# starting hypothesis -- here this generates at random
h0 = Utilities.make_h0()
hyps = TopN(N=options.TOP_COUNT)
hyps.add(lot_iter(MHSampler(h0, data, options.STEPS, trace=False)))
return hyps
if __name__ == "__main__":
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Main running
if is_master_process():
display_option_summary(options)
huge_data = generate_data(options.LARGE_DATA_SIZE)
# choose the appropriate map function
argarray = map(lambda x: [x], options.DATA_AMOUNTS * options.CHAINS)
seen = set()
for fs in MPI_unorderedmap(run, numpy.random.permutation(argarray)):
for h in fs.get_all():
if h not in seen:
seen.add(h)
h.compute_posterior(huge_data)
if h.prior > float("-inf"):
print h.prior, \
else:
data_amounts = [options.DATA]
# ========================================================================================================
# Load the model specified on the command line
# ========================================================================================================
from LOTlib.Examples.ExampleLoader import load_example
make_hypothesis, make_data = load_example(options.MODEL)
# ========================================================================================================
# Run, MPI friendly
# ========================================================================================================
if is_master_process():
display_option_summary(options)
eval_data = None
if options.EVAL_DATA > 0:
eval_data = make_data(options.EVAL_DATA)
# choose the appropriate map function
args = list(itertools.product([make_hypothesis],[make_data], data_amounts * options.CHAINS) )
# set the output codec -- needed to display lambda to stdout
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
seen = set()
for fs in MPI_unorderedmap(run, numpy.random.permutation(args)):
data_amount) + data[0].word + '.pkl'
with open(
'Chains/' + data[0].X + data[0].Y + str(data_amount) +
data[0].word + '.pkl', 'w') as f:
pickle.dump(hyps, f)
return hyps
###################################################################################
# Main Running
###################################################################################
argarray = map(lambda x: [x], options.data_pts * options.chains)
if is_master_process():
display_option_summary(options)
seen = set()
for fs in break_ctrlc(
MPI_map(run, numpy.random.permutation(argarray), progress_bar=False)):
for h in fs.get_all():
if h not in seen:
seen.add(h)
if h.prior > -Infinity:
print h.prior, \
h.likelihood, \
h \
#sys.stdout.flush()
assert options.DATA > 0, "*** Must specify either data or dmin, dmax, dstep"
data_amounts = [options.DATA]
# ========================================================================================================
# Load the model specified on the command line
# ========================================================================================================
from LOTlib.Examples import load_example
make_hypothesis, make_data = load_example(options.MODEL)
# ========================================================================================================
# Run, MPI friendly
# ========================================================================================================
if is_master_process():
display_option_summary(options)
eval_data = None
if options.EVAL_DATA > 0:
eval_data = make_data(options.EVAL_DATA)
# choose the appropriate map function
args = list(
itertools.product([make_hypothesis], [make_data],
data_amounts * options.CHAINS))
# set the output codec -- needed to display lambda to stdout
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
seen = set()