def test_convergence_with_map (self):
""" Tests if we can still achieve convergence when using
parallel_map. """
mu = [2, 1]
s = [[.01, 0], [0, .01]]
X = MultivariateLognormal (mu, s)
N = 2000
rvs_caller = lambda _: X.rvs ()
rand_vals = parallel_map (rvs_caller, range (N), 5)
mean = np.array ([.0, .0])
for val in rand_vals:
mean += val
mean /= N
assert all (abs (X.mean () - mean) / X.mean () < 1e-1)
mu = [2, 1]
s = [[.01, 0], [0, .01]]
X = MultivariateLognormal (mu, s)
N = 2000
rvs_caller = lambda _: X.rvs ()
rand_vals = parallel_map (rvs_caller, range (N), 5)
mean = np.array ([.0, .0])
for val in rand_vals:
mean += val
mean /= N
assert all (abs (X.mean () - mean) / X.mean () < 1e-1)
def test_get_mean (self):
""" Tests if one can get the mean of the random variable. """
mu = [1, 2, 3, 4]
S = np.array ([[2, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 4, 1],
[0, 0, 1, 2]])
X = MultivariateLognormal (mu, S)
assert all (abs (X.mean () - np.exp (np.array (mu) \
+ S.diagonal () / 2)) < 1e-4)
def test_convergence_to_mean (self):
""" Tests if the randomly generated values has a sample mean
that converges to the correct mean. """
mu = [2, 1]
s = [[.01, 0], [0, .01]]
X = MultivariateLognormal (mu, s)
N = 2000
mean = np.array ([.0, .0])
for i in range (N):
mean += X.rvs ()
mean /= N
assert all (abs (X.mean () - mean) / X.mean () < 1e-1)
mu = [2, 1]
s = [[.01, 0], [0, .01]]
X = MultivariateLognormal (mu, s)
N = 2000
mean = np.array ([.0, .0])
for i in range (N):
mean += X.rvs ()
mean /= N
assert all (abs (X.mean () - mean) / X.mean () < 1e-1)