def test_n_samples_2():
p = fmatrix()
u = fvector()
n = iscalar()
m = MultinomialFromUniform("auto")(p, u, n)
f = function([p, u, n], m, allow_input_downcast=True)
np.random.seed(12345)
for i in [1, 5, 10, 100, 1000]:
uni = np.random.rand(i).astype(config.floatX)
pvals = np.random.randint(1, 1000, (1, 1000)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, uni, i)
assert res.sum() == i
for i in [1, 5, 10, 100, 1000]:
uni = np.random.rand(i).astype(config.floatX)
pvals = np.random.randint(1, 1000000,
(1, 1000000)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, uni, i)
assert res.sum() == i
def test_multinomial_large():
p = fmatrix()
u = fvector()
m = MultinomialFromUniform("auto")(p, u)
f = function([p, u], m * 2, allow_input_downcast=True)
pval = np.arange(10000 * 4, dtype="float32").reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = np.ones_like(pval[:, 0]) * 0.5
mval = f(pval, uval)
assert mval.shape == pval.shape
if config.cast_policy == "custom":
assert mval.dtype == pval.dtype
elif config.cast_policy == "numpy+floatX":
assert mval.dtype == config.floatX
elif config.cast_policy == "numpy":
assert mval.dtype == "float64"
else:
raise NotImplementedError(config.cast_policy)
utt.assert_allclose(mval.sum(axis=1), 2)
asdf = np.asarray([0, 0, 2, 0]) + 0 * pval
utt.assert_allclose(mval, asdf) # broadcast over all rows