Undirected Graphical Model

class pyAgrum.MarkovRandomField(*args)

MarkovRandomField represents a Markov random field.

MarkovRandomField(name=’’) -> MarkovRandomField

Parameters:

name (str) – the name of the Bayes Net

MarkovRandomField(source) -> MarkovRandomField

Parameters:

source (pyAgrum.MarkovRandomField) – the Markov random field to copy

add(*args)

Add a variable to the pyAgrum.MarkovRandomField.

Parameters:

variable (pyAgrum.DiscreteVariable) – the variable added
name (str) – the variable name
nbrmod (int) – the number of modalities for the new variable
id (int) – the variable forced id in the pyAgrum.MarkovRandomField

Returns:

the id of the new node

Return type:

int

Raises:

pyAgrum.DuplicateLabel – If variable.name() is already used in this pyAgrum.MarkovRandomField.
pyAgrum.OperationNotAllowed – If nbrmod is less than 2
pyAgrum.DuplicateElement – If id is already used.

addFactor(*args)

Add a factor from a list or a set of id or str. If the argument is a set, the order is the order of the IDs of the variables

Parameters:: seq (sequence (list or set) of int or string) – The sequence (ordered or not) of node id or names
Return type:: Potential

addStructureListener(whenNodeAdded=None, whenNodeDeleted=None, whenEdgeAdded=None, whenedgeDeleted=None)

Add the listeners in parameters to the list of existing ones.

Parameters:

whenNodeAdded (lambda expression) – a function for when a node is added
whenNodeDeleted (lambda expression) – a function for when a node is removed
whenEdgeAdded (lambda expression) – a function for when an edge is added
whenEdgeDeleted (lambda expression) – a function for when an edge is removed

addVariables(listFastVariables, default_nbr_mod=2)

Add a list of variable in the form of ‘fast’ syntax.

Parameters:

listFastVariables (List[str]) – the list of variables in ‘fast’ syntax.
default_nbr_mod (int) – the number of modalities for the variable if not specified following fast syntax. Note that default_nbr_mod=1 is mandatory to create variables with only one modality (for utility for instance).

Returns:

the list of created ids.

Return type:

List[int]

adjacencyMatrix()

adjacency matrix from a graph/graphical models

Compute the adjacency matrix of a pyAgrum’s graph or graphical models (more generally an object that has nodes, children/parents or neighbours methods)

Returns:: adjacency matrix (as numpy.ndarray) with nodeId as key.
Return type:: numpy.ndarray

beginTopologyTransformation()

Return type:: None

changeVariableLabel(*args)

change the label of the variable associated to nodeId to the new value.

Parameters:

var (Union[int,str]) – a variable’s id (int) or name
old_label (str) – the old label
new_label (str) – the new label

Raises:

pyAgrum.NotFound – if id/name is not a variable or if old_label does not exist.

Return type:

None

changeVariableName(*args)

Changes a variable’s name in the pyAgrum.MarkovRandomField.

This will change the “pyAgrum.DiscreteVariable” names in the pyAgrum.MarkovRandomField.

Parameters:

car (Union[int,str]) – a variable’s id (int) or name
new_name (str) – the new name of the variable

Raises:

pyAgrum.DuplicateLabel – If new_name is already used in this MarkovRandomField.
pyAgrum.NotFound – If no variable matches id.

Return type:

None

clear()

Clear the whole MarkovRandomField

Return type:: None

completeInstantiation()

Give an instantiation over all the variables of the model

Returns:: a complete Instantiation for the model
Return type:: pyAgrum.Instantiation

connectedComponents()

connected components from a graph/graphical models

Compute the connected components of a pyAgrum’s graph or graphical models (more generally an object that has nodes, children/parents or neighbours methods)

The firstly visited node for each component is called a ‘root’ and is used as a key for the component. This root has been arbitrarily chosen during the algorithm.

Returns:: dict of connected components (as set of nodeIds (int)) with a nodeId (root) of each component as key.
Return type:: dict(int,Set[int])

dim()

Return type:: int

edges()

Return type:: object

empty()

Check if there are some variables in the model.

Returns:: True if there is no variable in the model.
Return type:: bool

endTopologyTransformation()

Terminates a sequence of insertions/deletions of arcs by adjusting all CPTs dimensions. End Multiple Change for all CPTs.

Return type:: pyAgrum.MarkovRandomField

erase(*args)

Remove a variable from the pyAgrum.MarkovRandomField.

Removes the corresponding variable from the pyAgrum.MarkovRandomField and from all of it’s children pyAgrum.Potential.

If no variable matches the given id, then nothing is done.

