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import pytest
import numpy as np
from numpy.testing import assert_allclose
from astroML.density_estimation import XDGMM
def test_XDGMM_1D_gaussian(N=100, sigma=0.1):
np.random.seed(0)
mu = 0
V = 1
X = np.random.normal(mu, V, size=(N, 1))
X += np.random.normal(0, sigma, size=(N, 1))
Xerr = sigma ** 2 * np.ones((N, 1, 1))
xdgmm = XDGMM(1).fit(X, Xerr)
# because of sample variance, results will be similar
# but not identical. We'll use a fudge factor of 0.1
assert_allclose(mu, xdgmm.mu[0], atol=0.1)
assert_allclose(V, xdgmm.V[0], atol=0.1)
@pytest.mark.parametrize("D", [1, 2, 3])
def test_single_gaussian(D, N=100, sigma=0.1):
np.random.seed(0)
mu = np.random.random(D)
V = np.random.random((D, D))
V = np.dot(V, V.T)
X = np.random.multivariate_normal(mu, V, size=N)
Xerr = np.zeros((N, D, D))
Xerr[:, range(D), range(D)] = sigma ** 2
X += np.random.normal(0, sigma, X.shape)
xdgmm = XDGMM(1)
xdgmm.fit(X, Xerr)
# because of sample variance, results will be similar
# but not identical. We'll use a fudge factor of 0.1
assert_allclose(mu, xdgmm.mu[0], atol=0.1)
assert_allclose(V, xdgmm.V[0], atol=0.1)
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