Median of Stream of Running Integers using STL
Given that integers are being read from a data stream. Find the median of all the elements read so far starting from the first integer till the last integer. This is also called the Median of Running Integers. The data stream can be any source of data, for example, a file, an array of integers, input stream etc.
What is Median?
Median can be defined as the element in the data set which separates the higher half of the data sample from the lower half. In other words, we can get the median element as, when the input size is odd, we take the middle element of sorted data. If the input size is even, we pick an average of middle two elements in the sorted stream.
Examples:
Input: 5 10 15
Output: 5, 7.5, 10
Explanation: Given the input stream as an array of integers [5,10,15]. Read integers one by one and print the median correspondingly. So, after reading first element 5,median is 5. After reading 10,median is 7.5 After reading 15 ,median is 10.
Input: 1, 2, 3, 4
Output: 1, 1.5, 2, 2.5
Explanation: Given the input stream as an array of integers [1, 2, 3, 4]. Read integers one by one and print the median correspondingly. So, after reading first element 1,median is 1. After reading 2,median is 1.5 After reading 3 ,median is 2.After reading 4 ,median is 2.5.
Approach: The idea is to use max heap and min heap to store the elements of higher half and lower half. Max heap and min heap can be implemented using priority_queue in C++ STL. Below is the step by step algorithm to solve this problem.
Algorithm:
- Create two heaps. One max heap to maintain elements of lower half and one min heap to maintain elements of higher half at any point of time..
- Take initial value of median as 0.
- For every newly read element, insert it into either max heap or min-heap and calculate the median based on the following conditions:
- If the size of max heap is greater than the size of min-heap and the element is less than the previous median then pop the top element from max heap and insert into min-heap and insert the new element to max heap else insert the new element to min-heap. Calculate the new median as the average of top of elements of both max and min heap.
- If the size of max heap is less than the size of min-heap and the element is greater than the previous median then pop the top element from min-heap and insert into the max heap and insert the new element to min heap else insert the new element to the max heap. Calculate the new median as the average of top of elements of both max and min heap.
- If the size of both heaps is the same. Then check if the current is less than the previous median or not. If the current element is less than the previous median then insert it to the max heap and a new median will be equal to the top element of max heap. If the current element is greater than the previous median then insert it to min-heap and new median will be equal to the top element of min heap.
Below is the implementation of above approach.
C++
// C++ program to find med in // stream of running integers #include<bits/stdc++.h> using namespace std; // function to calculate med of stream void printMedians(double arr[], int n) { // max heap to store the smaller half elements priority_queue<double> s; // min heap to store the greater half elements priority_queue<double,vector<double>,greater<double> > g; double med = arr[0]; s.push(arr[0]); cout << med << endl; // reading elements of stream one by one /* At any time we try to make heaps balanced and their sizes differ by at-most 1. If heaps are balanced,then we declare median as average of min_heap_right.top() and max_heap_left.top() If heaps are unbalanced,then median is defined as the top element of heap of larger size */ for (int i=1; i < n; i++) { double x = arr[i]; // case1(left side heap has more elements) if (s.size() > g.size()) { if (x < med) { g.push(s.top()); s.pop(); s.push(x); } else g.push(x); med = (s.top() + g.top())/2.0; } // case2(both heaps are balanced) else if (s.size()==g.size()) { if (x < med) { s.push(x); med = (double)s.top(); } else { g.push(x); med = (double)g.top(); } } // case3(right side heap has more elements) else { if (x > med) { s.push(g.top()); g.pop(); g.push(x); } else s.push(x); med = (s.top() + g.top())/2.0; } cout << med << endl; } } // Driver program to test above functions int main() { // stream of integers double arr[] = {5, 15, 10, 20, 3}; int n = sizeof(arr)/sizeof(arr[0]); printMedians(arr, n); return 0; } |
Java
// Java program to find med in // stream of running integers import java.util.Collections; import java.util.PriorityQueue; public class MedianMaintain { // method to calculate med of stream public static void printMedian(int[] a) { double med = a[0]; // max heap to store the smaller half elements PriorityQueue<Integer> smaller = new PriorityQueue<> (Collections.reverseOrder()); // min-heap to store the greater half elements PriorityQueue<Integer> greater = new PriorityQueue<>(); smaller.add(a[0]); System.out.println(med); // reading elements of stream one by one /* At any time we try to make heaps balanced and their sizes differ by at-most 1. If heaps are balanced,then we declare median as average of min_heap_right.top() and max_heap_left.top() If heaps are unbalanced,then median is defined as the top element of heap of larger size */ for(int i = 1; i < a.length; i++) { int x = a[i]; // case1(left side heap has more elements) if(smaller.size() > greater.size()) { if(x < med) { greater.add(smaller.remove()); smaller.add(x); } else greater.add(x); med = (double)(smaller.peek() + greater.peek())/2; } // case2(both heaps are balanced) else if(smaller.size() == greater.size()) { if(x < med) { smaller.add(x); med = (double)smaller.peek(); } else { greater.add(x); med = (double)greater.peek(); } } // case3(right side heap has more elements) else { if(x > med) { smaller.add(greater.remove()); greater.add(x); } else smaller.add(x); med = (double)(smaller.peek() + greater.peek())/2; } System.out.println(med); } } // Driver code public static void main(String []args) { // stream of integers int[] arr = new int[]{5, 15, 10, 20, 3}; printMedian(arr); } } // This code is contributed by Kaustav kumar Chanda. |
