def test_rvs_shape():
# Check that rvs parses the mean and covariance correctly, and returns
# an array of the right shape
N = 300
d = 4
sample = multivariate_normal.rvs(mean=np.zeros(d), cov=1, size=N)
assert_equal(sample.shape, (N, d))
sample = multivariate_normal.rvs(mean=None,
cov=np.array([[2, .1], [.1, 1]]),
size=N)
assert_equal(sample.shape, (N, 2))
u = multivariate_normal(mean=0, cov=1)
sample = u.rvs(N)
assert_equal(sample.shape, (N, ))
def test_rvs_shape():
# Check that rvs parses the mean and covariance correctly, and returns
# an array of the right shape
N = 300
d = 4
sample = multivariate_normal.rvs(mean=np.zeros(d), cov=1, size=N)
assert_equal(sample.shape, (N, d))
sample = multivariate_normal.rvs(mean=None,
cov=np.array([[2, .1], [.1, 1]]),
size=N)
assert_equal(sample.shape, (N, 2))
u = multivariate_normal(mean=0, cov=1)
sample = u.rvs(N)
assert_equal(sample.shape, (N, ))
def test_large_sample():
# Generate large sample and compare sample mean and sample covariance
# with mean and covariance matrix.
np.random.seed(2846)
n = 3
mean = np.random.randn(n)
M = np.random.randn(n, n)
cov = np.dot(M, M.T)
size = 5000
sample = multivariate_normal.rvs(mean, cov, size)
assert_allclose(numpy.cov(sample.T), cov, rtol=1e-1)
assert_allclose(sample.mean(0), mean, rtol=1e-1)
def test_large_sample():
# Generate large sample and compare sample mean and sample covariance
# with mean and covariance matrix.
np.random.seed(2846)
n = 3
mean = np.random.randn(n)
M = np.random.randn(n, n)
cov = np.dot(M, M.T)
size = 5000
sample = multivariate_normal.rvs(mean, cov, size)
assert_allclose(numpy.cov(sample.T), cov, rtol=1e-1)
assert_allclose(sample.mean(0), mean, rtol=1e-1)
