def mh_query2(model, pred, answer, samples_count, lag=1):
"""
Metropolis-Hastings algorithm for sampling
:param model: model to execute
:param samples_count: how much samples we want
:type samples_count: int
:return: samples
:rtype: list
"""
MCMC_shared.mh_flag = True
MCMC_shared.iteration = 0
samples = []
miss = True
model()
transitions = 0
rejection = 0
while len(samples) < samples_count:
MCMC_shared.iteration += 1
variables = MCMC_shared.trace.get_vector()
vector_vals_drift = variables.values()
vector = [val[0] for val in vector_vals_drift]
drifts = [val[1] for val in vector_vals_drift]
shifted_vector = numpy.random.multivariate_normal(vector, numpy.diag(drifts))
new_trace = Trace(MCMC_shared.trace)
new_trace.set_vector(dict(zip(variables.keys(), shifted_vector.tolist())), MCMC_shared.iteration)
old_trace = MCMC_shared.trace
MCMC_shared.trace = new_trace
sample = model()
while not miss and new_trace._likelihood == -float("inf"):
new_trace.clean(MCMC_shared.iteration)
new_trace._likelihood = 0
for name, (chunk, iteration) in new_trace.mem.items():
if chunk.erp.log_likelihood(chunk.x, *chunk.erp_parameters) == -float("inf"):
new_chunk = Chunk(chunk.erp,
numpy.random.normal(old_trace.get(name).x, chunk.drift / 2),
chunk.erp_parameters,
drift=chunk.drift)
new_trace.store(name, new_chunk, iteration)
sample = model()
MCMC_shared.trace = old_trace
probability = log(uniform())
# r = erp.log_proposal_prob()
if probability < new_trace._likelihood - old_trace._likelihood and (miss or pred(sample)):
if miss and pred(sample):
miss = False
MCMC_shared.drift = 0.05
transitions += 1
if (transitions % lag) == 0:
if not miss:
# print sample, rejection
samples.append(answer(sample))
rejection = 0
MCMC_shared.trace = new_trace
MCMC_shared.trace.clean(MCMC_shared.iteration)
else:
rejection += 1
return samples
def mh_query(model, pred, answer, samples_count, lag=1):
"""
Metropolis-Hastings algorithm for sampling
:param model: model to execute
:param samples_count: how much samples we want
:type samples_count: int
:return: samples
:rtype: list
"""
MCMC_shared.mh_flag = True
MCMC_shared.iteration = 0
samples = []
model()
prev_name_idx = 0
transitions = 0
rejected = 0
burn_in = 100
miss = True
while len(samples) < samples_count:
MCMC_shared.iteration += 1
variables = MCMC_shared.trace.names()
# index = random.randint(0, len(variables) - 1)
# selected_name = variables[index]
selected_name = variables[prev_name_idx % len(MCMC_shared.trace.names())]
prev_name_idx += 1
current = MCMC_shared.trace.get(selected_name)
erp, erp_params = current.erp, current.erp_parameters
new_value = erp.proposal_kernel(current.x, *erp_params)
# print erp_params
fwdProb = erp.log_proposal_prob(current.x, new_value, *erp_params)
rvsProb = erp.log_proposal_prob(new_value, current.x, *erp_params)
# r и f для flip == 0
# l = erp.log_likelihood(new_value, *erp_params)
# new_trace = deepcopy(trace)
new_trace = Trace(MCMC_shared.trace)
new_trace.store(selected_name, Chunk(erp, new_value, erp_params), MCMC_shared.iteration)
old_trace = MCMC_shared.trace
MCMC_shared.trace = new_trace
sample = model()
MCMC_shared.trace = old_trace
probability = log(uniform())
# print sample
# print new_trace._likelihood, old_trace._likelihood
if probability < new_trace._likelihood - old_trace._likelihood + rvsProb - fwdProb and \
(miss or pred(sample)):
if miss and pred(sample):
miss = False
transitions += 1
if not miss:
if burn_in:
burn_in -= 1
elif (transitions % lag) == 0:
# print len(samples), sample, new_trace._likelihood, rejected
samples.append(answer(sample))
rejected = 0
MCMC_shared.trace = new_trace
MCMC_shared.trace.clean(MCMC_shared.iteration)
else:
rejected += 1
return samples
