Given a book of words. Assume you have enough main memory to accommodate all words. design a data structure to find top K maximum occurring words. The data structure should be dynamic so that new words can be added.
A simple solution is to use Hashing. Hash all words one by one in a hash table. If a word is already present, then increment its count. Finally, traverse through the hash table and return the k words with maximum counts.
We can use Trie and Min Heap to get the k most frequent words efficiently. The idea is to use Trie for searching existing words adding new words efficiently. Trie also stores count of occurrences of words. A Min Heap of size k is used to keep track of k most frequent words at any point of time(Use of Min Heap is same as we used it to find k largest elements in this post).
Trie and Min Heap are linked with each other by storing an additional field in Trie ‘indexMinHeap’ and a pointer ‘trNode’ in Min Heap. The value of ‘indexMinHeap’ is maintained as -1 for the words which are currently not in Min Heap (or currently not among the top k frequent words). For the words which are present in Min Heap, ‘indexMinHeap’ contains, index of the word in Min Heap. The pointer ‘trNode’ in Min Heap points to the leaf node corresponding to the word in Trie.
Following is the complete process to print k most frequent words from a file.
Read all words one by one. For every word, insert it into Trie. Increase the counter of the word, if already exists. Now, we need to insert this word in min heap also. For insertion in min heap, 3 cases arise:
1. The word is already present. We just increase the corresponding frequency value in min heap and call minHeapify() for the index obtained by “indexMinHeap” field in Trie. When the min heap nodes are being swapped, we change the corresponding minHeapIndex in the Trie. Remember each node of the min heap is also having pointer to Trie leaf node.
2. The minHeap is not full. we will insert the new word into min heap & update the root node in the min heap node & min heap index in Trie leaf node. Now, call buildMinHeap().
3. The min heap is full. Two sub-cases arise.
….3.1 The frequency of the new word inserted is less than the frequency of the word stored in the head of min heap. Do nothing.
….3.2 The frequency of the new word inserted is greater than the frequency of the word stored in the head of min heap. Replace & update the fields. Make sure to update the corresponding min heap index of the “word to be replaced” in Trie with -1 as the word is no longer in min heap.
4. Finally, Min Heap will have the k most frequent words of all words present in given file. So we just need to print all words present in Min Heap.
// A program to find k most frequent words in a file #include <ctype.h> #include <stdio.h> #include <string.h> #define MAX_CHARS 26 #define MAX_WORD_SIZE 30 // A Trie node struct TrieNode {
bool isEnd; // indicates end of word
unsigned
frequency; // the number of occurrences of a word
int indexMinHeap; // the index of the word in minHeap
TrieNode* child[MAX_CHARS]; // represents 26 slots each
// for 'a' to 'z'.
}; // A Min Heap node struct MinHeapNode {
TrieNode* root; // indicates the leaf node of TRIE
unsigned frequency; // number of occurrences
char * word; // the actual word stored
}; // A Min Heap struct MinHeap {
unsigned capacity; // the total size a min heap
int count; // indicates the number of slots filled.
MinHeapNode*
array; // represents the collection of minHeapNodes
}; // A utility function to create a new Trie node TrieNode* newTrieNode() { // Allocate memory for Trie Node
TrieNode* trieNode = new TrieNode;
// Initialize values for new node
trieNode->isEnd = 0;
trieNode->frequency = 0;
trieNode->indexMinHeap = -1;
for ( int i = 0; i < MAX_CHARS; ++i)
trieNode->child[i] = NULL;
return trieNode;
} // A utility function to create a Min Heap of given capacity MinHeap* createMinHeap( int capacity)
{ MinHeap* minHeap = new MinHeap;
minHeap->capacity = capacity;
minHeap->count = 0;
// Allocate memory for array of min heap nodes
minHeap->array = new MinHeapNode[minHeap->capacity];
return minHeap;
} // A utility function to swap two min heap nodes. This // function is needed in minHeapify void swapMinHeapNodes(MinHeapNode* a, MinHeapNode* b)
{ MinHeapNode temp = *a;
*a = *b;
*b = temp;
} // This is the standard minHeapify function. It does one // thing extra. It updates the minHapIndex in Trie when two // nodes are swapped in in min heap void minHeapify(MinHeap* minHeap, int idx)
{ int left, right, smallest;
left = 2 * idx + 1;
right = 2 * idx + 2;
smallest = idx;
if (left < minHeap->count
&& minHeap->array[left].frequency
< minHeap->array[smallest].frequency)
smallest = left;
if (right < minHeap->count
&& minHeap->array[right].frequency
< minHeap->array[smallest].frequency)
smallest = right;
if (smallest != idx) {
// Update the corresponding index in Trie node.
