Example usage for weka.core Instance value

List of usage examples for weka.core Instance value

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Introduction

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

Prototype

public double value(Attribute att);

Source Link

Document

Returns an instance's attribute value in internal format.

Usage

From source file:decisiontree.MyID3.java

@Override
public double classifyInstance(Instance instance) throws Exception {
    if (instance.hasMissingValue()) {
        throw new Exception("Can't handle missing value(s)");
    }/*from   w w  w  .  j  a v a2 s.c  om*/
    if (splitAttr == null) {
        if (Utils.eq(leafValue, Double.NaN)) {
            return instance.value(classAttr);
        } else {
            return leafValue;
        }
    } else {
        return child[(int) instance.value(splitAttr)].classifyInstance(instance);
    }
}

From source file:decisiontree.MyID3.java

@Override
public double[] distributionForInstance(Instance instance) throws Exception {
    if (instance.hasMissingValue()) {
        throw new Exception("Can't handle missing value(s)");
    }/*  w w w.j ava 2  s. c  o m*/
    if (splitAttr == null) {
        return leafDist;
    } else {
        return child[(int) instance.value(splitAttr)].distributionForInstance(instance);
    }
}

From source file:decisiontree.MyID3.java

private Instances[] splitData(Instances data, Attribute att) {
    Instances[] splitData = new Instances[att.numValues()];
    for (int j = 0; j < att.numValues(); j++) {
        splitData[j] = new Instances(data, data.numInstances());
    }/*from ww w. j  a v  a  2 s  .co  m*/

    Enumeration instEnum = data.enumerateInstances();
    while (instEnum.hasMoreElements()) {
        Instance inst = (Instance) instEnum.nextElement();
        splitData[(int) inst.value(att)].add(inst);
    }
    for (Instances split : splitData) {
        split.compactify();
    }
    return splitData;
}

From source file:dewaweebtreeclassifier.Sujeong.java

public Instances[] splitInstancesOnAttribute(Instances data, Attribute attr) {
    Instances[] splitInstances = new Instances[attr.numValues()];

    for (int i = 0; i < attr.numValues(); i++) {
        splitInstances[i] = new Instances(data, data.numInstances());
    }//from   w  ww .  j av  a2s . com

    Enumeration enumInstance = data.enumerateInstances();
    while (enumInstance.hasMoreElements()) {
        Instance instance = (Instance) enumInstance.nextElement();
        splitInstances[(int) instance.value(attr)].add(instance);
    }

    for (int i = 0; i < attr.numValues(); i++) {
        splitInstances[i].compactify();
    }

    return splitInstances;
}

From source file:dewaweebtreeclassifier.Sujeong.java

public double classifyInstance(Instance instance) throws java.lang.Exception {
    if (filter != null) {
        filter.input(instance);// w w  w .j a  v  a  2s . com
        instance = filter.output();
    }
    if (bestAttr == null)
        return this.value;
    else {
        return children[(int) instance.value(bestAttr)].classifyInstance(instance);
    }
}

From source file:dewaweebtreeclassifier.veranda.VerandaTree.java

/**
 * //from  w  ww. j  av  a 2 s  .c  o m
 * @param instance
 * @return 
 * @throws java.lang.Exception 
 */
@Override
public double classifyInstance(Instance instance) throws Exception {
    if (mSplitAttribute == null) {
        return mClassValue;
    } else {
        return mChild[(int) instance.value(mSplitAttribute)].classifyInstance(instance);
    }
}

From source file:dewaweebtreeclassifier.veranda.VerandaTree.java

/**
 * //from  w  w  w .  ja  v a2s  .c  o  m
 * @param data
 * @param attr
 * @return 
 */
public Instances[] splitInstancesOnAttribute(Instances data, Attribute attr) {
    Instances[] splitInstances = new Instances[attr.numValues()];

    for (int i = 0; i < attr.numValues(); i++) {
        splitInstances[i] = new Instances(data, data.numInstances());
    }

    Enumeration enumInstance = data.enumerateInstances();
    while (enumInstance.hasMoreElements()) {
        Instance instance = (Instance) enumInstance.nextElement();
        splitInstances[(int) instance.value(attr)].add(instance);
    }

    for (int i = 0; i < attr.numValues(); i++) {
        splitInstances[i].compactify();
    }

    return splitInstances;
}

From source file:dkpro.similarity.experiments.rte.util.Evaluator.java

License:Open Source License

public static void runClassifierCV(WekaClassifier wekaClassifier, Dataset dataset) throws Exception {
    // Set parameters
    int folds = 10;
    Classifier baseClassifier = ClassifierSimilarityMeasure.getClassifier(wekaClassifier);

    // Set up the random number generator
    long seed = new Date().getTime();
    Random random = new Random(seed);

    // Add IDs to the instances
    AddID.main(new String[] { "-i", MODELS_DIR + "/" + dataset.toString() + ".arff", "-o",
            MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff" });
    Instances data = DataSource.read(MODELS_DIR + "/" + dataset.toString() + "-plusIDs.arff");
    data.setClassIndex(data.numAttributes() - 1);

    // Instantiate the Remove filter
    Remove removeIDFilter = new Remove();
    removeIDFilter.setAttributeIndices("first");

    // Randomize the data
    data.randomize(random);// w  w w  .ja  va2  s  .  c  o m

    // Perform cross-validation
    Instances predictedData = null;
    Evaluation eval = new Evaluation(data);

    for (int n = 0; n < folds; n++) {
        Instances train = data.trainCV(folds, n, random);
        Instances test = data.testCV(folds, n);

        // Apply log filter
        //          Filter logFilter = new LogFilter();
        //           logFilter.setInputFormat(train);
        //           train = Filter.useFilter(train, logFilter);        
        //           logFilter.setInputFormat(test);
        //           test = Filter.useFilter(test, logFilter);

