Given an array of red, blue and yellow objects, the task is to use an in-place sorting algorithm to sort the array in such a way that all the blue objects appear before all the red objects and all the red objects appear before all the yellow objects.
Examples:
Input: arr[] = {“blue”, “red”, “yellow”, “blue”, “yellow”}
Output: blue blue red yellow yellow
Input: arr[] = {“red”, “blue”, “red”, “yellow”, “blue”}
Output: blue blue red red yellow
Approach: First of all map the values of blue, red and yellow objects to 1, 2 and 3 respectively using a hash table. Now use these mapped values whenever a comparison of two objects is required. So, the algorithm will sort the array of objects such that all blue objects ( mapping to value 1 ) will appear first, then all red objects ( mapping to value 2 ) and then all yellow objects ( mapping to value 3 ).
Below is the implementation of the above approach:
C++
// C++ implementation of the approach#include <bits/stdc++.h>using namespace std;// Partition function which will partition// the array and into two partsint partition(vector<string>& objects, int l, int r, unordered_map<string, int>& hash){ int j = l - 1; int last_element = hash[objects[r]]; for (int i = l; i < r; i++) { // Compare hash values of objects if (hash[objects[i]] <= last_element) { j++; swap(objects[i], objects[j]); } } j++; swap(objects[j], objects[r]); return j;}// Classic quicksort algorithmvoid quicksort(vector<string>& objects, int l, int r, unordered_map<string, int>& hash){ if (l < r) { int mid = partition(objects, l, r, hash); quicksort(objects, l, mid - 1, hash); quicksort(objects, mid + 1, r, hash); }}// Function to sort and print the objectsvoid sortObj(vector<string>& objects){ // Create a hash table unordered_map<string, int> hash; // As the sorting order is blue objects, // red objects and then yellow objects hash["blue"] = 1; hash["red"] = 2; hash["yellow"] = 3; // Quick sort function quicksort(objects, 0, int(objects.size() - 1), hash); // Printing the sorted array for (int i = 0; i < objects.size(); i++) cout << objects[i] << " ";}// Driver codeint main(){ // Let's represent objects as strings vector<string> objects{ "red", "blue", "red", "yellow", "blue" }; sortObj(objects); return 0;} |
Java
// Java implementation of the approachimport java.util.*;class GFG {// Partition function which will partition// the array and into two partsstatic int partition(Vector<String> objects, int l, int r, Map<String, Integer> hash){ int j = l - 1; int last_element = hash.get(objects.get(r)); for (int i = l; i < r; i++) { // Compare hash values of objects if (hash.get(objects.get(i)) <= last_element) { j++; Collections.swap(objects, i, j); } } j++; Collections.swap(objects, j, r); return j;}// Classic quicksort algorithmstatic void quicksort(Vector<String> objects, int l, int r, Map<String, Integer> hash){ if (l < r) { int mid = partition(objects, l, r, hash); quicksort(objects, l, mid - 1, hash); quicksort(objects, mid + 1, r, hash); }}// Function to sort and print the objectsstatic void sortObj(Vector<String> objects){ // Create a hash table Map<String, Integer> hash = new HashMap<>(); // As the sorting order is blue objects, // red objects and then yellow objects hash. put("blue", 1); hash. put("red", 2); hash. put("yellow", 3); // Quick sort function quicksort(objects, 0, objects.size() - 1, hash); // Printing the sorted array for (int i = 0; i < objects.size(); i++) System.out.print(objects.get(i) + " ");}// Driver codepublic static void main(String []args){ // Let's represent objects as strings Vector<String> objects = new Vector<>(Arrays.asList( "red", "blue", "red", "yellow", "blue" )); sortObj(objects);}}// This code is contributed by PrinciRaj1992 |
Python3
