def main():
m = SimpleMacroModel()
prior = UniformDistribution([[0, 1], [0, 1]])
u = SMCUpdater(m, 1000, prior)
modelparams = prior.sample()
expparams = np.array([(12.0,)], dtype=m.expparams_dtype)
datum = m.simulate_experiment(modelparams, expparams)
print datum
u.update(datum, expparams)
print u.est_mean()
print m.call_count
def main():
m = SimpleMacroModel()
prior = UniformDistribution([[0, 1], [0, 1]])
u = SMCUpdater(m, 1000, prior)
modelparams = prior.sample()
expparams = np.array([(12.0, )], dtype=m.expparams_dtype)
datum = m.simulate_experiment(modelparams, expparams)
print datum
u.update(datum, expparams)
print u.est_mean()
print m.call_count
def perf_test(
model, n_particles, prior,
n_exp, heuristic_class,
true_model=None, true_prior=None
):
"""
Runs a trial of using SMC to estimate the parameters of a model, given a
number of particles, a prior distribution and an experiment design
heuristic.
:param qinfer.Model model: Model whose parameters are to
be estimated.
:param int n_particles: Number of SMC particles to use.
:param qinfer.Distribution prior: Prior to use in selecting
SMC particles.
:param int n_exp: Number of experimental data points to draw from the
model.
:param qinfer.Heuristic heuristic_class: Constructor function
for the experiment design heuristic to be used.
:param qinfer.Model true_model: Model to be used in
generating experimental data. If ``None``, assumed to be ``model``.
:param qinfer.Distribution true_prior: Prior to be used in
selecting the true model parameters. If ``None``, assumed to be
``prior``.
:rtype np.ndarray: See :ref:`perf_testing_struct` for more details on
the type returned by this function.
:return: A record array of performance metrics, indexed by the number
of experiments performed.
"""
if true_model is None:
true_model = model
if true_prior is None:
true_prior = prior
true_mps = true_prior.sample()
performance = np.zeros((n_exp,), dtype=PERFORMANCE_DTYPE)
updater = SMCUpdater(model, n_particles, prior)
heuristic = heuristic_class(updater)
for idx_exp in xrange(n_exp):
expparams = heuristic()
datum = true_model.simulate_experiment(true_mps, expparams)
with timing() as t:
updater.update(datum, expparams)
delta = updater.est_mean() - true_mps
performance[idx_exp]['elapsed_time'] = t.delta_t
performance[idx_exp]['loss'] = np.dot(model.Q, delta**2)
performance[idx_exp]['resample_count'] = updater.resample_count
return performance
def do_update(model, n_particles, prior, outcomes, expparams, return_all, resampler=None):
updater = SMCUpdater(model, n_particles, prior,
resampler=resampler
)
updater.batch_update(outcomes, expparams, resample_interval=1)
mean = updater.est_mean()
cov = updater.est_covariance_mtx()
if model.n_modelparams == 1:
mean = mean[0]
cov = cov[0, 0]
if not return_all:
return mean, cov
else:
return mean, cov, {
'updater': updater
}
def do_update(model, n_particles, prior, outcomes, expparams, return_all, resampler=None):
updater = SMCUpdater(model, n_particles, prior,
resampler=resampler
)
updater.batch_update(outcomes, expparams, resample_interval=1)
mean = updater.est_mean()
cov = updater.est_covariance_mtx()
if model.n_modelparams == 1:
mean = mean[0]
cov = cov[0, 0]
if not return_all:
return mean, cov
else:
return mean, cov, {
'updater': updater
}
class TestSMCUpdater(DerandomizedTestCase):
# True model parameter for test
MODELPARAMS = np.array([
1,
])
TEST_EXPPARAMS = np.linspace(1., 10., 100, dtype=np.float)
PRIOR = UniformDistribution([[0, 2]])
N_PARTICLES = 10000
TEST_TARGET_COV = np.array([[0.01]])
def setUp(self):
super(TestSMCUpdater, self).setUp()
self.precession_model = SimplePrecessionModel()
self.num_precession_model = NumericalSimplePrecessionModel()
self.expparams = TestSMCUpdater.TEST_EXPPARAMS.reshape(-1, 1)
self.outcomes = self.precession_model.simulate_experiment(
TestSMCUpdater.MODELPARAMS,
TestSMCUpdater.TEST_EXPPARAMS,
repeat=1).reshape(-1, 1)
self.updater = SMCUpdater(self.precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR)
self.updater_bayes = SMCUpdaterBCRB(self.precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR,
adaptive=True)
self.num_updater = SMCUpdater(self.num_precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR)
self.num_updater_bayes = SMCUpdaterBCRB(self.num_precession_model,
TestSMCUpdater.N_PARTICLES,
TestSMCUpdater.PRIOR,
adaptive=True)
def test_smc_fitting(self):
"""
Checks that the fitters converge on true value on simple precession_model. Is a stochastic
test but I ran 100 times and there were no fails, with these parameters.
