求一个或门逻辑关系函数
本帖最后由 hn10183051 于 2025-1-11 13:13 编辑研究一个表的或门逻辑关系，求高手指点！
(("1.85" "1800" "4000" "100") ("1.85" "1700" "5500" "100")
("1.85" "1600" "5000" "100") ("1.85" "1500" "4500" "100")
("1.85" "1400" "4200" "100") ("1.85" "1300" "4200" "100")
("1.85" "1200" "3500" "100") ("1.85" "1100" "2500" "100")
)
;当2个比较结果不一致时写入结果最大值,反之写入相同结果
(("1.85" "1800" "4000" "100" "4") ("1.85" "1700" "5500" "100" "4")
("1.85" "1600" "5000" "100" "3") ("1.85" "1500" "4500" "100" "2")
("1.85" "1400" "4200" "100" "1") ("1.85" "1300" "4200" "100" "1")
("1.85" "1200" "3500" "100" "0") ("1.85" "1100" "2500" "100" "0")
)(DEFUN C->TH (C D / TH);比较表第2个元素
(COND
((AND (>= 1300 C ) (>= 4000 D ))(setq TH 0))
((AND (>= 1400 C ) (>= 4200 D ))(setq TH 1))
((AND (>= 1500 C ) (>= 4500 D ))(setq TH 2))
((AND (>= 1600 C ) (>= 5000 D ))(setq TH 3))
(T (setq TH 4))
)
TH
)
(C->TH 900 4000) 0
(C->TH 900 5500) 4
(C->TH 1300 4400)2
(C->TH 1400 4400)2
(C->TH 1500 4400)2
(C->TH 1600 300) 3
(C->TH 1700 200) 4
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 (defun tt(lst)
(mapcar(function(lambda(x / a b)
(setq a(atoi(cadr x))
a(COND((>= 1300 a)0)((>= 1400 a)1)((>= 1500 a)2)((>= 1600 a)3)(t 4))
b(atoi(caddr x))
b(COND((>= 4000 b)0)((>= 4200 b)1)((>= 4500 b)2)((>= 5000 b)3)(t 4)))
(append x(list(itoa(max a b))))))lst))
(tt'(("1.85" "1800" "4000" "100") ("1.85" "1700" "5500" "100") ("1.85" "1600" "5000" "100") ("1.85" "1500" "4500" "100") ("1.85" "1400" "4200" "100")("1.85" "1300" "4200" "100") ("1.85" "1200" "3500" "100") ("1.85" "1100" "2500" "100") ))
=>
(("1.85" "1800" "4000" "100" "4") ("1.85" "1700" "5500" "100" "4") ("1.85" "1600" "5000" "100" "3") ("1.85" "1500" "4500" "100" "2") ("1.85" "1400" "4200" "100" "1") ("1.85" "1300" "4200" "100" "1") ("1.85" "1200" "3500" "100" "0") ("1.85" "1100" "2500" "100" "0"))
(defun tt1 (lst)
(mapcar (function (lambda (x / a b)
(setq C (atoi (cadr x)))
(setq D (atoi (caddr x)))
(COND
((AND (>= 1300 C ) (>= 4000 D ))(setq TH 0))
((AND (>= 1400 C ) (>= 4200 D ))(setq TH 1))
((AND (>= 1500 C ) (>= 4500 D ))(setq TH 2))
((AND (>= 1600 C ) (>= 5000 D ))(setq TH 3))
(T (setq TH 4))
)
(append x (list (itoa TH)))
)
)
lst
)
) llsheng_73 发表于 2025-1-11 12:46
(tt'(("1.85" "1800" "4000" "100") ("1.85" "1700" "5500" "100") ("1.85" "1600" "5000" "100") ("1.85 ...
谢谢，llsheng_73大神 hn10183051 发表于 2025-1-11 13:23
(defun tt1 (lst)
(mapcar (function (lambda (x / a b)
(setq C (atoi (cadr x)))
看错了，好象不会出现结果不正确的情况，因为几个梯度是逐级增大的。。。比如1300以下它可以返回01234
1600只能返回3，1600以上只能返回4，没注意到区间的这种划分办法可以直接隐含或逻辑... 坐标系中划分一下，不能有重叠或遗漏区域，没那么复杂。 (defun c->th (c d)
(cond ((and (>= 1300 c) (>= 4000 d)) 0)
((and (>= 1400 c) (>= 4200 d)) 1)
((and (>= 1500 c) (>= 4500 d)) 2)
((and (>= 1600 c) (>= 5000 d)) 3)
(t 4)
)
)
;(tt lst)
(defun tt (lst)(mapcar '(lambda (x)(append x (list (itoa (c->th (atoi (cadr x)) (atoi (caddr x))))))) lst))
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