5 Best Ways to Program Counting Palindromes of Size K from String Characters in Python

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πŸ’‘ Problem Formulation: We aim to find the number of distinct palindromic substrings of size k that can be created using the characters of a given string. For example, given the string "aabb" and k = 2, the output should be 2 since we can form the palindromes "aa" and "bb".

Method 1: Using Hashing and Combinations

This method involves counting the frequency of each character in the string and then using the combinatorial formula to calculate the number of distinct palindromes. It’s suitable for small-to-medium size strings and is quite efficient, as the frequency distribution of characters is the main factor determining the number of possible palindromes.

Here’s an example:

from collections import Counter
from math import factorial

def count_k_palindromes(s, k):
    if k % 2 == 1 and len(s) < k: return 0

    freq = Counter(s)
    even_slots = k // 2
    odd_allowed = k % 2
    evens = sum(val // 2 for val in freq.values())
    
    if evens < even_slots: return 0
    
    comb = factorial(evens) // (factorial(even_slots) * factorial(evens - even_slots))
    return comb * (1 + odd_allowed * (len(freq) - 1))

print(count_k_palindromes("aabb", 2))

Output: 2

This code snippet defines a function count_k_palindromes that calculates the number of distinct palindromes that can be formed from the given string characters. It first checks the basic conditions, then calculates the combinations of the even pair slots available, and returns the total palindromes by considering the middle character for when k is odd.

Method 2: Recursion with Memoization

Recursive approach takes advantage of breaking down the problem into subproblems and solves it efficiently by memoization. It explores all possible combinations of characters to form palindromes, thus providing an exact result if the input string is not too large.

Here’s an example:

from collections import Counter

def count_palindrome_recursive(s, k, memo):
    if (s, k) in memo: return memo[(s, k)]
    if k == 0: return 1
    if not s or k > len(s): return 0
    
    count = sum(count_palindrome_recursive(s[:i] + s[i + 1:], k - 2, memo) for i, char in enumerate(s))
    memo[(s, k)] = count
    return count

def count_k_palindromes(s, k):
    return count_palindrome_recursive(s, k, {}) // 2

print(count_k_palindromes("aabb", 2))

Output: 2

This code snippet showcases a recursive function count_palindrome_recursive which is called by another function count_k_palindromes. It utilizes memoization to avoid redundant calculations, effectively improving performance on larger inputs. The final count is halved to adjust for the double count that occurs in the recursive process.

Method 3: Dynamic Programming Approach

The dynamic programming approach builds upon previously solved subproblems to find the number of palindromes. This method typically uses a 2D array to store results for substrings and palindromic counts, ensuring that each subproblem is only solved once, which can be efficient for larger strings.

Here’s an example:

def count_k_palindromes(s, k):
    if k == 0 or not s: return 0
    
    # Dynamic programming table where dp[i][j] represents the
    # number of palindromes of size j that can end at position i
    dp = [[0] * (k + 1) for _ in range(len(s))]
    
    for i in range(len(s)):
        dp[i][1] = 1  # Base case: every character is a palindrome of size 1
    
    for length in range(2, k + 1):
        for i in range(length - 1, len(s)):
            dp[i][length] = dp[i - 1][length] + dp[i - 1][length - 2]
            if s[i] == s[i - length + 1]:
                dp[i][length] += 1
                
    return sum(row[k] for row in dp) // 2

print(count_k_palindromes("aabb", 2))

Output: 2

The code defines a function count_k_palindromes using a dynamic programming table to accumulate the number of palindromes of size k at each character position. The final sum is halved since every palindrome is counted twice when its center is reached.

Method 4: Bitmasking and Permutation

Bitmasking combined with permutation enumeration is another method that can be used to solve this problem by encoding the character frequencies to bitwise representation. This is helpful to create unique combinations to form palindromes, though it may not scale well for very large character sets due to its combinatorial nature.

Here’s an example:

from itertools import permutations

def count_k_palindromes(s, k):
    if k % 2 == 1 and len(s) < k: return 0
    freq = Counter(s)
    bitmask = sum((1 << ord(char)) for char in freq for _ in range(freq[char] // 2))
    return len(set(permutations(bin(bitmask)[2:].zfill(k // 2), k // 2)))

print(count_k_palindromes("aabb", 2))

Output: 2

This piece of code employs bitmasking to convert character frequencies to a binary representation. Permutations of the resulting binary string are then used to generate unique palindromes, and a set is used to ensure that duplicate palindromes are not counted.

Bonus One-Liner Method 5: Using Set and Permutations

If you want a compact solution, a one-liner involving a set and permutations from the itertools library can be used. This method may be of interest due to its brevity, but it’s not recommended for large strings or large values of k because it can be quite inefficient.

Here’s an example:

from itertools import permutations

def count_k_palindromes(s, k):
    return len({"".join(p) for p in permutations(s, k) if p == p[::-1]})

print(count_k_palindromes("aabb", 2))

Output: 2

This code utilizes list comprehension to create a set of all permutations of the string s that are palindromes of size k, and returns the length of this set as the count of unique palindromes.

Summary/Discussion

  • Method 1: Hashing and Combinations. Efficient with a clear computational logic. Not suitable for very long strings due to the factorial operation.
  • Method 2: Recursion with Memoization. Accurate and can handle moderate sized inputs. May be slow and not as efficient for large input sizes due to recursion overhead.
  • Method 3: Dynamic Programming. Great for longer strings, but uses more memory due to the dynamic programming table.
  • Method 4: Bitmasking and Permutation. Novel and interesting, but scales poorly for large inputs and diverse character sets.
  • Bonus Method 5: Using Set and Permutations. A one-liner solution that provides a quick and dirty way to get results but has poor scalability and efficiency.