"""
self.updater.batch_update(self.outcomes, self.expparams)
self.updater_bayes.batch_update(self.outcomes, self.expparams)
self.num_updater.batch_update(self.outcomes, self.expparams)
self.num_updater_bayes.batch_update(self.outcomes, self.expparams)
#Assert that models have learned true model parameters from data
#test means
assert_almost_equal(self.updater.est_mean(),
TestSMCUpdater.MODELPARAMS, 2)
assert_almost_equal(self.updater_bayes.est_mean(),
TestSMCUpdater.MODELPARAMS, 2)
assert_almost_equal(self.num_updater.est_mean(),
TestSMCUpdater.MODELPARAMS, 2)
assert_almost_equal(self.num_updater_bayes.est_mean(),
TestSMCUpdater.MODELPARAMS, 2)
#Assert that covariances have been reduced below thresholds
#test covs
assert_array_less(self.updater.est_covariance_mtx(),
TestSMCUpdater.TEST_TARGET_COV)
assert_array_less(self.updater_bayes.est_covariance_mtx(),
TestSMCUpdater.TEST_TARGET_COV)
assert_array_less(self.num_updater.est_covariance_mtx(),
TestSMCUpdater.TEST_TARGET_COV)
assert_array_less(self.num_updater_bayes.est_covariance_mtx(),
TestSMCUpdater.TEST_TARGET_COV)
def test_bim(self):
"""
Checks that the fitters converge on true value on simple precession_model. Is a stochastic
test but I ran 100 times and there were no fails, with these parameters.
"""
bim_currents = []
num_bim_currents = []
bim_adaptives = []
num_bim_adaptives = []
#track bims throughout experiments
for i in range(self.outcomes.shape[0]):
self.updater_bayes.update(self.outcomes[i], self.expparams[i])
self.num_updater_bayes.update(self.outcomes[i], self.expparams[i])
bim_currents.append(self.updater_bayes.current_bim)
num_bim_currents.append(self.num_updater_bayes.current_bim)
bim_adaptives.append(self.updater_bayes.adaptive_bim)
num_bim_adaptives.append(self.num_updater_bayes.adaptive_bim)
bim_currents = np.array(bim_currents)
num_bim_currents = np.array(num_bim_currents)
bim_adaptives = np.array(bim_adaptives)
num_bim_adaptives = np.array(num_bim_adaptives)
#compare numerical and analytical bims
assert_almost_equal(bim_currents, num_bim_currents, 2)
assert_almost_equal(bim_adaptives, num_bim_adaptives, 2)
#verify that array copying of properties is working
assert not np.all(bim_currents == bim_currents[0, ...])
assert not np.all(num_bim_currents == num_bim_currents[0, ...])
assert not np.all(bim_adaptives == bim_adaptives[0, ...])
assert not np.all(num_bim_adaptives == num_bim_adaptives[0, ...])
#verify that BCRB is approximately reached
assert_almost_equal(self.updater_bayes.est_covariance_mtx(),
np.linalg.inv(self.updater_bayes.current_bim), 2)
assert_almost_equal(self.updater_bayes.est_covariance_mtx(),
np.linalg.inv(self.updater_bayes.adaptive_bim), 2)
assert_almost_equal(self.num_updater_bayes.est_covariance_mtx(),
np.linalg.inv(self.updater_bayes.current_bim), 2)
assert_almost_equal(self.num_updater_bayes.est_covariance_mtx(),
np.linalg.inv(self.updater_bayes.adaptive_bim), 2)
mps_buf.release()
eps_buf.release()
dest_buf.release()
