Rabin Karp String Matching in DSA C++ - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize the base value for hashing.

DSA C++

const int base = [1];

Drag options to blanks, or click blank then click option'

A101

B1000

C256

D31

Attempts:

3 left

💡 Hint

Common Mistakes

Using a base value too small to cover all characters.

Choosing a prime number instead of the character set size.

The base value 256 is commonly used for ASCII character set in Rabin Karp hashing.

2fill in blank

medium

Complete the code to compute the initial hash of the pattern.

DSA C++

for (int i = 0; i < m; i++) {
    pattern_hash = (pattern_hash * base + pattern[i]) % [1];
}

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Apattern_hash

Bm

Cbase

Dprime_mod

Attempts:

3 left

💡 Hint

Common Mistakes

Using base as modulus causes incorrect hashing.

Using pattern length as modulus leads to collisions.

We use a large prime modulus to reduce hash collisions and keep hash values within integer limits.

3fill in blank

hard

Fix the error in the rolling hash update inside the loop.

DSA C++

text_hash = (base * (text_hash - text[i] * [1]) + text[i + m]) % prime_mod;

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Apow(base, 1)

Bpow(base, m-1)

Cpow(base, m+1)

Dpow(base, m)

Attempts:

3 left

💡 Hint

Common Mistakes

Using pow(base, m) causes incorrect subtraction.

Using pow(base, m+1) leads to wrong hash values.

We remove the leftmost character's contribution by multiplying it with base^(m-1).

4fill in blank

hard

Fill both blanks to correctly check and update the rolling hash.

DSA C++

if (pattern_hash == text_hash && text.substr(i, m) == [1]) {
    result.push_back(i);
}
text_hash = (base * (text_hash - text[i] * [2]) + text[i + m]) % prime_mod;

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Apattern

Btext.substr(i, m)

Cpow(base, m-1)

Dpow(base, m)

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing with text.substr(i, m) instead of pattern.

Using wrong power of base in hash update.

We compare the current substring with the pattern and update the hash by removing the leftmost character multiplied by base^(m-1).

5fill in blank

hard

Fill all three blanks to complete the Rabin Karp search function.

DSA C++

vector<int> rabinKarp(const string& text, const string& pattern) {
    int n = text.size(), m = pattern.size();
    const int base = [1];
    const int prime_mod = 101;
    int pattern_hash = 0, text_hash = 0;
    int power = 1;
    vector<int> result;

    for (int i = 0; i < m - 1; i++)
        power = (power * base) % prime_mod;

    for (int i = 0; i < m; i++) {
        pattern_hash = (pattern_hash * base + pattern[i]) % prime_mod;
        text_hash = (text_hash * base + text[i]) % prime_mod;
    }

    for (int i = 0; i <= n - m; i++) {
        if (pattern_hash == text_hash && text.substr(i, m) == [2]) {
            result.push_back(i);
        }
        if (i < n - m) {
            text_hash = (base * (text_hash - text[i] * [3]) + text[i + m]) % prime_mod;
            if (text_hash < 0) text_hash += prime_mod;
        }
    }
    return result;
}

Drag options to blanks, or click blank then click option'

A256

Bpattern

Cpower

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong base value.

Comparing with wrong substring.

Not using power for rolling hash update.

Base is 256 for ASCII, compare substring with pattern, and use power for rolling hash subtraction.

Practice

1. What is the main idea behind the Rabin Karp String Matching algorithm?

easy

A. Compare each character one by one without hashing

B. Use hashing to find pattern matches efficiently

C. Sort the text and pattern before matching

D. Use recursion to find all pattern occurrences

Solution

1. Step 1: Understand Rabin Karp's approach

   The algorithm uses a hash function to convert the pattern and text substrings into numbers.

2. Step 2: Use hashing for efficient matching

   By comparing hash values instead of characters, it quickly finds potential matches and then verifies them.

