/** * Main method for testing this class. * * @param argv should contain arguments to the filter: use -h for help */ public static void main(String[] argv) { runFilter(new Discretize(), argv); } }
/** Creates a default Discretize */ public Filter getFilter() { Discretize f= new Discretize(); return f; }
/** Creates a default Discretize */ public Filter getFilter() { Discretize f= new Discretize(); return f; }
/** * Main method for testing this class. * * @param argv should contain arguments to the filter: use -h for help */ public static void main(String[] argv) { runFilter(new Discretize(), argv); } }
/** Creates a specialized Discretize */ public Filter getFilter(String rangelist) { try { Discretize f = new Discretize(); f.setAttributeIndices(rangelist); return f; } catch (Exception ex) { ex.printStackTrace(); fail("Exception setting attribute range: " + rangelist + "\n" + ex.getMessage()); } return null; }
/** Creates a specialized Discretize */ public Filter getFilter(String rangelist) { try { Discretize f = new Discretize(); f.setAttributeIndices(rangelist); return f; } catch (Exception ex) { ex.printStackTrace(); fail("Exception setting attribute range: " + rangelist + "\n" + ex.getMessage()); } return null; }
this.discretizer = new Discretize(); try { discretizer.setInputFormat(this.instances);
m_Filter = new Discretize(); m_Filter.setInputFormat(new Instances(data, 0)); m_Filter.setBins(m_DiscretizeBin);
m_disTransform = new weka.filters.unsupervised.attribute.Discretize(); m_classIsNominal = false;
m_disTransform = new weka.filters.unsupervised.attribute.Discretize(); m_classIsNominal = false;
Discretize discretizer = new Discretize(); discretizer.setBins(m_NumIntervals); discretizer.setIgnoreClass(true);
Discretize discretizer = new Discretize(); discretizer.setBins(m_NumIntervals); discretizer.setIgnoreClass(true);