# Now we concatenate over outcomes.
return FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)
## SCRIPT ######################################################################
if __name__ == "__main__":
# NOTE: This is now redundant with the perf_testing module.
simple_model = SimplePrecessionModel()
for model in [AcceleratedPrecessionModel(), SimplePrecessionModel()]:
true = np.random.random(1)
updater = SMCUpdater(model, 100000, UniformDistribution([0, 1]))
tic = time.time()
for idx_exp in range(200):
if not (idx_exp % 20):
print(idx_exp)
expparams = np.array([(9 / 8)**idx_exp])
updater.update(simple_model.simulate_experiment(true, expparams),
expparams)
print(model, updater.est_mean(), true, time.time() - tic)
#NMR EXPERIMENT*************************************************
#USE this instead of simualate when doing experiments in NMR
# outcome=np.array([[[float(raw_input('Enter obtained Mz: '))]]])
# dummy=float(raw_input('waiting for Mz'))
# Mz_value=LF.lorentzfit(str(idx_trials+2)+'_spectrum.txt')
# outcome=np.array([[[Mz_value/abs(Mo_norm)]]])
#Run SMC and update the posterior distribution
updater.update(outcome,expparams,check_for_resample=True)
#STORE DATA******************************************
data[idx_trials]['est_mean'] = updater.est_mean()
data[idx_trials]['sim_outcome'] = outcome
data[idx_trials]['expparams'] = expparams
data[idx_trials]['covariance'] = updater.est_covariance_mtx()
save_exp.writelines(str(expparams)+'\n')
save_mean.write(str(updater.est_mean())+'\n')
save_out.write(str(outcome)+'\n')
save_cov.write(str(updater.est_covariance_mtx())+'\n')
# PLOT *******************************************
#plotting particles and weights
particles = updater.particle_locations
weights = updater.particle_weights
a=np.var(np.multiply(particles[:,0],weights))
outcome = sim_outcome
# NMR EXPERIMENT*************************************************
# USE this instead when doing experiments in NMR
# outcome=np.array([[[float(raw_input('Enter obtained Mz: '))]]])
# dummy=float(raw_input('waiting for Mz'))
# Mz_value=LF.lorentzfit(str(idx_trials+2)+'_spectrum.txt')
# outcome=np.array([[[Mz_value/abs(Mo_norm)]]])
# Run SMC and update the posterior distribution
updater.update(outcome, expparams)
# STORE DATA******************************************
data[idx_trials]['est_mean'] = updater.est_mean()
data[idx_trials]['sim_outcome'] = outcome
data[idx_trials]['expparams'] = expparams
# PLOT *******************************************
# plotting particles and weights
particles = updater.particle_locations
weights = updater.particle_weights
fig = plt.figure()
plt.axvline(updater.est_mean(), linestyle='--', c='blue', linewidth=2)
plt.axvline(true_model, linestyle='--', c='red', linewidth=2)
plt.scatter(
particles[:, 0], weights*10,
s=50*(1+(weights-1/N_particles)*N_particles)
# Copy the buffer back from the GPU and free memory there.
cl.enqueue_copy(self._queue, pr0, dest_buf)
mps_buf.release()
eps_buf.release()
dest_buf.release()
# Now we concatenate over outcomes.
return FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)
## SCRIPT ######################################################################
if __name__ == "__main__":
# NOTE: This is now redundant with the perf_testing module.
simple_model = SimplePrecessionModel()
for model in [AcceleratedPrecessionModel(), SimplePrecessionModel()]:
true = np.random.random(1)
updater = SMCUpdater(model, 100000, UniformDistribution([0, 1]))
tic = time.time()
for idx_exp in range(200):
if not (idx_exp % 20):
print(idx_exp)
expparams = np.array([(9 / 8) ** idx_exp])
updater.update(simple_model.simulate_experiment(true, expparams), expparams)
print(model, updater.est_mean(), true, time.time() - tic)
def perf_test(model,
n_particles,
prior,
n_exp,
heuristic_class,
true_model=None,
true_prior=None,
true_mps=None,
extra_updater_args=None):
"""
Runs a trial of using SMC to estimate the parameters of a model, given a
number of particles, a prior distribution and an experiment design
heuristic.
:param qinfer.Model model: Model whose parameters are to
be estimated.
