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"""
Test density estimation techniques
"""
import pytest
import numpy as np
from numpy.testing import assert_allclose
from scipy.stats import norm
from astroML.density_estimation import KNeighborsDensity, GaussianMixture1D
classifiers = [KNeighborsDensity(method='simple', n_neighbors=250),
KNeighborsDensity(method='bayesian', n_neighbors=250)]
@pytest.mark.parametrize("clf", classifiers)
def test_1D_density(clf, atol=100):
np.random.seed(0)
dist = norm(0, 1)
X = dist.rvs((5000, 1))
X2 = np.linspace(-5, 5, 10).reshape((10, 1))
true_dens = dist.pdf(X2[:, 0]) * X.shape[0]
clf.fit(X)
dens = clf.eval(X2)
assert_allclose(dens, true_dens, atol=atol)
def test_gaussian1d():
x = np.linspace(-6, 10, 1000)
means = np.array([-1.5, 0.0, 2.3])
sigmas = np.array([1, 0.25, 3.8])
weights = np.array([1, 1, 1])
gauss = GaussianMixture1D(means=means, sigmas=sigmas, weights=weights)
y = gauss.pdf(x)
# Check whether sampling works
gauss.sample(10)
dx = x[1] - x[0]
integral = np.sum(y*dx)
assert_allclose(integral, 1., atol=0.02)
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