def test_discrete_uniform_moments(self):
"""
Distributions: Checks that the discrete uniform dist. has the right moments.
"""
dist = DiscreteUniformDistribution(5)
samples = dist.sample(200000).astype(float)
assert_sigfigs_equal((2**10 - 1) / 12, np.var(samples), 1)
assert_sigfigs_equal(16, np.mean(samples), 1)
def test_mvuniform_moments(self):
"""
Distributions: Checks that ``MVUniformDistribution`` has the right moments.
"""
dist = MVUniformDistribution(dim=6)
samples = dist.sample(100000)
assert_sigfigs_equal(5/(36*7), samples[:,3].var(), 2)
assert_sigfigs_equal(np.array([1/6]*6), np.mean(samples, axis=0), 2)
def test_discrete_uniform_moments(self):
"""
Distributions: Checks that the discrete uniform dist. has the right moments.
"""
dist = DiscreteUniformDistribution(5)
samples = dist.sample(200000).astype(float)
assert_sigfigs_equal((2**10-1)/12, np.var(samples), 1)
assert_sigfigs_equal(16, np.mean(samples), 1)
def test_constrained_sum_moments(self):
"""
Distributions: Tests that the contstraint is met in
the constrained sum distribution.
"""
unif = UniformDistribution([[0,1],[0,1]])
dist = ConstrainedSumDistribution(unif, 1)
samples = dist.sample(100000)
assert_sigfigs_equal(np.array([1/2]*2), np.mean(samples, axis=0), 2)
def test_lognormal_moments(self):
"""
Distributions: Checks that the log normal
distribution has the right moments.
"""
mu, sig = 3, 2
dist = LogNormalDistribution(mu=mu, sigma=sig)
samples = dist.sample(150000)
assert_sigfigs_equal(scipy.stats.lognorm.mean(1, 3, 2), samples.mean(),
1)
assert_sigfigs_equal(np.round(scipy.stats.lognorm.var(1, 3, 2)),
np.round(samples.var()), 1)
def test_assert_sigfigs_equal(self):
"""
Tests to make sure assert_sigfigs_equal
only passes if the correct number of
significant figures match
"""
# these are the same to 6 sigfigs
assert_sigfigs_equal(np.array([3.141592]), np.array([3.141593]), 6)
# these are only the same to 5 sigfigs
self.assertRaises(AssertionError, assert_sigfigs_equal,
np.array([3.14159]), np.array([3.14158]), 6)
# these are the same to 3 sigfigs
assert_sigfigs_equal(np.array([1729]), np.array([1728]), 3)
# these are only the same to 3 sigfigs
self.assertRaises(AssertionError, assert_sigfigs_equal,
np.array([1729]), np.array([1728]), 4)
def test_lognormal_moments(self):
"""
Distributions: Checks that the log normal
distribution has the right moments.
"""
mu, sig = 3, 2
dist = LogNormalDistribution(mu=mu, sigma=sig)
samples = dist.sample(150000)
assert_sigfigs_equal(
scipy.stats.lognorm.mean(1,3,2),
samples.mean(),
1)
assert_sigfigs_equal(
np.round(scipy.stats.lognorm.var(1,3,2)),
np.round(samples.var()),
1)
def test_moments(self):
"""
Distributions: Tests the moment function (est_mean, etc)
of ParticleDistribution.
"""
dim = 5
n_particles = 100000
# draw particles from a randomly chosen mutivariate normal
mu = np.random.randn(dim)
cov = np.random.randn(dim, dim)
cov = np.dot(cov, cov.T)
particle_locations = np.random.multivariate_normal(
mu, cov, n_particles)
particle_weights = np.random.rand(n_particles)
dist = ParticleDistribution(particle_weights=particle_weights,
particle_locations=particle_locations)
assert_sigfigs_equal(mu, dist.est_mean(), 1)
assert_almost_equal(dist.est_meanfn(lambda x: x**2),
np.diag(cov) + mu**2, 0)
assert (np.linalg.norm(dist.est_covariance_mtx() - cov) < 0.5)
def test_slantednormal_moments(self):
"""
Distributions: Checks that the slanted normal
distribution has the right moments.
"""
ranges = [[0, 1], [0, 2], [2, 3]]
weight = 2
dist = SlantedNormalDistribution(ranges=ranges, weight=weight)
samples = dist.sample(150000)
assert_sigfigs_equal(np.mean(np.array(ranges), axis=1),
np.mean(samples, axis=0), 1)
assert_sigfigs_equal(1 / 12 + 4, samples[:, 0].var(), 1)
assert_sigfigs_equal(4 / 12 + 4, samples[:, 1].var(), 1)
assert_sigfigs_equal(1 / 12 + 4, samples[:, 2].var(), 1)
def test_assert_sigfigs_equal(self):
"""
Tests to make sure assert_sigfigs_equal
only passes if the correct number of
significant figures match
"""
# these are the same to 6 sigfigs
assert_sigfigs_equal(
np.array([3.141592]),
np.array([3.141593]),
6
)
# these are only the same to 5 sigfigs
self.assertRaises(
AssertionError,
assert_sigfigs_equal,
np.array([3.14159]),
np.array([3.14158]),
6
)
# these are the same to 3 sigfigs
assert_sigfigs_equal(
np.array([1729]),
np.array([1728]),
3
)
# these are only the same to 3 sigfigs
self.assertRaises(
AssertionError,
assert_sigfigs_equal,
np.array([1729]),
np.array([1728]),
4
)
def test_slantednormal_moments(self):
"""
Distributions: Checks that the slanted normal
distribution has the right moments.
"""
ranges = [[0,1],[0,2],[2,3]]
weight = 2
dist = SlantedNormalDistribution(ranges=ranges, weight=weight)
samples = dist.sample(150000)
assert_sigfigs_equal(
np.mean(np.array(ranges), axis=1),
np.mean(samples, axis=0),
1)
assert_sigfigs_equal(1/12+4, samples[:,0].var(), 1)
assert_sigfigs_equal(4/12+4, samples[:,1].var(), 1)
assert_sigfigs_equal(1/12+4, samples[:,2].var(), 1)
