def test_normal_lazy_mean_var_diag():
# The lazy `mean_var_diag` should only be called when neither the mean nor the
# diagonal of the variance exists. Otherwise, it's more efficient to just construct
# the other one. We go over all branches in the `if`-statement.
dist = Normal(lambda: B.ones(3, 1), lambda: B.eye(3), mean_var_diag=lambda: (8, 9))
approx(dist.marginals(), (8, 9))
approx(dist.mean, 8)
approx(dist.var_diag, 9)
dist = Normal(lambda: B.ones(3, 1), lambda: B.eye(3), mean_var_diag=lambda: (8, 9))
approx(dist.mean, B.ones(3, 1))
approx(dist.marginals(), (B.ones(3), B.ones(3)))
approx(dist.var_diag, B.ones(3))
dist = Normal(lambda: B.ones(3, 1), lambda: B.eye(3), mean_var_diag=lambda: (8, 9))
approx(dist.var_diag, B.ones(3))
approx(dist.marginals(), (B.ones(3), B.ones(3)))
approx(dist.mean, B.ones(3, 1))
dist = Normal(lambda: B.ones(3, 1), lambda: B.eye(3), mean_var_diag=lambda: (8, 9))
approx(dist.var_diag, B.ones(3))
approx(dist.mean, B.ones(3, 1))
approx(dist.marginals(), (B.ones(3), B.ones(3)))
def test_normal():
mean = np.random.randn(3, 1)
chol = np.random.randn(3, 3)
var = chol.dot(chol.T)
dist = Normal(var, mean)
dist_sp = multivariate_normal(mean[:, 0], var)
# Test second moment.
yield assert_allclose, dist.m2, var + mean.dot(mean.T)
# Test marginals.
marg_mean, lower, upper = dist.marginals()
yield assert_allclose, mean.squeeze(), marg_mean
yield assert_allclose, lower, marg_mean - 2 * np.diag(var)**.5
yield assert_allclose, upper, marg_mean + 2 * np.diag(var)**.5
# Test `logpdf` and `entropy`.
for _ in range(5):
x = np.random.randn(3, 10)
yield ok, allclose(dist.logpdf(x), dist_sp.logpdf(x.T)), 'logpdf'
yield ok, allclose(dist.entropy(), dist_sp.entropy()), 'entropy'
# Test that inputs to `logpdf` are converted appropriately.
yield assert_allclose, \
dist.logpdf(np.array([0, 1, 2])), \
dist.logpdf([0, 1, 2])
yield assert_allclose, \
dist.logpdf(np.array([0, 1, 2])), \
dist.logpdf((0, 1, 2))
# Test the the output of `logpdf` is flattened appropriately.
yield eq, np.shape(dist.logpdf(np.ones((3, 1)))), ()
yield eq, np.shape(dist.logpdf(np.ones((3, 2)))), (2, )
# Test KL with Monte Carlo estimate.
mean2 = np.random.randn(3, 1)
chol2 = np.random.randn(3, 3)
var2 = chol2.dot(chol2.T)
dist2 = Normal(var2, mean2)
samples = dist.sample(50000)
kl_est = np.mean(dist.logpdf(samples)) - np.mean(dist2.logpdf(samples))
kl = dist.kl(dist2)
yield ok, np.abs(kl_est - kl) / np.abs(kl) < 5e-2, 'kl sampled'
def test_normal():
mean = np.random.randn(3, 1)
chol = np.random.randn(3, 3)
var = chol.dot(chol.T)
dist = Normal(var, mean)
dist_sp = multivariate_normal(mean[:, 0], var)
# Test second moment.
allclose(dist.m2, var + mean.dot(mean.T))
# Test marginals.
marg_mean, lower, upper = dist.marginals()
allclose(mean.squeeze(), marg_mean)
allclose(lower, marg_mean - 2 * np.diag(var)**.5)
allclose(upper, marg_mean + 2 * np.diag(var)**.5)
# Test `logpdf` and `entropy`.
for _ in range(5):
x = np.random.randn(3, 10)
allclose(dist.logpdf(x), dist_sp.logpdf(x.T), desc='logpdf')
allclose(dist.entropy(), dist_sp.entropy(), desc='entropy')
# Test the the output of `logpdf` is flattened appropriately.
assert np.shape(dist.logpdf(np.ones((3, 1)))) == ()
assert np.shape(dist.logpdf(np.ones((3, 2)))) == (2, )
# Test KL with Monte Carlo estimate.
mean2 = np.random.randn(3, 1)
chol2 = np.random.randn(3, 3)
var2 = chol2.dot(chol2.T)
dist2 = Normal(var2, mean2)
samples = dist.sample(50000)
kl_est = np.mean(dist.logpdf(samples)) - np.mean(dist2.logpdf(samples))
kl = dist.kl(dist2)
assert np.abs(kl_est - kl) / np.abs(kl) < 5e-2, 'kl sampled'
