Example usage for weka.core Instances setClassIndex

Introduction

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

Prototype

public void setClassIndex(int classIndex) 

Source Link

Document

Sets the class index of the set.

Usage

From source file:entities.WekaNGGFeatureVector.java

public Instances fillInstanceSet(ArrayList<NGGFeatureVector> vList, ArrayList<NGGFeatureVector> vList2,
        String datasetType) throws IOException {
    ArrayList<Attribute> attributes = initializeWekaFeatureVector();
    Instances isSet = new Instances(vList.get(0).getLabel(), attributes, vList.size());

    isSet.setClassIndex(isSet.numAttributes() - 1);

    for (NGGFeatureVector NGGv : vList) {

        Instance i = fillFeatureVector(NGGv, isSet);

        isSet.add(i);/*from  ww w. ja  va  2 s  . c  o  m*/
    }

    for (NGGFeatureVector NGGv : vList2) {

        Instance i = fillFeatureVector(NGGv, isSet);

        isSet.add(i);
    }

    ArffSaver saver = new ArffSaver();
    saver.setInstances(isSet);
    saver.setFile(new File("./data/" + datasetType + ".arff"));
    saver.writeBatch();

    return isSet;
}

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From source file:entity.DatasetFileManager.java

License:Open Source License

public Instances generateWekaInstancesForDataset(String datasetFilePath) {

    // datasource creation from file
    DataSource datasource = null;
    try {// ww  w  . j  a  v a  2  s .  c om
        datasource = new DataSource(datasetFilePath);
    } catch (Exception e) {
        e.printStackTrace();
    }

    Instances instances = null;

    // instances creation from datasource
    try {
        instances = datasource.getDataSet();
    } catch (Exception e) {
        e.printStackTrace();
    }
    // last attribute is for classification
    instances.setClassIndex(instances.numAttributes() - 1);

    if (verbose)
        System.out.println("\nDataset:\n");
    if (verbose)
        System.out.println(instances);

    return instances;
}

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From source file:en_deep.mlprocess.manipulation.featmodif.FeatureModifierFilter.java

License:Open Source License

/**
 * Set the output format if the class is nominal.
 *///from www  .  j a va2 s .  c o  m
private void setOutputFormat() {

    FastVector newAtts;
    int newClassIndex;
    Instances outputFormat;

    newClassIndex = getInputFormat().classIndex();
    newAtts = new FastVector();

    BitSet attrSrc = new BitSet(), attrDest = new BitSet();

    int attSoFar = 0;

    for (int j = 0; j < getInputFormat().numAttributes(); j++) {

        Attribute att = getInputFormat().attribute(j);

        if (!m_Columns.isInRange(j)) {
            newAtts.addElement(att.copy());

            attrSrc.set(j);
            attrDest.set(attSoFar++);

        } else {

            ArrayList<Attribute> valueAttrs = getAttributeOutputFormat(att);

            if (newClassIndex >= 0 && j < getInputFormat().classIndex()) {
                newClassIndex += valueAttrs.size() - 1;
            }
            newAtts.addAll(valueAttrs);

            if (m_PreserveOriginals) {
                attrSrc.set(j);
                attrDest.set(attSoFar);
            }
            attSoFar += valueAttrs.size();
        }
    }

    outputFormat = new Instances(getInputFormat().relationName(), newAtts, 0);
    outputFormat.setClassIndex(newClassIndex);
    setOutputFormat(outputFormat);

    m_StringToCopySrc = new AttributeLocator(getInputFormat(), Attribute.STRING, MathUtils.findTrue(attrSrc));
    m_StringToCopyDst = new AttributeLocator(outputFormat, Attribute.STRING, MathUtils.findTrue(attrDest));
}

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From source file:en_deep.mlprocess.manipulation.featmodif.ReplaceMissing.java

License:Open Source License

/**
 * Set the output format if the class is nominal.
 *///from   w  ww  .j  a v  a  2  s  .c  o m
private void setOutputFormat() {

    FastVector newAtts;
    Instances outputFormat;

    newAtts = new FastVector();

    BitSet attrSrc = new BitSet();

    for (int j = 0; j < getInputFormat().numAttributes(); j++) {

        Attribute att = null;
        Attribute srcAtt = getInputFormat().attribute(j);

        if (!m_Columns.isInRange(j) || srcAtt.indexOfValue(m_ReplVal) >= 0) {
            att = (Attribute) srcAtt.copy();
        } else if (srcAtt.isNominal()) {

