[Solved] The following Python code tries to compute cosine similarity but gives an error. What is the main issue? import numpy as np A = np.array([1, 2, 3]) B = np.array([4, 5]) cos_sim = np.dot(A, B) / (np.... | NLP

NLP - Text Similarity and Search

The following Python code tries to compute cosine similarity but gives an error. What is the main issue?

import numpy as np
A = np.array([1, 2, 3])
B = np.array([4, 5])
cos_sim = np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B))
print(cos_sim)

Anp.linalg.norm is used incorrectly

BVectors A and B have different lengths causing dot product error

CDivision by zero error

DMissing import statement for numpy

Step-by-Step Solution

Solution:

Step 1: Check vector sizes
Vector A has length 3, vector B has length 2, so dot product is invalid.
Step 2: Understand dot product requirements
Dot product requires vectors of same length; mismatch causes error.
Final Answer:
Vectors A and B have different lengths causing dot product error -> Option B
Quick Check:
Dot product needs equal length vectors [OK]

Quick Trick: Dot product needs vectors of same length [OK]

Common Mistakes:

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The following Python code tries to compute cosine similarity but gives an error. What is the main issue?