Given an array of integers, sort the array according to frequency of elements. For example, if the input array is {2, 3, 2, 4, 5, 12, 2, 3, 3, 3, 12}, then modify the array to {3, 3, 3, 3, 2, 2, 2, 12, 12, 4, 5}.
In the previous post, we have discussed all methods for sorting according to frequency. In this post, method 2 is discussed in detail and C++ implementation for the same is provided.
In the previous post, we have discussed all methods for sorting according to frequency. In this post, method 2 is discussed in detail and C++ implementation for the same is provided.
Following is detailed algorithm.
1) Create a BST and while creating BST maintain the count i,e frequency of each coming element in same BST. This step may take O(nLogn) time if a self balancing BST is used.
2) Do Inorder traversal of BST and store every element and count of each element in an auxiliary array. Let us call the auxiliary array as ‘count[]’. Note that every element of this array is element and frequency pair. This step takes O(n) time.
3) Sort ‘count[]’ according to frequency of the elements. This step takes O(nLohn) time if a O(nLogn) sorting algorithm is used.
4) Traverse through the sorted array ‘count[]’. For each element x, print it ‘freq’ times where ‘freq’ is frequency of x. This step takes O(n) time.
1) Create a BST and while creating BST maintain the count i,e frequency of each coming element in same BST. This step may take O(nLogn) time if a self balancing BST is used.
2) Do Inorder traversal of BST and store every element and count of each element in an auxiliary array. Let us call the auxiliary array as ‘count[]’. Note that every element of this array is element and frequency pair. This step takes O(n) time.
3) Sort ‘count[]’ according to frequency of the elements. This step takes O(nLohn) time if a O(nLogn) sorting algorithm is used.
4) Traverse through the sorted array ‘count[]’. For each element x, print it ‘freq’ times where ‘freq’ is frequency of x. This step takes O(n) time.
Overall time complexity of the algorithm can be minimum O(nLogn) if we use a O(nLogn) sorting algorithm and use a self balancing BST with O(Logn) insert operation.
Following is C++ implementation of the above algorithm.
// Implementation of above algorithm in C++. #include <iostream> #include <stdlib.h> using namespace std; /* A BST node has data, freq, left and right pointers */ struct BSTNode { struct BSTNode *left; int data; int freq; struct BSTNode *right; }; // A structure to store data and its frequency struct dataFreq { int data; int freq; }; /* Function for qsort() implementation. Compare frequencies to sort the array according to decreasing order of frequency */ int compare( const void *a, const void *b) { return ( (*( const dataFreq*)b).freq - (*( const dataFreq*)a).freq ); } /* Helper function that allocates a new node with the given data, frequency as 1 and NULL left and right pointers.*/ BSTNode* newNode( int data) { struct BSTNode* node = new BSTNode; node->data = data; node->left = NULL; node->right = NULL; node->freq = 1; return (node); } // A utility function to insert a given key to BST. If element // is already present, then increases frequency BSTNode *insert(BSTNode *root, int data) { if (root == NULL) return newNode(data); if (data == root->data) // If already present root->freq += 1; else if (data < root->data) root->left = insert(root->left, data); else root->right = insert(root->right, data); return root; } // Function to copy elements and their frequencies to count[]. void store(BSTNode *root, dataFreq count[], int *index) { // Base Case if (root == NULL) return ; // Recur for left substree store(root->left, count, index); // Store item from root and increment index count[(*index)].freq = root->freq; count[(*index)].data = root->data; (*index)++; // Recur for right subtree store(root->right, count, index); } // The main function that takes an input array as an argument // and sorts the array items according to frequency void sortByFrequency( int arr[], int n) { // Create an empty BST and insert all array items in BST struct BSTNode *root = NULL; for ( int i = 0; i < n; ++i) root = insert(root, arr[i]); // Create an auxiliary array 'count[]' to store data and // frequency pairs. The maximum size of this array would // be n when all elements are different dataFreq count[n]; int index = 0; store(root, count, &index); // Sort the count[] array according to frequency (or count) qsort (count, index, sizeof (count[0]), compare); // Finally, traverse the sorted count[] array and copy the // i'th item 'freq' times to original array 'arr[]' int j = 0; for ( int i = 0; i < index; i++) { for ( int freq = count[i].freq; freq > 0; freq--) arr[j++] = count[i].data; } } // A utility function to print an array of size n void printArray( int arr[], int n) { for ( int i = 0; i < n; i++) cout << arr[i] << " " ; cout << endl; } /* Driver program to test above functions */ int main() { int arr[] = {2, 3, 2, 4, 5, 12, 2, 3, 3, 3, 12}; int n = sizeof (arr)/ sizeof (arr[0]); sortByFrequency(arr, n); printArray(arr, n); return 0; } |
Output:
3 3 3 3 2 2 2 12 12 5 4
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