Efficient Cosine Similarity | Dataford Interview Questions

Implement a function to calculate cosine similarity for high-dimensional embeddings efficiently. The function receives two equal-length numeric lists and returns their cosine similarity as a float. Return 0.0 if either vector has zero magnitude. Use the signature cosine_similarity(a, b). For example, [1, 0] and [0, 1] return 0.0, while [1, 2] and [1, 2] return 1.0. Inputs contain between 1 and 2,000 finite numeric values.

Constraints

1 <= len(a) = len(b) <= 2000
Each element is a finite integer or floating-point number
The input vectors are non-empty and have equal length
Return 0.0 when either vector has zero magnitude

Function Signature

def cosine_similarity(a, b):

Your question is Efficient Cosine Similarity. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem

Constraints

1 <= len(a) = len(b) <= 2000
Each element is a finite integer or floating-point number
The input vectors are non-empty and have equal length
Return 0.0 when either vector has zero magnitude

Function Signature

def cosine_similarity(a, b):

Your question is Efficient Cosine Similarity. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.