            Enumeration<String> valsEnum = srcAtt.enumerateValues();
            ArrayList<String> valsList = new ArrayList<String>();

            while (valsEnum.hasMoreElements()) {
                valsList.add(valsEnum.nextElement());
            }
            valsList.add(m_ReplVal);

            att = new Attribute(srcAtt.name(), valsList);
        } else { // string attributes
            att = (Attribute) srcAtt.copy();
            att.addStringValue(m_ReplVal);
        }

        newAtts.addElement(att);
        attrSrc.set(j);
    }

    outputFormat = new Instances(getInputFormat().relationName(), newAtts, 0);
    outputFormat.setClassIndex(getInputFormat().classIndex());

    setOutputFormat(outputFormat);

    m_StringToCopy = new AttributeLocator(getInputFormat(), Attribute.STRING, MathUtils.findTrue(attrSrc));
}

---

From source file:en_deep.mlprocess.manipulation.SetAwareNominalToBinary.java

License:Open Source License

/**
 * Set the output format if the class is nominal.
 *///from ww  w . jav  a 2s .  co m
private void setOutputFormat() {

    FastVector newAtts;
    int newClassIndex;
    Instances outputFormat;

    // Compute new attributes

    m_producedAttVals = new HashMap[getInputFormat().numAttributes()];
    newClassIndex = getInputFormat().classIndex();
    newAtts = new FastVector();

    for (int j = 0; j < getInputFormat().numAttributes(); j++) {
        Attribute att = getInputFormat().attribute(j);
        if (!att.isNominal() || (j == getInputFormat().classIndex()) || !m_Columns.isInRange(j)) {
            newAtts.addElement(att.copy());
        } else {
            if ((att.numValues() <= 2) && (!m_TransformAll)) {
                if (m_Numeric) {
                    newAtts.addElement(new Attribute(att.name()));
                } else {
                    newAtts.addElement(att.copy());
                }
            } else {

                ArrayList<Attribute> valueAttrs = convertAttribute(att);

                if (newClassIndex >= 0 && j < getInputFormat().classIndex()) {
                    newClassIndex += valueAttrs.size() - 1;
                }
                newAtts.addAll(valueAttrs);
            }
        }
    }
    outputFormat = new Instances(getInputFormat().relationName(), newAtts, 0);
    outputFormat.setClassIndex(newClassIndex);
    setOutputFormat(outputFormat);
}

---

From source file:es.bsc.autonomic.powermodeller.tools.classifiers.BaggingClassifier.java

License:Apache License

@Override
protected Classifier buildClassifier(DataSet training_ds) {
    logger.debug("Building Bagging classifier.");

    Classifier model = null;/*w  ww  .j a  v a  2 s. c om*/

    // Get the independent variable index
    String independent = training_ds.getIndependent();

    if (independent == null)
        throw new WekaWrapperException("Independent variable is not set in dataset.");

    try {

        // Read all the instances in the file (ARFF, CSV, XRFF, ...)
        ConverterUtils.DataSource source = new ConverterUtils.DataSource(training_ds.getFilePath());
        Instances instances = source.getDataSet();

        // Set the independent variable (powerWatts).
        instances.setClassIndex(instances.attribute(independent).index());

        // Builds a regression model for the given data.
        model = new weka.classifiers.meta.Bagging();

        // Build Linear Regression
        model.buildClassifier(instances);

    } catch (WekaWrapperException e) {
        logger.error("Error while creating Bagging classifier.", e);
        throw new WekaWrapperException("Error while creating Bagging classifier.");

    } catch (Exception e) {
        logger.error("Error while applying Bagging to data set instances.", e);
        throw new WekaWrapperException("Error while applying Bagging to data set instances.");
    }

    return model;

}

---

From source file:es.bsc.autonomic.powermodeller.tools.classifiers.LinearRegressionClassifier.java

License:Apache License

@Override
public Classifier buildClassifier(DataSet training_ds) {

    logger.debug("Building LinearRegression classifier.");

    Classifier model;/*from   w  ww .  j  av  a2  s .  c om*/

    // Get the independent variable index
    String independent = training_ds.getIndependent();

    if (independent == null)
        throw new WekaWrapperException("Independent variable is not set in dataset.");

    try {

        // Read all the instances in the file (ARFF, CSV, XRFF, ...)
        ConverterUtils.DataSource source = new ConverterUtils.DataSource(training_ds.getFilePath());
        Instances instances = source.getDataSet();

