List of sortInitialize() Examples

Examples of sortInitialize()

weka.classifiers.meta.ensembleSelection.ModelBag.sortInitialize()
Sort initialize the bag. @param num the Maximum number of models to initialize with @param greedy True if we do greedy addition, up to num. Greedy sort initialization adds models (up to num) in order of best to worst performance until performance no longer improves. @param instances the data set (needed for performance evaluation) @param metric metric for which to optimize. See EnsembleMetricHelper @return returns an array of indexes which were selected, in orderstarting from the model with best performance. @throws Exception if something goes wrong

Examples of weka.classifiers.meta.ensembleSelection.ModelBag.sortInitialize()

    } else if (m_algorithm == ALGORITHM_BEST) {
      // If we want to choose the best model, just make a model bag that
      // includes all the models, then sort initialize to find the 1 that
      // performs best.
      ModelBag model_bag = new ModelBag(predictions, 1.0, m_Debug);
      int[] modelPicked = model_bag.sortInitialize(1, false, data,
    m_hillclimbMetric);
      // Then give it a weight of 1, while all others remain 0.
      modelWeights[modelPicked[0]] = 1;
    } else {

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Examples of weka.classifiers.meta.ensembleSelection.ModelBag.sortInitialize()

      m_Debug);
  // And shuffle it.
  modelBag.shuffle(rand);
  if (getSortInitializationRatio() > 0.0) {
    // Sort initialize, if the ratio greater than 0.
    modelBag.sortInitialize((int) (getSortInitializationRatio()
        * getModelRatio() * numModels),
        getGreedySortInitialization(), data,
        m_hillclimbMetric);
  }

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Examples of weka.classifiers.meta.ensembleSelection.ModelBag.sortInitialize()

    if (m_verboseOutput) {
      // Output every model and its performance with respect to the
      // validation
      // data.
      ModelBag bag = new ModelBag(predictions, 1.0, m_Debug);
      int modelIndexes[] = bag.sortInitialize(modelNamesArray.length,
    false, data, m_hillclimbMetric);
      double modelPerformance[] = bag.getIndividualPerformance(data,
    m_hillclimbMetric);
      for (int i = 0; i < modelIndexes.length; ++i) {
  // TODO - Could do this in a more readable way.

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