Example usage for weka.core Instance classIndex

Introduction

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

Prototype

public int classIndex();

Source Link

Document

Returns the class attribute's index.

Usage

From source file:edu.oregonstate.eecs.mcplan.ml.Memorizer.java

License:Open Source License

/**
 * 'instance' must be labeled with the class label in the same position
 * as in the training instances./*  w  ww.  j av a  2s  .c o m*/
 * 
 * @see weka.classifiers.Classifier#classifyInstance(weka.core.Instance)
 */
@Override
public double classifyInstance(final Instance instance) throws Exception {
    assert (instance.classIndex() == class_idx_);
    final double[] x = instance.toDoubleArray();
    x[class_idx_] = 0;
    return m_.get(new ArrayHolder(x));
}

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From source file:edu.umbc.cs.maple.utils.WekaUtils.java

License:Open Source License

/**
 * Converts an instance to a feature vector excluding the class attribute.
 * @param instance The instance./* www  .jav a2 s .c o  m*/
 * @return A vector representation of the instance excluding the class attribute
 */
public static double[] instanceToDoubleArray(Instance instance) {
    double[] vector = new double[(instance.classIndex() != -1) ? instance.numAttributes() - 1
            : instance.numAttributes()];
    double[] instanceDoubleArray = instance.toDoubleArray();
    int attIdx = 0;
    for (int i = 0; i < vector.length; i++) {
        if (i == instance.classIndex()) {
            attIdx++;
        }
        vector[i] = instanceDoubleArray[attIdx++];
    }
    return vector;
}

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From source file:edu.umbc.cs.maple.utils.WekaUtils.java

License:Open Source License

/** Converts a set of instances to svm-light format
 * @param data the weka instances//from  w ww  .j  a va 2  s. c  o  m
 * @return the weka instances in svm-light format
 */
public static String arffToSVMLight(Instance data, SVMLightLabelFormat labelFormat) {

    if (labelFormat == SVMLightLabelFormat.CLASSIFICATION && data.numClasses() != 2) {
        throw new IllegalArgumentException(
                "SVM-light classification label format requires that the data contain only two classes.");
    }

    String str = "";
    String endline = System.getProperty("line.separator");

    int numAttributes = data.numAttributes();
    int classAttIdx = data.classIndex();

    // convert the instance label
    if (labelFormat == SVMLightLabelFormat.CLASSIFICATION) {
        str += (data.classValue() == 0) ? "-1" : "1";
    } else {
        str += data.classValue();
    }

    str += " ";

    // convert each feature
    for (int attIdx = 0; attIdx < numAttributes; attIdx++) {
        // skip the class attribute
        if (attIdx == classAttIdx)
            continue;
        str += (attIdx + 1) + ":" + data.value(attIdx) + " ";
    }

    // append the instance info string
    str += "#";

    str += endline;

    return str;
}

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From source file:elh.eus.absa.WekaWrapper.java

License:Open Source License

/**
 *   Simple function to print the results of a multilabel prediction.
 * /*  w  ww . j  a  va2  s .  co  m*/
 * @param HashMap<Instance, double[]> pred hashmap containing a set of instances and their corresponding
 *         multilabel prediction, as computed by the multiLabelPrediction() function in this class.
 */
public void printMultilabelPredictions(HashMap<Instance, double[]> pred) {
    for (Instance i : pred.keySet()) {
        double[] kk = pred.get(i);
        int c = 0;
        System.out.print("instance " + Integer.parseInt(Double.toString(i.value(0))) + " (" + i.classValue()
                + "|" + i.stringValue(i.classIndex()) + ") --> ");
        for (double d : kk) {
            System.out.print("cl_" + c + "=" + d + "; ");
            c++;
        }
        System.out.print("\n");
    }

}

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From source file:fantail.core.Tools.java

License:Open Source License

public static int getNumberTargets(Instance inst) throws Exception {
    if (inst == null) {
        throw new Exception("inst can't be null.");
    }// w ww . j  a  v  a 2 s  .  c o m
    Instances targets = inst.relationalValue(inst.classIndex());
    return targets.numAttributes();
}

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From source file:fantail.core.Tools.java

License:Open Source License

public static double[] getTargetVector(Instance inst) {
    Instances targetBag = inst.relationalValue(inst.classIndex());
    double[] values = new double[targetBag.numAttributes()];
    for (int i = 0; i < values.length; i++) {
        values[i] = targetBag.instance(0).value(i);
    }/*from   ww w .ja  va  2 s . com*/
    return values;
}

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From source file:fantail.core.Tools.java

License:Open Source License

public static String[] getTargetNames(Instance inst) {
    Instances targetBag = inst.relationalValue(inst.classIndex());
    String[] names = new String[targetBag.numAttributes()];
    for (int i = 0; i < names.length; i++) {
        names[i] = targetBag.attribute(i).name();
    }/*from   ww  w.j a  va  2s . c  o  m*/
    return names;
}

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From source file:fantail.core.Tools.java

