Example usage for weka.core Instance dataset

List of usage examples for weka.core Instance dataset

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Introduction

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

Prototype

public Instances dataset();

Source Link

Document

Returns the dataset this instance has access to.

Usage

From source file:edu.oregonstate.eecs.mcplan.abstraction.WekaUtil.java

License:Open Source License

public static double[] unlabeledFeatures(final Instance i) {
    assert (i.dataset() != null);
    assert (i.dataset().classIndex() == i.numAttributes() - 1);
    final double[] phi = new double[i.numAttributes() - 1];
    for (int j = 0; j < i.numAttributes() - 1; ++j) {
        phi[j] = i.value(j);//from w w w. j  av a2s. c o  m
    }
    return phi;
}

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

License:Open Source License

/**
 * Convert a single instance over if the class is nominal. The converted
 * instance is added to the end of the output queue.
 *
 * @param instance the instance to convert
 *///from   w  ww  . j  a v  a  2  s . c  o  m
private void convertInstance(Instance instance) {

    double[] vals = new double[outputFormatPeek().numAttributes()];
    String[] stringVals = new String[vals.length];
    int attSoFar = 0;

    for (int j = 0; j < getInputFormat().numAttributes(); j++) {
        Attribute att = instance.attribute(j);
        if (!m_Columns.isInRange(j)) {
            vals[attSoFar] = instance.value(j);
            attSoFar++;
        } else {
            // store new string values, make double values "missing" for now (if some string
            // values are missing, the double values will remain missing)
            if (instance.value(0) == 12 && instance.value(1) == 9 && att.name().equals("sempos")) {
                attSoFar = attSoFar;
            }
            attSoFar += getAttributeOutputValue(att, instance.value(j), vals, stringVals, attSoFar);
        }
    }
    Instance inst = null;
    if (instance instanceof SparseInstance) {
        inst = new SparseInstance(instance.weight(), vals);
    } else {
        inst = new DenseInstance(instance.weight(), vals);
    }

    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());

    // add new string values to the output data set and to the instance
    for (int i = 0; i < stringVals.length; ++i) {
        if (stringVals[i] != null) {
            vals[i] = inst.dataset().attribute(i).addStringValue(stringVals[i]);
        }
    }
    inst.replaceMissingValues(vals);

    inst.setDataset(getOutputFormat());
    push(inst);
}

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

License:Open Source License

/**
 * Convert a single instance over if the class is nominal. The converted
 * instance is added to the end of the output queue.
 *
 * @param instance the instance to convert
 *///w  w  w  . ja v a 2s .  c om
private void convertInstance(Instance instance) {

    // create a copy of the input instance
    Instance inst = null;

    if (instance instanceof SparseInstance) {
        inst = new SparseInstance(instance.weight(), instance.toDoubleArray());
    } else {
        inst = new DenseInstance(instance.weight(), instance.toDoubleArray());
    }

    // copy the string values from this instance as well (only the existing ones)
    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat()); // beware of weird behavior of this function (see source)!!
    inst.setDataset(getOutputFormat());

    // find the missing values to be filled + the double values for the new "missing" label and store it
    double[] vals = instance.toDoubleArray();

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

        Attribute att = instance.attribute(j);

        if (m_Columns.isInRange(j) && instance.isMissing(j)) {
            // find the "missing" value in the output nominal attribute
            if (att.isNominal()) {
                vals[j] = inst.dataset().attribute(j).indexOfValue(m_ReplVal);
            }
            // add a string value for the new "missing" label
            else if (att.isString()) {
                vals[j] = inst.dataset().attribute(j).addStringValue(m_ReplVal);
            }
        }
    }

    // fill in the missing values found
    inst.replaceMissingValues(vals);

    push(inst);
}

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

License:Open Source License

/**
 * Convert a single instance over if the class is nominal. The converted
 * instance is added to the end of the output queue.
 *
 * @param instance the instance to convert
 *///from  w w w . j ava2s  . c o m
private void convertInstance(Instance instance) {

    double[] vals = new double[outputFormatPeek().numAttributes()];
    int attSoFar = 0;

    for (int j = 0; j < getInputFormat().numAttributes(); j++) {
        Attribute att = getInputFormat().attribute(j);
        if (!att.isNominal() || (j == getInputFormat().classIndex()) || !m_Columns.isInRange(j)) {
            vals[attSoFar] = instance.value(j);
            attSoFar++;
        } else {
            if ((att.numValues() <= 2) && (!m_TransformAll)) {
                vals[attSoFar] = instance.value(j);
                attSoFar++;
            } else {
                attSoFar += setConvertedAttribute(att, instance.value(j), vals, attSoFar);
            }
        }
    }
    Instance inst = null;
    if (instance instanceof SparseInstance) {
        inst = new SparseInstance(instance.weight(), vals);
    } else {
        inst = new DenseInstance(instance.weight(), vals);
    }
    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());
    inst.setDataset(getOutputFormat());
    push(inst);
}

