def marginal_ve_e(bn, target, evidence={}):
"""
Perform Sum-Product Variable Elimination on
a Discrete Bayesian Network.
Arguments
---------
*bn* : a BayesNet object
*target* : a list of target RVs
*evidence* : a dictionary, where
key = rv and value = rv value
Returns
-------
*marginal_dict* : a dictionary, where
key = an rv in target and value =
a numpy array containing the key's
marginal conditional probability distribution.
Notes
-----
- Mutliple pieces of evidence often returns "nan"...numbers too small?
- dividing by zero -> perturb values in Factor class
"""
_phi = Factorization(bn)
order = copy(list(bn.nodes()))
order.remove(target)
#### EVIDENCE PROCESSING ####
for E, e in evidence.items():
_phi -= (E, e)
order.remove(E)
#### SUM-PRODUCT ELIMINATE VAR ####
for var in order:
_phi /= var
# multiply phi's together if there is evidence
final_phi = _phi.consolidate()
return np.round(final_phi.cpt, 4)
def marginal_ve_e(bn, target, evidence={}):
"""
Perform Sum-Product Variable Elimination on
a Discrete Bayesian Network.
Arguments
---------
*bn* : a BayesNet object
*target* : a list of target RVs
*evidence* : a dictionary, where
key = rv and value = rv value
Returns
-------
*marginal_dict* : a dictionary, where
key = an rv in target and value =
a numpy array containing the key's
marginal conditional probability distribution.
Notes
-----
- Mutliple pieces of evidence often returns "nan"...numbers too small?
- dividing by zero -> perturb values in Factor class
"""
_phi = Factorization(bn)
order = copy(list(bn.nodes()))
order.remove(target)
#### EVIDENCE PROCESSING ####
for E, e in evidence.items():
_phi -= (E,e)
order.remove(E)
#### SUM-PRODUCT ELIMINATE VAR ####
for var in order:
_phi /= var
# multiply phi's together if there is evidence
final_phi = _phi.consolidate()
return np.round(final_phi.cpt,4)
def ve_map(bn,
evidence={},
target=None,
prob=False):
"""
Perform Max-Sum Variable Elimination over a BayesNet object
for exact maximum a posteriori inference.
This has been validated w/ and w/out evidence
"""
_phi = Factorization(bn)
order = copy(list(bn.nodes()))
#### EVIDENCE PROCESSING ####
for E, e in evidence.items():
_phi -= (E,e)
order.remove(E)
#### MAX-PRODUCT ELIMINATE VAR ####
for var in order:
_phi //= var
#### TRACEBACK MAP ASSIGNMENT ####
max_assignment = _phi.traceback_map()
#### RETURN ####
if prob:
# multiply phi's together if there is evidence
final_phi = _phi.consolidate()
max_prob = round(final_phi.cpt[0],5)
if target is not None:
return max_prob, max_assignment[target]
else:
return max_prob, max_assignment
else:
if target is not None:
return max_assignment[target]
else:
return max_assignment
def ve_map(bn, evidence={}, target=None, prob=False):
"""
Perform Max-Sum Variable Elimination over a BayesNet object
for exact maximum a posteriori inference.
This has been validated w/ and w/out evidence
"""
_phi = Factorization(bn)
order = copy(list(bn.nodes()))
#### EVIDENCE PROCESSING ####
for E, e in evidence.items():
_phi -= (E, e)
order.remove(E)
#### MAX-PRODUCT ELIMINATE VAR ####
for var in order:
_phi //= var
#### TRACEBACK MAP ASSIGNMENT ####
max_assignment = _phi.traceback_map()
#### RETURN ####
if prob:
# multiply phi's together if there is evidence
final_phi = _phi.consolidate()
max_prob = round(final_phi.cpt[0], 5)
if target is not None:
return max_prob, max_assignment[target]
else:
return max_prob, max_assignment
else:
if target is not None:
return max_assignment[target]
else:
return max_assignment
