def crossover_lot(x,y):
t = copy(x.value)
n1, _ = t.sample_subnode(resampleProbability=lambdaOne)
n2, _ = y.value.sample_subnode(resampleProbability=lambda t: 1*(t.returntype==n1.returntype))
n1.setto(n2) # assign the value!
return Hypothesis.__copy__(x, value=t)
def crossover_lot(x, y):
t = copy(x.value)
n1, _ = t.sample_subnode(resampleProbability=lambdaOne)
n2, _ = y.value.sample_subnode(
resampleProbability=lambda t: 1 * (t.returntype == n1.returntype))
n1.setto(n2) # assign the value!
return Hypothesis.__copy__(x, value=t)
def propose(self):
if random.random() < 0.5:
ret = regeneration_proposal(self.grammar, self.value)
else:
ret = insert_delete_proposal(self.grammar, self.value)
p = Hypothesis.__copy__(self, value=ret[0])
ret[0] = p
return ret
def propose(self):
if random.random() < 0.5:
ret = regeneration_proposal(self.grammar, self.value)
else:
ret = insert_delete_proposal(self.grammar, self.value)
p = Hypothesis.__copy__(self, value=ret[0])
ret[0] = p
return ret
def propose(self, **kwargs):
while True: # keep trying to propose
try:
ret = self.propose_tree(self.value, **kwargs) # don't unpack, since we may return [newt,fb] or [newt,f,b]
break
except ProposalFailedException:
pass
p = Hypothesis.__copy__(self, value=ret[0])
ret[0] = p # really make the first a hypothesis, not a tree
return ret
def propose(self, **kwargs):
while True: # keep trying to propose
try:
ret = self.propose_tree(
self.value, **kwargs
) # don't unpack, since we may return [newt,fb] or [newt,f,b]
break
except ProposalFailedException:
pass
p = Hypothesis.__copy__(self, value=ret[0])
ret[0] = p # really make the first a hypothesis, not a tree
return ret
