POJ 2976 Dropping tests 题解 《挑战程序设计竞赛》-码农场



POJ 2976 Dropping tests 题解 《挑战程序设计竞赛》-码农场

乍看以为贪心或dp能解决,后来发现贪心策略与当前的总体准确率有关,行不通,于是二分解决。

依然需要确定一个贪心策略,每次贪心地去掉那些对正确率贡献小的考试。如何确定某个考试[a_i, b_i]对总体准确率x的贡献呢?a_i / b_i肯定是不行的,不然例子里的[0,1]会首当其冲被刷掉。在当前准确率为x的情况下,这场考试"额外"对的题目数量是a_i – x * b_i,当然这个值有正有负,恰好可以作为"贡献度"的测量。于是利用这个给考试排个降序,后k个刷掉就行了。

之后就是二分搜索了,从0到1之间搜一遍,我下面的注释应该很详细,不啰嗦了。

题目的大数据测试用例可以在http://ai.stanford.edu/~chuongdo/acm/2005/找到,有完整的输入输出可供检验自己的程序。

这题吃了点亏,开始没看清楚题目,给主循环的跳出逻辑写为while (cin >> n >> k && (n && k)),后来看到上面的测试用例里有1 0才恍然大悟,整个人表情变成下面这样:


Read full article from POJ 2976 Dropping tests 题解 《挑战程序设计竞赛》-码农场


No comments:

Post a Comment

Labels

Algorithm (219) Lucene (130) LeetCode (97) Database (36) Data Structure (33) text mining (28) Solr (27) java (27) Mathematical Algorithm (26) Difficult Algorithm (25) Logic Thinking (23) Puzzles (23) Bit Algorithms (22) Math (21) List (20) Dynamic Programming (19) Linux (19) Tree (18) Machine Learning (15) EPI (11) Queue (11) Smart Algorithm (11) Operating System (9) Java Basic (8) Recursive Algorithm (8) Stack (8) Eclipse (7) Scala (7) Tika (7) J2EE (6) Monitoring (6) Trie (6) Concurrency (5) Geometry Algorithm (5) Greedy Algorithm (5) Mahout (5) MySQL (5) xpost (5) C (4) Interview (4) Vi (4) regular expression (4) to-do (4) C++ (3) Chrome (3) Divide and Conquer (3) Graph Algorithm (3) Permutation (3) Powershell (3) Random (3) Segment Tree (3) UIMA (3) Union-Find (3) Video (3) Virtualization (3) Windows (3) XML (3) Advanced Data Structure (2) Android (2) Bash (2) Classic Algorithm (2) Debugging (2) Design Pattern (2) Google (2) Hadoop (2) Java Collections (2) Markov Chains (2) Probabilities (2) Shell (2) Site (2) Web Development (2) Workplace (2) angularjs (2) .Net (1) Amazon Interview (1) Android Studio (1) Array (1) Boilerpipe (1) Book Notes (1) ChromeOS (1) Chromebook (1) Codility (1) Desgin (1) Design (1) Divide and Conqure (1) GAE (1) Google Interview (1) Great Stuff (1) Hash (1) High Tech Companies (1) Improving (1) LifeTips (1) Maven (1) Network (1) Performance (1) Programming (1) Resources (1) Sampling (1) Sed (1) Smart Thinking (1) Sort (1) Spark (1) Stanford NLP (1) System Design (1) Trove (1) VIP (1) tools (1)

Popular Posts