Package rdkit :: Package ML :: Package InfoTheory :: Module entropy
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Module entropy

source code

Informational Entropy functions

The definitions used are the same as those in Tom Mitchell's
book "Machine Learning"

Functions [hide private]
 
PyInfoEntropy(results)
Calculates the informational entropy of a set of results.
source code
 
PyInfoGain(varMat)
calculates the information gain for a variable
source code
Variables [hide private]
  hascEntropy = 1
  _log2 = 0.69314718056
  __package__ = 'rdkit.ML.InfoTheory'

Imports: numpy, math, cEntropy, InfoEntropy, InfoGain


Function Details [hide private]

PyInfoEntropy(results)

source code 
Calculates the informational entropy of a set of results.

**Arguments**

  results is a 1D Numeric array containing the number of times a
  given set hits each possible result.
  For example, if a function has 3 possible results, and the
    variable in question hits them 5, 6 and 1 times each,
    results would be [5,6,1]

**Returns**

  the informational entropy

PyInfoGain(varMat)

source code 
calculates the information gain for a variable

**Arguments**

  varMat is a Numeric array with the number of possible occurances
    of each result for reach possible value of the given variable.

  So, for a variable which adopts 4 possible values and a result which
    has 3 possible values, varMat would be 4x3

**Returns**

  The expected information gain