ML.InfoTheory.entropy

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 **Arguments** 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(results) InfoEntropy( (AtomPairsParameters)arg1) -> float : 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(...) InfoGain( (AtomPairsParameters)arg1) -> float : 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

Variables | [hide private]
| hascEntropy = `1`
| _log2 = `0.69314718056`

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

### InfoEntropy(results) | source code
    InfoEntropy( (AtomPairsParameters)arg1) -> float :
        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 :
            double InfoEntropy(boost::python::api::object)

### InfoGain(...) | source code
    InfoGain( (AtomPairsParameters)arg1) -> float :
        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 :
            double InfoGain(boost::python::api::object)