:param int n_particles: Number of SMC particles to use.
:param qinfer.Distribution prior: Prior to use in selecting
SMC particles.
:param int n_exp: Number of experimental data points to draw from the
model.
:param qinfer.Heuristic heuristic_class: Constructor function
for the experiment design heuristic to be used.
:param qinfer.Model true_model: Model to be used in
generating experimental data. If ``None``, assumed to be ``model``.
Note that if the true and estimation models have different numbers
of parameters, the loss will be calculated by aligning the
respective model vectors "at the right," analogously to the
convention used by NumPy broadcasting.
:param qinfer.Distribution true_prior: Prior to be used in
selecting the true model parameters. If ``None``, assumed to be
``prior``.
:param numpy.ndarray true_mps: The true model parameters. If ``None``,
it will be sampled from ``true_prior``. Note that as this function
runs exactly one trial, only one model parameter vector may be passed.
In particular, this requires that ``len(true_mps.shape) == 1``.
:param dict extra_updater_args: Extra keyword arguments for the updater,
such as resampling and zero-weight policies.
:rtype np.ndarray: See :ref:`perf_testing_struct` for more details on
the type returned by this function.
:return: A record array of performance metrics, indexed by the number
of experiments performed.
"""
if true_model is None:
true_model = model
if true_prior is None:
true_prior = prior
if true_mps is None:
true_mps = true_prior.sample()
if extra_updater_args is None:
extra_updater_args = {}
n_min_modelparams = min(model.n_modelparams, true_model.n_modelparams)
dtype, is_scalar_exp = actual_dtype(model, true_model)
performance = np.zeros((n_exp, ), dtype=dtype)
updater = SMCUpdater(model, n_particles, prior, **extra_updater_args)
heuristic = heuristic_class(updater)
for idx_exp in range(n_exp):
# Set inside the loop to handle the case where the
# true model is time-dependent as well as the estimation model.
performance[idx_exp]['true'] = true_mps
expparams = heuristic()
datum = true_model.simulate_experiment(true_mps, expparams)
with timing() as t:
updater.update(datum, expparams)
# Update the true model.
true_mps = true_model.update_timestep(promote_dims_left(true_mps, 2),
expparams)[:, :, 0]
est_mean = updater.est_mean()
delta = np.subtract(*shorten_right(est_mean, true_mps))
loss = np.dot(delta**2, model.Q[-n_min_modelparams:])
performance[idx_exp]['elapsed_time'] = t.delta_t
performance[idx_exp]['loss'] = loss
performance[idx_exp]['resample_count'] = updater.resample_count
performance[idx_exp]['outcome'] = datum
performance[idx_exp]['est'] = est_mean
if is_scalar_exp:
performance[idx_exp]['experiment'] = expparams
else:
for param_name in [param[0] for param in model.expparams_dtype]:
performance[idx_exp][param_name] = expparams[param_name]
return performance
def perf_test(
model, n_particles, prior, n_exp, heuristic_class,
true_model=None, true_prior=None, true_mps=None,
extra_updater_args=None
):
"""
Runs a trial of using SMC to estimate the parameters of a model, given a
number of particles, a prior distribution and an experiment design
heuristic.
:param qinfer.Model model: Model whose parameters are to
be estimated.
:param int n_particles: Number of SMC particles to use.
:param qinfer.Distribution prior: Prior to use in selecting
SMC particles.
:param int n_exp: Number of experimental data points to draw from the
model.
:param qinfer.Heuristic heuristic_class: Constructor function
for the experiment design heuristic to be used.
:param qinfer.Model true_model: Model to be used in
generating experimental data. If ``None``, assumed to be ``model``.
:param qinfer.Distribution true_prior: Prior to be used in
selecting the true model parameters. If ``None``, assumed to be
``prior``.
:param np.ndarray true_mps: The true model parameters. If ``None``,
it will be sampled from ``true_prior``. Note that the performance
record can only handle one outcome and therefore ONLY ONE TRUE MODEL.
An error will occur if ``true_mps.shape[0] > 1`` returns ``True``.
:param dict extra_updater_args: Extra keyword arguments for the updater,
such as resampling and zero-weight policies.
:rtype np.ndarray: See :ref:`perf_testing_struct` for more details on
the type returned by this function.
