def in_concept_avg(self, domain):
"""
p(y in C | `self.hypotheses`)
for each hypothesis h, if y in C_h, accumulated w_h where w is the weight of a hypothesis,
determined by the hypothesis's posterior score p(h | y)
==> This is the weighted bayesian model averaging described in (Murphy, 2007)
"""
self.update()
probs_in_c = {}
for y in domain:
prob_in_c = 0
Z = logsumexp([h.posterior_score for h in self.hypotheses])
# for h in self.hypotheses:
# h.set_value(h.value)
# print self.hypotheses[0].prior, self.hypotheses[3].prior, self.hypotheses[5].prior
for h in self.hypotheses:
C = h()
w = h.posterior_score - Z
if y in C:
prob_in_c += exp(w)
probs_in_c[y] = prob_in_c
return probs_in_c
def in_concept_avg(self, domain):
"""
p(y in C | `self.hypotheses`)
for each hypothesis h, if y in C_h, accumulated w_h where w is the weight of a hypothesis,
determined by the hypothesis's posterior score p(h | y)
==> This is the weighted bayesian model averaging described in (Murphy, 2007)
"""
self.update()
probs_in_c = {}
for y in domain:
prob_in_c = 0
Z = logsumexp([h.posterior_score for h in self.hypotheses])
# for h in self.hypotheses:
# h.set_value(h.value)
# print self.hypotheses[0].prior, self.hypotheses[3].prior, self.hypotheses[5].prior
for h in self.hypotheses:
C = h()
w = h.posterior_score - Z
if y in C:
prob_in_c += exp(w)
probs_in_c[y] = prob_in_c
return probs_in_c
#comparison = GriceanQuantifierLexicon(make_my_hypothesis, my_weight_function)
#comparison.set_word('every', LOTHypothesis(G, value='SET_IN_TARGET', f=lambda A, B, S: presup_( nonempty_( A ), empty_( A ) )))
#comparison.set_word('some', LOTHypothesis(G, value='SET_IN_TARGET', f=lambda A, B, S: presup_( True, subset_( A, A ) )))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#This will debug -- for a given context and all_utterances, see if our likelihood is the same as empirical sampling
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
N_SAMPLES = 10000
if __name__ == "__main__":
context = sample_context()
cnt = defaultdict(int)
for _ in xrange(N_SAMPLES):
u = target.sample_utterance(context=context,
possible_utterances=target.all_words())
cnt[u] += 1
#for w,c in cnt.items():
#print w, float(c)/float(N_SAMPLES), exp(target.compute_single_likelihood( UtteranceData(utterance=w, possible_utterances=target.all_words(), context=context)))
print check_counts(
cnt, lambda w: exp(
target.compute_single_likelihood(
UtteranceData(utterance=w,
possible_utterances=target.all_words(),
context=context))))
from LOTlib.Miscellaneous import exp
from LOTlib.Examples.Quantifier.Model import *
#distribution of context sizes
#for i in xrange(1000):
#context = sample_context()
#comparison = GriceanQuantifierLexicon(make_my_hypothesis, my_weight_function)
#comparison.set_word('every', LOTHypothesis(G, value='SET_IN_TARGET', f=lambda A, B, S: presup_( nonempty_( A ), empty_( A ) )))
#comparison.set_word('some', LOTHypothesis(G, value='SET_IN_TARGET', f=lambda A, B, S: presup_( True, subset_( A, A ) )))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#This will debug -- for a given context and all_utterances, see if our likelihood is the same as empirical sampling
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
N_SAMPLES = 10000
if __name__ == "__main__":
context = sample_context()
cnt = defaultdict(int)
for _ in xrange(N_SAMPLES):
u = target.sample_utterance( context=context, possible_utterances=target.all_words())
cnt[u] += 1
#for w,c in cnt.items():
#print w, float(c)/float(N_SAMPLES), exp(target.compute_single_likelihood( UtteranceData(utterance=w, possible_utterances=target.all_words(), context=context)))
print check_counts( cnt, lambda w: exp(target.compute_single_likelihood( UtteranceData(utterance=w, possible_utterances=target.all_words(), context=context))))