def make_data(N=30):
"""
The data here consist of saffran-aslin-newport type strings. They have a geometric length distribution more like what
you might find in natural data, with more frequent shorter strings. This is modeled in the hypothesis with a flip to
whether or not you recurse to generate a longer string.
"""
data = []
cnt = Counter()
for _ in xrange(N):
cnt[''.join(sample_one(words) for _ in xrange(5))] += 1
return [FunctionData(input=[], output=cnt)]
def make_data(N=30):
"""
The data here consist of saffran-aslin-newport type strings. They have a geometric length distribution more like what
you might find in natural data, with more frequent shorter strings. This is modeled in the hypothesis with a flip to
whether or not you recurse to generate a longer string.
"""
data = []
cnt = Counter()
for _ in xrange(N):
cnt[''.join(sample_one(words) for _ in xrange(5))] += 1
return [FunctionData(input=[], output=cnt)]
def propose(self):
new = copy(self) ## Now we just copy the whole thing
while True:
try:
i = sample_one(range(self.N)) # random one
# i = max(self.value.keys()) # only propose to last
x, fb = self.get_word(i).propose()
new.set_word(i, x)
return new, fb
except ProposalFailedException:
pass
def propose(self):
new = copy(self) ## Now we just copy the whole thing
while True:
try:
i = sample_one(range(self.N)) # random one
# i = max(self.value.keys()) # only propose to last
x, fb = self.get_word(i).propose()
new.set_word(i, x)
return new, fb
except ProposalFailedException:
pass
def propose(self):
""" We only propose to the last in this kind of lexicon
"""
new = deepcopy(self) ## Now we just copy the whole thing
while True:
try:
i = sample_one(range(self.N))
x, fb = self.get_word(i).propose()
new.set_word(i, x)
new.grammar = self.value[0].grammar # keep the grammar the same object
return new, fb
except ProposalFailedException:
pass
def propose(self):
""" We only propose to the last in this kind of lexicon
"""
new = deepcopy(self) ## Now we just copy the whole thing
while True:
try:
i = sample_one(range(self.N)) # random one
# i = max(self.value.keys()) # only propose to last
x, fb = self.get_word(i).propose()
new.set_word(i, x)
new.grammar = self.value[0].grammar # keep the grammar the same object
return new, fb
except ProposalFailedException:
pass
def make_data(N=20, f=TargetConcepts[0]):
data = []
for _ in xrange(N):
o = sample_one(all_objects)
data.append(FunctionData(input=[o], output=f(o), alpha=0.90))
return data
def make_data(N=20, f=TargetConcepts[0]):
data = []
for _ in xrange(N):
o = sample_one(all_objects)
data.append(FunctionData(input=[o], output=f(o), alpha=0.90))
return data
def generate_data(N, f):
data = []
for _ in xrange(N):
o = sample_one(all_objects)
data.append(FunctionData(input=[o], output=f(o)))
return data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib.DataAndObjects import FunctionData, make_all_objects
from LOTlib.Miscellaneous import sample_one
from random import random
all_objects = make_all_objects( shape=SHAPES, color=COLORS )
# Generate all of the data, picking a different target hypothesis for each sequence
# "people" are shown
datas = []
for di in xrange(NDATASETS):
# pick a hypothesis at random
target = sample_one(hypotheses)
print "# Target:", target
data = []
for _ in xrange(DATASET_SIZE):
obj = sample_one(all_objects)
if random() < ALPHA:
output = target(obj)
else:
output = random() < BETA
data.append( FunctionData(input=[obj], output=output, alpha=ALPHA) )
datas.append(data)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib.DataAndObjects import FunctionData, make_all_objects
from LOTlib.Miscellaneous import sample_one
from random import random
all_objects = make_all_objects(shape=SHAPES, color=COLORS)
# Generate all of the data, picking a different target hypothesis for each sequence
# "people" are shown
datas = []
for di in xrange(NDATASETS):
# pick a hypothesis at random
target = sample_one(hypotheses)
print "# Target:", target
data = []
for _ in xrange(DATASET_SIZE):
obj = sample_one(all_objects)
if random() < ALPHA:
output = target(obj)
else:
output = random() < BETA
data.append(FunctionData(input=[obj], output=output, alpha=ALPHA))
datas.append(data)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
