ML.InfoTheory.rdInfoTheory

Package ML :: Package InfoTheory :: Module rdInfoTheory |
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# Module rdInfoTheory

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

Classes | [hide private]
| 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