Rabin Karp String Matching in DSA C# - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind the Rabin Karp string matching algorithm?

It uses hashing to find a pattern in a text by comparing hash values of the pattern and substrings of the text, reducing the number of direct character comparisons.

intermediate

Why does Rabin Karp use a rolling hash?

A rolling hash allows efficient calculation of hash values for consecutive substrings by updating the previous hash instead of recomputing from scratch.

intermediate

What happens if two substrings have the same hash value in Rabin Karp?

Since hash collisions can occur, the algorithm performs a direct character-by-character check to confirm if the substrings actually match.

intermediate

What is the average time complexity of Rabin Karp algorithm?

The average time complexity is O(n + m), where n is the length of the text and m is the length of the pattern, due to efficient hashing and rare collisions.

intermediate

In Rabin Karp, what is the role of the prime number used in hashing?

The prime number is used as a modulus to reduce the hash values and help distribute them uniformly, minimizing collisions.

What does Rabin Karp compare to find a pattern in text?

A Lengths of pattern and text

B Only characters one by one

C Hash values of pattern and text substrings

D ASCII values of characters

Rabin Karp uses hash values to quickly compare the pattern with substrings of the text.

What is a rolling hash used for in Rabin Karp?

A To quickly update hash for next substring

B To sort the text

C To encrypt the pattern

D To count characters

Rolling hash updates the hash value efficiently when moving to the next substring.

If two substrings have the same hash, what does Rabin Karp do next?

A Checks characters one by one to confirm match

B Assumes they match without checking

C Ignores the substrings

D Recalculates the hash

Due to possible hash collisions, Rabin Karp verifies by direct comparison.

What is the worst-case time complexity of Rabin Karp?

A O(m^2)

B O(n + m)

C O(n log m)

D O(n*m)

In worst case, many collisions cause character comparisons leading to O(n*m) time.

Why is a prime number used in the hashing function?

A To make the hash smaller

B To reduce collisions by spreading hash values evenly

C To speed up multiplication

D To sort the pattern

Prime modulus helps distribute hash values uniformly, reducing collisions.

Explain how the Rabin Karp algorithm uses hashing to find a pattern in a text.

Think about how hash values help avoid checking every character.

Describe the role of the rolling hash and why it is important in Rabin Karp.

Focus on how the algorithm moves from one substring to the next.

Practice

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

easy

A. Sort the text and pattern before matching

B. Compare each character of pattern with text directly

C. Use hashing to find pattern matches efficiently

D. Use recursion to find all pattern occurrences

Solution

1. Step 1: Understand Rabin Karp approach

   Rabin Karp uses hashing to convert strings into numbers for quick comparison.

2. Step 2: Compare hash values instead of characters

   It compares hash of pattern with hash of text substrings to find matches efficiently.

3. Final Answer:

   Use hashing to find pattern matches efficiently -> Option C

4. Quick Check:

   Hashing = Efficient matching [OK]

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

Common Mistakes:

• Thinking it compares characters one by one always
• Confusing with brute force matching
• Assuming sorting is involved

2. Which of the following is the correct way to calculate the initial hash for a substring in Rabin Karp (assuming base = 256 and prime = 101)?

easy

A. hash = (hash - char) % prime

B. hash = (hash + base * char) % prime

C. hash = (char * base) % prime

D. hash = (hash * base + char) % prime

Solution

1. Step 1: Understand rolling hash formula

   The rolling hash updates as hash = (hash * base + new_char) % prime.

2. Step 2: Apply formula for initial hash

   Initial hash is calculated by iterating characters and applying this formula cumulatively.

