Smith-Waterman Python implementation

smith_waterman.py

import numpy as np

def smith_waterman(a: str, b: str, alignment_score: float = 1, gap_cost: float = 1) -> float:
  """
  Compute the Smith-Waterman alignment score for two strings.

  See https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm#Algorithm
  This implementation has a fixed gap cost (i.e. extending a gap is considered
  free). In the terminology of the Wikipedia description, W_k = {c, c, c, ...}.
  This implementation also has a fixed alignment score, awarded if the relevant
  characters are equal.

  Kinda slow, especially for large (50+ char) inputs.
  """
  # H holds the alignment score at each point, computed incrementally
  H = np.zeros((len(a) + 1, len(b) + 1))
  for i in range(1, len(a) + 1):
    for j in range(1, len(b) + 1):
      # The score for substituting the letter a[i-1] for b[j-1]. Generally low
      # for mismatch, high for match.
      match = H[i-1,j-1] + (alignment_score if a[i-1] == b[j-1] else 0)
      # The scores for for introducing extra letters in one of the strings (or
      # by symmetry, deleting them from the other).
      delete = H[1:i,j].max() - gap_cost if i > 1 else 0
      insert = H[i,1:j].max() - gap_cost if j > 1 else 0
      H[i,j] = max(match, delete, insert, 0)
  # The highest score is the best local alignment.
  # For our purposes, we don't actually care _what_ the alignment was, just how
  # aligned the two strings were.
  return H.max()

thanks for this. helped me a lot with my project of implementing a method to prevent malicious name confusion.