Information Theory of Bayesian network

class pyAgrum.InformationTheory(*args)

This class gathers information theory concepts for subsets named X,Y and Z computed with only one inference.

Parameters:
  • nodeset (- Z (intstr or iterable[intstr] ) -- a third (an optional))

  • nodeset

  • nodeset

entropyX()
Returns:

The entropy of nodeset X.

Return type:

float

entropyXY()
Return type:

float

Returns:

float

The entropy of nodeset, union of X and Y.

entropyXYgivenZ()
Return type:

float

entropyXgivenY()
Return type:

float

Returns:

float

The conditional entropy of nodeset X conditionned by nodeset Y

entropyY()
Return type:

float

Returns:

float

The entropy of nodeset X.

entropyYgivenX()
Return type:

float

Returns:

float

The conditional entropy of nodeset Y conditionned by nodeset X

mutualInformationXY()
Return type:

float

mutualInformationXYgivenZ()
Return type:

float

Returns:

float

The conditional mutual information between nodeset X and nodeset Y conditionned by nodeset Z

variationOfInformationXY()
Return type:

float

Returns:

float

The variation of information between nodeset X and nodeset Y