# Carl is free software; you can redistribute it and/or modify it
# under the terms of the Revised BSD License; see LICENSE file for
# more details.
import numpy as np
import scipy.stats as st
from numpy.testing import assert_array_almost_equal
from sklearn.utils import check_random_state
from carl.distributions import Exponential
def check_exponential(inverse_scale):
rng = check_random_state(1)
p_carl = Exponential(inverse_scale=inverse_scale)
p_scipy = st.expon(scale=1. / inverse_scale)
X = rng.rand(50, 1)
assert_array_almost_equal(p_carl.pdf(X),
p_scipy.pdf(X.ravel()))
assert_array_almost_equal(p_carl.cdf(X),
p_scipy.cdf(X.ravel()))
assert_array_almost_equal(-np.log(p_carl.pdf(X)),
p_carl.nll(X))
def test_exponential():
for inverse_scale in [1, 2, 5]:
yield check_exponential, inverse_scale
def check_rvs(inverse_scale, random_state):
p = Exponential(inverse_scale=inverse_scale)
samples = p.rvs(1000, random_state=random_state)
assert np.abs(np.mean(samples) - 1. / inverse_scale) <= 0.05
def test_rvs():
for inverse_scale, random_state in [(1, 0), (1, 1),
(2, 3), (0.5, 4)]:
yield check_rvs, inverse_scale, random_state
def check_fit(inverse_scale):
p = Exponential()
X = st.expon(scale=1. / inverse_scale).rvs(5000,
random_state=0).reshape(-1, 1)
p.fit(X)
assert np.abs(p.inverse_scale.get_value() - inverse_scale) <= 0.1
def test_fit():
for inverse_scale in [1, 2, 5]:
yield check_fit, inverse_scale