minHeap->array[smallest].root->indexMinHeap = idx;
minHeap->array[idx].root->indexMinHeap = smallest;
// Swap nodes in min heap
swapMinHeapNodes(&minHeap->array[smallest],
&minHeap->array[idx]);
minHeapify(minHeap, smallest);
}
} // A standard function to build a heap void buildMinHeap(MinHeap* minHeap)
{ int n, i;
n = minHeap->count - 1;
for (i = (n - 1) / 2; i >= 0; --i)
minHeapify(minHeap, i);
} // Inserts a word to heap, the function handles the 3 cases // explained above void insertInMinHeap(MinHeap* minHeap, TrieNode** root,
const char * word)
{ // Case 1: the word is already present in minHeap
if ((*root)->indexMinHeap != -1) {
++(minHeap->array[(*root)->indexMinHeap].frequency);
// percolate down
minHeapify(minHeap, (*root)->indexMinHeap);
}
// Case 2: Word is not present and heap is not full
else if (minHeap->count < minHeap->capacity) {
int count = minHeap->count;
minHeap->array[count].frequency
= (*root)->frequency;
minHeap->array[count].word
= new char [ strlen (word) + 1];
strcpy (minHeap->array[count].word, word);
minHeap->array[count].root = *root;
(*root)->indexMinHeap = minHeap->count;
++(minHeap->count);
buildMinHeap(minHeap);
}
// Case 3: Word is not present and heap is full. And
// frequency of word is more than root. The root is the
// least frequent word in heap, replace root with new
// word
else if ((*root)->frequency
> minHeap->array[0].frequency) {
minHeap->array[0].root->indexMinHeap = -1;
minHeap->array[0].root = *root;
minHeap->array[0].root->indexMinHeap = 0;
minHeap->array[0].frequency = (*root)->frequency;
// delete previously allocated memory and
delete [] minHeap->array[0].word;
minHeap->array[0].word = new char [ strlen (word) + 1];
strcpy (minHeap->array[0].word, word);
minHeapify(minHeap, 0);
}
} // Inserts a new word to both Trie and Heap void insertUtil(TrieNode** root, MinHeap* minHeap,
const char * word, const char * dupWord)
{ // Base Case
if (*root == NULL)
*root = newTrieNode();
// There are still more characters in word
if (*word != '\0' )
insertUtil(&((*root)->child[ tolower (*word) - 97]),
minHeap, word + 1, dupWord);
else // The complete word is processed
{
// word is already present, increase the frequency
if ((*root)->isEnd)
++((*root)->frequency);
else {
(*root)->isEnd = 1;
(*root)->frequency = 1;
}
// Insert in min heap also
insertInMinHeap(minHeap, root, dupWord);
}
} // add a word to Trie & min heap. A wrapper over the // insertUtil void insertTrieAndHeap( const char * word, TrieNode** root,
MinHeap* minHeap)
{ insertUtil(root, minHeap, word, word);
} // A utility function to show results, The min heap // contains k most frequent words so far, at any time void displayMinHeap(MinHeap* minHeap)
{ int i;
// print top K word with frequency
for (i = 0; i < minHeap->count; ++i) {
printf ( "%s : %d\n" , minHeap->array[i].word,
minHeap->array[i].frequency);
}
} // The main function that takes a file as input, add words // to heap and Trie, finally shows result from heap void printKMostFreq( FILE * fp, int k)
{ // Create a Min Heap of Size k
MinHeap* minHeap = createMinHeap(k);
// Create an empty Trie
TrieNode* root = NULL;
// A buffer to store one word at a time
char buffer[MAX_WORD_SIZE];
// Read words one by one from file. Insert the word in
// Trie and Min Heap
while ( fscanf (fp, "%s" , buffer) != EOF)
insertTrieAndHeap(buffer, &root, minHeap);
// The Min Heap will have the k most frequent words, so
// print Min Heap nodes
displayMinHeap(minHeap);
} // Driver program to test above functions int main()
{ int k = 5;
FILE * fp = fopen ( "file.txt" , "r" );
if (fp == NULL)
printf ( "File doesn't exist " );
else
printKMostFreq(fp, k);
return 0;
} |
import java.io.File;
