def probvec(m, k, random_state=None):
    if k == 1:
        return np.ones((m, k))

    # if k >= 2
    random_state = check_random_state(random_state)
    r = random_state.random_sample(size=(m, k-1))

    r.sort(axis=-1)
    x = np.empty((m, k))
    _diff(r, out=x)

    return x
Return m randomly sampled probability vectors of dimension k.
dataset/ETHPy150Open QuantEcon/QuantEcon.py/quantecon/random/utilities.py/probvec