def test_multinomial(self):
# Test that raw_random.multinomial generates the same results as numpy.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
post_r, out = multinomial(rng_R, (7, 3), 6, [0.2] * 5)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
(val0, ) = f()
(val1, ) = f()
numpy_val0 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
numpy_val1 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
assert val0.shape == (7, 3, 5)
assert val1.shape == (7, 3, 5)
def test_multinomial_vector(self):
rng_R = random_state_type()
n = tensor.lvector()
pvals = tensor.matrix()
post_r, out = multinomial(rng_R, n=n, pvals=pvals)
assert out.ndim == 2
f = compile.function([rng_R, n, pvals], [post_r, out],
accept_inplace=True)
n_val = [1, 2, 3]
pvals_val = [[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]]
pvals_val = np.asarray(pvals_val, dtype=config.floatX)
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, n_val, pvals_val)
numpy_val0 = np.asarray([
numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val, pvals_val)
])
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, n_val[:-1], pvals_val[:-1])
numpy_val1 = np.asarray([
numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val[:-1], pvals_val[:-1])
])
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, n, pvals],
multinomial(rng_R, n=n, pvals=pvals, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, n_val, pvals_val)
numpy_val2 = np.asarray([
numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val, pvals_val)
])
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, n_val[:-1], pvals_val[:-1])
def test_multinomial_tensor3_b(self):
# Test the examples given in the multinomial documentation regarding
# tensor3 objects
rng_R = random_state_type()
n = 9
pvals = tensor.dtensor3()
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(10, 1, -1))
assert out.ndim == 4
assert out.broadcastable == (False, True, False, False)
f = function([rng_R, pvals], [post_r, out], accept_inplace=True)
rng = np.random.RandomState(utt.fetch_seed())
pvals_val = np.asarray([[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]]])
assert pvals_val.shape == (1, 3, 2)
out_rng, draw = f(rng, pvals_val)
assert draw.shape == (10, 1, 3, 2)
assert np.allclose(draw.sum(axis=3), 9)
def test_default_shape(self):
rng_R = random_state_type()
post_r, out = uniform(rng_R)
f = compile.function([rng_R], [post_r, out], accept_inplace=True)
rng_state0 = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0)
post1, val1 = f(post0)
numpy_val0 = np.asarray(numpy_rng.uniform(), dtype=theano.config.floatX)
numpy_val1 = np.asarray(numpy_rng.uniform(), dtype=theano.config.floatX)
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
post_r, out = multinomial(rng_R)
g = compile.function([rng_R], [post_r, out], accept_inplace=True)
post2, val2 = g(post1)
numpy_val2 = np.asarray(
numpy_rng.multinomial(n=1, pvals=[0.5, 0.5]), dtype=theano.config.floatX
)
assert np.all(val2 == numpy_val2)