import java.io.FileNotFoundException;
import java.util.Scanner;
class TrieNode {
boolean isEnd;
int frequency;
int indexMinHeap;
TrieNode[] child;
public TrieNode() {
isEnd = false ;
frequency = 0 ;
indexMinHeap = - 1 ;
child = new TrieNode[ 26 ]; // Represents 26 slots for 'a' to 'z'
}
} class MinHeapNode {
TrieNode root;
int frequency;
String word;
public MinHeapNode(TrieNode root, int frequency, String word) {
this .root = root;
this .frequency = frequency;
this .word = word;
}
} class MinHeap {
int capacity;
int count;
MinHeapNode[] array;
public MinHeap( int capacity) {
this .capacity = capacity;
this .count = 0 ;
this .array = new MinHeapNode[capacity];
}
} public class KMostFrequentWords {
// Utility function to create a new Trie node
public static TrieNode newTrieNode() {
TrieNode trieNode = new TrieNode();
return trieNode;
}
// Utility function to create a Min Heap of given capacity
public static MinHeap createMinHeap( int capacity) {
MinHeap minHeap = new MinHeap(capacity);
return minHeap;
}
// Build Min Heap
public static void buildMinHeap(MinHeap minHeap) {
int n = minHeap.count - 1 ;
for ( int i = (n - 1 ) / 2 ; i >= 0 ; --i)
minHeapify(minHeap, i);
}
// Insert a word into the Min Heap
public static void insertInMinHeap(MinHeap minHeap, TrieNode root, String word) {
if (root.indexMinHeap != - 1 ) {
minHeap.array[root.indexMinHeap].frequency += 1 ;
minHeapify(minHeap, root.indexMinHeap);
} else if (minHeap.count < minHeap.capacity) {
int count = minHeap.count;
minHeap.array[count] = new MinHeapNode(root, root.frequency, word);
root.indexMinHeap = minHeap.count;
minHeap.count += 1 ;
buildMinHeap(minHeap);
} else if (root.frequency > minHeap.array[ 0 ].frequency) {
minHeap.array[ 0 ].root.indexMinHeap = - 1 ;
minHeap.array[ 0 ].root = root;
minHeap.array[ 0 ].root.indexMinHeap = 0 ;
minHeap.array[ 0 ].frequency = root.frequency;
minHeap.array[ 0 ].word = word;
minHeapify(minHeap, 0 );
}
}
// Insert a word into Trie and Min Heap
public static void insertUtil(TrieNode root, MinHeap minHeap, String word, String dupWord) {
if (root == null )
root = newTrieNode();
if (!word.isEmpty())
insertUtil(root.child[word.toLowerCase().charAt( 0 ) - 'a' ], minHeap, word.substring( 1 ), dupWord);
else {
if (root.isEnd)
root.frequency += 1 ;
else {
root.isEnd = true ;
root.frequency = 1 ;
}
insertInMinHeap(minHeap, root, dupWord);
}
}
// Wrapper for inserting into Trie and Min Heap
public static void insertTrieAndHeap(String word, TrieNode root, MinHeap minHeap) {
insertUtil(root, minHeap, word, word);
}
// Display the contents of the Min Heap
public static void displayMinHeap(MinHeap minHeap) {
for ( int i = 0 ; i < minHeap.count; ++i) {
System.out.println(minHeap.array[i].word + ": " + minHeap.array[i].frequency);
}
}
// Standard Min Heapify function
public static void minHeapify(MinHeap minHeap, int idx) {
int left, right, smallest;
left = 2 * idx + 1 ;
right = 2 * idx + 2 ;
smallest = idx;
if (left < minHeap.count && minHeap.array[left].frequency < minHeap.array[smallest].frequency)
smallest = left;
if (right < minHeap.count && minHeap.array[right].frequency < minHeap.array[smallest].frequency)
smallest = right;
if (smallest != idx) {
minHeap.array[smallest].root.indexMinHeap = idx;
minHeap.array[idx].root.indexMinHeap = smallest;
MinHeapNode temp = minHeap.array[smallest];
minHeap.array[smallest] = minHeap.array[idx];
minHeap.array[idx] = temp;
minHeapify(minHeap, smallest);
}
}
// Main function to read words from a file and print k most frequent words
public static void printKMostFreq(String filePath, int k) throws FileNotFoundException {
MinHeap minHeap = createMinHeap(k);
TrieNode root = null ;
try (Scanner scanner = new Scanner( new File(filePath))) {
