标签:
原题链接:http://lintcode.com/en/problem/majority-number/#
Given an array of integers, the majority number is the number that occurs more than half of the size of the array. Find it.
For [1, 1, 1, 1, 2, 2, 2], return 1
O(n) time and O(1) space
http://www.geeksforgeeks.org/majority-element/
这里是一篇论文: http://www.cs.utexas.edu/~moore/best-ideas/mjrty/
这里用的算法是:MJRTY - A Fast Majority Vote Algorithm
1. 简单来讲,就是不断对某个议案投票,如果有人有别的议案,则将前面认为的议案的cnt减1,减到0换一个议案。
如果存在majority number,那么这个议案一定不会被减到0,最后会胜出。
2. 投票完成后,要对majority number进行检查,以排除不存在majority number的情况。如 1,2,3,4这样的数列,是没有majory number的。
很简单,统计一下结果议案的票数,没有过半就是没有majority number.
摘录一段解释:
METHOD 3 (Using Moore’s Voting Algorithm)
This is a two step process.
1. Get an element occurring most of the time in the array. This phase
will make sure that if there is a majority element then it will return
that only.
2. Check if the element obtained from above step is majority element.
1. Finding a Candidate:
The algorithm for first phase that works in O(n) is known as Moore’s
Voting Algorithm. Basic idea of the algorithm is if we cancel out each
occurrence of an element e with all the other elements that are
different from e then e will exist till end if it is a majority element.
findCandidate(a[], size) 1. Initialize index and count of majority element maj_index = 0, count = 1 2. Loop for i = 1 to size – 1 (a)If a[maj_index] == a[i] count++ (b)Else count--; (c)If count == 0 maj_index = i; count = 1 3. Return a[maj_index]
Above algorithm loops through each element and maintains a count of a[maj_index], If next element is same then increments the count, if next element is not same then decrements the count, and if the count reaches 0 then changes the maj_index to the current element and sets count to 1.
First Phase algorithm gives us a candidate element. In second phase we
need to check if the candidate is really a majority element. Second
phase is simple and can be easily done in O(n). We just need to check if
count of the candidate element is greater than n/2.
Example:
A[] = 2, 2, 3, 5, 2, 2, 6
Initialize:
maj_index = 0, count = 1 –> candidate ‘2?
2, 2, 3, 5, 2, 2, 6
Same as a[maj_index] => count = 2
2, 2, 3, 5, 2, 2, 6
Different from a[maj_index] => count = 1
2, 2, 3, 5, 2, 2, 6
Different from a[maj_index] => count = 0
Since count = 0, change candidate for majority element to 5 => maj_index = 3, count = 1
2, 2, 3, 5, 2, 2, 6
Different from a[maj_index] => count = 0
Since count = 0, change candidate for majority element to 2 => maj_index = 4
2, 2, 3, 5, 2, 2, 6
Same as a[maj_index] => count = 2
2, 2, 3, 5, 2, 2, 6
Different from a[maj_index] => count = 1
Finally candidate for majority element is 2.
First step uses Moore’s Voting Algorithm to get a candidate for majority element.
2. Check if the element obtained in step 1 is majority
printMajority (a[], size) 1. Find the candidate for majority 2. If candidate is majority. i.e., appears more than n/2 times. Print the candidate 3. Else Print "NONE"
1 package Algorithms.lintcode.math; 2 3 import java.util.ArrayList; 4 5 public class MajorityNumber { 6 /** 7 * @param nums: a list of integers 8 * @return: find a majority number 9 */ 10 public int majorityNumber(ArrayList<Integer> nums) { 11 // write your code 12 if (nums == null || nums.size() == 0) { 13 // No majority number. 14 return -1; 15 } 16 17 int candidate = nums.get(0); 18 19 // The phase 1: Voting. 20 int cnt = 1; 21 for (int i = 1; i < nums.size(); i++) { 22 if (nums.get(i) == candidate) { 23 cnt++; 24 } else { 25 cnt--; 26 if (cnt == 0) { 27 candidate = nums.get(i); 28 cnt = 1; 29 } 30 } 31 } 32 33 // The phase 2: Examing. 34 cnt = 0; 35 for (int i = 0; i < nums.size(); i++) { 36 if (nums.get(i) == candidate) { 37 cnt++; 38 } 39 } 40 41 // No majory number. 42 if (cnt <= nums.size() / 2) { 43 return -1; 44 } 45 46 return candidate; 47 } 48 }
https://github.com/yuzhangcmu/LeetCode_algorithm/blob/master/lintcode/math/MajorityNumber.java
Lintcode: Majority Number 解题报告
标签:
原文地址:http://www.cnblogs.com/yuzhangcmu/p/4175046.html