解释这串代码 mf(U(:, :) > lamda1) = mf1(U(:, :) > lamda1); mf(U(:, :) < lamda2) = mf2(U(:, :) < lamda2);
时间: 2023-06-07 18:09:36 浏览: 128
这段代码使用了逻辑判断运算符 `>` 和 `<`,用于判断 `U` 矩阵中元素是否大于或小于 `lamda1` 和 `lamda2`,并根据判断结果将 `U` 矩阵中符合条件的元素替换为 `mf1` 和 `mf2` 中对应位置的元素。具体地,代码中 `mf` 表示一个和 `U` 矩阵维度相同的矩阵,`mf1` 和 `mf2` 分别表示替换为的元素矩阵。`U(:, :)` 表示访问矩阵 `U` 的所有行和列。因此,这段代码的作用是将 `U` 中大于 `lamda1` 的元素替换为 `mf1` 中对应位置的元素,将小于 `lamda2` 的元素替换为 `mf2` 中对应位置的元素。
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mf1 = U.^mi1(i); mf2 = U.^mi2(i); mf = U.^mt(i); mf(U(:, :) > lamda1) = mf1(U(:, :) > lamda1); mf(U(:, :) < lamda2) = mf2(U(:, :) < lamda2);
This code segment computes the membership values of a fuzzy set based on three membership functions: mf1, mf2, and mf.
The input variable U is a matrix of data points whose membership values are to be computed. The mi1, mi2, and mt are vectors of parameters that determine the shape and location of the three membership functions.
The first line of code computes the membership values of U using the mf1 function for those values of U that are greater than a threshold value lamda1. The second line computes the membership values using the mf2 function for those values of U that are less than a threshold value lamda2. Finally, the third line computes the membership values using the mf function for all other values of U.
The resulting membership values are stored in the mf matrix, which is then returned as the output of the function. The overall effect of this code is to create a hybrid membership function that combines the characteristics of mf1, mf2, and mf, and that varies smoothly between these three functions depending on the value of U.
将这段代码转换为伪代码:算法函数3-2:LM 1: def levenberg_marquardt(fun, grad, jacobian, x0, iterations, tol): 2: while (gnorm > tol) and (k < iterations): 3: if updateJ == 1: 4: x_log = np.append(x_log, xk.T) 5: yk = fun(xk) 6: y_log = np.append(y_log, yk) 7: H_lm = H + (lamda * np.eye(9)) 8: gfk = grad(xk) 9: pk = - np.linalg.inv(H_lm).dot(gfk) 10: pk = pk.A.reshape(1, -1)[0] 11: xk = xk + pk 12: fval = fun(xk) 13: if fval < old_fval: 14: lamda = lamda / 10 15: old_fval = fval
算法函数3-2:LM
输入:目标函数 fun,目标函数的梯度 grad,雅可比矩阵 jacobian,起始点 x0,最大迭代次数 iterations,收敛精度 tol
输出:最优解 xk
1. k = 0, lamda = 0.01, H = jacobian(x0),old_fval = fun(x0)
2. while (gnorm > tol) and (k < iterations) do
3. if updateJ == 1 then
4. x_log = append(x_log, xk.T)
5. yk = fun(xk)
6. y_log = append(y_log, yk)
7. end if
8. H_lm = H + (lamda * eye(9))
9. gfk = grad(xk)
10. pk = - inv(H_lm) · gfk
11. pk = pk.A.reshape(1, -1)[0]
12. xk = xk + pk
13. fval = fun(xk)
14. if fval < old_fval then
15. lamda = lamda / 10
16. old_fval = fval
17. else
18. lamda = lamda * 10
19. end if
20. H = jacobian(xk)
21. k = k + 1
22. end while
23. return xk
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