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:meka.classifiers.multitarget.NSR.java

License:Open Source License

@Override
public double[] distributionForInstance(Instance x) throws Exception {

    int L = x.classIndex();

    //if there is only one class (as for e.g. in some hier. mtds) predict it
    //if(L == 1) return new double[]{1.0};

    Instance x_sl = PSUtils.convertInstance(x, L, m_InstancesTemplate); // the sl instance
    //x_sl.setDataset(m_InstancesTemplate);                     // where y in {comb_1,comb_2,...,comb_k}

    double w[] = m_Classifier.distributionForInstance(x_sl); // w[j] = p(y_j) for each j = 1,...,L
    int max_j = Utils.maxIndex(w); // j of max w[j]
    //int max_j = (int)m_Classifier.classifyInstance(x_sl);         // where comb_i is selected
    String y_max = m_InstancesTemplate.classAttribute().value(max_j); // comb_i e.g. "0+3+0+0+1+2+0+0"

    double y[] = Arrays.copyOf(A.toDoubleArray(MLUtils.decodeValue(y_max)), L * 2); // "0+3+0+0+1+2+0+0" -> [0.0,3.0,0.0,...,0.0]

    HashMap<Double, Double> votes[] = new HashMap[L];
    for (int j = 0; j < L; j++) {
        votes[j] = new HashMap<Double, Double>();
    }/*from   w  w w .  j  a va  2s.c  o  m*/

    for (int i = 0; i < w.length; i++) {
        double y_i[] = A.toDoubleArray(MLUtils.decodeValue(m_InstancesTemplate.classAttribute().value(i)));
        for (int j = 0; j < y_i.length; j++) {
            votes[j].put(y_i[j], votes[j].containsKey(y_i[j]) ? votes[j].get(y_i[j]) + w[i] : w[i]);
        }
    }

    // some confidence information
    for (int j = 0; j < L; j++) {
        y[j + L] = votes[j].size() > 0 ? Collections.max(votes[j].values()) : 0.0;
    }

    return y;
}

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

License:Open Source License

@Override
public double[] distributionForInstance(Instance x) throws Exception {

    int L = x.classIndex();

    HashMap<Integer, Double> votes[] = new HashMap[L];
    for (int j = 0; j < L; j++) {
        votes[j] = new HashMap<Integer, Double>();
    }//from   www. j  ava2 s.c o m

    for (int m = 0; m < m_M; m++) {

        // Transform instance
        Instance x_m = PSUtils.convertInstance(x, L, m_InstancesTemplates[m]);
        x_m.setDataset(m_InstancesTemplates[m]);

        // Get a meta classification
        int yp_j = (int) m_Classifiers[m].classifyInstance(x_m); // e.g., 2

        int values[] = SuperLabelUtils.decodeValue(m_InstancesTemplates[m].classAttribute().value(yp_j));

        int k_indices[] = SuperLabelUtils.decodeClass(m_InstancesTemplates[m].classAttribute().name());

        // Vote with classification
        for (int j_k = 0; j_k < k_indices.length; j_k++) {
            //int i = k_indices[j_k];         // original indices
            int j = kMap[m][j_k]; // original indices
            Double score = votes[j].get(values[j_k]);
            votes[j].put(values[j_k], (score == null) ? 1. : score + 1.);
        }

    }

    double y[] = SuperLabelUtils.convertVotesToDistributionForInstance(votes);

    return y;
}

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

License:Open Source License

@Override
public double[] distributionForInstance(Instance x) throws Exception {

    //return mt.distributionForInstance(x);
    int L = x.classIndex();
    double y[] = new double[L * 2];

    // Convert (x,y) to (x_,y_)
    int L_ = m_InstancesTemplate.classIndex(); // == L-NUM
    Instance x_ = MLUtils.setTemplate(x, f.getTemplate(), m_InstancesTemplate);

    // Get a classification y_ = h(x_)
    double y_[] = null;
    try {/*from www  .ja v  a2  s  .  c  o  m*/
        y_ = ((ProblemTransformationMethod) m_Classifier).distributionForInstance(x_);
    } catch (Exception e) {
        System.err.println("EXCEPTION !!! setting to " + Arrays.toString(y_));
        return y;
        //e.printStackTrace();
        //System.exit(1);
    }

