def run(data_size):
print "Running ", data_size
# We store the top 100 from each run
hypset = FiniteBestSet(TOP_COUNT, max=True)
# initialize the data
data = generate_data(data_size)
# starting hypothesis -- here this generates at random
learner = GriceanQuantifierLexicon(make_my_hypothesis, my_weight_function)
# We will defautly generate from null the grammar if no value is specified
for w in target.all_words(): learner.set_word(w)
# populate the finite sample by running the sampler for this many steps
for x in mh_sample(learner, data, SAMPLES, skip=0):
hypset.push(x, x.posterior_score)
return hypset
def run(data_size):
print "Running ", data_size
# We store the top 100 from each run
hypset = FiniteBestSet(TOP_COUNT, max=True)
# initialize the data
data = generate_data(data_size)
# starting hypothesis -- here this generates at random
learner = GriceanQuantifierLexicon(make_my_hypothesis, my_weight_function)
# We will defautly generate from null the grammar if no value is specified
for w in target.all_words():
learner.set_word(w)
# populate the finite sample by running the sampler for this many steps
for x in mh_sample(learner, data, SAMPLES, skip=0):
hypset.push(x, x.posterior_score)
return hypset
show_baseline_distribution(TESTING_SET)
print "\n\n"
# intialize a learner lexicon, at random
h0 = GriceanQuantifierLexicon(make_my_hypothesis, my_weight_function)
for w in target.all_words():
h0.set_word(w) # We will defautly generate from null the grammar if no value is specified
### sample the target data
data = generate_data(300)
### Update the target with the data
target.compute_likelihood(data)
print h0
#### Now we have built the data, so run MCMC
for h in mh_sample(h0, data, 10000000, skip=0):
sstr = str(h)
sstr = re.sub("[_ ]", "", sstr)
sstr = re.sub("presup", u"\u03BB A B . presup", sstr)
print h.posterior_score, "\t", h.prior, "\t", h.likelihood, "\t", target.likelihood, "\n", sstr, "\n\n"
# for t in data:
# print h(t.utterance, t.context), t
print "\n\n"
# intialize a learner lexicon, at random
h0 = GriceanQuantifierLexicon(make_my_hypothesis, my_weight_function)
for w in target.all_words():
h0.set_word(
w
) # We will defautly generate from null the grammar if no value is specified
### sample the target data
data = generate_data(300)
### Update the target with the data
target.compute_likelihood(data)
print h0
#### Now we have built the data, so run MCMC
for h in mh_sample(h0, data, 10000000, skip=0):
sstr = str(h)
sstr = re.sub("[_ ]", "", sstr)
sstr = re.sub("presup", u"\u03BB A B . presup", sstr)
print h.posterior_score, "\t", h.prior, "\t", h.likelihood, "\t", target.likelihood, "\n", sstr, "\n\n"
#for t in data:
#print h(t.utterance, t.context), t
