def get_independencies(self, latex=False):
"""
Computes independencies in the Bayesian Network, by checking d-seperation.
Parameters
----------
latex: boolean
If latex=True then latex string of the independence assertion
would be created.
Examples
--------
>>> from pgmpy.models import BayesianModel
>>> chain = BayesianModel([('X', 'Y'), ('Y', 'Z')])
>>> chain.get_independencies()
(X _|_ Z | Y)
(Z _|_ X | Y)
"""
independencies = Independencies()
for start in (self.nodes()):
rest = set(self.nodes()) - {start}
for r in range(len(rest)):
for observed in itertools.combinations(rest, r):
d_seperated_variables = rest - set(observed) - set(
self.active_trail_nodes(start, observed=observed))
if d_seperated_variables:
independencies.add_assertions(
[start, d_seperated_variables, observed])
independencies.reduce()
if not latex:
return independencies
else:
return independencies.latex_string()
def get_independencies(self, latex=False):
"""
Computes independencies in the Bayesian Network, by checking d-seperation.
Parameters
----------
latex: boolean
If latex=True then latex string of the independence assertion
would be created.
Examples
--------
>>> from pgmpy.models import BayesianModel
>>> chain = BayesianModel([('X', 'Y'), ('Y', 'Z')])
>>> chain.get_independencies()
(X _|_ Z | Y)
(Z _|_ X | Y)
"""
independencies = Independencies()
for start in (self.nodes()):
rest = set(self.nodes()) - {start}
for r in range(len(rest)):
for observed in itertools.combinations(rest, r):
d_seperated_variables = rest - set(observed) - set(
self.active_trail_nodes(start, observed=observed)[start])
if d_seperated_variables:
independencies.add_assertions([start, d_seperated_variables, observed])
independencies.reduce()
if not latex:
return independencies
else:
return independencies.latex_string()
def get_independencies(self, latex=False):
"""
Compute independencies in Bayesian Network.
Parameters
----------
latex: boolean
If latex=True then latex string of the independence assertion
would be created.
Examples
--------
>>> from pgmpy.models import BayesianModel
>>> student = BayesianModel()
>>> student.add_nodes_from(['diff', 'intel', 'grades', 'letter', 'sat'])
>>> student.add_edges_from([('diff', 'grades'), ('intel', 'grades'), ('grade', 'letter'),
... ('intel', 'sat')])
>>> student.get_independencies()
"""
independencies = Independencies()
for start in (self.nodes()):
for r in (1, len(self.nodes())):
for observed in itertools.combinations(self.nodes(), r):
independent_variables = self.active_trail_nodes(start, observed=observed)
independent_variables = set(independent_variables) - {start}
if independent_variables:
independencies.add_assertions([start, independent_variables,
observed])
independencies.reduce()
if not latex:
return independencies
else:
return independencies.latex_string()
def get_local_independencies(self, latex=False):
"""
Returns all the local independencies present in the markov model.
Local independencies are the independence assertion in the form of
.. math:: {X \perp W - {X} - MB(X) | MB(X)}
where MB is the markov blanket of all the random variables in X
Parameters
----------
latex: boolean
If latex=True then latex string of the indepedence assertion would
be created
Examples
--------
>>> from pgmpy.models import MarkovModel
>>> mm = MarkovModel()
>>> mm.add_nodes_from(['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7'])
>>> mm.add_edges_from([('x1', 'x3'), ('x1', 'x4'), ('x2', 'x4'),
... ('x2', 'x5'), ('x3', 'x6'), ('x4', 'x6'),
... ('x4', 'x7'), ('x5', 'x7')])
>>> mm.get_local_independecies()
"""
from pgmpy.exceptions import RequiredError
local_independencies = Independencies()
all_vars = set(self.nodes())
for node in self.nodes():
markov_blanket = set(self.markov_blanket(node))
rest = all_vars - set([node]) - markov_blanket
try:
local_independencies.add_assertions(
[node, list(rest), list(markov_blanket)])
except RequiredError:
pass
local_independencies.reduce()
if latex:
return local_independencies.latex_string()
else:
return local_independencies
def get_local_independencies(self, latex=False):
"""
Returns all the local independencies present in the markov model.
Local independencies are the independence assertion in the form of
.. math:: {X \perp W - {X} - MB(X) | MB(X)}
where MB is the markov blanket of all the random variables in X
Parameters
----------
latex: boolean
If latex=True then latex string of the indepedence assertion would
be created
Examples
--------
>>> from pgmpy.models import MarkovModel
>>> mm = MarkovModel()
>>> mm.add_nodes_from(['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7'])
>>> mm.add_edges_from([('x1', 'x3'), ('x1', 'x4'), ('x2', 'x4'),
... ('x2', 'x5'), ('x3', 'x6'), ('x4', 'x6'),
... ('x4', 'x7'), ('x5', 'x7')])
>>> mm.get_local_independecies()
"""
from pgmpy.exceptions import RequiredError
local_independencies = Independencies()
all_vars = set(self.nodes())
for node in self.nodes():
markov_blanket = set(self.markov_blanket(node))
rest = all_vars - set([node]) - markov_blanket
try:
local_independencies.add_assertions([node, list(rest), list(markov_blanket)])
except RequiredError:
pass
local_independencies.reduce()
if latex:
return local_independencies.latex_string()
else:
return local_independencies
def get_independencies(self, latex=False):
"""
Compute independencies in Bayesian Network.
Parameters
----------
latex: boolean
If latex=True then latex string of the independence assertion
would be created.
Examples
--------
>>> from pgmpy.models import BayesianModel
>>> student = BayesianModel()
>>> student.add_nodes_from(['diff', 'intel', 'grades', 'letter', 'sat'])
>>> student.add_edges_from([('diff', 'grades'), ('intel', 'grades'), ('grade', 'letter'),
... ('intel', 'sat')])
>>> student.get_independencies()
"""
independencies = Independencies()
for start in (self.nodes()):
for r in (1, len(self.nodes())):
for observed in itertools.combinations(self.nodes(), r):
independent_variables = self.active_trail_nodes(
start, observed=observed)
independent_variables = set(independent_variables) - {
start
}
if independent_variables:
independencies.add_assertions(
[start, independent_variables, observed])
independencies.reduce()
if not latex:
return independencies
else:
return independencies.latex_string()