def compute_single_likelihood(self, di):
"""
Compute the likelihood, where di.input is a [f arg1 arg2 arg3 ...], curried
"""
freduced = self(*di.input) # this is where the magic happens
# Okay with 1.0-ALPHA we'll sample uniformly from words
# and with probability ALPHA we'll sample from all the words which have
# identical associated lambdas
p = (1.0 - self.alpha) / len(self.all_words())
freduced_str = lambdastring(freduced)
# TODO: maybe we also reduce self.value[w]?
matches = [
w for w in self.all_words()
if lambdastring(self.value[w].value) == freduced_str
]
# print ">>>", di.input, freduced_str, matches
if di.output in matches:
p += self.alpha / len(matches) # else outlier likelihood
return log(p)
def print_lexicon_and_data(L, data):
"""
Friendlier printing of the lexicon and associated data inputs and outputs
"""
print(L.posterior_score, L.prior, L.likelihood)
print(L)
for di in data:
outstr = lambdastring(L(*di.input))
print("\t", di.input, "->", di.output, "\t==>", [
w for w in list(L.value.keys())
if lambdastring(L.value[w].value) == outstr
], "\t", outstr)
print("\n")
def compute_single_likelihood(self, di):
"""
Compute the likelihood, where di.input is a [f arg1 arg2 arg3 ...], curried
"""
freduced = self(*di.input) # this is where the magic happens
# Okay with 1.0-ALPHA we'll sample uniformly from words
# and with probability ALPHA we'll sample from all the words which have
# identical associated lambdas
p = (1.0-self.alpha) / len(self.all_words())
freduced_str = lambdastring(freduced)
# TODO: maybe we also reduce self.value[w]?
matches = filter(lambda w: lambdastring(self.value[w].value) == freduced_str, self.all_words())
# print ">>>", di.input, freduced_str, matches
if di.output in matches:
p += self.alpha / len(matches) # else outlier likelihood
return log(p)
def print_lexicon_and_data(L, data):
"""
Friendlier printing of the lexicon and associated data inputs and outputs
"""
print L.posterior_score, L.prior, L.likelihood
print L
for di in data:
outstr = lambdastring(L(*di.input))
print "\t", di.input, "->", di.output, "\t==>", [ w for w in L.value.keys() if lambdastring(L.value[w].value)==outstr], "\t", outstr
print "\n"
def __str__(self):
return ('\n'.join([u"%-15s: %s" % (qq(w), lambdastring(v.value)) for w, v in sorted(self.value.iteritems())]) + '\0').encode('utf-8')
def __str__(self):
return '\n'.join([
"%-15s: %s" % (qq(w), lambdastring(v.value))
for w, v in sorted(self.value.items())
]) + '\0'
def __str__(self):
return ('\n'.join([
u"%-15s: %s" % (qq(w), lambdastring(v.value))
for w, v in sorted(self.value.iteritems())
]) + '\0').encode('utf-8')