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Tutorial on Precomputation techniques in Strings

Precomputation in strings refers to the process of performing calculations on a given string before actual operations or queries are performed.

The goal of precomputation is to process and store certain information about the string that can be used to optimize and speed up various string-related operations or queries that might be performed later. By pre-computing certain information of the string, redundant calculations can be avoided, and faster runtime for specific tasks can be achieved. Now let’s consider the following problem for better understanding.

Problem Statement:

Given a string s of length n and a set of q queries, each query defined by a triplet (l, r, c) where: For each query, you need to calculate and output the number of occurrences of the character c within the substring s[l...r].

Naïve Approach:

The idea is to iterate from index l to r and count the frequency of character c for each query separately.

For each query, this approach requires iterating through the substring characters and checking if each character is equal to the given character c. The time complexity of this approach for a single query is O(n). Since there are q queries, the overall time complexity is O(q * n).

The naïve approach is simple to implement, but it might be inefficient for large values of q and m since it involves repetitive calculations for each query.

Approach (Using PreComputation):

To efficiently solve this problem, precomputation technique can be used involving the use of a 2D array that stores prefix sums of each character occurrences. Then, for each query, count of the desired character within the specified substring can be computed in O(1) time using the prefix sum.

Steps to implement the above idea:

  • Initialize a 2D array prefixSum of size n x 26, where n is the length of the input string s, and 26 represents the number of English alphabet characters (‘a’ to ‘z’).
  • prefixSum[i][ch] denotes the number of times character ch occurs till i.
  • For each index i from 0 to n - 1:
    • For each character ch from ‘a‘ to ‘z‘, calculate prefixSum[i][ch - 'a'] by adding prefixSum[i - 1][ch - 'a'] to the count of character ch at index i in string s.
  • For each query (l, r, c):
    • The count of character c is prefixSum[r] - prefixSum[l - 1].

Below is the implementation of above approach:

C++

// C++ code
#include <bits/stdc++.h>
using namespace std;
 
vector<int> preComputation(string s,
                           vector<vector<int> > queries,
                           vector<char> character)
{
    int n = s.size();
 
    // Create a 2D vector to store prefix sum of
    // character occurrences
    vector<vector<int> > psum(n + 1, vector<int>(26, 0));
 
    // Calculate prefix sums
    for (int i = 1; i <= n; i++) {
        for (int j = 0; j < 26; j++) {
 
            // Update prefix sum
            psum[i][j] += psum[i - 1][j];
        }
 
        // Increment the count of current character
        psum[i][s[i - 1] - 97]++;
    }
 
    int m = queries.size();
    vector<int> ans(m);
 
    // Calculate answers for each query
    for (int i = 0; i < m; i++) {
 
        // Calculate the count of character within the
        // specified range
        ans[i]
            = psum[queries[i][1]][character[i] - 97]
              - psum[queries[i][0] - 1][character[i] - 97];
    }
 
    return ans;
}
 
// Driver Code
int main()
{
    string s = "baabcaba";
    vector<vector<int> > queries{
        { 2, 6 }, { 4, 5 }, { 1, 6 }, { 3, 6 }
    };
    vector<char> character{ 'a', 'a', 'b', 'c' };
    // ans[i] stores answer to the ith query
    vector<int> ans = preComputation(s, queries, character);
 
    // Output the results
    for (int i = 0; i < ans.size(); i++) {
        cout << "Frequency of character " << character[i]
             << " in the range " << queries[i][0] << " to "
             << queries[i][1] << " :" << ans[i] << "\n";
    }
}

Java

public class PrefixSum {
    static int[] preComputation(String s, int[][] queries, char[] characters) {
        int n = s.length();
 
        // Create a 2D array to store prefix sum of character occurrences
        int[][] psum = new int[n + 1][26];
 
        // Calculate prefix sums
        for (int i = 1; i <= n; i++) {
            for (int j = 0; j < 26; j++) {
                // Update prefix sum
                psum[i][j] = psum[i - 1][j];
            }
 
            // Increment the count of current character
            psum[i][s.charAt(i - 1) - 'a']++;
        }
 
        int m = queries.length;
        int[] ans = new int[m];
 
        // Calculate answers for each query
        for (int i = 0; i < m; i++) {
            // Calculate the count of character within the specified range
            ans[i] = psum[queries[i][1]][characters[i] - 'a'] - psum[queries[i][0] - 1][characters[i] - 'a'];
        }
 
        return ans;
    }
 
    public static void main(String[] args) {
        String s = "baabcaba";
        int[][] queries = {{2, 6}, {4, 5}, {1, 6}, {3, 6}};
        char[] characters = {'a', 'a', 'b', 'c'};
 
        // ans[i] stores the answer to the ith query
        int[] ans = preComputation(s, queries, characters);
 
        // Output the results
        for (int i = 0; i < ans.length; i++) {
            System.out.println("Frequency of character " + characters[i] + " in the range "
                    + queries[i][0] + " to " + queries[i][1] + ": " + ans[i]);
        }
    }
}

