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- 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
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