[Solved] Identify the bug in this Levenshtein distance initialization code snippet: def lev_dist(s, t): dp = [[0]*(len(t)) for _ in range(len(s))] for i in range(len(s)): dp[i][0] = i for j in range(len(t)):... | NLP

NLP - Text Similarity and Search

Identify the bug in this Levenshtein distance initialization code snippet:

def lev_dist(s, t):
    dp = [[0]*(len(t)) for _ in range(len(s))]
    for i in range(len(s)):
        dp[i][0] = i
    for j in range(len(t)):
        dp[0][j] = j
    # rest omitted

AThe dp matrix size should be (len(s)+1) x (len(t)+1) to include empty prefixes

BThe loops initializing dp should start from 1 instead of 0

CThe dp matrix should be initialized with ones instead of zeros

DThe code incorrectly assigns dp[0][0] twice

Step-by-Step Solution

Solution:

Step 1: Check dp matrix size
It is initialized as len(s) x len(t), but it must be (len(s)+1) x (len(t)+1) to handle empty string cases.
Step 2: Understand initialization loops
Loops correctly assign first row and column, but indices go out of range if matrix is smaller.
Step 3: Analyze options
The dp matrix size should be (len(s)+1) x (len(t)+1) to include empty prefixes correctly identifies the size bug. Others are incorrect or irrelevant.
Final Answer:
Option A -> Option A
Quick Check:
Matrix must include empty string row/column. [OK]

Quick Trick: Matrix size must be lengths + 1 for empty prefixes [OK]

Common Mistakes:

MISTAKES

Not adding +1 to matrix dimensions
Starting loops at wrong indices

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Identify the bug in this Levenshtein distance initialization code snippet:

Step 1: Check dp matrix size

Step 2: Understand initialization loops

Step 3: Analyze options

Final Answer:

Quick Check:

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