def test_draw_samples(self):
np.random.seed(0)
samples_1_np = np.random.randn(5)
samples_1 = mx.nd.array(samples_1_np)
samples_2_np = np.random.randn(50)
samples_2 = mx.nd.array(samples_2_np)
m = Model()
v = Variable(shape=(1,))
m.v2 = Normal.define_variable(mean=v, variance=mx.nd.array([1]), rand_gen=MockMXNetRandomGenerator(samples_1))
m.v3 = Normal.define_variable(mean=m.v2, variance=mx.nd.array([0.1]), shape=(10,), rand_gen=MockMXNetRandomGenerator(samples_2))
np.random.seed(0)
v_np =np.random.rand(1)
v_mx = mx.nd.array(v_np)
v_rt = add_sample_dimension(mx.nd, v_mx)
variance = m.v2.factor.variance
variance2 = m.v3.factor.variance
variance_rt = add_sample_dimension(mx.nd, variance.constant)
variance2_rt = add_sample_dimension(mx.nd, variance2.constant)
samples = m.draw_samples(F=mx.nd, num_samples=5, targets=[m.v3.uuid],
variables={v.uuid: v_rt, variance.uuid: variance_rt, variance2.uuid: variance2_rt})[0]
samples_np = v_np + samples_1_np[:, None] + np.sqrt(0.1)*samples_2_np.reshape(5,10)
assert array_has_samples(mx.nd, samples) and get_num_samples(mx.nd, samples)==5
assert np.allclose(samples.asnumpy(), samples_np)
def test_compute_log_prob(self):
m = Model()
v = Variable(shape=(1,))
m.v2 = Normal.define_variable(mean=v, variance=mx.nd.array([1]))
m.v3 = Normal.define_variable(mean=m.v2, variance=mx.nd.array([1]), shape=(10,))
np.random.seed(0)
v_mx = mx.nd.array(np.random.randn(1))
v2_mx = mx.nd.array(np.random.randn(1))
v3_mx = mx.nd.array(np.random.randn(10))
v_rt = add_sample_dimension(mx.nd, v_mx)
v2_rt = add_sample_dimension(mx.nd, v2_mx)
v3_rt = add_sample_dimension(mx.nd, v3_mx)
variance = m.v2.factor.variance
variance2 = m.v3.factor.variance
variance_rt = add_sample_dimension(mx.nd, variance.constant)
variance2_rt = add_sample_dimension(mx.nd, variance2.constant)
log_pdf = m.log_pdf(F=mx.nd, variables={m.v2.uuid: v2_rt, m.v3.uuid:v3_rt, variance.uuid: variance_rt, variance2.uuid: variance2_rt, v.uuid: v_rt}).asscalar()
variables = {m.v2.factor.mean.uuid: v_rt, m.v2.factor.variance.uuid: variance_rt, m.v2.factor.random_variable.uuid: v2_rt}
log_pdf_1 = mx.nd.sum(m.v2.factor.log_pdf(F=mx.nd, variables=variables))
variables = {m.v3.factor.mean.uuid: v2_rt, m.v3.factor.variance.uuid: variance2_rt, m.v3.factor.random_variable.uuid: v3_rt}
log_pdf_2 = mx.nd.sum(m.v3.factor.log_pdf(F=mx.nd, variables=variables))
assert log_pdf == (log_pdf_1 + log_pdf_2).asscalar()