Python3

def pre_computation(s, queries, characters):
    n = len(s)
 
    # Create a 2D list to store prefix sum of character occurrences
    psum = [[0] * 26 for _ in range(n + 1)]
 
    # Calculate prefix sums
    for i in range(1, n + 1):
        for j in range(26):
 
            # Update prefix sum
            psum[i][j] = psum[i - 1][j]
 
        # Increment the count of current character
        psum[i][ord(s[i - 1]) - ord('a')] += 1
 
    m = len(queries)
    ans = [0] * m
 
    # Calculate answers for each query
    for i in range(m):
        # Calculate the count of character within the specified range
        ans[i] = psum[queries[i][1]][ord(characters[i]) - ord('a')] - psum[queries[i][0] - 1][ord(characters[i]) - ord('a')]
 
    return ans
 
# Driver Code
if __name__ == "__main__":
    s = "baabcaba"
    queries = [[2, 6], [4, 5], [1, 6], [3, 6]]
    characters = ['a', 'a', 'b', 'c']
 
    # ans[i] stores the answer to the ith query
    ans = pre_computation(s, queries, characters)
 
    # Output the results
    for i in range(len(ans)):
        print(f"Frequency of character {characters[i]} in the range {queries[i][0]} to {queries[i][1]}: {ans[i]}")

C#

using System;
using System.Collections.Generic;
 
class GFG
{
    static List<int> PreComputation(string s, List<List<int>> queries, List<char> character)
    {
        int n = s.Length;
 
        // Create a 2D list to store prefix sum of character occurrences
        List<List<int>> psum = new List<List<int>>(n + 1);
        for (int i = 0; i <= n; i++)
        {
            psum.Add(new List<int>(26));
            for (int j = 0; j < 26; j++)
            {
                // Update prefix sum
                psum[i].Add(0);
            }
        }
 
        // Calculate prefix sums
        for (int i = 1; i <= n; i++)
        {
            for (int j = 0; j < 26; j++)
            {
                // Update prefix sum
                psum[i][j] += psum[i - 1][j];
            }
 
            // Increment the count of current character
            psum[i][s[i - 1] - 'a']++;
        }
 
        int m = queries.Count;
        List<int> ans = new List<int>(m);
 
        // Calculate answers for each query
        for (int i = 0; i < m; i++)
        {
            // Calculate the count of character within the specified range
            ans.Add(psum[queries[i][1]][character[i] - 'a'] - psum[queries[i][0] - 1][character[i] - 'a']);
        }
 
        return ans;
    }
 
    // Driver Code
    static void Main()
    {
        string s = "baabcaba";
        List<List<int>> queries = new List<List<int>>
        {
            new List<int> {2, 6},
            new List<int> {4, 5},
            new List<int> {1, 6},
            new List<int> {3, 6}
        };
 
        List<char> character = new List<char> { 'a', 'a', 'b', 'c' };
 
        // ans[i] stores answer to the ith query
        List<int> ans = PreComputation(s, queries, character);
 
        // Output the results
        for (int i = 0; i < ans.Count; i++)
        {
            Console.WriteLine($"Frequency of character {character[i]} in the range {queries[i][0]} to {queries[i][1]}: {ans[i]}");
        }
    }
}

Javascript

// javaScript code for the above approach
 
function preComputation(s, queries, character) {
    const n = s.length;
 
    // Create a 2D array to store prefix
    // sum of character occurrences
    const psum = new Array(n + 1).fill(0).map(() => new Array(26).fill(0));
 
    // Calculate prefix sums
    for (let i = 1; i <= n; i++) {
        for (let j = 0; j < 26; j++) {
            // Update prefix sum
            psum[i][j] += psum[i - 1][j];
        }
 
        // Increment the count of current
        // character
        const charIndex = s.charCodeAt(i - 1) - 97;
        psum[i][charIndex]++;
    }
 
    const m = queries.length;
    const ans = new Array(m);
 
    // Calculate answers for each query
    for (let i = 0; i < m; i++) {
        // Calculate the count of character
        // within the specified range
        const charIndex = character[i].charCodeAt(0) - 97;
        ans[i] =
            psum[queries[i][1]][charIndex] -
            psum[queries[i][0] - 1][charIndex];
    }
 
    return ans;
}
 
// Driver Code
 
const s = "baabcaba";
const queries = [
    [2, 6],
    [4, 5],
    [1, 6],
    [3, 6]
];
const character = ['a', 'a', 'b', 'c'];
 
// ans[i] stores answer to the ith query
const ans = preComputation(s, queries, character);
 
// Output the results
for (let i = 0; i < ans.length; i++) {
    console.log(
        `Frequency of character ${character[i]} in the range ${
            queries[i][0]
        } to ${queries[i][1]} :${ans[i]}`
    );
}

Time Complexity: O(n+m), where n is length of string and m is number of queries.
Auxiliary Space: O(n)


 


Reffered: https://www.geeksforgeeks.org

DSA

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Category:
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Sub Category:
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