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 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)
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)
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)