题目描述
如何得到一个数据流中的中位数?如果从数据流中读出奇数个数值,那么中位数就是所有数值排序之后位于中间的数值。如果从数据流中读出偶数个数值,那么中位数就是所有数值排序之后中间两个数的平均值。我们使用Insert()方法读取数据流,使用GetMedian()方法获取当前读取数据的中位数。
import java.util.*;
public class Solution {
public ArrayList<Integer> list = new ArrayList<>();
public Solution() {
// TODO Auto-generated constructor stub
}
public void Insert(Integer num) {
int size = list.size();
list.add(num);
int index = bs(num);
for (int i = size-1; i >= index; i--)
swap(i+1, i);
}
public Double GetMedian() {
int size = list.size() - 1;
if (size % 2 == 1)
return Double.valueOf((list.get(size / 2) + list.get(size / 2 + 1)) / 2.0);
else
return Double.valueOf(list.get(size / 2));
}
public int bs(int val) {
int l = 0;
int r = list.size() - 1;
int mid = 0;
while (l <= r) {
mid = (l + r) >> 1;
if (list.get(mid) <= val) {
l = mid + 1;
} else {
r = mid - 1;
}
}
return l;
}
public void swap(int i, int j) {
int tmp = list.get(i);
list.set(i, list.get(j));
list.set(j, tmp);
}
}
用插入排序重新排序。这种做法比较垃圾。重新写了一个用优先级队列的方法。一个大根堆和一个小根堆
import java.util.*;
public class Solution {
public PriorityQueue<Integer> maxHeap;
public PriorityQueue<Integer> minHeap;
public Solution() {
maxHeap = new PriorityQueue<Integer>(new Comparator<Integer>() {
@Override
public int compare(Integer o1, Integer o2) {
return o2 - o1;
}
});
minHeap = new PriorityQueue<>(new Comparator<Integer>() {
@Override
public int compare(Integer o1, Integer o2) {
return o1 - o2;
}
});
}
public void Insert(Integer num) {
if (this.maxHeap.isEmpty() || num <= this.maxHeap.peek()) {
this.maxHeap.add(num);
} else {
this.minHeap.add(num);
}
while (Math.abs(this.maxHeap.size() - this.minHeap.size()) > 1) {
if (this.maxHeap.size() > this.minHeap.size()) {
this.minHeap.add(this.maxHeap.poll());
} else {
this.maxHeap.add(this.minHeap.poll());
}
}
}
public Double GetMedian() {
int maxsize = this.maxHeap.size();
int minsize = this.minHeap.size();
if ((minsize + maxsize) % 2 == 0) {
return Double.valueOf((this.maxHeap.peek() + this.minHeap.peek()) / 2.0);
} else {
if (maxsize > minsize) {
return Double.valueOf(this.maxHeap.peek());
} else {
return Double.valueOf(this.minHeap.peek());
}
}
}
}