def test_marginal_distribution():
"""Test moments from marginal MVN."""
random_state = check_random_state(0)
mvn = MVN(mean=mean, covariance=covariance, random_state=random_state)
marginalized = mvn.marginalize(np.array([0]))
assert_equal(marginalized.mean, np.array([0.0]))
assert_equal(marginalized.covariance, np.array([0.5]))
marginalized = mvn.marginalize(np.array([1]))
assert_equal(marginalized.mean, np.array([1.0]))
assert_equal(marginalized.covariance, np.array([5.0]))
def test_marginal_distribution():
"""Test moments from marginal MVN."""
random_state = check_random_state(0)
mvn = MVN(mean=mean, covariance=covariance, random_state=random_state)
marginalized = mvn.marginalize(np.array([0]))
assert_equal(marginalized.mean, np.array([0.0]))
assert_equal(marginalized.covariance, np.array([0.5]))
marginalized = mvn.marginalize(np.array([1]))
assert_equal(marginalized.mean, np.array([1.0]))
assert_equal(marginalized.covariance, np.array([5.0]))
def test_marginal_distribution():
random_state = check_random_state(0)
mean = np.array([0.0, 1.0])
covariance = np.array([[0.5, -1.0], [-1.0, 5.0]])
mvn = MVN(mean=mean, covariance=covariance, random_state=random_state)
marginalized = mvn.marginalize(np.array([0]))
assert_equal(marginalized.mean, np.array([0.0]))
assert_equal(marginalized.covariance, np.array([0.5]))
mvn = MVN(random_state=random_state)
X = random_state.multivariate_normal([0.0, 1.0], [[0.5, -2.0], [-2.0, 5.0]],
size=(100,))
mvn.from_samples(X)
X_sampled = mvn.sample(n_samples=100)
plt.figure(figsize=(15, 5))
plt.subplot(1, 3, 1)
plt.xlim((-10, 10))
plt.ylim((-10, 10))
plot_error_ellipse(plt.gca(), mvn)
plt.scatter(X[:, 0], X[:, 1], c="g", label="Training data")
plt.scatter(X_sampled[:, 0], X_sampled[:, 1], c="r", label="Samples")
plt.title("Bivariate Gaussian")
plt.legend(loc="best")
x = np.linspace(-10, 10, 100)
plt.subplot(1, 3, 2)
plt.xticks(())
marginalized = mvn.marginalize(np.array([1]))
plt.plot(marginalized.to_probability_density(x[:, np.newaxis]), x)
plt.title("Marginal distribution over y")
plt.subplot(1, 3, 3)
plt.yticks(())
marginalized = mvn.marginalize(np.array([0]))
plt.plot(x, marginalized.to_probability_density(x[:, np.newaxis]))
plt.title("Marginal distribution over x")
plt.show()
mvn = MVN(random_state=random_state)
X = random_state.multivariate_normal([0.0, 1.0], [[0.5, 1.5], [1.5, 5.0]],
size=(100,))
mvn.from_samples(X)
X_sampled = mvn.sample(n_samples=100)
plt.figure(figsize=(15, 5))
plt.subplot(1, 3, 1)
plt.xlim((-10, 10))
plt.ylim((-10, 10))
plot_error_ellipse(plt.gca(), mvn)
plt.scatter(X[:, 0], X[:, 1], c="g", label="Training data")
plt.scatter(X_sampled[:, 0], X_sampled[:, 1], c="r", label="Samples")
plt.title("Bivariate Gaussian")
plt.legend(loc="best")
x = np.linspace(-10, 10, 100)
plt.subplot(1, 3, 2)
plt.xticks(())
marginalized = mvn.marginalize(np.array([1]))
plt.plot(marginalized.to_probability_density(x[:, np.newaxis]), x)
plt.title("Marginal distribution over y")
plt.subplot(1, 3, 3)
plt.yticks(())
marginalized = mvn.marginalize(np.array([0]))
plt.plot(x, marginalized.to_probability_density(x[:, np.newaxis]))
plt.title("Marginal distribution over x")
plt.show()