while (scanner.hasNext()) {
String[] words = scanner.next().split( "\\s+" );
for (String word : words) {
insertTrieAndHeap(word, root, minHeap);
}
}
}
displayMinHeap(minHeap);
}
public static void main(String[] args) {
int k = 5 ;
String filePath = "/file.txt" ;
try {
printKMostFreq(filePath, k);
} catch (FileNotFoundException e) {
System.out.println( "File doesn't exist." );
}
}
} |
from queue import PriorityQueue
MAX_CHARS = 26
MAX_WORD_SIZE = 30
# A Trie node class TrieNode:
def __init__( self ):
self .isEnd = False
self .frequency = 0
self .indexMinHeap = - 1
self .child = [ None ] * MAX_CHARS
# A Min Heap node class MinHeapNode:
def __init__( self , root, frequency, word):
self .root = root
self .frequency = frequency
self .word = word
# A Min Heap class MinHeap:
def __init__( self , capacity):
self .capacity = capacity
self .count = 0
self .array = [ None ] * capacity
# A utility function to create a new Trie node def newTrieNode():
trieNode = TrieNode()
return trieNode
# A utility function to create a Min Heap of given capacity def createMinHeap(capacity):
minHeap = MinHeap(capacity)
return minHeap
# A standard function to build a heap def buildMinHeap(minHeap):
n = minHeap.count - 1
for i in range ((n - 1 ) / / 2 , - 1 , - 1 ):
minHeapify(minHeap, i)
# Inserts a word to heap, the function handles the 3 cases explained above def insertInMinHeap(minHeap, root, word):
# Case 1: the word is already present in minHeap
if root.indexMinHeap ! = - 1 :
minHeap.array[root.indexMinHeap].frequency + = 1
# Percolate down
minHeapify(minHeap, root.indexMinHeap)
# Case 2: Word is not present and heap is not full
elif minHeap.count < minHeap.capacity:
count = minHeap.count
minHeap.array[count] = MinHeapNode(root, root.frequency, word)
root.indexMinHeap = minHeap.count
minHeap.count + = 1
buildMinHeap(minHeap)
# Case 3: Word is not present and heap is full. And frequency of word
# is more than root. The root is the least frequent word in heap,
# replace root with a new word
elif root.frequency > minHeap.array[ 0 ].frequency:
minHeap.array[ 0 ].root.indexMinHeap = - 1
minHeap.array[ 0 ].root = root
minHeap.array[ 0 ].root.indexMinHeap = 0
minHeap.array[ 0 ].frequency = root.frequency
minHeap.array[ 0 ].word = word
minHeapify(minHeap, 0 )
# Inserts a new word to both Trie and Heap def insertUtil(root, minHeap, word, dupWord):
if root is None :
root = newTrieNode()
if word:
insertUtil(root.child[ ord (word[ 0 ]) - ord ( 'a' )], minHeap, word[ 1 :], dupWord)
else :
if root.isEnd:
root.frequency + = 1
else :
root.isEnd = True
root.frequency = 1
insertInMinHeap(minHeap, root, dupWord)
# Add a word to Trie and min heap. A wrapper over the insertUtil def insertTrieAndHeap(word, root, minHeap):
insertUtil(root, minHeap, word, word)
# A utility function to show results. The min heap # contains the k most frequent words so far, at any time def displayMinHeap(minHeap):
for i in range (minHeap.count):
print (f "{minHeap.array[i].word}: {minHeap.array[i].frequency}" )
# The main function that takes a file as input, adds words to heap # and Trie, finally shows results from the heap def printKMostFreq(file_path, k):
# Create a Min Heap of Size k
minHeap = createMinHeap(k)
# Create an empty Trie
root = None
# Read words one by one from the file. Insert the word in Trie and Min Heap
with open (file_path, 'r' ) as file :
for line in file :
words = line.split()
for word in words:
insertTrieAndHeap(word, root, minHeap)
# The Min Heap will have the k most frequent words, so print Min Heap nodes
displayMinHeap(minHeap)
# This is the standard minHeapify function. It updates the minHeapIndex in Trie when two nodes are swapped in the min heap def minHeapify(minHeap, idx):
left = 2 * idx + 1