3. Final Answer:

   Use hashing to find pattern matches efficiently -> Option B

4. Quick Check:

   Hashing = Efficient matching [OK]

Hint: Remember: Rabin Karp uses hashing for fast pattern search [OK]

Common Mistakes:

• Thinking it compares characters directly always
• Assuming it sorts strings before matching
• Confusing it with recursive search methods

2. Which of the following is the correct way to update the rolling hash in Rabin Karp when moving from substring s[i..i+m-1] to s[i+1..i+m]?

easy

A. Add leftmost char's hash, multiply by base, subtract new char's hash

B. Multiply entire hash by base and add new char's hash only

C. Remove leftmost char's hash, multiply by base, add new char's hash

D. Recalculate hash from scratch for new substring

Solution

1. Step 1: Understand rolling hash update

   Remove the leftmost character's contribution, multiply remaining hash by base, then add new character's value.

2. Step 2: Apply formula for next substring

   This keeps hash calculation efficient without recomputing from scratch.

3. Final Answer:

   Remove leftmost char's hash, multiply by base, add new char's hash -> Option C

4. Quick Check:

   Rolling hash update = Remove + multiply + add [OK]

Hint: Rolling hash = remove old char + shift + add new char [OK]

Common Mistakes:

• Recalculating hash fully each time
• Adding instead of removing old char's hash
• Multiplying after adding new char instead of before

3. What will be the output of the following C++ code snippet using Rabin Karp to find pattern "abc" in text "ababc"?

std::string text = "ababc";
std::string pattern = "abc";
// Assume correct Rabin Karp implementation
std::vector<int> matches = rabinKarpSearch(text, pattern);
for (int pos : matches) std::cout << pos << " ";

medium

A. 1

B. 0 2

C. No output

D. 2

Solution

1. Step 1: Identify pattern occurrences in text

   Pattern "abc" appears starting at index 2 in "ababc" (0-based indexing).

2. Step 2: Confirm no other matches

   Substring at index 0 is "aba", not "abc"; index 1 is "bab", not "abc".

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Pattern found at index 2 [OK]

Hint: Check substring positions carefully for exact matches [OK]

Common Mistakes:

• Assuming match at index 0 incorrectly
• Confusing overlapping substrings
• Using 1-based indexing instead of 0-based

4. Identify the bug in this Rabin Karp rolling hash update snippet:

hash = (hash - text[i] * pow(base, m-1)) * base + text[i+m];

What is the likely problem?

medium

A. Missing modulo operation causing overflow

B. Wrong power used in subtraction term

C. Adding instead of subtracting old char's hash

D. Incorrect base multiplier order

Solution

1. Step 1: Check arithmetic operations in rolling hash

   The formula subtracts old char's hash, multiplies by base, adds new char's value.

2. Step 2: Identify missing modulo usage

   Without modulo, hash values can overflow integer limits causing wrong results.

3. Final Answer:

   Missing modulo operation causing overflow -> Option A

4. Quick Check:

   Modulo needed to prevent overflow [OK]

Hint: Always apply modulo after each hash update [OK]

Common Mistakes:

• Ignoring modulo leading to wrong hash values
• Using wrong power exponent
• Mixing addition and subtraction signs

5. Given a large text and multiple patterns, how can Rabin Karp be adapted to efficiently find all pattern occurrences?

hard

A. Use a combined hash set of all patterns and check text substrings against it

B. Run Rabin Karp separately for each pattern on the entire text

C. Sort all patterns and use binary search on text substrings

D. Use recursion to match patterns one by one

Solution

1. Step 1: Understand multi-pattern matching challenge

   Running Rabin Karp separately for each pattern is inefficient for many patterns.

2. Step 2: Use combined hashing approach

   Hash all patterns and store in a set. Then slide over text, hashing substrings and checking set membership.

3. Step 3: Efficiently find all matches

   This reduces repeated work and speeds up multi-pattern search.

4. Final Answer:

   Use a combined hash set of all patterns and check text substrings against it -> Option A

5. Quick Check:

   Combined hashing = efficient multi-pattern search [OK]

Hint: Hash all patterns once, then check text substrings [OK]

Common Mistakes:

• Running separate searches for each pattern
• Sorting patterns instead of hashing
• Using recursion unnecessarily