def rejection_sampling(self,
X: str,
N: int,
e: dict = None) -> ProbDist: #533
"""
Estimate the probability distribution of variable X given using
N samples and evidence e. If not evidence is given, use the default
for the Inference object.
"""
if e == None:
e = self.evidence
# Q = ProbDist(X)
"""YOUR CODE"""
countV = ProbDist(X)
for j in range(N): #N is the sample spaces.
x = self.sample() # x←PRIOR-SAMPLE(bn)
# px=ProbDist(x)
#new way. want to find if e is subset of x items.
setx = set(x.items())
sete = set(e.items())
if (sete.issubset(setx)): #yay
if x[X] is True:
countV[x[X]] = countV[x[X]] + 1
if (x[X] is False):
countV[x[X]] = countV[x[X]] + 1
# print(ProbDist.normalize(countV))
return ProbDist.normalize(countV) #<-- fix this too
def enumeration_infer(self, X, e=None) -> ProbDist: #page 525-528
"""
Return the conditional probability distribution of variable X
given evidence e
"""
'''
Use evidence passed to function call, otherwise use default
'''
if e == None:
e = self.evidence
assert X not in e, 'Query variable must be distinct from evidence'
assert X in self.net.variables, 'Variable needs to be in network'
"""
* Initialize a probability distribution
* For each outcome sum over all the hidden variables
* Normalize
"""
Q = ProbDist(X)
# print(Q)
# print('im here')
# for each value xi of X do
for xi in self.net.variable_values(X):
# e[xi] = X
exi = self.copy_assign(e, X, xi)
# print(exi,'exiGHGHGHGHGHHGHGHGHGHGHG')
# Q(xi)←ENUMERATE-ALL(bn.VARS, exi )
Q[xi] = self.enumerate_all(self.net.variables, exi)
# print(e)
"""YOUR CODE"""
# return NORMALIZE(Q(X))
# print(Q, "this is Q")
return Q.normalize()
def likelihood_weighting(self,
X: str,
N: int,
e: dict = None) -> ProbDist: #page 534
"""Estimate the probability distribution of variable X given
evidence e
"""
if e == None:
e = self.evidence
W = ProbDist(X)
for j in range(N):
x, w = self.weighted_sample(e.copy())
W[x[X]] = W[x[X]] + w
return ProbDist.normalize(W)