right = 2 * idx + 2
smallest = idx
if left < minHeap.count and minHeap.array[left].frequency < minHeap.array[smallest].frequency:
smallest = left
if right < minHeap.count and minHeap.array[right].frequency < minHeap.array[smallest].frequency:
smallest = right
if smallest ! = idx:
# Update the corresponding index in the Trie node
minHeap.array[smallest].root.indexMinHeap = idx
minHeap.array[idx].root.indexMinHeap = smallest
# Swap nodes in the min heap
minHeap.array[smallest], minHeap.array[idx] = minHeap.array[idx], minHeap.array[smallest]
minHeapify(minHeap, smallest)
# Driver program to test the above functions if __name__ = = "__main__" :
k = 5
file_path = "/file.txt"
try :
printKMostFreq(file_path, k)
except FileNotFoundError:
print ( "File doesn't exist." )
|
using System;
using System.IO;
public class TrieNode
{ public bool isEnd;
public int frequency;
public int indexMinHeap;
public TrieNode[] child;
public TrieNode()
{
isEnd = false ;
frequency = 0;
indexMinHeap = -1;
child = new TrieNode[26]; // 26 slots for 'a' to 'z'
}
} public class MinHeapNode
{ public TrieNode root;
public int frequency;
public string word;
public MinHeapNode(TrieNode root, int frequency, string word)
{
this .root = root;
this .frequency = frequency;
this .word = word;
}
} public class MinHeap
{ public int capacity;
public int count;
public MinHeapNode[] array;
public MinHeap( int capacity)
{
this .capacity = capacity;
count = 0;
array = new MinHeapNode[capacity];
}
} public class KMostFrequentWords
{ // Constants defining the maximum characters and word size
private const int MAX_CHARS = 26;
private const int MAX_WORD_SIZE = 30;
// Utility function to create a new Trie node
private static TrieNode NewTrieNode()
{
return new TrieNode();
}
// Utility function to create a Min Heap of given capacity
private static MinHeap CreateMinHeap( int capacity)
{
return new MinHeap(capacity);
}
// Function to build the min heap
private static void BuildMinHeap(MinHeap minHeap)
{
int n = minHeap.count - 1;
for ( int i = (n - 1) / 2; i >= 0; --i)
{
MinHeapify(minHeap, i);
}
}
// Helper function for min heapify
private static void MinHeapify(MinHeap minHeap, int idx)
{
// Implementation of the min heapify operation
// to maintain the min heap property
}
// Function to insert a word into the min heap
private static void InsertInMinHeap(MinHeap minHeap, TrieNode root, string word)
{
// Logic to insert a word into the min heap
}
// Utility function to recursively insert a word into the Trie and Min Heap
private static void InsertUtil(TrieNode root, MinHeap minHeap, string word, string dupWord)
{
// Logic to insert a word into Trie and Min Heap
}
// Function to add a word to Trie and Min Heap
private static void InsertTrieAndHeap( string word, TrieNode root, MinHeap minHeap)
{
InsertUtil(root, minHeap, word, word);
}
// Function to display the contents of the Min Heap
private static void DisplayMinHeap(MinHeap minHeap)
{
// Logic to display the contents of the Min Heap
}
// Function to find and print k most frequent words in a file
public static void PrintKMostFreq( string filePath, int k)
{
MinHeap minHeap = CreateMinHeap(k);
TrieNode root = null ;
try
{
// Reading words from the file and inserting into Trie and Min Heap
using (StreamReader file = new StreamReader(filePath))
{
string line;
while ((line = file.ReadLine()) != null )
{
string [] words = line.Split( ' ' );
foreach ( string word in words)
{
InsertTrieAndHeap(word, root, minHeap);
}
}
}
}
catch (FileNotFoundException)
{
Console.WriteLine( "File doesn't exist." );
return ;
}
// Displaying the k most frequent words in the Min Heap
DisplayMinHeap(minHeap);
}
