def systematic_resample(sess, weights):
    N = len(weights)

    # make N subdivisions and choose positions with a random offset
    positions = (sess.run(tf.random.uniform((1, ))) + np.arange(N)) / N

    indexes = np.zeros(N, 'i')
    cum_sum = np.cumsum(weights)
    i, j = 0, 0
    while i < N:
        if positions[i] < cum_sum[j]:
            indexes[i] = j
            i = i + 1
        else:
            j = j + 1
    return indexes

  def resample_from_index(particles, weights, indexes):
    particles = particles[:, indexes]
    weights = weights[indexes]
    weights.fill(1.0 / len(weights))

    return particles, weights, indexes