def test_univ_uniform_range(self):
"""
Distributions: Checks that the univariate uniform dist obeys limits.
"""
for lower, upper in [(0, 1), (-1, 1), (-1, 5)]:
dist = UniformDistribution([lower, upper])
samples = dist.sample(1000)
assert np.all(samples >= lower)
assert np.all(samples <= upper)
def test_univ_uniform_range(self):
"""
Distributions: Checks that the univariate uniform dist obeys limits.
"""
for lower, upper in [(0, 1), (-1, 1), (-1, 5)]:
dist = UniformDistribution([lower, upper])
samples = dist.sample(1000)
assert np.all(samples >= lower)
assert np.all(samples <= upper)
def test_univ_uniform_moments(self):
"""
Distributions: Checks that the univ. uniform dist. has the right moments.
"""
dist = UniformDistribution([[0, 1]])
samples = dist.sample(10000)
# We use low-precision checks here, since the error goes as 1/sqrt{N}.
# Determinism helps us be sure that once we pass, we'll keep passing,
# but it does nothing to make the moments accurate.
assert_almost_equal(1 / 12, samples.var(), 2)
assert_almost_equal(1 / 2, samples.mean(), 2)
def test_univ_uniform_moments(self):
"""
Distributions: Checks that the univ. uniform dist. has the right moments.
"""
dist = UniformDistribution([[0, 1]])
samples = dist.sample(10000)
# We use low-precision checks here, since the error goes as 1/sqrt{N}.
# Determinism helps us be sure that once we pass, we'll keep passing,
# but it does nothing to make the moments accurate.
assert_almost_equal(1 / 12, samples.var(), 2)
assert_almost_equal(1 / 2, samples.mean(), 2)
import logging
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
model = T1Model()
prior = UniformDistribution(np.array([0, 10]))
N_particles = 1000000
updater = SMCUpdater(
model, N_particles, prior, resampler=LiuWestResampler(),
zero_weight_policy='reset'
)
designer = ExperimentDesigner(updater, opt_algo=1)
# Set the value of T1 to Learn, pick 1 value from prior
true_model = prior.sample()
# true_model=np.array([11.032], dtype=model.expparams_dtype)
performance_dtype = [
('expparams', 'float'),
('sim_outcome', 'float'),
('est_mean', 'float'),
]
# NMR EXPERIMENT Initialization*******************************
# going to normalize Mo max of 1.
# model.Mo=float(raw_input('Please enter Mo: '))
# dummy=float(raw_input('Waiting for Mo: '))
# Mo_norm=LF.lorentzfit('1_spectrum.txt')
# model.Mo=(Mo_norm/Mo_norm)
def test_uniform_shape(self):
"""
Distributions: Checks that the multivar. uni. dist has the right shape.
"""
dist = UniformDistribution([[0, 1], [0, 2], [0, 3]])
assert dist.sample(100).shape == (100, 3)
def test_uniform_shape(self):
"""
Distributions: Checks that the multivar. uni. dist has the right shape.
"""
dist = UniformDistribution([[0, 1], [0, 2], [0, 3]])
assert dist.sample(100).shape == (100, 3)