:return: A record array of performance metrics, indexed by the number
of experiments performed.
"""
if true_model is None:
true_model = model
if true_prior is None:
true_prior = prior
if true_mps is None:
true_mps = true_prior.sample()
if extra_updater_args is None:
extra_updater_args = {}
dtype, is_scalar_exp = actual_dtype(model)
performance = np.zeros((n_exp,), dtype=dtype)
updater = SMCUpdater(model, n_particles, prior, **extra_updater_args)
heuristic = heuristic_class(updater)
performance['true'] = true_mps
for idx_exp in xrange(n_exp):
expparams = heuristic()
datum = true_model.simulate_experiment(true_mps, expparams)
with timing() as t:
updater.update(datum, expparams)
est_mean = updater.est_mean()
delta = est_mean - true_mps
loss = np.dot(delta**2, model.Q)
performance[idx_exp]['elapsed_time'] = t.delta_t
performance[idx_exp]['loss'] = loss
performance[idx_exp]['resample_count'] = updater.resample_count
performance[idx_exp]['outcome'] = datum
performance[idx_exp]['est'] = est_mean
if is_scalar_exp:
performance[idx_exp]['experiment'] = expparams
else:
for param_name in [param[0] for param in model.expparams_dtype]:
performance[idx_exp][param_name] = expparams[param_name]
return performance
class TestSMCUpdater(DerandomizedTestCase): # True model parameter for test MODELPARAMS = np.array([1,]) TEST_EXPPARAMS = np.linspace(1.,10.,100,dtype=np.float) PRIOR = UniformDistribution([[0,2]]) N_PARTICLES = 10000 TEST_TARGET_COV = np.array([[0.01]]) def setUp(self): super(TestSMCUpdater,self).setUp() self.precession_model = SimplePrecessionModel() self.num_precession_model = NumericalSimplePrecessionModel() self.expparams = TestSMCUpdater.TEST_EXPPARAMS.reshape(-1,1) self.outcomes = self.precession_model.simulate_experiment(TestSMCUpdater.MODELPARAMS, TestSMCUpdater.TEST_EXPPARAMS,repeat=1 ).reshape(-1,1) self.updater = SMCUpdater(self.precession_model, TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR) self.updater_bayes = SMCUpdaterBCRB(self.precession_model, TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR,adaptive=True) self.num_updater = SMCUpdater(self.num_precession_model, TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR) self.num_updater_bayes = SMCUpdaterBCRB(self.num_precession_model, TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR,adaptive=True) def test_smc_fitting(self): """ Checks that the fitters converge on true value on simple precession_model. Is a stochastic test but I ran 100 times and there were no fails, with these parameters. """ self.updater.batch_update(self.outcomes,self.expparams) self.updater_bayes.batch_update(self.outcomes,self.expparams) self.num_updater.batch_update(self.outcomes,self.expparams) self.num_updater_bayes.batch_update(self.outcomes,self.expparams) #Assert that models have learned true model parameters from data #test means assert_almost_equal(self.updater.est_mean(),TestSMCUpdater.MODELPARAMS,2) assert_almost_equal(self.updater_bayes.est_mean(),TestSMCUpdater.MODELPARAMS,2) assert_almost_equal(self.num_updater.est_mean(),TestSMCUpdater.MODELPARAMS,2) assert_almost_equal(self.num_updater_bayes.est_mean(),TestSMCUpdater.MODELPARAMS,2) #Assert that covariances have been reduced below thresholds #test covs assert_array_less(self.updater.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV) assert_array_less(self.updater_bayes.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV) assert_array_less(self.num_updater.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV) assert_array_less(self.num_updater_bayes.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV) def test_bim(self): """ Checks that the fitters converge on true value on simple precession_model. Is a stochastic test but I ran 100 times and there were no fails, with these parameters. """ bim_currents = [] num_bim_currents = [] bim_adaptives = [] num_bim_adaptives = [] #track bims throughout experiments for i in range(self.outcomes.shape[0]): self.updater_bayes.update(self.outcomes[i],self.expparams[i]) self.num_updater_bayes.update(self.outcomes[i],self.expparams[i]) bim_currents.append(self.updater_bayes.current_bim) num_bim_currents.append(self.num_updater_bayes.current_bim) bim_adaptives.append(self.updater_bayes.adaptive_bim) num_bim_adaptives.append(self.num_updater_bayes.adaptive_bim) bim_currents = np.array(bim_currents) num_bim_currents = np.array(num_bim_currents) bim_adaptives = np.array(bim_adaptives) num_bim_adaptives = np.array(num_bim_adaptives) #compare numerical and analytical bims assert_almost_equal(bim_currents,num_bim_currents,2) assert_almost_equal(bim_adaptives,num_bim_adaptives,2) #verify that array copying of properties is working assert not np.all(bim_currents == bim_currents[0,...]) assert not np.all(num_bim_currents == num_bim_currents[0,...]) assert not np.all(bim_adaptives == bim_adaptives[0,...]) assert not np.all(num_bim_adaptives == num_bim_adaptives[0,...]) #verify that BCRB is approximately reached assert_almost_equal(self.updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.current_bim),2) assert_almost_equal(self.updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.adaptive_bim),2) assert_almost_equal(self.num_updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.current_bim),2) assert_almost_equal(self.num_updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.adaptive_bim),2)
def perf_test(
model, n_particles, prior, n_exp, heuristic_class,
true_model=None, true_prior=None, true_mps=None,
extra_updater_args=None
):
"""
Runs a trial of using SMC to estimate the parameters of a model, given a
number of particles, a prior distribution and an experiment design
heuristic.
:param qinfer.Model model: Model whose parameters are to
be estimated.
:param int n_particles: Number of SMC particles to use.
:param qinfer.Distribution prior: Prior to use in selecting
SMC particles.
:param int n_exp: Number of experimental data points to draw from the
model.
:param qinfer.Heuristic heuristic_class: Constructor function
for the experiment design heuristic to be used.
:param qinfer.Model true_model: Model to be used in
generating experimental data. If ``None``, assumed to be ``model``.
Note that if the true and estimation models have different numbers
of parameters, the loss will be calculated by aligning the
respective model vectors "at the right," analogously to the
convention used by NumPy broadcasting.
:param qinfer.Distribution true_prior: Prior to be used in
selecting the true model parameters. If ``None``, assumed to be
``prior``.
:param numpy.ndarray true_mps: The true model parameters. If ``None``,
it will be sampled from ``true_prior``. Note that as this function
runs exactly one trial, only one model parameter vector may be passed.
In particular, this requires that ``len(true_mps.shape) == 1``.
:param dict extra_updater_args: Extra keyword arguments for the updater,
such as resampling and zero-weight policies.
:rtype np.ndarray: See :ref:`perf_testing_struct` for more details on
the type returned by this function.
:return: A record array of performance metrics, indexed by the number
of experiments performed.
"""
if true_model is None:
true_model = model
if true_prior is None:
true_prior = prior
if true_mps is None:
true_mps = true_prior.sample()
if extra_updater_args is None:
extra_updater_args = {}
n_min_modelparams = min(model.n_modelparams, true_model.n_modelparams)
dtype, is_scalar_exp = actual_dtype(model, true_model)
performance = np.zeros((n_exp,), dtype=dtype)
updater = SMCUpdater(model, n_particles, prior, **extra_updater_args)
heuristic = heuristic_class(updater)
for idx_exp in range(n_exp):
# Set inside the loop to handle the case where the
# true model is time-dependent as well as the estimation model.
performance[idx_exp]['true'] = true_mps
expparams = heuristic()
datum = true_model.simulate_experiment(true_mps, expparams)
with timing() as t:
updater.update(datum, expparams)
# Update the true model.
true_mps = true_model.update_timestep(
promote_dims_left(true_mps, 2), expparams
)[:, :, 0]
est_mean = updater.est_mean()
delta = np.subtract(*shorten_right(est_mean, true_mps))
loss = np.dot(delta**2, model.Q[-n_min_modelparams:])
performance[idx_exp]['elapsed_time'] = t.delta_t
performance[idx_exp]['loss'] = loss
performance[idx_exp]['resample_count'] = updater.resample_count
performance[idx_exp]['outcome'] = datum
performance[idx_exp]['est'] = est_mean
if is_scalar_exp:
performance[idx_exp]['experiment'] = expparams
else:
for param_name in [param[0] for param in model.expparams_dtype]:
performance[idx_exp][param_name] = expparams[param_name]
return performance
