Introduction to pyAgrum¶
pyAgrum is a scientific C++ and Python library dedicated to Bayesian networks (BN) and other Probabilistic Graphical Models. Based on the C++ aGrUM library, it provides a high-level interface to the C++ part of aGrUM allowing to create, manage and perform efficient computations with Bayesian networks and others probabilistic graphical models : Markov networks (MN), influence diagrams (ID) and LIMIDs, credal networks (CN), dynamic BN (dBN), probabilistic relational models (PRM).
The module is generated using the SWIG interface generator. Custom-written code was added to make the interface more user friendly.
pyAgrum aims to allow to easily use (as well as to prototype new algorithms on) Bayesian network and other graphical models.
- pyAgrum contains :
Reference manual¶
- Functions from pyAgrum
- Other functions from aGrUM
- Exceptions from aGrUM
GumException
DefaultInLabel
DuplicateElement
DuplicateLabel
FatalError
FormatNotFound
GraphError
IOError
InvalidArc
InvalidArgument
InvalidArgumentsNumber
InvalidDirectedCycle
InvalidEdge
InvalidNode
NoChild
NoNeighbour
NoParent
NotFound
NullElement
OperationNotAllowed
OutOfBounds
ArgumentError
SizeError
SyntaxError
UndefinedElement
UndefinedIteratorKey
UndefinedIteratorValue
UnknownLabelInDatabase
DatabaseError
CPTError