def get_transition_graph(self):
g = gum.DiGraph()
for i in range(self.__len__()):
g.addNode() #on ajoute autant de noeud qu'il n y a d'états
mat = self.get_transition_matrix()
for i in range(len(mat)):
l = mat[i] #une ligne de la matrice
for j in range(len(l)):
p = l[j] #on recupere la proba correspondante dans la matrice
if (p != 0):
g.addArc(
i, j
) #si la proba n'est pas null on ajoute un arc entre les deux etats
return g
def get_transition_graph(self):
#creer un graph oriente
g = gum.DiGraph()
#creer autant de noeuds qu'il y a d'etats
for i in range(len(self.get_states())):
# g.addNodeWithId(i+1)
g.addNode()
#recup la matrice de transitions
matrix = self.get_transition_matrix()
#ajouter les transitions au graph
for i in range(len(matrix)):
for j in range(len(matrix[i])):
c = matrix[i][j]
if (c > 0):
g.addArc(i, j)
return g
def testConstructorFromDG(self):
dg = gum.DiGraph()
dg.addNodes(4)
dg.addArc(0, 2)
dg.addArc(1, 2)
dg.addArc(2, 3)
mixed_graph = gum.MixedGraph()
mixed_graph.addNodes(4)
mixed_graph.addArc(0, 2)
mixed_graph.addArc(1, 2)
mixed_graph.addArc(2, 3)
mg = gum.MixedGraph(dg)
self.assertEqual(mixed_graph, mg)
def backdoor_generator(bn: "pyAgrum.BayesNet",
cause: NodeId,
effect: NodeId,
not_bd: NodeSet = None):
"""
Generates backdoor sets for the pair of nodes `(cause, effect)` in the graph `bn` excluding the nodes in the set
`not_bd` (optional)
Parameters
----------
bn: pyAgrum.BayesNet
cause: int
effect: int
not_bd: Set[int] default=None
Yields
-------
List[int]
the different backdoors
"""
if len(bn.parents(cause)) == 0: # no parent of cause, no backdoor
return
if isParent(effect, cause, bn): # causalDagFromBN(bn)):
return
if not_bd is None:
not_bd = set()
# simplifying the graph
interest = {cause, effect}
G = dSep_reduce(bn, interest)
# removing the non connected in G without descendants
# GG is a trash graph just to find the disjointed nodes in G
GG = pyAgrum.DiGraph(G)
for i in descendants(bn, cause, set()):
GG.eraseNode(i)
# we only keep interesting connex components
for nodes in GG.connectedComponents().values():
if nodes.isdisjoint(interest):
for n in nodes:
G.eraseNode(n)
possible = set(
G.nodes()) - (descendants(bn, cause, set()) | interest | not_bd)
if len(possible) == 0:
return
backdoors = set()
for i in range(len(possible)):
for subset in it.combinations(possible, i + 1):
sub = frozenset(subset)
worth_testing = True
for s in backdoors:
if s <= sub:
worth_testing = False
if worth_testing and isDSep_parents(G, {cause}, {effect}, sub):
backdoors.add(sub)
yield list(subset)
def testAddNodes(self):
self._testAddNodes(gum.DiGraph())
self._testAddNodes(gum.UndiGraph())
self._testAddNodes(gum.MixedGraph())
self._testAddNodes(gum.DAG())