    // For each super node ...
    for (int j = 0; j < L_; j++) {

        int idxs[] = SuperNodeFilter.decodeClasses(m_InstancesTemplate.attribute(j).name()); // 3,4   (partition)
        String vals[] = SuperNodeFilter
                .decodeValue(m_InstancesTemplate.attribute(j).value((int) Math.round(y_[j]))); // 1,0   (clases)

        for (int i = 0; i < idxs.length; i++) {
            y[idxs[i]] = x.dataset().attribute(idxs[i]).indexOfValue(vals[i]); // y_j = v
            y[idxs[i] + L] = y_[j + L_]; // P(Y_j = v), hence, MUST be a multi-target classifier
        }
    }

    return y;
}

From source file:meka.core.CCUtils.java

License:Open Source License

/**
 * SetPath - set 'path[]' into the first L attributes of Instance 'xy'.
 * @param   xy      an Example (x,y)//  w w  w. j a  v a2s  .  c  om
 * @param   path   a label vector
 */
public static void setPath(Instance xy, double path[]) {
    int L = xy.classIndex(); // = path.length
    for (int j = 0; j < L; j++) {
        xy.setValue(j, (int) Math.round(path[j])); // x = x + path_j
    }
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * Instance with L labels to double[] of length L, where L = x.classIndex().
 * Rounds to the nearest whole number./*from   w w  w .j a  v a  2 s .  c o m*/
 */
public static final double[] toDoubleArray(Instance x) {
    int L = x.classIndex();
    return toDoubleArray(x, L);
}

From source file:meka.core.MLUtils.java

License:Open Source License

public static final Instance setTemplate(Instance x, Instances instancesTemplate) {
    int L = x.classIndex();
    int L_t = instancesTemplate.classIndex();
    x = (Instance) x.copy();/*from  w ww .  jav  a2 s . c o  m*/
    x.setDataset(null);
    for (int i = L_t; i < L; i++)
        x.deleteAttributeAt(0);
    x.setDataset(instancesTemplate);
    return x;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * SetTemplate - returns a copy of x_template, set with x's attributes, and set to dataset D_template (of which x_template) is a template of this.
 * This function is very useful when Weka throws a strange IndexOutOfBounds exception for setTemplate(x,Template)
 *///from www . jav a  2  s  .c  o  m
public static final Instance setTemplate(Instance x, Instance x_template, Instances D_template) {
    Instance x_ = (Instance) x_template.copy();
    int L_y = x.classIndex();
    int L_z = D_template.classIndex();
    // copy over x space
    MLUtils.copyValues(x_, x, L_y, L_z);
    // set class values to missing
    MLUtils.setLabelsMissing(x_, L_z);
    // set dataset
    x_.setDataset(D_template);
    return x_;
}

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * SetLabelsMissing - Set all labels in x to missing.
 *//*from   w  w  w  . java  2  s.c om*/
public static Instance setLabelsMissing(Instance x) {
    return setLabelsMissing(x, x.classIndex());
}

From source file:meka.core.MLUtils.java

License:Open Source License

public static final String toDebugString(Instance x) {
    int L = x.classIndex();
    StringBuilder sb = new StringBuilder();
    sb.append("y = [");
    for (int j = 0; j < L; j++) {
        sb.append(x.value(j) + " ");
    }/*from  www . ja  v a  2  s.  com*/
    sb.append("], x = [");
    for (int j = L; j < L + 10; j++) {
        sb.append(x.value(j) + " ");
    }
    sb.append(" ... ]");
    return sb.toString();
}

From source file:meka.core.MLUtils.java

License:Open Source License

/** Clear Labels -- set the value of all label attributes to 0.0 */
public static void clearLabels(Instance x) {
    int L = x.classIndex();
    for (int j = 0; j < L; j++)
        x.setValue(j, 0.0);//w w  w .ja  va2  s. c  o m
}