getNNI

def getNNI(W1, b1, W2, b2, alpha_s: list, beta_s: list, n):
    a = np.asarray(alpha_s)
    b = np.asarray(beta_s)
    ba_term = np.prod(b - a)
    ab = np.array([a, b])

    X = []
    L = get_L(n)
    for l in L:
        x = - b1 - W1[0]*ab[l[0],0] - W1[1]*ab[l[1],1] - W1[2]*ab[l[2],2] - W1[3]*ab[l[3],3] - W1[4]*ab[l[4],4] - W1[5]*ab[l[5],5]
        X.append(x)
    Xsi = get_Xsi(n, False)

    Fi = np.array([xsi * Li(n, -np.exp(x, dtype=np.float128)) for xsi, x in zip(Xsi, X)])
    Fi = np.sum(Fi, axis=0)

    I = b2[0] * ba_term + np.sum(W2.flatten() * (ba_term + Fi / np.prod(W1, axis=0) ))
    return I