        // Set the independent variable (powerWatts).
        instances.setClassIndex(instances.attribute(independent).index());

        // Builds a regression model for the given data.
        model = new weka.classifiers.functions.LinearRegression();

        // Build Linear Regression
        model.buildClassifier(instances);

    } catch (WekaWrapperException e) {
        logger.error("Error while creating Linear Regression classifier.", e);
        throw new WekaWrapperException("Error while creating Linear Regression classifier.");

    } catch (Exception e) {
        logger.error("Error while applying Linear Regression to data set instances.", e);
        throw new WekaWrapperException("Error while applying Linear Regression to data set instances.");
    }

    return model;

}

---

From source file:es.bsc.autonomic.powermodeller.tools.classifiers.MultilayerPerceptronClassifier.java

License:Apache License

@Override
protected Classifier buildClassifier(DataSet training_ds) {

    logger.debug("Building MultilayerPerceptron classifier.");

    MultilayerPerceptron model;/*from   w w w. ja v a 2s.c  o  m*/

    // Get the independent variable index
    String independent = training_ds.getIndependent();

    if (independent == null)
        throw new WekaWrapperException("Independent variable is not set in dataset.");

    try {

        // Read all the instances in the file (ARFF, CSV, XRFF, ...)
        ConverterUtils.DataSource source = new ConverterUtils.DataSource(training_ds.getFilePath());
        Instances instances = source.getDataSet();

        // Set the independent variable (powerWatts).
        instances.setClassIndex(instances.attribute(independent).index());

        // Builds a regression model for the given data.
        model = new weka.classifiers.functions.MultilayerPerceptron();
        model.setHiddenLayers("4");
        model.setTrainingTime(20);

        // Build Linear Regression
        model.buildClassifier(instances);

    } catch (WekaWrapperException e) {
        logger.error("Error while creating Linear Regression classifier.", e);
        throw new WekaWrapperException("Error while creating Linear Regression classifier.");

    } catch (Exception e) {
        logger.error("Error while applying Linear Regression to data set instances.", e);
        throw new WekaWrapperException("Error while applying Linear Regression to data set instances.");
    }

    return model;
}

---

From source file:es.bsc.autonomic.powermodeller.tools.classifiers.RepTreeClassifier.java

License:Apache License

@Override
public Classifier buildClassifier(DataSet training_ds) {

    logger.debug("Building RepTree classifier.");

    Classifier model;/*w  ww . j  a  v  a 2s  .c o  m*/

    // Get the independent variable index
    String independent = training_ds.getIndependent();

    if (independent == null)
        throw new WekaWrapperException("Independent variable is not set in dataset.");

    try {

        // Read all the instances in the file (ARFF, CSV, XRFF, ...)
        ConverterUtils.DataSource source = new ConverterUtils.DataSource(training_ds.getFilePath());
        Instances instances = source.getDataSet();

        // Set the independent variable (powerWatts).
        instances.setClassIndex(instances.attribute(independent).index());

        // Builds a regression model for the given data.
        model = new weka.classifiers.trees.REPTree();

        // Build Linear Regression
        model.buildClassifier(instances);

    } catch (WekaWrapperException e) {
        logger.error("Error while creating Linear Regression classifier.", e);
        throw new WekaWrapperException("Error while creating Linear Regression classifier.");

    } catch (Exception e) {
        logger.error("Error while applying Linear Regression to data set instances.", e);
        throw new WekaWrapperException("Error while applying Linear Regression to data set instances.");
    }

    return model;

}

---

From source file:es.bsc.autonomic.powermodeller.tools.classifiers.WekaWrapper.java

License:Apache License

public static Instances convertDataSetToInstances(DataSet ds) {

    Instances instances;
    try {//from w  w  w  .j  a v a 2 s.com
        // Read all the instances in the file and initialize data

        ConverterUtils.DataSource source = new ConverterUtils.DataSource(ds.getFilePath());
        instances = source.getDataSet();

        instances.setClassIndex(instances.attribute(ds.getIndependent()).index());

    } catch (Exception e) {
        logger.error("Error while reading input DataSet", e);
        throw new WekaWrapperException("Error while reading input DataSet");
    }

    return instances;
}

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