def test_mutual_info_regression():
# We generate sample from multivariate normal distribution, using
# transformation from initially uncorrelated variables. The zero
# variables after transformation is selected as the target vector,
# it has the strongest correlation with the variable 2, and
# the weakest correlation with the variable 1.
T = np.array([[1, 0.5, 2, 1], [0, 1, 0.1, 0.0], [0, 0.1, 1, 0.1],
[0, 0.1, 0.1, 1]])
cov = T.dot(T.T)
mean = np.zeros(4)
rng = check_random_state(0)
Z = rng.multivariate_normal(mean, cov, size=1000)
X = Z[:, 1:]
y = Z[:, 0]
mi = mutual_info_regression(X, y, random_state=0)
assert_array_equal(np.argsort(-mi), np.array([1, 2, 0]))
def test_mutual_info_regression():
# We generate sample from multivariate normal distribution, using
# transformation from initially uncorrelated variables. The zero
# variables after transformation is selected as the target vector,
# it has the strongest correlation with the variable 2, and
# the weakest correlation with the variable 1.
T = np.array([
[1, 0.5, 2, 1],
[0, 1, 0.1, 0.0],
[0, 0.1, 1, 0.1],
[0, 0.1, 0.1, 1]
])
cov = T.dot(T.T)
mean = np.zeros(4)
rng = check_random_state(0)
Z = rng.multivariate_normal(mean, cov, size=1000)
X = Z[:, 1:]
y = Z[:, 0]
mi = mutual_info_regression(X, y, random_state=0)
assert_array_equal(np.argsort(-mi), np.array([1, 2, 0]))