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import numpy as np
from numpy.testing import assert_array_almost_equal, assert_allclose
from astroML.utils import (log_multivariate_gaussian,
convert_2D_cov,
completeness_contamination,
split_samples)
def positive_definite_matrix(N, M=None):
"""return an array of M positive-definite matrices with shape (N, N)"""
if M is None:
V = np.random.random((N, N))
V = np.dot(V, V.T)
else:
V = np.random.random((M, N, N))
for i in range(M):
V[i] = np.dot(V[i], V[i].T)
return V
def test_log_multivariate_gaussian_methods():
np.random.seed(0)
x = np.random.random(3)
mu = np.random.random(3)
V = positive_definite_matrix(3, M=10)
res1 = log_multivariate_gaussian(x, mu, V, method=0)
res2 = log_multivariate_gaussian(x, mu, V, method=1)
assert_array_almost_equal(res1, res2)
def test_log_multivariate_gaussian():
np.random.seed(0)
x = np.random.random((2, 1, 1, 3))
mu = np.random.random((3, 1, 3))
V = positive_definite_matrix(3, M=4)
res1 = log_multivariate_gaussian(x, mu, V)
assert res1.shape == (2, 3, 4)
res2 = np.zeros_like(res1)
for i in range(2):
for j in range(3):
for k in range(4):
res2[i, j, k] = log_multivariate_gaussian(x[i, 0, 0],
mu[j, 0],
V[k])
assert_array_almost_equal(res1, res2)
def test_log_multivariate_gaussian_Vinv():
np.random.seed(0)
x = np.random.random((2, 1, 1, 3))
mu = np.random.random((3, 1, 3))
V = positive_definite_matrix(3, M=4)
Vinv = np.array([np.linalg.inv(Vi) for Vi in V])
res1 = log_multivariate_gaussian(x, mu, V)
res2 = log_multivariate_gaussian(x, mu, V, Vinv=Vinv)
assert_array_almost_equal(res1, res2)
def test_2D_cov():
s1 = 1.3
s2 = 1.0
alpha = 0.2
cov = convert_2D_cov(s1, s2, alpha)
assert_array_almost_equal([s1, s2, alpha],
convert_2D_cov(cov))
def test_completeness_contamination():
completeness, contamination = completeness_contamination(np.ones(100),
np.ones(100))
assert_allclose(completeness, 1)
assert_allclose(contamination, 0)
completeness, contamination = completeness_contamination(np.zeros(100),
np.zeros(100))
assert_allclose(completeness, 0)
assert_allclose(contamination, 0)
completeness, contamination = completeness_contamination(
np.concatenate((np.ones(50), np.zeros(50))),
np.concatenate((np.ones(25), np.zeros(50), np.ones(25)))
)
assert_allclose(completeness, 0.5)
assert_allclose(contamination, 0.5)
def test_split_samples():
X = np.arange(100.)
y = np.arange(100.)
X_divisions, y_divisions = split_samples(X, y)
assert (len(X_divisions[0]) == len(y_divisions[0]) == 75)
assert (len(X_divisions[1]) == len(y_divisions[1]) == 25)
assert (len(set(X_divisions[0]) | set(X_divisions[1])) == 100)
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