def get_knower_pattern(self):
# compute a string describing the behavior of this knower-level
resp = [
self(set(sample_sets_of_objects(n, all_objects)))
for n in xrange(1, 10)
]
return ''.join([
str(word_to_number[x]) if
(x is not None and x is not 'undef') else 'U' for x in resp
])
def generate_data(data_size):
"""
Sample some data according to the target
"""
data = []
for i in range(data_size):
# how many in this set
set_size = weighted_sample( range(1,10+1), probs=[7187, 1484, 593, 334, 297, 165, 151, 86, 105, 112] )
# get the objects in the current set
s = set(sample_sets_of_objects(set_size, all_objects))
# sample according to the target
if random() < ALPHA: r = WORDS[len(s)-1]
else: r = weighted_sample( WORDS )
# and append the sampled utterance
data.append(FunctionData(input=[s], output=r)) # convert to "FunctionData" and store
return data
def make_data(data_size=300, alpha=0.75):
"""
Sample some data according to the target
"""
data = []
for i in range(data_size):
# how many in this set
set_size = weighted_sample(
range(1, 10 + 1),
probs=[7187, 1484, 593, 334, 297, 165, 151, 86, 105, 112])
# get the objects in the current set
s = set(sample_sets_of_objects(set_size, all_objects))
# sample according to the target
if random() < alpha: r = WORDS[len(s) - 1]
else: r = weighted_sample(WORDS)
# and append the sampled utterance
data.append(FunctionData(input=[s], output=r, alpha=alpha))
return data
def get_knower_pattern(ne):
out = ''
resp = [ ne(set(sample_sets_of_objects(n, all_objects))) for n in xrange(1, 10)]
return ''.join([str(word_to_number[x]) if (x is not None and x is not 'undef') else 'U' for x in resp])
for i in range(data_size):
# how many in this set
set_size = weighted_sample( range(1,10+1), probs=[7187, 1484, 593, 334, 297, 165, 151, 86, 105, 112] )
# get the objects in the current set
s = set(sample_sets_of_objects(set_size, all_objects))
# sample according to the target
if random() < ALPHA: r = WORDS[len(s)-1]
else: r = weighted_sample( WORDS )
# and append the sampled utterance
data.append(FunctionData(input=[s], output=r)) # convert to "FunctionData" and store
return data
# compute a string describing the behavior of this knower-level
def get_knower_pattern(ne):
out = ''
resp = [ ne(set(sample_sets_of_objects(n, all_objects))) for n in xrange(1, 10)]
return ''.join([str(word_to_number[x]) if (x is not None and x is not 'undef') else 'U' for x in resp])
# ============================================================================================================
# All objects -- not very exciting
#here this is really just a dummy -- one type of object, which is replicated in sample_sets_of_objects
all_objects = make_all_objects(shape=['duck'])
# all possible data sets on 10 objects
all_possible_data = [ ('', set(sample_sets_of_objects(n, all_objects))) for n in xrange(1,10) ]
def get_knower_pattern(self):
# compute a string describing the behavior of this knower-level
resp = [self(set(sample_sets_of_objects(n, all_objects))) for n in xrange(1, 10)]
return "".join([str(word_to_number[x]) if (x is not None and x is not "undef") else "U" for x in resp])
s = set(sample_sets_of_objects(set_size, all_objects))
# sample according to the target
if random() < alpha: r = WORDS[len(s) - 1]
else: r = weighted_sample(WORDS)
# and append the sampled utterance
data.append(FunctionData(input=[s], output=r, alpha=alpha))
return data
#here this is really just a dummy -- one type of object, which is replicated in sample_sets_of_objects
all_objects = make_all_objects(shape=['duck'])
# all possible data sets on 10 objects
all_possible_data = [('', set(sample_sets_of_objects(n, all_objects)))
for n in xrange(1, 10)]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Grammar
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib.Grammar import Grammar
from LOTlib.Miscellaneous import q
# The priors here are somewhat hierarchical by type in generation, tuned to be a little more efficient
# (but the actual RR prior does not care about these probabilities)
grammar = Grammar()
grammar.add_rule('START', '', ['WORD'], 1.0)
