def check_normal(mu, sigma):
rng = check_random_state(1)
p_carl = Normal(mu=mu, sigma=sigma)
p_scipy = st.norm(loc=mu, scale=sigma)
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_linear_transform_1d():
p0 = Normal()
pt = LinearTransform(p0, A=np.array([[0.5]]))
X0 = p0.rvs(10, random_state=0)
Xt = pt.rvs(10, random_state=0)
assert X0.shape == Xt.shape
assert_array_equal(X0 * 0.5, Xt)
assert_array_equal(p0.pdf(X0), pt.pdf(Xt))
assert_array_equal(p0.nll(X0), pt.nll(Xt))
def test_linear_transform_1d():
p0 = Normal()
pt = LinearTransform(p0, A=np.array([[0.5]]))
X0 = p0.rvs(10, random_state=0)
Xt = pt.rvs(10, random_state=0)
assert X0.shape == Xt.shape
assert_array_equal(X0 * 0.5, Xt)
assert_array_equal(p0.pdf(X0), pt.pdf(Xt))
assert_array_equal(p0.nll(X0), pt.nll(Xt))
def check_normal(mu, sigma):
rng = check_random_state(1)
p_carl = Normal(mu=mu, sigma=sigma)
p_scipy = st.norm(loc=mu, scale=sigma)
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_kde():
# Test API
p = Normal()
X = p.rvs(10000, random_state=1)
k = KernelDensity()
k.fit(X)
reals = np.linspace(-3, 3).reshape(-1, 1)
assert np.mean(np.abs(p.pdf(reals) - k.pdf(reals))) < 0.05
assert np.mean(np.abs(p.nll(reals) - k.nll(reals))) < 0.05
# Test sampling
X = k.rvs(10000, random_state=1)
assert np.abs(np.mean(X)) < 0.05
