Examples of weka.attributeSelection.AttributeSelection.SelectAttributes()

Class weka.attributeSelection.AttributeSelection

Examples of weka.attributeSelection.AttributeSelection.SelectAttributes()

weka.attributeSelection.AttributeSelection.SelectAttributes()
Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc. @param ASEvaluator an evaluator object @param options an array of options, not only for the evaluatorbut also the search method (if any) and an input data file @return the results of attribute selection as a String @exception Exception if no training file is set

   */
  public void evaluateAttributes(String corpusName, String featureSetName,
      String splitName, Instances inst, String label, Integer run,
      Integer fold) throws Exception {
    AttributeSelection ae = this.getAttributeSelection();
    ae.SelectAttributes(inst);
    double rankedAttributes[][] = ae.rankedAttributes();
    FeatureEvaluation fe = initializeFeatureEvaluation(corpusName,
        featureSetName, splitName, label, run, fold);
    List<FeatureRank> featureRanks = new ArrayList<FeatureRank>(
        rankedAttributes.length);

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      }
            
      switch (testMode) {
        case 0: // select using training
        m_Log.statusMessage(Messages.getInstance().getString("AttributeSelectionPanel_StartAttributeSelection_Run_Log_StatusMessage_Text_First"));
        eval.SelectAttributes(inst);
        break;


        case 1: // CV mode
        m_Log.statusMessage(Messages.getInstance().getString("AttributeSelectionPanel_StartAttributeSelection_Run_Log_StatusMessage_Text_Second"));
        Random random = new Random(seed);

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      }
            
      switch (testMode) {
        case 0: // select using training
        m_Log.statusMessage("Evaluating on training data...");
        eval.SelectAttributes(inst);
        break;


        case 1: // CV mode
        m_Log.statusMessage("Randomizing instances...");
        Random random = new Random(seed);

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      }
            
      switch (testMode) {
        case 0: // select using training
        m_Log.statusMessage("Evaluating on training data...");
        eval.SelectAttributes(inst);
        break;


        case 1: // CV mode
        m_Log.statusMessage("Randomizing instances...");
        Random random = new Random(seed);

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