Package nltk_lite :: Package contrib :: Package classifier :: Module discretise
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Module discretise

source code

Classes [hide private]
  Discretise
  Discretiser
Functions [hide private]
 
get_chunks_with_frequency(values, freq) source code
 
ranges_from_chunks(chunks) source code
Variables [hide private]
  a_help = 'Selects the discretisation algorithm ...
  f_help = 'Base name of attribute, klass, training, test and go...
  t_help = 'Base name of training file for discretisation. '
  T_help = 'Base name of test file to be discterised. '
  g_help = 'Base name of gold file to be discretised. '
  A_help = 'Comma separated list of attribute indices. '
  o_help = 'Algorithm specific options ...
  UNSUPERVISED_EQUAL_WIDTH = 'UEW'
  UNSUPERVISED_EQUAL_FREQUENCY = 'UEF'
  NAIVE_SUPERVISED = 'NS'
  NAIVE_SUPERVISED_V1 = 'NS1'
  NAIVE_SUPERVISED_V2 = 'NS2'
  ENTROPY_BASED_SUPERVISED = 'ES'
  ALGORITHM_MAPPINGS = {'ES': 'entropy_based_supervised', 'NS': ...
Variables Details [hide private]

a_help

Value:
'Selects the discretisation algorithm                 Options: UEW for\
 Unsupervised Equal Width                     UEF for Unsupervised Equ\
al Frequency                 NS for Naive Supervised                  \
            NS1 for Naive Supervised version 1                   NS2 f\
or Naive Supervised version 2                   ES for Entropy Based S\
upervised             Default: UEW.'

f_help

Value:
'Base name of attribute, klass, training, test and gold files.        \
                                      '

o_help

Value:
'Algorithm specific options                           UEW: Comma separ\
ated list of number of parts in which     each attribute should be spl\
it.                 '

ALGORITHM_MAPPINGS

Value:
{'ES': 'entropy_based_supervised',
 'NS': 'naive_supervised',
 'NS1': 'naive_supervised_v1',
 'NS2': 'naive_supervised_v2',
 'UEF': 'unsupervised_equal_frequency',
 'UEW': 'unsupervised_equal_width'}