Example usage for weka.core Instance value

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:mulan.transformations.regression.SingleTargetTransformation.java

License:Open Source License

/**
 * Remove all target attributes except labelToKeep
 * /*  w w w . ja  va 2s. c  om*/
 * @param instance the instance to be transformed
 * @param targetToKeep the target to keep
 * @return transformed Instance
 */
public Instance transformInstance(Instance instance, int targetToKeep) {
    Instance transformedInstance;
    remove.input(instance);
    transformedInstance = remove.output();
    add.input(transformedInstance);
    transformedInstance = add.output();
    transformedInstance.setDataset(shell);

    int[] targetIndices = data.getLabelIndices();
    transformedInstance.setValue(shell.numAttributes() - 1, instance.value(targetIndices[targetToKeep]));

    return transformedInstance;
}

From source file:mulan.transformations.regression.SingleTargetTransformation.java

License:Open Source License

/**
 * Remove all target attributes except target at position indexToKeep.
 * // w  ww  .j a  v  a2s .  c  o  m
 * @param instance the instance to be transformed
 * @param targetIndices the target indices to transform
 * @param indexToKeep the target to keep
 * @return transformed Instance
 */
public static Instance transformInstance(Instance instance, int[] targetIndices, int indexToKeep) {
    double[] values = instance.toDoubleArray();
    double[] transformedValues = new double[values.length - targetIndices.length + 1];

    int counterTransformed = 0;
    boolean isTarget = false;

    for (int i = 0; i < values.length; i++) {
        for (int j = 0; j < targetIndices.length; j++) {
            if (i == targetIndices[j] && i != indexToKeep) {
                isTarget = true;
                break;
            }
        }

        if (!isTarget) {
            transformedValues[counterTransformed] = instance.value(i);
            counterTransformed++;
        }
        isTarget = false;
    }

    Instance transformedInstance = DataUtils.createInstance(instance, 1, transformedValues);
    return transformedInstance;
}

From source file:myclassifier.MyC45.java

/**
 * Classifies a given test instance using the decision tree.
 *
 * @param instance the instance to be classified
 * @return the classification/*  w  w  w.ja v a  2 s .  c  o m*/
 */
public double classifyInstance(Instance instance) {
    Instance ins = instance;//discreting(instance);
    if (m_Attribute == null)
        return m_ClassValue;
    else
        return m_Successors[(int) ins.value(m_Attribute)].classifyInstance(ins);
}

From source file:myclassifier.MyC45.java

/**
 * Computes class distribution for instance using decision tree.
 *
 * @param instance the instance for which distribution is to be computed
 * @return the class distribution for the given instance
 *///from  w ww  . ja va2 s.  c o  m
public double[] distributionForInstance(Instance instance) throws NoSupportForMissingValuesException {
    //instance = discreting(instance);
    if (m_Attribute == null) {
        return m_Distribution;
    } else {
        return m_Successors[(int) instance.value(m_Attribute)].distributionForInstance(instance);
    }
}

From source file:myclassifier.MyC45.java

/**
 * Splits a dataset according to the values of a nominal attribute.
 *
 * @param data the data which is to be split
 * @param att the attribute to be used for splitting
 * @return the sets of instances produced by the split
 *//*ww  w  . j ava 2 s  . c om*/
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());

    Enumeration instEnum = data.enumerateInstances();

    while (instEnum.hasMoreElements()) {
        Instance inst = (Instance) instEnum.nextElement();
        splitData[(int) inst.value(att)].add(inst);
    }
    return splitData;
}

From source file:myclassifier.myC45Pack.SplitModel.java

private void handleNominalAttribute(Instances dataSet) throws Exception {

    Instance instance;
    classDist = new ClassDistribution(numOfBranches, dataSet.numClasses());
    Enumeration instanceEnum = dataSet.enumerateInstances();
    while (instanceEnum.hasMoreElements()) {
        instance = (Instance) instanceEnum.nextElement();
        if (!instance.isMissing(attribIndex)) {
            classDist.addInstance((int) instance.value(attribIndex), instance);
        }//w w  w  . j  a va2 s .  co m
    }

