Vector Similarity From Scratch | Dataford Interview Questions

Implement a vector similarity function from scratch, focusing on computational efficiency.

Use cosine similarity for two dense numeric vectors. Implement vector_similarity(a, b), returning a float in [-1.0, 1.0]; return 0.0 if either vector has zero magnitude. Inputs have equal nonzero lengths, and you may use standard-library arithmetic but no numerical or machine-learning libraries.

Constraints

1 <= len(a) = len(b) <= 100000
Each vector element is a finite integer or floating-point value
At least one vector may be all zeros
Do not use NumPy, SciPy, scikit-learn, or other numerical libraries

Function Signature

def vector_similarity(a, b):

Your question is Vector Similarity From Scratch. 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

Implement a vector similarity function from scratch, focusing on computational efficiency.

Constraints

1 <= len(a) = len(b) <= 100000
Each vector element is a finite integer or floating-point value
At least one vector may be all zeros
Do not use NumPy, SciPy, scikit-learn, or other numerical libraries

Function Signature

def vector_similarity(a, b):

Your question is Vector Similarity From Scratch. 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.