Package rdkit :: Package ML :: Package InfoTheory
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Package InfoTheory

source code

Information Theory functionality

Submodules [hide private]

Functions [hide private]
 
ChiSquare(...)
ChiSquare( (AtomPairsParameters)arg1) -> float : Calculates the chi squared value for a variable
source code
 
InfoEntropy(results)
InfoEntropy( (AtomPairsParameters)arg1) -> float : calculates the informational entropy of the values in an array
source code
 
InfoGain(...)
InfoGain( (AtomPairsParameters)arg1) -> float : Calculates the information gain for a variable
source code

Imports: BitCorrMatGenerator, InfoBitRanker, InfoType, entropy, rdInfoTheory


Function Details [hide private]

ChiSquare(...)

source code 

ChiSquare( (AtomPairsParameters)arg1) -> float :
    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 :
        double ChiSquare(boost::python::api::object)

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)