def test_likelihood_free_mixture():
p1 = Normal(random_state=1)
p2 = Normal(mu=2.0, random_state=1)
h1 = Histogram(bins=50).fit(p1.rvs(10000))
h2 = Histogram(bins=50).fit(p2.rvs(10000))
m1 = Mixture(components=[p1, p2])
m2 = Mixture(components=[h1, h2])
# Check whether pdf, nnlf and cdf have been overriden
assert isinstance(m1.pdf, theano.compile.function_module.Function)
assert isinstance(m1.nnlf, theano.compile.function_module.Function)
assert isinstance(m1.cdf, theano.compile.function_module.Function)
assert isinstance(m2.pdf, types.MethodType)
assert isinstance(m2.nnlf, types.MethodType)
assert isinstance(m2.cdf, types.MethodType)
# Compare pdfs
rng = check_random_state(1)
X = rng.rand(100, 1) * 10 - 5
assert np.mean(np.abs(m1.pdf(X) - m2.pdf(X))) < 0.05
# Test sampling
X = m2.rvs(10)
assert X.shape == (10, 1)
# Check errors
assert_raises(NotImplementedError, m2.fit, X)
def test_likelihood_free_mixture():
p1 = Normal(random_state=1)
p2 = Normal(mu=2.0, random_state=1)
h1 = Histogram(bins=50).fit(p1.rvs(10000))
h2 = Histogram(bins=50).fit(p2.rvs(10000))
m1 = Mixture(components=[p1, p2])
m2 = Mixture(components=[h1, h2])
# Check whether pdf, nnlf and cdf have been overriden
assert isinstance(m1.pdf, theano.compile.function_module.Function)
assert isinstance(m1.nnlf, theano.compile.function_module.Function)
assert isinstance(m1.cdf, theano.compile.function_module.Function)
assert isinstance(m2.pdf, types.MethodType)
assert isinstance(m2.nnlf, types.MethodType)
assert isinstance(m2.cdf, types.MethodType)
# Compare pdfs
rng = check_random_state(1)
X = rng.rand(100, 1) * 10 - 5
assert np.mean(np.abs(m1.pdf(X) - m2.pdf(X))) < 0.05
# Test sampling
X = m2.rvs(10)
assert X.shape == (10, 1)
# Check errors
assert_raises(NotImplementedError, m2.fit, X)
def test_mv_mixture():
p1 = MultivariateNormal(mu=np.array([0.0, 0.0]), sigma=np.eye(2))
p2 = MultivariateNormal(mu=np.array([2.0, 2.0]), sigma=0.5 * np.eye(2))
m = Mixture(components=[p1, p2])
assert m.ndim == 2
X = m.rvs(100)
assert X.shape == (100, 2)
assert_raises(ValueError, Mixture, components=[p1, Normal()])
def test_mv_mixture():
p1 = MultivariateNormal(mu=np.array([0.0, 0.0]),
sigma=np.eye(2))
p2 = MultivariateNormal(mu=np.array([2.0, 2.0]),
sigma=0.5 * np.eye(2))
m = Mixture(components=[p1, p2])
assert m.ndim == 2
X = m.rvs(100)
assert X.shape == (100, 2)
assert_raises(ValueError, Mixture, components=[p1, Normal()])
def test_rvs():
p1 = Normal(mu=0.0, sigma=T.constant(1.0), random_state=0)
p2 = Normal(mu=2.0, sigma=2.0, random_state=0)
m = Mixture(components=[p1, p2], weights=[0.25], random_state=0)
X = m.rvs(2000)
assert (np.mean(X) - (0.25 * p1.mu.eval() + 0.75 * p2.mu.eval())) < 0.1
def test_rvs():
p1 = Normal(mu=0.0, sigma=T.constant(1.0), random_state=0)
p2 = Normal(mu=2.0, sigma=2.0, random_state=0)
m = Mixture(components=[p1, p2], weights=[0.25], random_state=0)
X = m.rvs(2000)
assert (np.mean(X) - (0.25 * p1.mu.eval() + 0.75 * p2.mu.eval())) < 0.1