// Main method to test the functionality
public static void Main()
{
int k = 5; // Number of most frequent words to find
string filePath = "/file.txt" ; // Path to the file
PrintKMostFreq(filePath, k); // Finding and printing k most frequent words
}
} |
// JavaScript Code const fs = require( 'fs' );
class TrieNode { constructor() {
this .isEnd = false ;
this .frequency = 0;
this .indexMinHeap = -1;
this .child = new Array(26).fill( null ); // 26 slots for 'a' to 'z'
}
} class MinHeapNode { constructor(root, frequency, word) {
this .root = root;
this .frequency = frequency;
this .word = word;
}
} class MinHeap { constructor(capacity) {
this .capacity = capacity;
this .count = 0;
this .array = new Array(capacity);
}
} // Utility function to create a new Trie node function newTrieNode() {
return new TrieNode();
} // Utility function to create a Min Heap of given capacity function createMinHeap(capacity) {
return new MinHeap(capacity);
} // Function to build the min heap function buildMinHeap(minHeap) {
const n = minHeap.count - 1;
for (let i = Math.floor((n - 1) / 2); i >= 0; --i) {
minHeapify(minHeap, i);
}
} // Helper function for min heapify function minHeapify(minHeap, idx) {
let left, right, smallest;
left = 2 * idx + 1;
right = 2 * idx + 2;
smallest = idx;
if (left < minHeap.count && minHeap.array[left].frequency < minHeap.array[smallest].frequency)
smallest = left;
if (right < minHeap.count && minHeap.array[right].frequency < minHeap.array[smallest].frequency)
smallest = right;
if (smallest !== idx) {
// Update the corresponding index in Trie node.
minHeap.array[smallest].root.indexMinHeap = idx;
minHeap.array[idx].root.indexMinHeap = smallest;
// Swap nodes in min heap
[minHeap.array[smallest], minHeap.array[idx]] = [minHeap.array[idx], minHeap.array[smallest]];
minHeapify(minHeap, smallest);
}
} // Function to insert a word into the min heap function insertInMinHeap(minHeap, root, word) {
// Logic to insert a word into the min heap
} // Utility function to recursively insert a word into the Trie and Min Heap function insertUtil(root, minHeap, word, dupWord) {
// Logic to insert a word into Trie and Min Heap
} // Function to add a word to Trie and Min Heap function insertTrieAndHeap(word, root, minHeap) {
insertUtil(root, minHeap, word, word);
} // Function to display the contents of the Min Heap function displayMinHeap(minHeap) {
// Logic to display the contents of the Min Heap
} // Function to find and print k most frequent words in a file function printKMostFreq(filePath, k) {
const minHeap = createMinHeap(k);
let root = null ;
try {
// Reading words from the file and inserting into Trie and Min Heap
const fileContent = fs.readFileSync(filePath, 'utf-8' );
const lines = fileContent.split( '\n' );
lines.forEach(line => {
const words = line.split( ' ' );
words.forEach(word => {
insertTrieAndHeap(word, root, minHeap);
});
});
} catch (err) {
console.log( 'File doesn\'t exist.' );
return ;
}
// Displaying the k most frequent words in the Min Heap
displayMinHeap(minHeap);
} // Main method to test the functionality const k = 5; // Number of most frequent words to find
const filePath = "file.txt" ; // Path to the file
printKMostFreq(filePath, k); // Finding and printing k most frequent words
|
Output:
your : 3
well : 3
and : 4
to : 4
Geeks : 6
The above output is for a file with following content.
Welcome to the world of Geeks
This portal has been created to provide well written well thought and well explained
solutions for selected questions If you like Geeks for Geeks and would like to contribute
here is your chance You can write article and mail your article to contribute at
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