def test_draw_random_only(self, random_effects):
np.random.seed(127)
dimensions = [9, 3, 2, 2]
N = np.prod(dimensions)
Y_true = np.zeros(N)
dct = {}
for i, effect in enumerate(random_effects):
Z = rutils.kronecker(effect, dimensions, 0)
u = np.random.randn(np.prod(effect))
Y_true += Z.dot(u)
dct['intercept' + str(i)] = ([
effect[j] == dimensions[j] for j in range(len(dimensions))
], None)
delta_true = .005
Y = Y_true + np.random.randn(N) * np.sqrt(delta_true)
model = LME(dimensions, 1, Y, {}, {}, {}, False, random_effects=dct)
model.optimize(inner_print_level=0)
model.postVarRandom()
n_draws = 1000
_, u_samples = model.draw(n_draws=n_draws)
u1 = np.concatenate(
[u[:np.prod(random_effects[0][1:])] for u in model.u_soln])
u1_sample_mean = np.mean(u_samples[0].reshape((-1, n_draws)), axis=1)
assert np.linalg.norm(u1 - u1_sample_mean) / np.linalg.norm(u1) < .05
u2 = np.concatenate([
u[np.prod(random_effects[0][1:]):np.prod(random_effects[0][1:]) +
np.prod(random_effects[1][1:])] for u in model.u_soln
])
u2_sample_mean = np.mean(u_samples[1].reshape((-1, n_draws)), axis=1)
assert np.linalg.norm(u2 - u2_sample_mean) / np.linalg.norm(u2) < .05
model.outputDraws()
return
def test_draw(self, dimensions, random_effects):
np.random.seed(127)
#dimensions = [9, 3, 2, 2]
N = np.prod(dimensions)
X = np.ones((N, 2))
X[:, 1] = np.random.randn(N)
beta_true = [1., -0.6]
Y_true = X.dot(beta_true)
dct = {}
for i, effect in enumerate(random_effects):
Z = rutils.kronecker(effect, dimensions, 0)
u = np.random.randn(np.prod(effect))
Y_true += Z.dot(u)
dct['intercept' + str(i)] = ([
effect[j] == dimensions[j] for j in range(len(dimensions))
], None)
delta_true = .005
Y = Y_true + np.random.randn(N) * np.sqrt(delta_true)
model = LME(dimensions,
1,
Y, {'cov': (X[:, 1], [True] * len(dimensions))}, {},
{'cov': [-float('inf'), float('inf')]},
True,
random_effects=dct)
model.optimize(inner_print_level=0)
model.postVarGlobal()
if len(random_effects) > 0:
model.postVarRandom()
n_draws = 1000
beta_samples, u_samples = model.draw(n_draws=n_draws)
beta_sample_mean = np.mean(beta_samples, axis=1)
assert np.linalg.norm(beta_sample_mean - model.beta_soln
) / np.linalg.norm(model.beta_soln) < .02
if len(random_effects) > 0:
u1 = np.concatenate(
[u[:np.prod(random_effects[0][1:])] for u in model.u_soln])
u1_sample_mean = np.mean(u_samples[0].reshape((-1, n_draws)),
axis=1)
assert np.linalg.norm(u1 -
u1_sample_mean) / np.linalg.norm(u1) < .05
u2 = np.concatenate([
u[np.prod(random_effects[0][1:]
):np.prod(random_effects[0][1:]) +
np.prod(random_effects[1][1:])] for u in model.u_soln
])
u2_sample_mean = np.mean(u_samples[1].reshape((-1, n_draws)),
axis=1)
assert np.linalg.norm(u2 -
u2_sample_mean) / np.linalg.norm(u2) < .05
model.outputDraws()