Python3
# python3 program to find med in # stream of running integers from heapq import * # function to calculate med of stream def printMedians(arr, n): # max heap to store the smaller half elements s = [] # min heap to store the greater half elements g = [] heapify(s) heapify(g) med = arr[0] heappush(s, arr[0]) print(med) # reading elements of stream one by one for i in range(1, n): x = arr[i] # case1(left side heap has more elements) if len(s) > len(g): if x < med: heappush(g, heappop(s)) heappush(s, x) else: heappush(g, x) med = (nlargest(1, s)[0] + nsmallest(1, g)[0])/2 # case2(both heaps are balanced) elif len(s) == len(g): if x < med: heappush(s, x) med = nlargest(1, s)[0] else: heappush(g, x) med = nsmallest(1, g)[0] # case3(right side heap has more elements) else: if x > med: heappush(s, heappop(g)) heappush(g, x) else: heappush(s, x) med = (nlargest(1, s)[0] + nsmallest(1, g)[0])/2 print(med) # Driver program to test above functions arr = [5, 15, 10, 20, 3] printMedians(arr, len(arr)) # This code is contributed by cavi4762. |
C#
// C# program to find med in // stream of running integers using System; using System.Collections.Generic; public class MedianMaintain { // method to calculate med of stream public static void printMedian(int[] a) { double med = a[0]; // max heap to store the smaller half elements List<int> smaller = new List<int>(); // min-heap to store the greater half elements List<int> greater = new List<int>(); smaller.Add(a[0]); Console.WriteLine(med); // reading elements of stream one by one /* At any time we try to make heaps balanced and their sizes differ by at-most 1. If heaps are balanced,then we declare median as average of min_heap_right.top() and max_heap_left.top() If heaps are unbalanced,then median is defined as the top element of heap of larger size */ for(int i = 1; i < a.Length; i++) { int x = a[i]; // case1(left side heap has more elements) if(smaller.Count > greater.Count) { if(x < med) { smaller.Sort(); smaller.Reverse(); greater.Add(smaller[0]); smaller.RemoveAt(0); smaller.Add(x); } else greater.Add(x); smaller.Sort(); smaller.Reverse(); greater.Sort(); med = (double)(smaller[0] + greater[0])/2; } // case2(both heaps are balanced) else if(smaller.Count == greater.Count) { if(x < med) { smaller.Add(x); smaller.Sort(); smaller.Reverse(); med = (double)smaller[0]; } else { greater.Add(x); greater.Sort(); med = (double)greater[0]; } } // case3(right side heap has more elements) else { if(x > med) { greater.Sort(); smaller.Add(greater[0]); greater.RemoveAt(0); greater.Add(x); } else smaller.Add(x); smaller.Sort(); smaller.Reverse(); med = (double)(smaller[0] + greater[0])/2; } Console.WriteLine(med); } } // Driver code public static void Main(String []args) { // stream of integers int[] arr = new int[]{5, 15, 10, 20, 3}; printMedian(arr); } } // This code is contributed by Rajput-Ji |
Javascript
<script> // Javascript program to find med in // stream of running integers // method to calculate med of stream function printMedian(a) { let med = a[0]; // max heap to store the smaller half elements let smaller = []; // min-heap to store the greater half elements let greater = []; smaller.push(a[0]); document.write(med+"<br>"); // reading elements of stream one by one /* At any time we try to make heaps balanced and their sizes differ by at-most 1. If heaps are balanced,then we declare median as average of min_heap_right.top() and max_heap_left.top() If heaps are unbalanced,then median is defined as the top element of heap of larger size */ for(let i = 1; i < a.length; i++) { let x = a[i]; // case1(left side heap has more elements) if(smaller.length > greater.length) { if(x < med) { smaller.sort(function(a,b){return b-a;}); greater.push(smaller.shift()); smaller.push(x); } else { greater.push(x);} smaller.sort(function(a,b){return b-a;}); greater.sort(function(a,b){return a-b;}); med = (smaller[0] + greater[0])/2; } // case2(both heaps are balanced) else if(smaller.length == greater.length) { if(x < med) { smaller.push(x); smaller.sort(function(a,b){return b-a;}); med = smaller[0]; } else { greater.push(x); greater.sort(function(a,b){return a-b;}); med = greater[0]; } } // case3(right side heap has more elements) else { if(x > med) { greater.sort(function(a,b){return a-b;}); smaller.push(greater.shift()); greater.push(x); } else { smaller.push(x);} smaller.sort(function(a,b){return b-a;}); med = (smaller[0] + greater[0])/2; } document.write(med+"<br>"); } } // Driver code // stream of integers let arr=[5, 15, 10, 20, 3]; printMedian(arr); // This code is contributed by avanitrachhadiya2155 </script> |
Output:
5 10 10 12.5 10
Complexity Analysis:
- Time Complexity: O(n Log n).
Time Complexity to insert element in min heap is log n. So to insert n element is O( n log n). - Auxiliary Space : O(n).
The Space required to store the elements in Heap is O(n).
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