How to use the quantecon.util.searchsorted function in quantecon

equal to (num_reps, ts_length-1)

    out : ndarray(int, ndim=2)
        Array to store the sample paths.

    Notes
    -----
    This routine is jit-complied by Numba.

    """
    num_reps, ts_length = out.shape

    for i in range(num_reps):
        out[i, 0] = init_states[i]
        for t in range(ts_length-1):
            out[i, t+1] = searchsorted(P_cdfs[out[i, t]], random_values[i, t])

equal to (num_reps, ts_length-1)

    out : ndarray(int, ndim=2)
        Array to store the sample paths.

    Notes
    -----
    This routine is jit-complied by Numba.

    """
    num_reps, ts_length = out.shape

    for i in range(num_reps):
        out[i, 0] = init_states[i]
        for t in range(ts_length-1):
            k = searchsorted(P_cdfs1d[indptr[out[i, t]]:indptr[out[i, t]+1]],
                             random_values[i, t])
            out[i, t+1] = indices[indptr[out[i, t]]+k]

def draw_impl(cdf, size):
            rs = np.random.random(size)
            out = np.empty(size, dtype=np.int_)
            for i in range(size):
                out[i] = searchsorted(cdf, rs[i])
            return out
    else:

def draw_impl(cdf, size):
            r = np.random.random()
            return searchsorted(cdf, r)
    return draw_impl

used.

    Returns
    -------
    X : array_like(int, ndim=1)
        The simulation of states.

    """
    random_state = check_random_state(random_state)

    if isinstance(init, numbers.Integral):
        X_0 = init
    else:
        cdf0 = np.cumsum(init)
        u_0 = random_state.random_sample()
        X_0 = searchsorted(cdf0, u_0)

    mc = MarkovChain(P)
    return mc.simulate(ts_length=sample_size, init=X_0,
                       random_state=random_state)