def compute(self):
logging.debug("Entering")
# needed for both matrices
Q = self.ozone_posterior.create_Q_matrix(self.kappa);
if self.matrix_type == "Q":
logging.info("Matrix type Q")
logdet = self.ozone_posterior.log_det_method(Q)
elif self.matrix_type == "M":
logging.info("Matrix type M")
_, A = OzonePosterior.load_ozone_data()
AtA = A.T.dot(A)
M = Q + self.tau * AtA;
logdet = self.ozone_posterior.log_det_method(M)
else:
raise ValueError("Unknown matrix type: %s" % self.matrix_type)
result = ScalarResult(logdet)
self.aggregator.submit_result(result)
logging.debug("Leaving")
def compute(self):
logging.debug("Entering")
# needed for both matrices
Q = self.ozone_posterior.create_Q_matrix(self.kappa)
if self.matrix_type == "Q":
logging.info("Matrix type Q")
logdet = self.ozone_posterior.log_det_method(Q)
elif self.matrix_type == "M":
logging.info("Matrix type M")
_, A = OzonePosterior.load_ozone_data()
AtA = A.T.dot(A)
M = Q + self.tau * AtA
logdet = self.ozone_posterior.log_det_method(M)
else:
raise ValueError("Unknown matrix type: %s" % self.matrix_type)
result = ScalarResult(logdet)
self.aggregator.submit_result(result)
logging.debug("Leaving")
def precompute_likelihood_estimates(self, tau, kappa):
logging.debug("Entering")
# submit all jobs for log-determinant Q
aggregators_Q = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa,
"Q")
aggregators_Q.append(self.computation_engine.submit_job(job))
# submit all jobs for log-determinant M
aggregators_M = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa,
"M")
aggregators_M.append(self.computation_engine.submit_job(job))
# submit job for remainder of likelihood
job = OzoneLikelihoodWithoutLogDetJob(ScalarResultAggregator(), self,
tau, kappa)
aggregator_remainder = self.computation_engine.submit_job(job)
# grab a coffee
self.computation_engine.wait_for_all()
# collect results from all aggregators
log_dets_Q = zeros(self.num_estimates)
log_dets_M = zeros(self.num_estimates)
for i in range(self.num_estimates):
aggregators_Q[i].finalize()
aggregators_M[i].finalize()
log_dets_Q[i] = aggregators_Q[i].get_final_result().result
log_dets_M[i] = aggregators_M[i].get_final_result().result
aggregators_Q[i].clean_up()
aggregators_M[i].clean_up()
aggregator_remainder.finalize()
result_remainder = aggregator_remainder.get_final_result().result
aggregator_remainder.clean_up()
# load n since needed for likelihood
y, _ = OzonePosterior.load_ozone_data()
n = len(y)
# construct all likelihood estimates
log_det_parts = 0.5 * log_dets_Q + 0.5 * n * log(
tau) - 0.5 * log_dets_M
estimates = log_det_parts + result_remainder
# crude check for an overflow to print error details
limit = 1e100
indices = where(abs(estimates) > limit)[0]
if len(indices) > 0:
logging.info(
"Log-likelihood estimates overflow occured at the following indices:"
)
for idx in indices:
logging.info("At index %d. Details are: " % idx)
logging.info("log-det Q: " + aggregators_Q[idx].job_name +
". Result is %f" % log_dets_Q[idx])
logging.info("log-det M: " + aggregators_M[idx].job_name +
". Result is %f" % log_dets_M[idx])
logging.info("log-lik-without-log-det: " +
aggregator_remainder.job_name +
". Result is %f" % result_remainder[idx])
logging.info("Removing mentioned estimates from list")
estimates = estimates[abs(estimates) < limit]
logging.info("New number of estimates is %d, old was %d" %
(len(estimates), self.num_estimates))
logging.debug("Leaving")
return estimates
def precompute_likelihood_estimates(self, tau, kappa):
logging.debug("Entering")
# submit all jobs for log-determinant Q
aggregators_Q = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa, "Q")
aggregators_Q.append(self.computation_engine.submit_job(job))
# submit all jobs for log-determinant M
aggregators_M = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa, "M")
aggregators_M.append(self.computation_engine.submit_job(job))
# submit job for remainder of likelihood
job = OzoneLikelihoodWithoutLogDetJob(ScalarResultAggregator(), self, tau, kappa)
aggregator_remainder = self.computation_engine.submit_job(job)
# grab a coffee
self.computation_engine.wait_for_all()
# collect results from all aggregators
log_dets_Q = zeros(self.num_estimates)
log_dets_M = zeros(self.num_estimates)
for i in range(self.num_estimates):
aggregators_Q[i].finalize()
aggregators_M[i].finalize()
log_dets_Q[i] = aggregators_Q[i].get_final_result().result
log_dets_M[i] = aggregators_M[i].get_final_result().result
aggregators_Q[i].clean_up()
aggregators_M[i].clean_up()
aggregator_remainder.finalize()
result_remainder = aggregator_remainder.get_final_result().result
aggregator_remainder.clean_up()
# load n since needed for likelihood
y, _ = OzonePosterior.load_ozone_data()
n = len(y)
# construct all likelihood estimates
log_det_parts = 0.5 * log_dets_Q + 0.5 * n * log(tau) - 0.5 * log_dets_M
estimates = log_det_parts + result_remainder
# crude check for an overflow to print error details
limit = 1e100
indices = where(abs(estimates) > limit)[0]
if len(indices) > 0:
logging.info("Log-likelihood estimates overflow occured at the following indices:")
for idx in indices:
logging.info("At index %d. Details are: " % idx)
logging.info("log-det Q: " + aggregators_Q[idx].job_name +
". Result is %f" % log_dets_Q[idx])
logging.info("log-det M: " + aggregators_M[idx].job_name +
". Result is %f" % log_dets_M[idx])
logging.info("log-lik-without-log-det: " +
aggregator_remainder.job_name + ". Result is %f" % result_remainder[idx])
logging.info("Removing mentioned estimates from list")
estimates = estimates[abs(estimates) < limit]
logging.info("New number of estimates is %d, old was %d" %
(len(estimates), self.num_estimates))
logging.debug("Leaving")
return estimates
