Examples of weka.filters.unsupervised.attribute.Discretize

weka.filters.unsupervised.attribute.Discretize

An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.

Valid options are:

 -unset-class-temporarily Unsets the class index temporarily before the filter is applied to the data. (default: no)

 -B <num> Specifies the (maximum) number of bins to divide numeric attributes into. (default = 10)

 -M <num> Specifies the desired weight of instances per bin for equal-frequency binning. If this is set to a positive number then the -B option will be ignored. (default = -1)

 -F Use equal-frequency instead of equal-width discretization.

 -O Optimize number of bins using leave-one-out estimate of estimated entropy (for equal-width discretization). If this is set then the -B option will be ignored.

 -R <col1,col2-col4,...> Specifies list of columns to Discretize. First and last are valid indexes. (default: first-last)

 -V Invert matching sense of column indexes.

 -D Output binary attributes for discretized attributes.

@author Len Trigg (trigg@cs.waikato.ac.nz) @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 6568 $

  /* private object used for parsing */
  private String[] class_bounds;
  private double min, max;
  
  public WekaBinClassifier(){
    discretize = new Discretize();
    super.classifier = new J48();
  }

    data.deleteAttributeAt(0); //remove the bagIndex attribute;




    // Assume the order of the instances are preserved in the Discretize filter
    if(m_DiscretizeBin > 0){
      m_Filter = new Discretize();
      m_Filter.setInputFormat(new Instances(data, 0));
      m_Filter.setBins(m_DiscretizeBin);
      data = Filter.useFilter(data, m_Filter);
    }

                filt_hold = Filter.useFilter(filt_hold, theRemove);
            }




            if (!aDiscrete.isEmpty()){
                Discretize disc = new Discretize();
                disc.setAttributeIndices((String) aDiscrete.get(0));
                disc.setBins((Integer) aDiscrete.get(1));
                disc.setFindNumBins(((JCheckBox) aDiscrete.get(2)).isSelected());
                disc.setUseEqualFrequency(aDiscrete.get(3).equals("True"));
                disc.setInputFormat(filt_learn);
                filt_learn = Filter.useFilter(filt_learn, disc);
                disc.setInputFormat(filt_hold);
                filt_hold = Filter.useFilter(filt_hold, disc);
            }




                        Classifier cpyCls[];

Examples of weka.filters.unsupervised.attribute.Discretize

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