IGweight.py 文件源码

def entropy(v, axis=0):
  """
  Optimized implementation of entropy. This version is faster than that in 
  scipy.stats.distributions, particularly over long vectors.
  """
  v = numpy.array(v, dtype='float')
  s = numpy.sum(v, axis=axis)
  with numpy.errstate(divide='ignore', invalid='ignore'):
    rhs = numpy.nansum(v * numpy.log(v), axis=axis) / s
    r = numpy.log(s) - rhs
  # Where dealing with binarized events, it is possible that an event always
  # occurs and thus has 0 information. In this case, the negative class
  # will have frequency 0, resulting in log(0) being computed as nan.
  # We replace these nans with 0
  nan_index = numpy.isnan(rhs)
  if nan_index.any():
    r[nan_index] = 0
  return r