def test_truncated_distribution_cdf():
n = Normal()
truncated = TruncatedDistribution(Normal(), lower_bound=0)
assert truncated.cdf(-1) == 0
assert truncated.cdf(0) == 0
assert truncated.cdf(1) == 2 * (n.cdf(1) - n.cdf(0))
assert truncated.cdf(np.inf) == 1
def test_fixed_values(self):
data = np.random.standard_normal(1000)
raw = Normal.fit(data)
assert raw.loc() == approx(0.0)
assert raw.scale() == approx(1.0)
fixed = Normal.fit(data, loc=1.0)
assert fixed.loc() == 1.0
def test_log_likelihood_is_correct(normal_data):
ndata = normal_data[:1000]
n = Normal(loc=kernels.linear(np.arange(len(ndata)))).fit_instance(ndata)
actual = log_likelihood(n, ndata)
expected = sum(
norm.logpdf(x, loc=loc, scale=n.scale())
for x, loc in zip(ndata, n.loc()))
assert actual == approx(expected)
def test_truncated_distribution_fit():
n = Normal(1)
data = n.rvs(10000)
trunc_data = data[data >= 0]
truncated = TruncatedDistribution(Normal(), lower_bound=0)
fitted_all_data = truncated.fit_instance(data)
fitted_trunc = truncated.fit_instance(trunc_data)
for p_trunc, p_all in zip(fitted_trunc.flattened_params,
fitted_all_data.flattened_params):
assert p_trunc() == p_all()
def test_cache():
n = Normal(0, 1)
np.testing.assert_array_almost_equal(n.pdf([-1, 0, 1]),
[0.24197072, 0.39894228, 0.24197072])
np.testing.assert_array_almost_equal(n.pdf([1, 2, 3]),
[0.24197072, 0.05399097, 0.00443185])
np.testing.assert_array_almost_equal(n.cdf([-1, 0, 1]),
[0.15865525, 0.5, 0.84134475])
np.testing.assert_array_almost_equal(n.pdf([-1, 0, 1]),
[0.24197072, 0.39894228, 0.24197072])
def test_fit_instance_fixed_params_extra_levels(dataset):
covariate = np.arange(len(dataset))
n = Normal(loc=kernels.linear(covariate, a=kernels.linear(covariate)))
m = n.fit_instance(dataset, loc_a_a=5)
assert m.loc.a.a() == 5
def test_fit_instance_fixed_params_multi_level(dataset, linear_kernel):
n = Normal(loc=linear_kernel)
m = n.fit_instance(dataset, loc_a=5)
assert m.loc.a() == 5
def test_fit_instance_fixed_params(dataset):
n = Normal().fit_instance(dataset, loc=5)
assert n.loc() == 5
def test_fit_instance(dataset):
std_fit = Normal.fit(dataset)
instance_fit = Normal(loc=kernels.constant()).fit_instance(dataset)
assert std_fit.loc() == approx(instance_fit.loc())
def test_named_with_params_partial_assignment():
n = Normal()
m = n.with_params(scale=3)
assert m.loc() == 0
assert m.scale() == 3
def test_named_with_params_multi_level(linear_kernel):
n = Normal(loc=linear_kernel, scale=1)
m = n.with_params(loc_a=2, scale=3)
assert m.loc.a() == 2
assert m.scale() == 3
def test_named_with_params():
n = Normal()
m = n.with_params(loc=2, scale=3)
assert m.loc() == 2
assert m.scale() == 3
def test_basic_with_params():
n = Normal()
m = n.with_params([2, 3])
assert m.loc() == 2
assert m.scale() == 3
def test_rvs_random_state():
n = Normal()
rand_state = 10
sample = n.rvs(10000, random_state=rand_state)
sample2 = n.rvs(10000, random_state=rand_state)
assert (sample == sample2).all()
def test_rvs():
n = Normal(1)
sample = n.rvs(10000, scale=2)
assert np.mean(sample) == approx(1)
assert np.std(sample) == approx(2)
def normal_data():
return np.array(Normal().rvs(100000))