ML.InfoTheory.rdInfoTheory

Module rdInfoTheory

Module containing bunch of functions for information metrics and a ranker to rank bits

Classes

BitCorrMatGenerator
A class to generate a pariwise correlation matrix between a list of bits...

InfoBitRanker
A class to rank the bits from a series of labelled fingerprints A simple demonstration may help clarify what this class does.

Functions

[hide private]

ChiSquare(...)
Calculates the chi squared value for a variable ARGUMENTS: - varMat: a Numeric Array object varMat is a Numeric array with the number of possible occurances of each result for reach possible value of the given variable.

source code

InfoEntropy(...)
calculates the informational entropy of the values in an array ARGUMENTS: - resMat: pointer to a long int array containing the data - dim: long int containing the length of the _tPtr_ array.

source code

InfoGain(varMat)
Calculates the information gain for a variable ARGUMENTS: - varMat: a Numeric Array object varMat is a Numeric array with the number of possible occurances of each result for reach possible value of the given variable.

source code

Function Details

[hide private]

ChiSquare(...)

source code

Calculates the chi squared value for a variable

   ARGUMENTS:

     - varMat: a Numeric Array object
       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:

     - a Python float object

C++ signature:
    ChiSquare(boost::python::api::object) -> double

InfoEntropy(...)

source code

calculates the informational entropy of the values in an array

  ARGUMENTS:
    
    - resMat: pointer to a long int array containing the data
    - dim: long int containing the length of the _tPtr_ array.

  RETURNS:

    a double

C++ signature:
    InfoEntropy(boost::python::api::object) -> double

InfoGain(varMat)

source code

Calculates the information gain for a variable

   ARGUMENTS:

     - varMat: a Numeric Array object
       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:

     - a Python float object

   NOTES

     - this is a dropin replacement for _PyInfoGain()_ in entropy.py

C++ signature:
    InfoGain(boost::python::api::object) -> double