        // Copy the classifier
        Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);

        // Instantiate the FilteredClassifier
        FilteredClassifier filteredClassifier = new FilteredClassifier();
        filteredClassifier.setFilter(removeIDFilter);
        filteredClassifier.setClassifier(classifier);

        // Build the classifier
        filteredClassifier.buildClassifier(train);

        // Evaluate
        eval.evaluateModel(filteredClassifier, test);

        // Add predictions
        AddClassification filter = new AddClassification();
        filter.setClassifier(classifier);
        filter.setOutputClassification(true);
        filter.setOutputDistribution(false);
        filter.setOutputErrorFlag(true);
        filter.setInputFormat(train);
        Filter.useFilter(train, filter); // trains the classifier

        Instances pred = Filter.useFilter(test, filter); // performs predictions on test set
        if (predictedData == null)
            predictedData = new Instances(pred, 0);
        for (int j = 0; j < pred.numInstances(); j++)
            predictedData.add(pred.instance(j));
    }

    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());

    // Prepare output scores
    String[] scores = new String[predictedData.numInstances()];

    for (Instance predInst : predictedData) {
        int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1;

        int valueIdx = predictedData.numAttributes() - 2;

        String value = predInst.stringValue(predInst.attribute(valueIdx));

        scores[id] = value;
    }

    // Output classifications
    StringBuilder sb = new StringBuilder();
    for (String score : scores)
        sb.append(score.toString() + LF);

    FileUtils.writeStringToFile(new File(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString()
            + "/" + dataset.toString() + ".csv"), sb.toString());

    // Output prediction arff
    DataSink.write(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString() + "/"
            + dataset.toString() + ".predicted.arff", predictedData);

    // Output meta information
    sb = new StringBuilder();
    sb.append(baseClassifier.toString() + LF);
    sb.append(eval.toSummaryString() + LF);
    sb.append(eval.toMatrixString() + LF);

    FileUtils.writeStringToFile(new File(OUTPUT_DIR + "/" + dataset.toString() + "/" + wekaClassifier.toString()
            + "/" + dataset.toString() + ".meta.txt"), sb.toString());
}

From source file:dkpro.similarity.experiments.sts2013.filter.LogFilter.java

License:Open Source License

@Override
protected Instance process(Instance inst) throws Exception {
    Instance newInst = new DenseInstance(inst.numAttributes());

    newInst.setValue(0, inst.value(0));

    for (int i = 1; i < inst.numAttributes() - 1; i++) {
        double newVal = Math.log(inst.value(i) + 1);
        newInst.setValue(i, newVal);//from www .ja  va 2s  .c  o  m
    }

    newInst.setValue(inst.numAttributes() - 1, inst.value(inst.numAttributes() - 1));

    return newInst;
}

From source file:dkpro.similarity.experiments.sts2013.util.Evaluator.java

License:Open Source License

public static void runLinearRegressionCV(Mode mode, Dataset... datasets) throws Exception {
    for (Dataset dataset : datasets) {
        // Set parameters
        int folds = 10;
        Classifier baseClassifier = new LinearRegression();

        // Set up the random number generator
        long seed = new Date().getTime();
        Random random = new Random(seed);

        // Add IDs to the instances
        AddID.main(new String[] { "-i",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".arff", "-o",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString()
                        + "-plusIDs.arff" });
        Instances data = DataSource.read(
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff");
        data.setClassIndex(data.numAttributes() - 1);

        // Instantiate the Remove filter
        Remove removeIDFilter = new Remove();
        removeIDFilter.setAttributeIndices("first");

        // Randomize the data
        data.randomize(random);/* w  ww . j  a  v a 2s  .c o  m*/

        // Perform cross-validation
        Instances predictedData = null;
        Evaluation eval = new Evaluation(data);

        for (int n = 0; n < folds; n++) {
            Instances train = data.trainCV(folds, n, random);
            Instances test = data.testCV(folds, n);

            // Apply log filter
            Filter logFilter = new LogFilter();
            logFilter.setInputFormat(train);
            train = Filter.useFilter(train, logFilter);
            logFilter.setInputFormat(test);
            test = Filter.useFilter(test, logFilter);

            // Copy the classifier
            Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);

            // Instantiate the FilteredClassifier
            FilteredClassifier filteredClassifier = new FilteredClassifier();
            filteredClassifier.setFilter(removeIDFilter);
            filteredClassifier.setClassifier(classifier);

            // Build the classifier
            filteredClassifier.buildClassifier(train);

            // Evaluate
            eval.evaluateModel(classifier, test);

            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);
            filter.setOutputDistribution(false);
            filter.setOutputErrorFlag(true);
            filter.setInputFormat(train);
            Filter.useFilter(train, filter); // trains the classifier

            Instances pred = Filter.useFilter(test, filter); // performs predictions on test set
            if (predictedData == null) {
                predictedData = new Instances(pred, 0);
            }
            for (int j = 0; j < pred.numInstances(); j++) {
                predictedData.add(pred.instance(j));
            }
        }

        // Prepare output scores
        double[] scores = new double[predictedData.numInstances()];

        for (Instance predInst : predictedData) {
            int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1;

            int valueIdx = predictedData.numAttributes() - 2;

            double value = predInst.value(predInst.attribute(valueIdx));

            scores[id] = value;

            // Limit to interval [0;5]
            if (scores[id] > 5.0) {
                scores[id] = 5.0;
            }
            if (scores[id] < 0.0) {
                scores[id] = 0.0;
            }
        }

        // Output
        StringBuilder sb = new StringBuilder();
        for (Double score : scores) {
            sb.append(score.toString() + LF);
        }

        FileUtils.writeStringToFile(
                new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv"),
                sb.toString());
    }
}