3. Final Answer:

   hash = (hash * base + char) % prime -> Option D

4. Quick Check:

   Rolling hash formula = hash = (hash * base + char) % prime [OK]

Hint: Use (hash * base + char) % prime for rolling hash [OK]

Common Mistakes:

• Adding instead of multiplying by base
• Subtracting characters incorrectly
• Ignoring modulo operation

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

string text = "ababc";
string pattern = "abc";
List<int> matches = RabinKarpSearch(text, pattern);
Console.WriteLine(string.Join(",", matches));

List<int> RabinKarpSearch(string txt, string pat) {
    int baseVal = 256, prime = 101;
    int m = pat.Length, n = txt.Length;
    int patHash = 0, txtHash = 0, h = 1;
    List<int> result = new List<int>();

    for (int i = 0; i < m - 1; i++) h = (h * baseVal) % prime;

    for (int i = 0; i < m; i++) {
        patHash = (baseVal * patHash + pat[i]) % prime;
        txtHash = (baseVal * txtHash + txt[i]) % prime;
    }

    for (int i = 0; i <= n - m; i++) {
        if (patHash == txtHash) {
            bool match = true;
            for (int j = 0; j < m; j++) {
                if (txt[i + j] != pat[j]) {
                    match = false;
                    break;
                }
            }
            if (match) result.Add(i);
        }
        if (i < n - m) {
            txtHash = (baseVal * (txtHash - txt[i] * h) + txt[i + m]) % prime;
            if (txtHash < 0) txtHash += prime;
        }
    }
    return result;
}

medium

A. 1,2

B. 2

C. 0,2

D. No matches

Solution

1. Step 1: Identify pattern occurrences in text

   Pattern "abc" appears starting at index 2 in "ababc". Also, substring at index 0 is "aba" which is not a match.

2. Step 2: Check hash and character matches

   Hashes match only at index 2 (initial hashes differ at index 0 and 1), and character check passes at index 2. So only index 2 is added.

3. Final Answer:

   2 -> Option B

4. Quick Check:

   Pattern found at index 2 [OK]

Hint: Check both hash and characters to confirm matches [OK]

Common Mistakes:

• Assuming all hash matches are valid
• Ignoring character verification after hash match
• Miscounting indices starting from 1

4. Identify the bug in this Rabin Karp substring search code snippet:

int baseVal = 256, prime = 101;
int m = pattern.Length, n = text.Length;
int patHash = 0, txtHash = 0, h = 1;
for (int i = 0; i <= m - 1; i++) h = (h * baseVal) % prime;
for (int i = 0; i < m; i++) {
    patHash = (baseVal * patHash + pattern[i]) % prime;
    txtHash = (baseVal * txtHash + text[i]) % prime;
}
// rest of code omitted

medium

A. Loop for h calculation should run till m-2, not m-1

B. Modulo prime missing in hash calculations

C. Using <= in loop causes out of range error

D. Base value should be 128, not 256

Solution

1. Step 1: Analyze h calculation loop

   h is used as base^(m-1) % prime, so loop should run m-1 times, i.e., i from 0 to m-2.

2. Step 2: Identify off-by-one error

   Loop runs from i=0 to i=m-1 (inclusive), which is m times, causing incorrect h value.

3. Final Answer:

   Loop for h calculation should run till m-2, not m-1 -> Option A

4. Quick Check:

   h loop runs m-1 times [OK]

Hint: Calculate h with loop running m-1 times only [OK]

Common Mistakes:

• Using <= instead of < in loop
• Forgetting modulo in hash updates
• Wrong base value choice

5. You want to find all occurrences of pattern "test" in a very large text using Rabin Karp. Which optimization helps reduce false positives caused by hash collisions?

hard

A. Use a larger prime number for modulo operations

B. Skip character comparison after hash match

C. Use base value as 1 to simplify hash

D. Only check first character after hash match

Solution

1. Step 1: Understand hash collisions in Rabin Karp

   Hash collisions occur when different substrings produce same hash value, causing false positives.

2. Step 2: Use larger prime to reduce collisions

   A larger prime number reduces chances of collisions by spreading hash values more evenly.

3. Final Answer:

   Use a larger prime number for modulo operations -> Option A

4. Quick Check:

   Larger prime = fewer collisions [OK]

Hint: Choose bigger prime to minimize hash collisions [OK]

Common Mistakes:

• Skipping character checks after hash match
• Using base 1 which weakens hash uniqueness
• Checking only first character after hash match