License:Open Source License

public static ReasonerComponent[] getTargetObjects(Instance inst) {

    Instances targetBag = inst.relationalValue(inst.classIndex());
    String[] names = new String[targetBag.numAttributes()];
    ReasonerComponent res[] = new ReasonerComponent[names.length];

    for (int i = 0; i < names.length; i++) {
        ReasonerComponent cl = new ReasonerComponent(i, targetBag.instance(0).value(i));
        res[i] = cl;/*  ww w . j ava2s .c  o m*/
    }
    return res;
}

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From source file:faster_pca.faster_pca.java

License:Open Source License

/**
* Transform an instance in original (unormalized) format.
* 
* @param instance an instance in the original (unormalized) format
* @return a transformed instance//from w ww  .j  a va 2 s .  co m
* @throws Exception if instance can't be transformed
*/
protected Instance convertInstance(Instance instance) throws Exception {
    Instance result;
    double[] newVals;
    Instance tempInst;
    double cumulative;
    int i;
    int j;
    double tempval;
    int numAttsLowerBound;

    newVals = new double[m_OutputNumAtts];
    tempInst = (Instance) instance.copy();

    /*m_ReplaceMissingFilter.input(tempInst);
    m_ReplaceMissingFilter.batchFinished();
    tempInst = m_ReplaceMissingFilter.output();*/

    m_NominalToBinaryFilter.input(tempInst);
    m_NominalToBinaryFilter.batchFinished();
    tempInst = m_NominalToBinaryFilter.output();

    if (m_AttributeFilter != null) {
        m_AttributeFilter.input(tempInst);
        m_AttributeFilter.batchFinished();
        tempInst = m_AttributeFilter.output();
    }

    if (!super.getCenterData()) {

        tempInst = f_norm.filter(tempInst);
    } else {

        tempInst = f_center.filter(tempInst);
    }

    if (m_HasClass) {
        newVals[m_OutputNumAtts - 1] = instance.value(instance.classIndex());
    }

    if (m_MaxAttributes > 0) {
        numAttsLowerBound = m_NumAttribs - m_MaxAttributes;
    } else {
        numAttsLowerBound = 0;
    }
    if (numAttsLowerBound < 0) {
        numAttsLowerBound = 0;
    }

    double tempInstCpy[] = new double[m_NumAttribs];
    for (j = 0; j < m_NumAttribs; j++) {
        tempInstCpy[j] = tempInst.value(j);
    }

    cumulative = 0;
    for (i = m_NumAttribs - 1; i >= numAttsLowerBound; i--) {
        tempval = 0.0;
        for (j = 0; j < m_NumAttribs; j++) {
            tempval += m_Eigenvectors[j][m_SortedEigens[i]] * tempInstCpy[j];
        }

        newVals[m_NumAttribs - i - 1] = tempval;
        cumulative += m_Eigenvalues[m_SortedEigens[i]];
        if ((cumulative / m_SumOfEigenValues) >= m_CoverVariance) {
            break;
        }
    }

    // create instance
    if (instance instanceof SparseInstance) {
        result = new SparseInstance(instance.weight(), newVals);
    } else {
        result = new DenseInstance(instance.weight(), newVals);
    }

    return result;
}

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From source file:GClass.EvaluationInternal.java

License:Open Source License

/**
 * Prints the predictions for the given dataset into a String variable.
 *///from   w  w  w.ja  v  a 2s  .  c  o  m
protected static String printClassifications(Classifier classifier, Instances train, String testFileName,
        int classIndex, Range attributesToOutput) throws Exception {

    StringBuffer text = new StringBuffer();
    if (testFileName.length() != 0) {
        BufferedReader testReader = null;
        try {
            testReader = new BufferedReader(new FileReader(testFileName));
        } catch (Exception e) {
            throw new Exception("Can't open file " + e.getMessage() + '.');
        }
        Instances test = new Instances(testReader, 1);
        if (classIndex != -1) {
            test.setClassIndex(classIndex - 1);
        } else {
            test.setClassIndex(test.numAttributes() - 1);
        }
        int i = 0;
        while (test.readInstance(testReader)) {
            Instance instance = test.instance(0);
            Instance withMissing = (Instance) instance.copy();
            withMissing.setDataset(test);
            double predValue = ((Classifier) classifier).classifyInstance(withMissing);
            if (test.classAttribute().isNumeric()) {
                if (Instance.isMissingValue(predValue)) {
                    text.append(i + " missing ");
                } else {
                    text.append(i + " " + predValue + " ");
                }
                if (instance.classIsMissing()) {
                    text.append("missing");
                } else {
                    text.append(instance.classValue());
                }
                text.append(" " + attributeValuesString(withMissing, attributesToOutput) + "\n");
            } else {
                if (Instance.isMissingValue(predValue)) {
                    text.append(i + " missing ");
                } else {
                    text.append(i + " " + test.classAttribute().value((int) predValue) + " ");
                }
                if (Instance.isMissingValue(predValue)) {
                    text.append("missing ");
                } else {
                    text.append(classifier.distributionForInstance(withMissing)[(int) predValue] + " ");
                }
                text.append(instance.toString(instance.classIndex()) + " "
                        + attributeValuesString(withMissing, attributesToOutput) + "\n");
            }
            test.delete(0);
            i++;
        }
        testReader.close();
    }
    return text.toString();
}

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