Example usage for weka.core Instances instance

List of usage examples for weka.core Instances instance

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Introduction

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

Prototype



publicInstance instance(int index)

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Document

Returns the instance at the given position.

Usage

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

License:Open Source License

/**
 * Transforms the instance in the prediction process before given to the internal multi-label
 * or multi-target classifier. The instance is passed having the original set of labels, these
 * must be replaced with the transformed labels (attributes) so that the internla classifier
 * can predict them.//w ww. jav a 2  s .  co  m
 *
 * @param x The instance to transform. Consists of features and labels.
 * @return The transformed instance. Consists of features and transformed labels.
 */
@Override
public Instance transformInstance(Instance x) throws Exception {
    Instances tmpInst = new Instances(x.dataset());

    tmpInst.delete();
    tmpInst.add(x);

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

    Instances labels = new Instances(this.m_PatternInstances);

    labels.add(new DenseInstance(labels.numAttributes()));

    Instances result = Instances.mergeInstances(labels, features);

    result.setClassIndex(labels.numAttributes());

    return result.instance(0);
}

From source file:meka.classifiers.multitarget.NSR.java

License:Open Source License

public Instances convertInstances(Instances D, int L) throws Exception {

    //Gather combinations
    HashMap<String, Integer> distinctCombinations = MLUtils.classCombinationCounts(D);
    if (getDebug())
        System.out.println("Found " + distinctCombinations.size() + " unique combinations");

    //Prune combinations
    MLUtils.pruneCountHashMap(distinctCombinations, m_P);
    if (getDebug())
        System.out.println("Pruned to " + distinctCombinations.size() + " with P=" + m_P);

    // Remove all class attributes
    Instances D_ = MLUtils.deleteAttributesAt(new Instances(D), MLUtils.gen_indices(L));
    // Add a new class attribute
    D_.insertAttributeAt(new Attribute("CLASS", new ArrayList(distinctCombinations.keySet())), 0); // create the class attribute
    D_.setClassIndex(0);//  w w w . j  a  v a  2s. co m

    //Add class values
    for (int i = 0; i < D.numInstances(); i++) {
        String y = MLUtils.encodeValue(MLUtils.toIntArray(D.instance(i), L));
        // add it
        if (distinctCombinations.containsKey(y)) //if its class value exists
            D_.instance(i).setClassValue(y);
        // decomp
        else if (m_N > 0) {
            String d_subsets[] = SuperLabelUtils.getTopNSubsets(y, distinctCombinations, m_N);
            for (String s : d_subsets) {
                int w = distinctCombinations.get(s);
                Instance copy = (Instance) (D_.instance(i)).copy();
                copy.setClassValue(s);
                copy.setWeight(1.0 / d_subsets.length);
                D_.add(copy);
            }
        }
    }

    // remove with missing class
    D_.deleteWithMissingClass();

    // keep the header of new dataset for classification
    m_InstancesTemplate = new Instances(D_, 0);

    if (getDebug())
        System.out.println("" + D_);

    return D_;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/** 
 * LabelCardinality - return the label cardinality of dataset D of L labels.
 *//*from  w  w  w  .  ja v a 2 s  . com*/
public static final double labelCardinality(Instances D, int L) {
    double sum = 0.0;
    double numInstances = (double) D.numInstances();
    for (int i = 0; i < D.numInstances(); i++) {
        for (int j = 0; j < L; j++) {
            if (!D.instance(i).isMissing(j)) {
                sum += D.instance(i).value(j);
            }
        }
    }
    return (double) sum / numInstances;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/** 
 * LabelCardinalities - return the frequency of each label of dataset D.
 */// ww w .ja  v a  2  s.c  o m
public static final double[] labelCardinalities(Instances D) {
    int L = D.classIndex();
    double lc[] = new double[L];
    for (int j = 0; j < L; j++) {
        int count = 0;
        for (int i = 0; i < D.numInstances(); i++) {
            //if for missing valueses
            if (!D.instance(i).isMissing(j)) {
                lc[j] += D.instance(i).value(j);
                count++;
            }
        }
        lc[j] /= count; //D.numInstances();
    }
    return lc;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * MostCommonCombination -  Most common label combination in D (of L labels).
 *///from w  ww .  java  2 s.c  o  m
public static final String mostCommonCombination(Instances D, int L) {
    HashMap<String, Integer> hm = new HashMap<String, Integer>(D.numInstances());
    double max_v = 0.0;
    int max_i = 0;

