def test_sample_confidence_region():
"""Test sampling of confidence region."""
random_state = check_random_state(42)
mvn = MVN(mean=np.array([1.0, 2.0]),
covariance=np.array([[1.0, 0.0], [0.0, 4.0]]),
random_state=random_state)
samples = mvn.sample_confidence_region(100, 0.9)
for sample in samples:
assert_true(mvn.is_in_confidence_region(sample, 0.9))
n_samples = 1000
plt.figure(figsize=(15, 5))
ax = plt.subplot(131)
ax.set_title("Unconstrained Sampling")
samples = mvn.sample(n_samples)
ax.scatter(samples[:, 0], samples[:, 1], alpha=0.9, s=1, label="Samples")
plot_error_ellipse(ax, mvn, factors=(1.0, 2.0), color="orange")
ax.set_xlim((-5, 5))
ax.set_ylim((-10, 10))
ax = plt.subplot(132)
ax.set_title(r"95.45 % Confidence Region ($2\sigma$)")
samples = mvn.sample_confidence_region(n_samples, 0.9545)
ax.scatter(samples[:, 0], samples[:, 1], alpha=0.9, s=1, label="Samples")
plot_error_ellipse(ax, mvn, factors=(1.0, 2.0), color="orange")
ax.set_xlim((-5, 5))
ax.set_ylim((-10, 10))
ax = plt.subplot(133)
ax.set_title(r"68.27 % Confidence Region ($\sigma$)")
samples = mvn.sample_confidence_region(n_samples, 0.6827)
ax.scatter(samples[:, 0], samples[:, 1], alpha=0.9, s=1, label="Samples")
plot_error_ellipse(ax, mvn, factors=(1.0, 2.0), color="orange")
ax.set_xlim((-5, 5))
ax.set_ylim((-10, 10))
ax.legend()
plt.show()
