本系列导航：剑指offer（第二版）java实现导航帖
面试题41：数据流中的中位数
题目要求：
得到一个数据流中的中位数。
解题思路：
此题的关键在于“数据流”，即数字不是一次性给出，解题的关键在于重新写一个结构记录历史数据。下面给出三种思路，分别借助于链表、堆、二叉搜索树完成。
思路1：使用未排序的链表存储数据，使用快排的分区函数求中位数；也可以在插入时进行排序，这样中位数的获取会很容易，但插入费时。
思路2：使用堆存储，两个堆能够完成最大堆-最小堆这样的简单分区，两个堆的数字个数相同或最大堆数字个数比最小堆数字个数大1，中位数为两个堆堆顶的均值或者最大堆的堆顶。
思路3：使用二叉搜索树存储，每个树节点添加一个表征子树节点数目的字段用于找到中位数。
package chapter5;
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.List;
/**
* Created with IntelliJ IDEA.
* Author: ryder
* Date : 2017/8/1
* Time : 11:06
* Description:数据流中的中位数
**/
public class P214_StreamMedian {
public static interface MedianFinder{
//模拟读取数据流的过程
void addNum(double num);
double findMedian();
}
//使用未排序的链表存储数据，使用快排的分区函数求中位数；
//也可以在插入时进行排序，这样中位数的获取会很容易，但插入费时。
public static class MedianFinder1 implements MedianFinder{
List list = null;
public MedianFinder1() {
list = new LinkedList<>();
}
@Override
public void addNum(double num) {
list.add(num);
}
@Override
public double findMedian() {
if(list.size()==0)
return 0;
//如果长度为奇数，求中间的那个数;如果为偶数，求中间两个数的均值
if((list.size()&1)==1)
return findKth(list.size()>>>1);
else
return (findKth(list.size()>>>1)+findKth((list.size()>>>1)-1))/2;
}
//得到从小到大排序后下标为k的数据值
private double findKth(int k){
int start=0,end=list.size()-1;
int index = partition(start,end);
while (index!=k){
if(index=end)
return end;
double pivot = list.get(start);
//[start,bound)小于等于pivot,bound值为pivot，(bound，end]大于pivot
int bound = start;
for(int i=start+1;i<=end;i++){
if(list.get(i)<=pivot){
list.set(bound,list.get(i));
bound++;
list.set(i,list.get(bound));
}
}
list.set(bound,pivot);
return bound;
}
}
//思路二：使用堆存储
//两个堆能够完成最大堆-最小堆这样的简单分区，两个堆的数字个数相同或heapMax比heapMin大1
//中位数为最大堆的堆顶或者两个堆堆顶的均值
public static class MedianFinder2 implements MedianFinder{
List maxHeap = null;
List minHeap = null;
public MedianFinder2() {
maxHeap = new ArrayList<>();
minHeap = new ArrayList<>();
maxHeap.add(0.0);
minHeap.add(0.0);
}
@Override
public void addNum(double num) {
if(maxHeap.size()==minHeap.size()){
if(minHeap.size()<=1||num<=minHeap.get(1))
addItemToMaxHeap(num);
else{
addItemToMaxHeap(minHeap.get(1));
minHeap.set(1,num);
adjustMinHeap(1);
}
}
else{
if(num>=maxHeap.get(1))
addItemToMinHeap(num);
else{
addItemToMinHeap(maxHeap.get(1));
maxHeap.set(1,num);
adjustMaxHeap(1);
}
}
}
private void addItemToMaxHeap(double value){
maxHeap.add(value);
int curIndex = maxHeap.size()-1;
while (curIndex>1 && maxHeap.get(curIndex)>maxHeap.get(curIndex>>>1)){
double temp = maxHeap.get(curIndex);
maxHeap.set(curIndex,maxHeap.get(curIndex>>>1));
maxHeap.set(curIndex>>>1,temp);
curIndex = curIndex>>>1;
}
}
private void adjustMaxHeap(int index){
int left = index*2,right=left+1;
int max = index;
if(leftmaxHeap.get(max))
max = left;
if(rightmaxHeap.get(max))
max = right;
if(max!=index){
double temp = maxHeap.get(index);
maxHeap.set(index,maxHeap.get(max));
maxHeap.set(max,temp);
adjustMaxHeap(max);
}
}
private void addItemToMinHeap(double value){
minHeap.add(value);
int curIndex = maxHeap.size()-1;
while (curIndex>1 && minHeap.get(curIndex)>>1)){
double temp = minHeap.get(curIndex);
minHeap.set(curIndex,minHeap.get(curIndex>>>1));
minHeap.set(curIndex>>>1,temp);
curIndex = curIndex>>>1;
}
}
private void adjustMinHeap(int index){
int left = index*2,right=left+1;
int min = index;
if(leftminHeap.size())
return maxHeap.get(1);
else
return (maxHeap.get(1)+minHeap.get(1))/2;
}
}
//思路三：使用二叉搜索树存储，每个树节点添加一个表征子树节点数目的字段用于计算中位数。
public static class TreeNodeWithNums {
public T val;
public int nodes;
public TreeNodeWithNums left;
public TreeNodeWithNums right;
public TreeNodeWithNums(T val){
this.val = val;
this.nodes = 1;
this.left = null;
this.right = null;
}
}
public static class MedianFinder3 implements MedianFinder {
//左孩子小于根节点，右孩子大于等于根节点
TreeNodeWithNums root = null;
public MedianFinder3() {
}
@Override
public void addNum(double num) {
if(root==null)
root = new TreeNodeWithNums<>(num);
else
addNode(root,num);
}
public void addNode(TreeNodeWithNums node, double num){
if(num(num);
node.nodes++;
}
else
addNode(node.left,num);
}
else{
if(node.right==null){
node.right = new TreeNodeWithNums<>(num);
node.nodes++;
}
else
addNode(node.right,num);
}
}
@Override
public double findMedian() {
if(root==null)
return 0;
if((root.nodes&1)==1)
return findMedian(root,root.nodes/2+1);
else
return (findMedian(root,root.nodes/2)+findMedian(root,root.nodes/2+1))/2;
}
private double findMedian(TreeNodeWithNums node,int index){
if(node.left!=null){
if(index==node.left.nodes+1)
return node.val;
else if(index<=node.left.nodes)
return findMedian(node.left,index);
else
return findMedian(node.right,index-node.left.nodes-1);
}
else if(node.right!=null){
return findMedian(node.right,index-1);
}
else
return node.val;
}
}
public static void main(String[] args){
MedianFinder medianFinder1 = new MedianFinder1();
medianFinder1.addNum(2);
medianFinder1.addNum(1);
System.out.println(medianFinder1.findMedian());
MedianFinder medianFinder2 = new MedianFinder2();
medianFinder2.addNum(2);
medianFinder2.addNum(1);
System.out.println(medianFinder2.findMedian());
MedianFinder medianFinder3 = new MedianFinder3();
medianFinder3.addNum(2);
medianFinder3.addNum(1);
System.out.println(medianFinder3.findMedian());
medianFinder1.addNum(4);
medianFinder2.addNum(4);
medianFinder3.addNum(4);
System.out.println(medianFinder1.findMedian());
System.out.println(medianFinder2.findMedian());
System.out.println(medianFinder3.findMedian());
}
}
运行结果
1.5
1.5
1.5
2.0
2.0
2.0