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Sorting objects using In-Place sorting algorithm

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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 parts
int 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 algorithm
void 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 objects
void 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 code
int main()
{
 
    // Let's represent objects as strings
    vector<string> objects{ "red", "blue",
                            "red", "yellow", "blue" };
 
    sortObj(objects);
 
    return 0;
}

                    

Java

// Java implementation of the approach
import java.util.*;
class GFG
{
 
// Partition function which will partition
// the array and into two parts
static 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 algorithm
static 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 objects
static 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 code
public 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 parts
objects = []
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 algorithm
def quicksort(l, r):
    if (l < r):
        mid = partition(l, r)
        quicksort(l, mid - 1)
        quicksort(mid + 1, r)
 
# Function to sort and print the objects
def 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 strings
objects = ["red", "blue", "red",
               "yellow", "blue"]
 
sortObj()
 
# This code is contributed
# by Mohit Kumar

                    

C#

// C# implementation of the approach
using System;
using System.Collections.Generic;
 
class GFG
{
 
// Partition function which will partition
// the array and into two parts
static 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 algorithm
static 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 objects
static 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 code
public 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 parts
function 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 algorithm
function 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 objects
function 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 code
let 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 



Last Updated : 01 Sep, 2022
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