Example usage for weka.core Instance setDataset

Introduction

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

Prototype

public void setDataset(Instances instances);

Source Link

Document

Sets the reference to the dataset.

Usage

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

License:Open Source License

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

    int L = x.classIndex();
    //ArrayList<double[]> collection = new ArrayList<double[]>(100);

    double y[] = new double[L]; // for collectiing marginal
    int sequence[] = A.make_sequence(L);

    double likelihood[] = new double[L];

    for (int i = 0; i < I; i++) {
        Collections.shuffle(Arrays.asList(sequence));
        for (int j : sequence) {
            // x = [x,y[1],...,y[j-1],y[j+1],...,y[L]]
            x.setDataset(D_templates[j]);
            // q = h_j(x)    i.e. p(y_j | x)

            double dist[] = h[j].distributionForInstance(x);
            int k = A.samplePMF(dist, m_R);
            x.setValue(j, k);/*from w  ww . j  ava2 s  .  c o  m*/
            likelihood[j] = dist[k];
            // likelihood
            double s = Utils.sum(likelihood);
            // collect  // and where is is good 
            if (i > (I - I_c)) {
                y[j] += x.value(j);
            }
            // else still burning in
        }
    }
    // finish, calculate marginals
    for (int j = 0; j < L; j++) {
        y[j] /= I_c;
    }

    return y;
}

From source file:meka.classifiers.multilabel.incremental.BRUpdateable.java

License:Open Source License

@Override
public void updateClassifier(Instance x) throws Exception {

    int L = x.classIndex();

    if (getDebug())
        System.out.print("-: Updating " + L + " models");

    for (int j = 0; j < L; j++) {
        Instance x_j = (Instance) x.copy();
        x_j.setDataset(null);
        x_j = MLUtils.keepAttributesAt(x_j, new int[] { j }, L);
        x_j.setDataset(m_InstancesTemplates[j]);
        ((UpdateableClassifier) m_MultiClassifiers[j]).updateClassifier(x_j);
    }//from w  w w  .j a  v a2  s  .  com

    if (getDebug())
        System.out.println(":- ");
}

From source file:meka.classifiers.multilabel.meta.DeepML.java

License:Open Source License

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

    int L = xy.classIndex();

    double z[] = dbm.prob_z(MLUtils.getxfromInstance(xy));

    Instance zy = (Instance) m_InstancesTemplate.firstInstance().copy();

    MLUtils.setValues(zy, z, L);/* www .j  a  va2 s . c  o m*/
    zy.setDataset(m_InstancesTemplate);

    return m_Classifier.distributionForInstance(zy);
}

From source file:meka.classifiers.multilabel.meta.MBR.java

License:Open Source License

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

    int c = instance.classIndex();

    double result[] = m_BASE.distributionForInstance(instance);

    instance.setDataset(null);

    for (int i = 0; i < c; i++) {
        instance.insertAttributeAt(c);//from w ww .ja  va 2  s.  c o m
    }

    instance.setDataset(m_InstancesTemplate);

    for (int i = 0; i < c; i++) {
        instance.setValue(c + i, result[i]);
    }

    return m_META.distributionForInstance(instance);
}

From source file:meka.classifiers.multilabel.meta.RandomSubspaceML.java

License:Open Source License

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

    int L = x.classIndex();
    double p[] = new double[L];

    for (int i = 0; i < m_NumIterations; i++) {
        // Use a template Instance from training, and copy values over
        // (this is faster than copying x and cutting it to shape)
        Instance x_ = (Instance) m_InstanceTemplates[i];
        MLUtils.copyValues(x_, x, m_IndicesCut[i]);
        x_.setDataset(m_InstancesTemplates[i]);

        // TODO, use generic voting scheme somewhere?
        double d[] = ((ProblemTransformationMethod) m_Classifiers[i]).distributionForInstance(x_);
        for (int j = 0; j < d.length; j++) {
            p[j] += d[j];/* w ww .j  a va2  s  .  co m*/
        }
    }

    return p;
}

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

License:Open Source License

@Override
public double[] distributionForInstance(Instance instance) throws Exception {
    int L = instance.classIndex();
    Instance x = F.meka2mulan((Instance) instance.copy(), L);
    x.setDataset(m_InstancesTemplate);
    double y[] = m_MULAN.makePrediction(x).getConfidences();
    return y;//from www .  ja v  a  2  s .  c o  m
}

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

License:Open Source License

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

    int L = x.classIndex();

    // If there is only one label, predict it
    //if(L == 1) return new double[]{1.0};

    double y[] = new double[L];
    //int c[] = new int[L]; // to scale it between 0 and 1

    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 i_m = (int) m_Classifiers[m].classifyInstance(x_m); // e.g., 2
        int k_indices[] = mapBack(m_InstancesTemplates[m], i_m); // e.g., [3,8]

        // Vote with classification
        for (int i : k_indices) {
            int index = kMap[m][i];
            y[index] += 1.;/*from  w w w.ja  v  a2s . c o m*/
        }

    }

    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>();
    }/*ww w . j av  a 2  s  .  com*/

    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.core.CCUtils.java

License:Open Source License

/**
 * LinkTransform - prepare 'x' for testing at a node 'j' of the chain, by excluding 'exl'.
 * @param   x      instance//from ww w.j a va 2s  .com
 * @param   excl   indices of labels which are NOT parents of j
 * @param   _D      the dataset template to use
 * @return   the transformed instance
 */
public static Instance linkTransformation(Instance x, int excl[], Instances _D) {
    // copy
    Instance copy = (Instance) x.copy();
    copy.setDataset(null);

    // delete attributes we don't need
    for (int i = excl.length - 1; i >= 0; i--) {
        copy.deleteAttributeAt(excl[i]);
    }

    //set template
    copy.setDataset(_D);

    return copy;
}

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 w  w .  j ava2s  . c  o m
    x.setDataset(null);
    for (int i = L_t; i < L; i++)
        x.deleteAttributeAt(0);
    x.setDataset(instancesTemplate);
    return x;
}