Пример #1
0
  @Override
  public double[] distributionForInstance(Instance xy) throws Exception {

    int L = xy.classIndex();

    double y[] = new double[L];
    double w = 0.0;

    /*
     * e.g. K = [3,3,5]
     * we push y_[] from [0,0,0] to [2,2,4] over all necessary iterations.
     */
    int K[] = getKs(xy.dataset());
    if (getDebug()) System.out.println("K[] = " + Arrays.toString(K));
    double y_[] = new double[L];

    for (int i = 0; i < 1000000; i++) { // limit to 1m
      // System.out.println(""+i+" "+Arrays.toString(y_));
      double w_ = A.product(super.probabilityForInstance(xy, y_));
      if (w_ > w) {
        if (getDebug()) System.out.println("y' = " + Arrays.toString(y_) + ", :" + w_);
        y = Arrays.copyOf(y_, y_.length);
        w = w_;
      }
      if (push(y_, K, 0)) {
        // Done !
        if (getDebug()) System.out.println("Tried all " + (i + 1) + " combinations.");
        break;
      }
    }

    return y;
  }
Пример #2
0
  /**
   * Generates the classifier.
   *
   * @param data set of instances serving as training data
   * @throws Exception if the classifier has not been generated successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    m_theInstances = new Instances(data);
    m_theInstances.deleteWithMissingClass();

    m_rr = new Random(1);

    if (m_theInstances.classAttribute().isNominal()) { // 	 Set up class priors
      m_classPriorCounts = new double[data.classAttribute().numValues()];
      Arrays.fill(m_classPriorCounts, 1.0);
      for (int i = 0; i < data.numInstances(); i++) {
        Instance curr = data.instance(i);
        m_classPriorCounts[(int) curr.classValue()] += curr.weight();
      }
      m_classPriors = m_classPriorCounts.clone();
      Utils.normalize(m_classPriors);
    }

    setUpEvaluator();

    if (m_theInstances.classAttribute().isNumeric()) {
      m_disTransform = new weka.filters.unsupervised.attribute.Discretize();
      m_classIsNominal = false;

      // use binned discretisation if the class is numeric
      ((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setBins(10);
      ((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setInvertSelection(true);

      // Discretize all attributes EXCEPT the class
      String rangeList = "";
      rangeList += (m_theInstances.classIndex() + 1);
      // System.out.println("The class col: "+m_theInstances.classIndex());

      ((weka.filters.unsupervised.attribute.Discretize) m_disTransform)
          .setAttributeIndices(rangeList);
    } else {
      m_disTransform = new weka.filters.supervised.attribute.Discretize();
      ((weka.filters.supervised.attribute.Discretize) m_disTransform).setUseBetterEncoding(true);
      m_classIsNominal = true;
    }

    m_disTransform.setInputFormat(m_theInstances);
    m_theInstances = Filter.useFilter(m_theInstances, m_disTransform);

    m_numAttributes = m_theInstances.numAttributes();
    m_numInstances = m_theInstances.numInstances();
    m_majority = m_theInstances.meanOrMode(m_theInstances.classAttribute());

    // Perform the search
    int[] selected = m_search.search(m_evaluator, m_theInstances);

    m_decisionFeatures = new int[selected.length + 1];
    System.arraycopy(selected, 0, m_decisionFeatures, 0, selected.length);
    m_decisionFeatures[m_decisionFeatures.length - 1] = m_theInstances.classIndex();

    // reduce instances to selected features
    m_delTransform = new Remove();
    m_delTransform.setInvertSelection(true);

    // set features to keep
    m_delTransform.setAttributeIndicesArray(m_decisionFeatures);
    m_delTransform.setInputFormat(m_theInstances);
    m_dtInstances = Filter.useFilter(m_theInstances, m_delTransform);

    // reset the number of attributes
    m_numAttributes = m_dtInstances.numAttributes();

    // create hash table
    m_entries = new Hashtable((int) (m_dtInstances.numInstances() * 1.5));

    // insert instances into the hash table
    for (int i = 0; i < m_numInstances; i++) {
      Instance inst = m_dtInstances.instance(i);
      insertIntoTable(inst, null);
    }

    // Replace the global table majority with nearest neighbour?
    if (m_useIBk) {
      m_ibk = new IBk();
      m_ibk.buildClassifier(m_theInstances);
    }

    // Save memory
    if (m_saveMemory) {
      m_theInstances = new Instances(m_theInstances, 0);
      m_dtInstances = new Instances(m_dtInstances, 0);
    }
    m_evaluation = null;
  }