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题目:
Given a non negative integer number num. For every numbers i in the range 0 ≤ i ≤ num calculate the number of 1’s in their binary representation and return them as an array.
Example:
For num = 5 you should return [0,1,1,2,1,2].
Follow up:
It is very easy to come up with a solution with run time O(n*sizeof(integer)). But can you do it in linear time O(n) /possibly in a single pass?
Space complexity should be O(n).
Can you do it like a boss? Do it without using any builtin function like __builtin_popcount in c++ or in any other language.
Hint:
You should make use of what you have produced already.
翻译:
给定一个非负整数num,对于每个0<=i<=num的整数i,计算i的二进制表示中1的个数,返回这些个数作为一个数组。
例如,输入num = 5 你应该返回 [0,1,1,2,1,2].
分析:
按照常规思路,很容易得出“Java代码2”的方案,但是这个方案的时间复杂度是O(nlogn)。
通过对数组的前64个元素进行分析(num=63),我们发现数组呈现一定的规律,不断重复,如下图所示:
0
1
1 2
1 2 2 3
1 2 2 3 2 3 3 4
1 2 2 3 2 3 3 4 2 3 3 4 3 4 4 5
1 2 2 3 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6
由此我们发现0112是一个基础元素,不断循环反复,可以推论:如果已知第一个元素是result[0],那么第二第三个元素为result[0]+1,第四个元素为result[0]+2,由此获得前4个元素result[0]~result[3];以这4个元素为基础,我们可以得到
result[4]=result[0]+1,result[5]=result[1]+1…,
result[8]=result[0]+1,result[9]=result[1]+1… ,
result[12]=result[0]+2,result[13]=result[1]+2…;
以此类推可以获得全部的数组。
Java版代码1:
public class Solution {
public int[] countBits(int num) {
int[] result = new int[num + 1];
int range = 1;
result[0] = 0;
boolean stop = false;
while (!stop) {
stop = fillNum(result, range);
range *= 4;
}
return result;
}
public boolean fillNum(int[] nums, int range) {
for (int i = 0; i < range; i++) {
if (range + i < nums.length) {
nums[range + i] = nums[i] + 1;
} else {
return true;
}
if (2 * range + i < nums.length) {
nums[2 * range + i] = nums[i] + 1;
}
if (3 * range + i < nums.length) {
nums[3 * range + i] = nums[i] + 2;
}
}
return false;
}
}
Java版代码2:
public class Solution {
public int[] countBits(int num) {
int[] result=new int[num+1];
result[0]=0;
for(int i=1;i<=num;i++){
result[i]=getCount(i);
}
return result;
}
public int getCount(int num){
int count=0;
while(num!=0){
if((num&1)==1){
count++;
}
num/=2;
}
return count;
}
}
Leet Code OJ 338. Counting Bits [Difficulty: Easy]
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原文地址:http://blog.csdn.net/lnho2015/article/details/50924299