    // Check if minimum number of Instances in at least two
    // subsets.
    if (classDist.isSplitable(minInstances)) {
        numSubsets = numOfBranches;
        infoGain = classDist.calculateInfoGain(totalWeights);
        gainRatio = classDist.calculateGainRatio(infoGain);
    }
}

From source file:myclassifier.myC45Pack.SplitModel.java

public final void setSplitPoint(Instances allInstances) {

    double newSplitPoint = -Double.MAX_VALUE;
    double temp;//w w  w . ja  va 2  s  .c  o  m
    Instance instance;

    if ((allInstances.attribute(attribIndex).isNumeric()) && (numSubsets > 1)) {
        Enumeration instancesEnum = allInstances.enumerateInstances();
        while (instancesEnum.hasMoreElements()) {
            instance = (Instance) instancesEnum.nextElement();
            if (!instance.isMissing(attribIndex)) {
                temp = instance.value(attribIndex);
                if ((temp > newSplitPoint) && (temp <= splitPointValue)) {
                    newSplitPoint = temp;
                }
            }
        }
        splitPointValue = newSplitPoint;
    }
}

From source file:myclassifier.myC45Pack.SplitModel.java

@Override
public int getSubsetIndex(Instance instance) throws Exception {
    if (instance.isMissing(attribIndex)) {
        return -1;
    } else {/*from  w w w .j av  a  2s.c  o m*/
        if (instance.attribute(attribIndex).isNominal())
            return (int) instance.value(attribIndex);
        else if (instance.value(attribIndex) <= splitPointValue) {
            return 0;
        } else {
            return 1;
        }
    }
}

From source file:myclusterer.MyKMeans.java

private Instance createCentroid(Instances members) {
    double[] vals = new double[members.numAttributes()];
    double[][] nominalDists = new double[members.numAttributes()][];
    double[] weightMissing = new double[members.numAttributes()];
    double[] weightNonMissing = new double[members.numAttributes()];

    for (int j = 0; j < members.numAttributes(); j++) {
        if (members.attribute(j).isNominal()) {
            nominalDists[j] = new double[members.attribute(j).numValues()];
        }//w w  w  .  j  a va2 s .  com
    }
    for (int i = 0; i < members.numInstances(); ++i) {
        Instance inst = members.instance(i);
        for (int j = 0; j < members.numAttributes(); j++) {
            if (inst.isMissing(j)) {
                weightMissing[j] += inst.weight();
            } else {
                weightNonMissing[j] += inst.weight();
                if (members.attribute(j).isNumeric())
                    vals[j] += inst.weight() * inst.value(j);
                else
                    nominalDists[j][(int) inst.value(j)] += inst.weight();
            }
        }
    }
    for (int i = 0; i < members.numAttributes(); i++) {
        if (members.attribute(i).isNumeric()) {
            if (weightNonMissing[i] > 0) {
                vals[i] /= weightNonMissing[i];
            } else {
                vals[i] = Instance.missingValue();
            }
        } else {
            double max = -Double.MAX_VALUE;
            double maxIndex = -1;
            for (int j = 0; j < nominalDists[i].length; j++) {
                if (nominalDists[i][j] > max) {
                    max = nominalDists[i][j];
                    maxIndex = j;
                }
                vals[i] = max < weightMissing[i] ? Instance.missingValue() : maxIndex;
            }
        }
    }
    return new Instance(1.0, vals);
}

From source file:myID3.MyId3.java

/**
 * Create split of data based on the value of attribute
 * @param data//w  ww.j  a  va2 s.co m
 * @param attribute
 * @return 
 */
private Instances[] splitDataByAttribute(Instances data, Attribute attribute) {

    // Init the object first
    Instances[] subSet = new Instances[attribute.numValues()];
    for (int i = 0; i < attribute.numValues(); i++) {
        subSet[i] = new Instances(data, data.numInstances());
    }

    // Split it!
    Enumeration instanceEnum = data.enumerateInstances();
    while (instanceEnum.hasMoreElements()) {
        Instance instance = (Instance) instanceEnum.nextElement();
        subSet[(int) instance.value(attribute)].add(instance);
    }

    // Compact the array of object by removing the empty array
    for (int i = 0; i < attribute.numValues(); i++) {
        subSet[i].compactify();
        // System.out.println(subSet[i]);
    }

    return subSet;
}