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:de.unidue.langtech.grading.tc.ClusterTrainTask.java

License:Open Source License

private ConditionalFrequencyDistribution<Integer, String> getClusterCfd(Map<Integer, Set<Integer>> clusterMap,
        Instances data, List<String> outcomeValues) {
    ConditionalFrequencyDistribution<Integer, String> clusterAssignments = new ConditionalFrequencyDistribution<Integer, String>();

    for (Integer clusterId : clusterMap.keySet()) {
        for (Integer offset : clusterMap.get(clusterId)) {

            // get instance ID from instance
            Instance instance = data.get(offset);

            Double classOffset = new Double(instance.value(data.classAttribute()));
            String label = outcomeValues.get(classOffset.intValue());

            clusterAssignments.addSample(clusterId, label);
        }//from ww  w.  j  a va  2  s . c  o  m
    }

    return clusterAssignments;
}

From source file:de.uniheidelberg.cl.swp.mlprocess.AblationTesting.java

License:Apache License

/**
 * Determines the attribute value for a Instance object and the specified attribute name.
 * /*w w w.jav  a  2  s  .c o m*/
 * @param inst The instance object from which the value is extracted.
 * @param featureName The name of the attribute.
 * @return A double representation of the value used by WEKA.
 */
private double getAttributeValue(Instance inst, String featureName) {
    for (int i = 0; i < inst.numAttributes(); i++) {
        if (inst.attribute(i).name().equals(featureName))
            return inst.value(i);
    }
    return 0;
}

From source file:de.uni_potsdam.hpi.bpt.promnicat.util.WeightedEditDistance.java

License:Open Source License

/**
 * Updates the ranges given a new instance.
 * //w w  w  .j  av a  2  s .  c o  m
 * @param instance    the new instance
 * @param ranges    low, high and width values for all attributes
 * @return      the updated ranges
 */
public double[][] updateRanges(Instance instance, double[][] ranges) {
    // updateRangesFirst must have been called on ranges
    for (int j = 0; j < ranges.length; j++) {
        double value = instance.value(j);
        if (!((ProcessInstance) instance).isStringMissing(j)) {
            if (value < ranges[j][R_MIN]) {
                ranges[j][R_MIN] = value;
                ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
            } else {
                if (instance.value(j) > ranges[j][R_MAX]) {
                    ranges[j][R_MAX] = value;
                    ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
                }
            }
        }
    }

    return ranges;
}

From source file:de.uni_potsdam.hpi.bpt.promnicat.util.WeightedEditDistance.java

License:Open Source License

/**
 * Updates the minimum and maximum and width values for all the attributes
 * based on a new instance./*from  ww  w.  j  a  v a2 s.c om*/
 * 
 * @param instance    the new instance
 * @param numAtt    number of attributes in the model
 * @param ranges    low, high and width values for all attributes
 */
public void updateRanges(Instance instance, int numAtt, double[][] ranges) {
    // updateRangesFirst must have been called on ranges
    for (int j = 0; j < numAtt; j++) {
        double value = instance.value(j);
        if (!((ProcessInstance) instance).isMissing(j)) {
            if (value < ranges[j][R_MIN]) {
                ranges[j][R_MIN] = value;
                ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
                if (value > ranges[j][R_MAX]) { //if this is the first value that is
                    ranges[j][R_MAX] = value; //not missing. The,0
                    ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
                }
            } else {
                if (value > ranges[j][R_MAX]) {
                    ranges[j][R_MAX] = value;
                    ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
                }
            }
        }
    }
}