def mh_query(model, pred, answer, samples_count, lag=1):
"""
Metropolis-Hastings algorithm for sampling
:param model: model to execute
:param samples_count: how much samples we want
:type samples_count: int
:return: samples
:rtype: list
"""
MCMC_shared.mh_flag = True
MCMC_shared.iteration = 0
samples = []
model()
prev_name_idx = 0
transitions = 0
rejected = 0
burn_in = 100
miss = True
while len(samples) < samples_count:
MCMC_shared.iteration += 1
variables = MCMC_shared.trace.names()
# index = random.randint(0, len(variables) - 1)
# selected_name = variables[index]
selected_name = variables[prev_name_idx %
len(MCMC_shared.trace.names())]
prev_name_idx += 1
current = MCMC_shared.trace.get(selected_name)
erp, erp_params = current.erp, current.erp_parameters
new_value = erp.proposal_kernel(current.x, *erp_params)
# print erp_params
fwdProb = erp.log_proposal_prob(current.x, new_value, *erp_params)
rvsProb = erp.log_proposal_prob(new_value, current.x, *erp_params)
# r и f для flip == 0
# l = erp.log_likelihood(new_value, *erp_params)
# new_trace = deepcopy(trace)
new_trace = Trace(MCMC_shared.trace)
new_trace.store(selected_name, Chunk(erp, new_value, erp_params),
MCMC_shared.iteration)
old_trace = MCMC_shared.trace
MCMC_shared.trace = new_trace
sample = model()
MCMC_shared.trace = old_trace
probability = log(uniform())
# print sample
# print new_trace._likelihood, old_trace._likelihood
if probability < new_trace._likelihood - old_trace._likelihood + rvsProb - fwdProb and \
(miss or pred(sample)):
if miss and pred(sample):
miss = False
transitions += 1
if not miss:
if burn_in:
burn_in -= 1
elif (transitions % lag) == 0:
# print len(samples), sample, new_trace._likelihood, rejected
samples.append(answer(sample))
rejected = 0
MCMC_shared.trace = new_trace
MCMC_shared.trace.clean(MCMC_shared.iteration)
else:
rejected += 1
return samples
def mh_query2(model, pred, answer, samples_count, lag=1):
"""
Metropolis-Hastings algorithm for sampling
:param model: model to execute
:param samples_count: how much samples we want
:type samples_count: int
:return: samples
:rtype: list
"""
MCMC_shared.mh_flag = True
MCMC_shared.iteration = 0
samples = []
miss = True
model()
transitions = 0
rejection = 0
while len(samples) < samples_count:
MCMC_shared.iteration += 1
variables = MCMC_shared.trace.get_vector()
vector_vals_drift = variables.values()
vector = [val[0] for val in vector_vals_drift]
drifts = [val[1] for val in vector_vals_drift]
shifted_vector = numpy.random.multivariate_normal(
vector, numpy.diag(drifts))
new_trace = Trace(MCMC_shared.trace)
new_trace.set_vector(
dict(zip(variables.keys(), shifted_vector.tolist())),
MCMC_shared.iteration)
old_trace = MCMC_shared.trace
MCMC_shared.trace = new_trace
sample = model()
while not miss and new_trace._likelihood == -float("inf"):
new_trace.clean(MCMC_shared.iteration)
new_trace._likelihood = 0
for name, (chunk, iteration) in new_trace.mem.items():
if chunk.erp.log_likelihood(
chunk.x, *chunk.erp_parameters) == -float("inf"):
new_chunk = Chunk(chunk.erp,
numpy.random.normal(
old_trace.get(name).x,
chunk.drift / 2),
chunk.erp_parameters,
drift=chunk.drift)
new_trace.store(name, new_chunk, iteration)
sample = model()
MCMC_shared.trace = old_trace
probability = log(uniform())
# r = erp.log_proposal_prob()
if probability < new_trace._likelihood - old_trace._likelihood and (
miss or pred(sample)):
if miss and pred(sample):
miss = False
MCMC_shared.drift = 0.05
transitions += 1
if (transitions % lag) == 0:
if not miss:
# print sample, rejection
samples.append(answer(sample))
rejection = 0
MCMC_shared.trace = new_trace
MCMC_shared.trace.clean(MCMC_shared.iteration)
else:
rejection += 1
return samples
