rdkit.ML.InfoTheory.entropy module — The RDKit 2016.09.1 documentation

# rdkit.ML.InfoTheory.entropy module

Informational Entropy functions

The definitions used are the same as those in Tom Mitchell’s book “Machine Learning”

`rdkit.ML.InfoTheory.entropy.` `PyInfoEntropy`(results)

  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

`rdkit.ML.InfoTheory.entropy.` `PyInfoGain`(varMat)

  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