Parameters:: var (Union[int,str,pyAgrum.DiscreteVariable]) – a variable’s id (int) or name of variable or a reference of this variable to remove.
Return type:: None

eraseFactor(*args)

Return type:: None

exists(*args)

Check if a node with this name or id exists

Parameters:: norid (str|int) – name or id of the searched node
Returns:: True if there is a node with such a name or id
Return type:: bool

existsEdge(*args)

Return type:: bool

factor(*args)

Returns the factor of a set of variables (if existing).

Parameters:: vars (Union[Set[int],Set[str]]) – A set of ids or names of variable the pyAgrum.MarkovRandomField.
Returns:: The factor of the set of nodes.
Return type:: pyAgrum.Potential
Raises:: pyAgrum.NotFound – If no variable’s id matches varId.

factors()

Return type:: List[Set[int]]

family(*args)

Return type:: List[int]

static fastPrototype(*args)

Create a Markov random field with a modified dot-like syntax which specifies:

the structure a-b-c;b-d-e;. The substring a-b-c indicates a factor with the scope (a,b,c).
the type of the variables with different syntax (cf documentation).

Examples

>>> import pyAgrum as gum
>>> bn=pyAgrum.MarkovRandomField.fastPrototype('A--B[1,3]-C{yes|No}--D[2,4]--E[1,2.5,3.9]',6)

Parameters:

dotlike (str) – the string containing the specification
domainSize (int or str) – the default domain size or the default domain for variables

Returns:

the resulting Markov random field

Return type:

pyAgrum.MarkovRandomField

static fromBN(bn)

Parameters:: bn (BayesNet)
Return type:: MarkovRandomField

generateFactor(vars)

Randomly generate factor parameters for a given factor in a given structure.

Parameters:

node (Union[int,str]) – a variable’s id (int) or name
vars (List[int])

Return type:

None

generateFactors()

Randomly generates factors parameters for a given structure.

Return type:: None

graph()

Return type:: UndiGraph

hasSameStructure(other)

idFromName(name)

Parameters:: name (str)
Return type:: int

ids(names)

List of ids for a list of names of variables in the model

Parameters:

lov (List[str]) – List of variable names
names (List[str])

Returns:

The ids for the list of names of the graph variables

Return type:

List[int]

isIndependent(*args)

check if nodes X and nodes Y are independent given nodes Z

Parameters:

X (str|intList[str|int]) – a list of of nodeIds or names
Y (str|intList[str|int]) – a list of of nodeIds or names
Z (str|intList[str|int]) – a list of of nodeIds or names

Raises:

InvalidArgument – if X and Y share variables

Returns:

True if X and Y are independent given Z in the model

Return type:

bool

loadUAI(*args)

Load an UAI file.

Parameters:

name (str) – the name’s file
l (list) – list of functions to execute

Raises:

pyAgrum.IOError – If file not found
pyAgrum.FatalError – If file is not valid

Return type:

str

log10DomainSize()

returns the log10 of the domain size of the model defined as the product of the domain sizes of the variables in the model.

Returns:: the log10 domain size.
Return type:: float

maxNonOneParam()

Return type:: float

maxParam()

Return type:: float

maxVarDomainSize()

Return type:: int

minNonZeroParam()

Return type:: float

minParam()

Return type:: float

minimalCondSet(*args)

Return type:: object

names()

Set of names of variables in the model

Returns:: The names of the graph variables
Return type:: Set[str]

neighbours(norid)

Parameters:: norid (object)
Return type:: object

nodeId(var)

Parameters:: var (DiscreteVariable)
Return type:: int

nodes()

Return type:: object

nodeset(names)

Set of ids for a list of names of variables in the model

Parameters:

lov (List[str]) – List of variable names
names (List[str])

Returns:

The set of ids for the list of names of the graph variables

Return type:

Set[int]

properties()

Return type:: List[str]

saveUAI(name)

Save the MarkovRandomField in an UAI file.

Parameters:: name (str) – the file’s name
Return type:: None

size()

Return type:: int

sizeEdges()

Return type:: int

smallestFactorFromNode(node)

Parameters:: node (int)
Return type:: List[int]

property thisown: The membership flag

toDot()

Return type:: str

toDotAsFactorGraph()

Return type:: str

toFast(filename=None)

Export the MRF as fast syntax (in a string or in a python file)

Parameters:: filename (Optional[str]) – the name of the file (including the prefix), if None , use sys.stdout
Return type:: str

variable(*args)

Return type:: DiscreteVariable

variableFromName(name)

Parameters:: name (str)
Return type:: DiscreteVariable

variableNodeMap()