def _test(shape, n):
K = shape[-1]
rv = Multinomial(shape, pi=tf.constant(1.0/K, shape=shape))
rv_sample = rv.sample(n)
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.float32)
pi = rv.pi.eval()
if len(shape) == 1:
assert np.allclose(
rv.log_prob_idx((), x_tf).eval(),
multinomial_logpmf_vec(x[:, :], 1, pi[:]))
elif len(shape) == 2:
for i in range(shape[0]):
assert np.allclose(
rv.log_prob_idx((i, ), x_tf).eval(),
multinomial_logpmf_vec(x[:, i, :], 1, pi[i, :]))
else:
assert False
def _test(shape, p, size):
x = Multinomial(shape, p)
val_est = tuple(get_dims(x.sample(size=size)))
val_true = (size, ) + shape
assert val_est == val_true
def _test(shape, p, n):
x = Multinomial(shape, p)
val_est = tuple(get_dims(x.sample(n)))
val_true = (n, ) + shape
assert val_est == val_true
def _test(shape, p, n):
x = Multinomial(shape, p)
val_est = tuple(get_dims(x.sample(n)))
val_true = (n, ) + shape
assert val_est == val_true
