上篇用有限狀態機來求解,其實也是進行了一遍掃描,只是我把問題考慮的複雜了。python
對於掃描,我以爲首先要問本身3個問題:算法
1. 如何掃描 (這裏是遍歷數組元素)數組
2. 每次掃描會改變什麼 (這裏的算法會改變maxendinghere,前一篇的算法是改變狀態)spa
3. 改變的東西會對結果有影響麼 (maxendinghere若是大於maxsofar,那麼maxsofar就被賦值爲maxendinghere).net
不一樣的考慮問題的方式引入不一樣的解決方案,其中的差距太大了!!前一篇我太關注正負號了,致使我採用了序列分段,狀態轉移的方式去解決問題;這裏的解法關注最大和,以及有可能影響最大和的因素,maxsofar和maxendinghere的相對大小。雖然時間複雜度都是O(n),可是,高下立現!code
代碼以下(python):get
#!/usr/bin/python def scan(vector): # return (maxsofar, low, high) length = len(vector) maxsofar = 0 maxendinghere = 0 low = 0 high = 0 low2 = 0 high2 = 0 for i in range(0, length): if maxendinghere + vector[i] > 0: maxendinghere = maxendinghere + vector[i] high2 = i+1 else: maxendinghere = 0 low2 = i+1 if maxsofar >= maxendinghere: high = high low = low maxsofar = maxsofar else: high = high2 low = low2 maxsofar = maxendinghere return (maxsofar, low, high) def test(): vector = [-1, -1, -1, -1] (maxsofar, low, high) = scan(vector) print vector print maxsofar, low, high print vector = [1, -1, -1, -1] (maxsofar, low, high) = scan(vector) print vector print maxsofar, low, high print vector = [-1, -1, -1, 1] (maxsofar, low, high) = scan(vector) print vector print maxsofar, low, high print vector = [-1, 2, 3, -4] (maxsofar, low, high) = scan(vector) print vector print maxsofar, low, high print vector = [1, 2, 3, 4] (maxsofar, low, high) = scan(vector) print vector print maxsofar, low, high print vector = [31, -41, 59, 26, -53, 58, 97, -93, -23, 84] (maxsofar, low, high) = scan(vector) print vector print maxsofar, low, high print def main(): test() if __name__ == '__main__': main()
運行結果:class
root@localhost :/home/James/mypro/Python# ./scan.py
[-1, -1, -1, -1]
0 0 0test
[1, -1, -1, -1]
1 0 1遍歷
[-1, -1, -1, 1]
1 3 4
[-1, 2, 3, -4]
5 1 3
[1, 2, 3, 4]
10 0 4
[31, -41, 59, 26, -53, 58, 97, -93, -23, 84]
187 2 7
root@localhost :/home/James/mypro/Python#
後記:感受python真的很適合寫這種小demo,又快又方便。