# Python3 implementation of the approach# Partition function which will partition# the array and into two partsobjects = []hash = dict()def partition(l, r): global objects, hash j = l - 1 last_element = hash[objects[r]] for i in range(l, r): # Compare hash values of objects if (hash[objects[i]] <= last_element): j += 1 (objects[i], objects[j]) = (objects[j], objects[i]) j += 1 (objects[j], objects[r]) = (objects[r], objects[j]) return j# Classic quicksort algorithmdef quicksort(l, r): if (l < r): mid = partition(l, r) quicksort(l, mid - 1) quicksort(mid + 1, r)# Function to sort and print the objectsdef sortObj(): global objects, hash # As the sorting order is blue objects, # red objects and then yellow objects hash["blue"] = 1 hash["red"] = 2 hash["yellow"] = 3 # Quick sort function quicksort(0, int(len(objects) - 1)) # Printing the sorted array for i in objects: print(i, end = " ")# Driver code# Let's represent objects as stringsobjects = ["red", "blue", "red", "yellow", "blue"]sortObj()# This code is contributed # by Mohit Kumar |
C#
// C# implementation of the approachusing System;using System.Collections.Generic;class GFG {// Partition function which will partition// the array and into two partsstatic int partition(List<String> objects, int l, int r, Dictionary<String, int> hash){ int j = l - 1; String temp; int last_element = hash[objects[r]]; for (int i = l; i < r; i++) { // Compare hash values of objects if (hash[objects[i]] <= last_element) { j++; temp = objects[i]; objects[i] = objects[j]; objects[j] = temp; } } j++; temp = objects[r]; objects[r] = objects[j]; objects[j] = temp; return j;}// Classic quicksort algorithmstatic void quicksort(List<String> objects, int l, int r, Dictionary<String, int> hash){ if (l < r) { int mid = partition(objects, l, r, hash); quicksort(objects, l, mid - 1, hash); quicksort(objects, mid + 1, r, hash); }}// Function to sort and print the objectsstatic void sortObj(List<String> objects){ // Create a hash table Dictionary<String, int> hash = new Dictionary<String, int>(); // As the sorting order is blue objects, // red objects and then yellow objects hash.Add("blue", 1); hash.Add("red", 2); hash.Add("yellow", 3); // Quick sort function quicksort(objects, 0, objects.Count - 1, hash); // Printing the sorted array for (int i = 0; i < objects.Count; i++) Console.Write(objects[i] + " ");}// Driver codepublic static void Main(String []args){ // Let's represent objects as strings List<String> objects = new List<String>{"red", "blue", "red", "yellow", "blue"}; sortObj(objects);}}// This code is contributed by Rajput-Ji |
Javascript
<script>// Javascript implementation of the approach// Partition function which will partition// the array and into two partsfunction partition(objects, l, r, hash){ let j = l - 1; let last_element = hash.get(objects[r]); for (let i = l; i < r; i++) { // Compare hash values of objects if (hash.get(objects[i]) <= last_element) { j++; let temp = objects[i]; objects[i] = objects[j]; objects[j] = temp; } } j++; let temp = objects[r]; objects[r] = objects[j]; objects[j] = temp; return j;}// Classic quicksort algorithmfunction quicksort(objects, l, r, hash){ if (l < r) { let mid = partition(objects, l, r, hash); quicksort(objects, l, mid - 1, hash); quicksort(objects, mid + 1, r, hash); }}// Function to sort and print the objectsfunction sortObj(objects){ // Create a hash table let hash = new Map(); // As the sorting order is blue objects, // red objects and then yellow objects hash. set("blue", 1); hash. set("red", 2); hash. set("yellow", 3); // Quick sort function quicksort(objects, 0, objects.length - 1, hash); // Printing the sorted array for (let i = 0; i < objects.length; i++) document.write(objects[i] + " ");}// Driver codelet objects = ["red", "blue","red", "yellow", "blue"];sortObj(objects);// This code is contributed by patel2127</script> |
Output:
blue blue red red yellow
Time complexity: O(n^2) since using quicksort
Auxiliary space: O(n) because using hash table
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