From source file:de.uni_potsdam.hpi.bpt.promnicat.util.WeightedEditDistance.java

License:Open Source License

/**
 * Used to initialize the ranges. For this the values of the first
 * instance is used to save time.//  w  ww  . j  av  a  2 s.  c  o  m
 * Sets low and high to the values of the first instance and
 * width to zero.
 * 
 * @param instance    the new instance
 * @param numAtt    number of attributes in the model
 * @param ranges    low, high and width values for all attributes
 */
public void updateRangesFirst(Instance instance, int numAtt, double[][] ranges) {
    for (int j = 0; j < numAtt; j++) {
        if (!((ProcessInstance) instance).isStringMissing(j)) {
            ranges[j][R_MIN] = instance.value(j);
            ranges[j][R_MAX] = instance.value(j);
            ranges[j][R_WIDTH] = 0.0;
        } else { // if value was missing
            ranges[j][R_MIN] = Double.POSITIVE_INFINITY;
            ranges[j][R_MAX] = -Double.POSITIVE_INFINITY;
            ranges[j][R_WIDTH] = Double.POSITIVE_INFINITY;
        }
    }
}

From source file:de.uni_potsdam.hpi.bpt.promnicat.util.WeightedEditDistance.java

License:Open Source License

/**
 * Test if an instance is within the given ranges.
 * //from  ww w . j a v a  2 s. c om
 * @param instance    the instance
 * @param ranges    the ranges the instance is tested to be in
 * @return true    if instance is within the ranges
 */
public boolean inRanges(Instance instance, double[][] ranges) {
    boolean isIn = true;

    // updateRangesFirst must have been called on ranges
    for (int j = 0; isIn && (j < ranges.length); j++) {
        if (!((ProcessInstance) instance).isStringMissing(j)) {
            double value = instance.value(j);
            isIn = value <= ranges[j][R_MAX];
            if (isIn)
                isIn = value >= ranges[j][R_MIN];
        }
    }

    return isIn;
}

From source file:decisiontree.ID3tree.java

private double calculateEntropy(ArrayList<Instance> aList, int attr, int index) {
    Instance tempInst;
    int numInst = aList.size();
    int[] numAttr;
    double ent = 0.0;
    double temp;//from   ww  w . ja v  a 2s  . c  o  m
    numAttr = new int[attr];
    Arrays.fill(numAttr, 0);

    // Count number of classes         
    for (int i = 0; i < numInst; i++) {
        tempInst = aList.get(i);
        for (int j = 0; j < attr - 1; j++) {
            if (tempInst.value(index) == j)
                numAttr[j] += 1;
        }
    }

    //System.out.println(attr);
    for (int j = 0; j < attr - 1; j++) {
        temp = numAttr[j] / (double) numInst;

        if (Double.isNaN(temp))
            ent -= 0.0;
        else
            ent -= (temp * (Math.log(temp) / Math.log(2)));
    }

    return ent;
}

From source file:decisiontree.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
*//*from  w w w  .  ja va2s.  com*/
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);
    }
    for (int i = 0; i < splitData.length; i++) {
        splitData[i].compactify();
    }
    return splitData;
}

From source file:decisiontree.MyC45.java

/**
* Splits a dataset according to the values of a numeric 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
*//*from w  w w. j av a  2  s.  c  o m*/
private Instances[] splitData(Instances data, Attribute att, double threshold) {
    Instances[] splitData = new Instances[2];
    for (int i = 0; i < 2; i++) {
        splitData[i] = new Instances(data, data.numInstances());
    }

    Enumeration instEnum = data.enumerateInstances();
    while (instEnum.hasMoreElements()) {
        Instance inst = (Instance) instEnum.nextElement();
        if (inst.value(att) >= threshold) {
            inst.setValue(att, threshold);
            splitData[1].add(inst);
        } else {
            inst.setValue(att, 0);
            splitData[0].add(inst);
        }
    }
    for (int i = 0; i < splitData.length; i++) {
        splitData[i].compactify();
    }
    return splitData;
}

From source file:decisiontree.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  om*/
*/
public double classifyInstance(Instance instance) {
    if (m_Attribute == null) {
        return m_ClassValue;
    } else {
        return m_Successors[(int) instance.value(m_Attribute)].classifyInstance(instance);
    }
}