_id = new Attribute("twitter_id");
    //        Attribute body = new Attribute("body");

    FastVector classVal = new FastVector(2);
    classVal.addElement("pos");
    classVal.addElement("neg");

    Attribute class_attr = new Attribute("class_attr", classVal);

    // Add them to a list
    FastVector attrVector = new FastVector(3);
    //        attrVector.addElement(twitter_id);
    //        attrVector.addElement(new Attribute("body", (FastVector) null));
    //        attrVector.addElement(class_attr);

    // Get the number of tweets and then create predictSet
    int numTweets = tweets.length;
    Enumeration structAttrs = dataStructure.enumerateAttributes();

    //        ArrayList<Attribute> attrList = new ArrayList<Attribute>(dataStructure.numAttributes());
    while (structAttrs.hasMoreElements()) {
        attrVector.addElement((Attribute) structAttrs.nextElement());
    }
    Instances predictSet = new Instances("predictInstances", attrVector, numTweets);
    //        Instances predictSet = new Instances(dataStructure);
    predictSet.setClassIndex(2);

    // init prediction
    double prediction = -1;

    System.out.println("PredictSet matches source structure: " + predictSet.equalHeaders(dataStructure));

    System.out.println("PredSet struct: " + predictSet.attribute(0));
    System.out.println("PredSet struct: " + predictSet.attribute(1));
    System.out.println("PredSet struct: " + predictSet.attribute(2));
    // Array to return predictions 
    //double[] tweetsClassified = new double[2][numTweets];
    //List<Integer, Double> tweetsClass = new ArrayList<Integer, Double>(numTweets);
    for (int i = 0; i < numTweets; i++) {
        String content = (String) tweets[i].getPreprocessedText();

        System.out.println("Tweet content: " + content);

        //            attrList
        Instance tweetInstance = new Instance(predictSet.numAttributes());

        tweetInstance.setDataset(predictSet);

        tweetInstance.setValue(predictSet.attribute(0), i);
        tweetInstance.setValue(predictSet.attribute(1), content);
        tweetInstance.setClassMissing();

        predictSet.add(tweetInstance);

        try {
            // Apply string filter
            StringToWordVector filter = new StringToWordVector();

            filter.setInputFormat(predictSet);
            Instances filteredPredictSet = Filter.useFilter(predictSet, filter);

            // Apply model
            prediction = trainedModel.classifyInstance(filteredPredictSet.instance(i));
            filteredPredictSet.instance(i).setClassValue(prediction);
            System.out.println("Classification: " + filteredPredictSet.instance(i).toString());
            System.out.println("Prediction: " + prediction);

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

    return 0;
}

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

License:Apache License

protected void addLastXWekaInstances(Instances instances, int numberOfInstances, List<Object> linkedObjects) {
    // TODO: use ResultEntry instead of List<Object>
    if (numberOfInstances <= 0) {
        return;//ww  w.j a  va 2s.  c om
    }
    List<DActivationPatternPackage> dActivationPatternPackages = mAnalysisChain.getFinalLayers().get(0)
            .getResultAnalysisPackage().getActivationPatternPackages();
    int counter = 0;
    for (DActivationPatternPackage dActivationPatternPackage : dActivationPatternPackages) {
        DActivationPattern activationPatternTemp = dActivationPatternPackage.getActivationPatterns().get(0);
        if (counter > 0) {
            throw new RuntimeException("More than one DActivationPatternPackage found!");
        }
        counter++;

        for (int i = numberOfInstances - 1; i >= 0; i--) {
            DActivationPattern activationPattern = dActivationPatternPackage.getActivationPatterns()
                    .get(dActivationPatternPackage.getActivationPatterns().size() - 1 - i);
            Instance instance = new Instance(activationPatternTemp.getRawPattern().length + 1);

            for (int j = 0; j < activationPattern.getRawPattern().length; j++) {
                instance.setValue(j, activationPattern.getRawPattern()[j]);
            }
            instances.add(instance);
            instance.setDataset(instances);

            if (linkedObjects != null) {
                DBasicMetaData d = activationPattern.getMetaData();
                if (d instanceof DObjectLinkMetaData) {
                    Object o = ((DObjectLinkMetaData) d).getLinkedObject();
                    linkedObjects.add(o);
                } else {
                    throw new IllegalArgumentException(
                            "Wrong metadata type attached! Must be DObjectLinkMetaData!");
                }
            }
        }

    }
}

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

License:Apache License

public Instances getWekaInstances() {
    Instances instances = null;/*from  w  w  w. j a  v a2  s .c  o  m*/
    List<DActivationPatternPackage> dActivationPatternPackages = mAnalysisChain.getFinalLayers().get(0)
            .getResultAnalysisPackage().getActivationPatternPackages();
    int counter = 0;
    for (DActivationPatternPackage dActivationPatternPackage : dActivationPatternPackages) {
        if (counter > 0) {
            throw new RuntimeException("More than one DActivationPatternPackage found!");
        }
        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(mAnalysisConfig.getApplicationAnalysisName(), 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);
                    instance.setClassValue(label);
                    break;
                }
            }

            instances.add(instance);
        }

    }
    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;//from w  w w .  j av  a2 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//from w  ww. ja  v a2 s. c o 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);
}