Examples with Distribution weka.classifiers.trees.j48.Distribution used on opensource projects

Example 6 with Distribution

use of weka.classifiers.trees.j48.Distribution in project umple by umple.

the class ClassifierDecList method buildDecList.

/**
 * Builds the partial tree without hold out set.
 *
 * @exception Exception if something goes wrong
 */
public void buildDecList(Instances data, boolean leaf) throws Exception {
    Instances[] localInstances;
    int ind;
    int i, j;
    double sumOfWeights;
    NoSplit noSplit;
    m_train = null;
    m_test = null;
    m_isLeaf = false;
    m_isEmpty = false;
    m_sons = null;
    indeX = 0;
    sumOfWeights = data.sumOfWeights();
    noSplit = new NoSplit(new Distribution(data));
    if (leaf) {
        m_localModel = noSplit;
    } else {
        m_localModel = m_toSelectModel.selectModel(data);
    }
    if (m_localModel.numSubsets() > 1) {
        localInstances = m_localModel.split(data);
        data = null;
        m_sons = new ClassifierDecList[m_localModel.numSubsets()];
        i = 0;
        do {
            i++;
            ind = chooseIndex();
            if (ind == -1) {
                for (j = 0; j < m_sons.length; j++) {
                    if (m_sons[j] == null) {
                        m_sons[j] = getNewDecList(localInstances[j], true);
                    }
                }
                if (i < 2) {
                    m_localModel = noSplit;
                    m_isLeaf = true;
                    m_sons = null;
                    if (Utils.eq(sumOfWeights, 0)) {
                        m_isEmpty = true;
                    }
                    return;
                }
                ind = 0;
                break;
            } else {
                m_sons[ind] = getNewDecList(localInstances[ind], false);
            }
        } while ((i < m_sons.length) && (m_sons[ind].m_isLeaf));
        // Choose rule
        indeX = chooseLastIndex();
    } else {
        m_isLeaf = true;
        if (Utils.eq(sumOfWeights, 0)) {
            m_isEmpty = true;
        }
    }
}

Example 7 with Distribution

use of weka.classifiers.trees.j48.Distribution in project umple by umple.

the class ResidualSplit method buildClassifier.

/**
 * Builds the split.
 * Needs the Z/W values of LogitBoost for the set of instances.
 */
public void buildClassifier(Instances data, double[][] dataZs, double[][] dataWs) throws Exception {
    m_numClasses = data.numClasses();
    m_numInstances = data.numInstances();
    if (m_numInstances == 0)
        throw new Exception("Can't build split on 0 instances");
    // save data/Zs/Ws
    m_data = data;
    m_dataZs = dataZs;
    m_dataWs = dataWs;
    m_attribute = data.attribute(m_attIndex);
    // determine number of subsets and split point for numeric attributes
    if (m_attribute.isNominal()) {
        m_splitPoint = 0.0;
        m_numSubsets = m_attribute.numValues();
    } else {
        getSplitPoint();
        m_numSubsets = 2;
    }
    // create distribution for data
    m_distribution = new Distribution(data, this);
}