def __init__(self, Counts, L, GroupLength, prior_offset, Nyes, Ntrials, ModelResponse, value=None):
"""
Counts - nonterminal -> #h x #rules counts
L - group -> #h x 1 array
GroupLength - #groups (vector) - contains the number of trials per group
Nyes - #item ( #item = sum(GroupLength))
Ntrials - #item
ModelResponse - #h x #item - each hypothesis' response to the i'th item (1 or 0)
"""
assert sum(GroupLength) == len(Nyes) == len(Ntrials)
L = numpy.array(L)
self_update(self,locals())
self.N_groups = len(GroupLength)
self.nts = Counts.keys() # all nonterminals
self.nrules = { nt: Counts[nt].shape[1] for nt in self.nts} # number of rules for each nonterminal
self.N_hyps = Counts[self.nts[0]].shape[0]
if value is None:
value = {
'rulep': { nt: DirichletDistribution(alpha=np.ones(self.nrules[nt]), proposal_scale=1000.) for nt in self.nts },
'alpha': BetaDistribution(1,1),
'beta': BetaDistribution(1,1),
'likelihood_temperature': GammaDistribution(a=1, scale=1, proposal_scale=10.),
'prior_temperature': GammaDistribution(a=1, scale=1, proposal_scale=10.)
}
Hypothesis.__init__(self, value=value) # sets the value
def __init__(self, make_hypothesis, words=(), **kwargs): """ hypothesis - a function to generate hypotheses words -- words to initially add (sampling from the prior) """ Hypothesis.__init__(self, value=dict(), **kwargs) self.__dict__.update(locals()) assert isroutine(make_hypothesis) # check that we can call # update with the supplied words, each generating from the grammar for w in words: self.set_word(w, v=None)
def __init__(self, make_hypothesis, words=(), **kwargs):
"""
hypothesis - a function to generate hypotheses
words -- words to initially add (sampling from the prior)
"""
Hypothesis.__init__(self, value=dict(), **kwargs)
self.__dict__.update(locals())
assert isroutine(make_hypothesis) # check that we can call
# update with the supplied words, each generating from the grammar
for w in words:
self.set_word(w, v=None)
def __init__(self, value=None, propose_p=0.5, **kwargs):
"""
make_hypothesis -- a function to make each individual word meaning. None will leave it empty (for copying)
words -- words to initially add (sampling from the prior)
propose_p -- the probability of proposing to each word
"""
if value is None:
value = dict()
else:
assert isinstance(self.value, dict)
Hypothesis.__init__(self, value=value, **kwargs)
self.propose_p = propose_p
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
def __init__(self, value=None, ll_decay=0.0, **kwargs):
Hypothesis.__init__(self, value=value, **kwargs)
self.ll_decay = ll_decay # store this
self.stored_likelihood = None
def __init__(self, value=None, ll_decay=0.0, **kwargs):
Hypothesis.__init__(self, value=value, **kwargs)
self.ll_decay = ll_decay # store this
self.stored_likelihood = None