    for (int i = 0; i < D.numInstances(); i++) {
        String y = MLUtils.toBitString(D.instance(i), L);

        Integer v = hm.get(y);
        if (v == null) {
            hm.put(y, 0);
        } else {
            if (v > max_v) {
                max_v = v;
                max_i = i;
            }
            hm.put(y, v + 1);
        }
    }

    return MLUtils.toBitString(D.instance(max_i), L);
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * CountCombinations - return a mapping of each distinct label combination and its count.
 * NOTE: A sparse representation would be much better for many applications, i.e., instead of using toBitString(...), use toSparseRepresentation(...) instead.
 * @param   D   dataset /*w  ww  .j  a v  a2 s.  c  o m*/
 * @param   L   number of labels
 * @return   a HashMap where a String representation of each label combination is associated with an Integer count, e.g., "00010010",3
 */
public static final HashMap<String, Integer> countCombinations(Instances D, int L) {
    HashMap<String, Integer> map = new HashMap<String, Integer>();
    for (int i = 0; i < D.numInstances(); i++) {
        //String y = MLUtils.toSparseRepresentation(D.instance(i),L);
        String y = MLUtils.toBitString(D.instance(i), L);
        Integer c = map.get(y);
        map.put(y, c == null ? 1 : c + 1);
    }
    return map;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * ClassCombinationCounts - multi-target version of countCombinations(...).
 * NOTE: uses the encodeValue(...) function which does NOT consider sparse data.
 * TODO: use LabelVector instead of Strings
 * @param   D   dataset //www.  j  av a 2  s  . c o m
 * @return   a HashMap where a String representation of each class combination is associated with an Integer count, e.g. [0,2,2,3,2],5
 */
public static final HashMap<String, Integer> classCombinationCounts(Instances D) {
    int L = D.classIndex();
    HashMap<String, Integer> map = new HashMap<String, Integer>();
    for (int i = 0; i < D.numInstances(); i++) {
        String y = encodeValue(toIntArray(D.instance(i), L));
        Integer c = map.get(y);
        map.put(y, c == null ? 1 : c + 1);
    }
    return map;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * SetLabelsMissing - Set all labels in D to missing.
 *//*ww  w .  ja  v  a  2  s. c  om*/
public static Instances setLabelsMissing(Instances D) {
    int L = D.classIndex();
    for (int i = 0; i < D.numInstances(); i++) {
        for (int j = 0; j < L; j++) {
            D.instance(i).setMissing(j);
        }
    }
    return D;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * Stack two Instances together row-wise.
 *///from   ww  w.j  a  v a2  s  .  c  om
public static final Instances combineInstances(Instances D1, Instances D2) {
    Instances D = new Instances(D1);
    for (int i = 0; i < D2.numInstances(); i++) {
        D.add(D2.instance(i));
    }
    return D;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * GetXfromD - Extract attributes as a double X[][] from Instances D.
 * TODO: getXfromInstances would be a better name.
 *//* w  w  w . java2s . c  om*/
public static double[][] getXfromD(Instances D) {
    int N = D.numInstances();
    int L = D.classIndex();
    int d = D.numAttributes() - L;
    //System.out.println("d="+d);
    double X[][] = new double[N][d];
    for (int i = 0; i < N; i++) {
        for (int k = 0; k < d; k++) {
            X[i][k] = D.instance(i).value(k + L);
        }
    }
    return X;
}