From source file:fantail.algorithms.RankingByPairwiseComparison.java

License:Open Source License

@Override
public double[] recommendRanking(Instance testInst) throws Exception {
    Instances tempData = new Instances(testInst.dataset(), 0);
    tempData.add((Instance) testInst.copy());
    // remove the relation att
    tempData.setClassIndex(-1);//  w w w. j a v a 2s  .c  om
    tempData.deleteAttributeAt(tempData.numAttributes() - 1);
    tempData = Filter.useFilter(tempData, m_Add);
    tempData.setClassIndex(tempData.numAttributes() - 1);
    double predRanking[] = new double[m_NumLabels];
    for (int i = 0; i < predRanking.length; i++) {
        predRanking[i] = m_NumLabels - 1;
    }
    for (int i = 0; i < m_Classifiers.size(); i++) {
        double predIndex = m_Classifiers.get(i).classifyInstance(tempData.instance(0));
        String algoPair = m_AlgoPairs.get(i);
        String[] parts = algoPair.split("\\|");
        int trueIndex = Integer.parseInt(parts[(int) predIndex]);
        predRanking[trueIndex] -= 1;
    }
    predRanking = Tools.doubleArrayToRanking(predRanking);
    return predRanking;
}

From source file:fantail.algorithms.RankingByPairwiseComparison.java

License:Open Source License

public double[] recommendRanking2(Instance testInst) throws Exception {
    Instances tempData = new Instances(testInst.dataset(), 0);
    tempData.add((Instance) testInst.copy());
    // remove the relation att
    tempData.setClassIndex(-1);/*from   w  w w.  j a  v a  2  s.  c  om*/
    tempData.deleteAttributeAt(tempData.numAttributes() - 1);
    tempData = Filter.useFilter(tempData, m_Add);
    tempData.setClassIndex(tempData.numAttributes() - 1);
    double predRanking[] = new double[m_NumLabels];
    for (int i = 0; i < m_Classifiers.size(); i++) {
        double predIndex = m_Classifiers.get(i).classifyInstance(tempData.instance(0));
        double predProb = m_Classifiers.get(i).distributionForInstance(tempData.instance(0))[0];
        String algoPair = m_AlgoPairs.get(i);
        String[] parts = algoPair.split("\\|");
        int trueIndex = Integer.parseInt(parts[(int) predIndex]);
        predRanking[trueIndex] -= predProb;
    }
    return Tools.doubleArrayToRanking(predRanking);
}

From source file:ffnn.FFNNTubesAI.java

public static Instance filterNominalNumeric(Instance i) throws Exception {
    NominalToBinary filter = new NominalToBinary();
    filter.setInputFormat(i.dataset());
    filter.input(i);/*from   w  ww .  j  av  a 2  s .c  o m*/
    return filter.output();
}

From source file:mao.datamining.RemoveUselessColumnsByMissingValues.java

License:Open Source License

/**
 * Input an instance for filtering./*from   w  w w.  jav a  2  s.c  o  m*/
 *
 * @param instance the input instance
 * @return true if the filtered instance may now be
 * collected with output().
 */
public boolean input(Instance instance) {

    if (getInputFormat() == null) {
        throw new IllegalStateException("No input instance format defined");
    }
    if (m_NewBatch) {
        resetQueue();
        m_NewBatch = false;
    }
    if (m_removeFilter != null) {
        m_removeFilter.input(instance);
        Instance processed = m_removeFilter.output();
        processed.setDataset(getOutputFormat());
        copyValues(processed, false, instance.dataset(), getOutputFormat());
        push(processed);
        return true;
    }
    bufferInput(instance);
    return false;
}

From source file:meka.classifiers.multilabel.Maniac.java

License:Open Source License

@Override
public Instance transformInstance(Instance x) throws Exception {

    Instances tmpInst = new Instances(x.dataset());

    tmpInst.delete();/* w ww.j a  v a2s  .  c  o m*/
    tmpInst.add(x);

    Instances features = this.extractPart(tmpInst, false);

    Instances pseudoLabels = new Instances(this.compressedTemplateInst);
    Instance tmpin = pseudoLabels.instance(0);
    pseudoLabels.delete();

    pseudoLabels.add(tmpin);

    for (int i = 0; i < pseudoLabels.classIndex(); i++) {
        pseudoLabels.instance(0).setMissing(i);
    }

    Instances newDataSet = Instances.mergeInstances(pseudoLabels, features);
    newDataSet.setClassIndex(pseudoLabels.numAttributes());

    return newDataSet.instance(0);
}

From source file:meka.classifiers.multilabel.MLCBMaD.java

License:Open Source License

@Override
public Instance transformInstance(Instance x) throws Exception {
    Instances tmpInst = new Instances(x.dataset());

    tmpInst.delete();//from  www.j a  v  a  2 s.  c o  m
    tmpInst.add(x);

    Instances features = this.extractPart(tmpInst, false);

    Instances pseudoLabels = new Instances(this.compressedMatrix);
    Instance tmpin = pseudoLabels.instance(0);
    pseudoLabels.delete();

    pseudoLabels.add(tmpin);

    for (int i = 0; i < pseudoLabels.classIndex(); i++) {
        pseudoLabels.instance(0).setMissing(i);
    }

    Instances newDataSet = Instances.mergeInstances(pseudoLabels, features);
    newDataSet.setClassIndex(this.size);

    return newDataSet.instance(0);
}