def test_select_proportional_to_weight(self): # Tests that multinomial_wo_replacement selects elements, on average, # proportional to the their probabilities th_rng = RandomStreams(12345) p = tensor.fmatrix() n = tensor.iscalar() m = th_rng.multinomial_wo_replacement(pvals=p, n=n) f = function([p, n], m, allow_input_downcast=True) n_elements = 100 n_selected = 10 mean_rtol = 0.0005 np.random.seed(12345) pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX) pvals /= pvals.sum(1) avg_pvals = np.zeros((n_elements, ), dtype=config.floatX) for rep in range(10000): res = f(pvals, n_selected) res = np.squeeze(res) avg_pvals[res] += 1 avg_pvals /= avg_pvals.sum() avg_diff = np.mean(abs(avg_pvals - pvals)) assert avg_diff < mean_rtol
def test_target_parameter(): srng = MRG_RandomStreams() pvals = np.array([[0.98, 0.01, 0.01], [0.01, 0.49, 0.50]]) def basic_target_parameter_test(x): f = aesara.function([], x) assert isinstance(f(), np.ndarray) basic_target_parameter_test(srng.uniform((3, 2), target="cpu")) basic_target_parameter_test(srng.normal((3, 2), target="cpu")) basic_target_parameter_test(srng.truncated_normal((3, 2), target="cpu")) basic_target_parameter_test(srng.binomial((3, 2), target="cpu")) basic_target_parameter_test( srng.multinomial(pvals=pvals.astype("float32"), target="cpu")) basic_target_parameter_test( srng.choice(p=pvals.astype("float32"), replace=False, target="cpu")) basic_target_parameter_test( srng.multinomial_wo_replacement(pvals=pvals.astype("float32"), target="cpu"))
def test_fail_select_alot(self): # Tests that multinomial_wo_replacement fails when asked to sample more # elements than the actual number of elements th_rng = RandomStreams(12345) p = tensor.fmatrix() n = tensor.iscalar() m = th_rng.multinomial_wo_replacement(pvals=p, n=n) f = function([p, n], m, allow_input_downcast=True) n_elements = 100 n_selected = 200 np.random.seed(12345) pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX) pvals /= pvals.sum(1) with pytest.raises(ValueError): f(pvals, n_selected)
def test_select_distinct(self): # Tests that multinomial_wo_replacement always selects distinct elements th_rng = RandomStreams(12345) p = tensor.fmatrix() n = tensor.iscalar() m = th_rng.multinomial_wo_replacement(pvals=p, n=n) f = function([p, n], m, allow_input_downcast=True) n_elements = 1000 all_indices = range(n_elements) np.random.seed(12345) for i in [5, 10, 50, 100, 500, n_elements]: pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX) pvals /= pvals.sum(1) res = f(pvals, i) res = np.squeeze(res) assert len(res) == i assert np.all(np.in1d(np.unique(res), all_indices)), res