class MultiNormalTests(unittest.TestCase):
def setUp(self):
np.random.seed(1)
self.mean = np.array([-13.0, .0, 7.0])
self.cov = np.eye(3)
self.distribution = MultiNormal(self.mean, self.cov, seed=1)
def test_sample(self):
samples = self.distribution.sample(100)
computed_means = samples.mean(axis=0)
computed_vars = samples.var(axis=0)
expected_means = np.array([-12.9820723, 0.08671813, 7.11855369])
expected_vars = np.array([0.99725084, 0.8610233, 0.8089557])
self.assertTrue((computed_means - expected_means < 1e-5).all())
self.assertTrue((computed_vars - expected_vars < 1e-5).all())
def test_set_parameters(self):
new_mean = np.array([130.0, 10.0, .0, .0])
new_cov = np.eye(4) * 1e-2
self.distribution.set_parameters([new_mean, new_cov])
pdf_value = self.distribution.pdf(new_mean)
self.assertLess(abs(pdf_value - 253.302959106), 1e-6)
samples = self.distribution.sample(100)
computed_means = samples.mean(axis=0)
computed_vars = samples.var(axis=0)
expected_means = np.array(
[1.30004201e+02, 1.00043990e+01, 1.08618430e-02, 8.21679910e-04])
expected_vars = np.array(
[0.01023298, 0.00919317, 0.00876968, 0.00987364])
self.assertTrue((computed_means - expected_means < 1e-5).all())
self.assertTrue((computed_vars - expected_vars < 1e-5).all())
def setUp(self):
# find spark and initialize it
self.backend = BackendDummy()
# define a uniform prior distribution
lb = np.array([-5, 0])
ub = np.array([5, 10])
prior = Uniform(lb, ub, seed=1)
# define a Gaussian model
self.model = Gaussian(prior, mu=2.1, sigma=5.0, seed=1)
# define a distance function
stat_calc = Identity(degree=2, cross=0)
self.dist_calc = Euclidean(stat_calc)
# create fake observed data
self.observation = self.model.simulate(1)
# define kernel
mean = np.array([-13.0, .0, 7.0])
cov = np.eye(3)
self.kernel = MultiNormal(mean, cov, seed=1)
def setUp(self):
np.random.seed(1)
self.mean = np.array([-13.0, .0, 7.0])
self.cov = np.eye(3)
self.distribution = MultiNormal(self.mean, self.cov, seed=1)
def test_sample(self):
# setup backend
backend = BackendDummy()
# define a uniform prior distribution
lb = np.array([-5, 0])
ub = np.array([5, 10])
prior = Uniform(lb, ub, seed=1)
# define a Gaussian model
model = Gaussian(prior, mu=2.1, sigma=5.0, seed=1)
# define sufficient statistics for the model
stat_calc = Identity(degree=2, cross=0)
# create fake observed data
y_obs = model.simulate(1)
# Define the likelihood function
likfun = SynLiklihood(stat_calc)
# use the PMC scheme for T = 1
mean = np.array([-13.0, .0, 7.0])
cov = np.eye(3)
kernel = MultiNormal(mean, cov, seed=1)
T, n_sample, n_samples_per_param = 1, 10, 100
sampler = PMC(model, likfun, kernel, backend, seed=1)
journal = sampler.sample(y_obs,
T,
n_sample,
n_samples_per_param,
covFactor=np.array([.1, .1]),
iniPoints=None)
samples = (journal.get_parameters(), journal.get_weights())
# Compute posterior mean
mu_post_sample, sigma_post_sample, post_weights = np.array(
samples[0][:, 0]), np.array(samples[0][:,
1]), np.array(samples[1][:,
0])
mu_post_mean, sigma_post_mean = np.average(
mu_post_sample,
weights=post_weights), np.average(sigma_post_sample,
weights=post_weights)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(
mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10, ))
self.assertEqual(sigma_sample_shape, (10, ))
self.assertEqual(weights_sample_shape, (10, ))
self.assertLess(abs(mu_post_mean - (-1.48953333102)), 1e-10)
self.assertLess(abs(sigma_post_mean - 6.50695612708), 1e-10)
# use the PMC scheme for T = 2
T, n_sample, n_samples_per_param = 2, 10, 100
sampler = PMC(model, likfun, kernel, backend, seed=1)
journal = sampler.sample(y_obs,
T,
n_sample,
n_samples_per_param,
covFactor=np.array([.1, .1]),
iniPoints=None)
samples = (journal.get_parameters(), journal.get_weights())
# Compute posterior mean
mu_post_sample, sigma_post_sample, post_weights = np.asarray(
samples[0][:, 0]), np.asarray(samples[0][:, 1]), np.asarray(
samples[1][:, 0])
mu_post_mean, sigma_post_mean = np.average(
mu_post_sample,
weights=post_weights), np.average(sigma_post_sample,
weights=post_weights)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(
mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10, ))
self.assertEqual(sigma_sample_shape, (10, ))
self.assertEqual(weights_sample_shape, (10, ))
self.assertLess(abs(mu_post_mean - (-1.4033145848)), 1e-10)
self.assertLess(abs(sigma_post_mean - 7.05175546876), 1e-10)
#Define model
lb, ub = [50, 5, 0.1, 0.5e-3, 0], [150, 20, 1.5, 3e-3, 10]
prior = Uniform(lb=[50, 5, 0.1, 0.5e-3, 0], ub=[150, 20, 1.5, 3e-3, 10])
pAd, pAg, pT, pF, aT = 110, 14.6, 0.6, 1.7e-3, 6
model = Deposition(prior, pAd, pAg, pT, pF, aT, seed=1)
# Observed data
a = np.array([[0, 0, 0, 172200, 4808], [20, 1689, 26.8, 155100, 1683],
[60, 2004, 29.9, 149400, 0], [120, 1968, 31.3, 140700, 0],
[300, 1946, 36.6, 125800, 0]])
np.save('depo_experimental.npy', a)
BE = np.zeros(shape=(10, 5))
index = 5
y_obs = np.load('depo_experimental.npy')[index]
# Define summary stat and distance
stat_calc = DepositionStatistics(degree=1, cross=0)
dist_calc = DepositionDistance(stat_calc)
mean = np.array([-13.0, .0, 7.0])
cov = np.eye(3)
kernel = MultiNormal(mean, cov, seed=1)
steps, epsilon, n_samples, n_samples_per_param = 2, 40, 1, 1
sampler = SABC(model, dist_calc, kernel, backend, seed=1)
journal_sabc = sampler.sample([y_obs], steps, epsilon, n_samples,
n_samples_per_param)
journal_sabc.save('experimental_5.jrnl')
samples = (journal_sabc.get_parameters(), journal_sabc.get_weights())