Add cosine similarity algorithm

[anandmt]

Description

Added cosine_similarity() to maths/ — a fundamental similarity metric missing from the collection.

The repository has Jaccard similarity, Euclidean distance, Manhattan distance, Minkowski distance, and Chebyshev distance, but no cosine similarity. This is one of the most widely used similarity metrics in machine learning, NLP, information retrieval, and recommendation systems.

Implementation

• Pure Python implementation with no external dependencies
• Calculates (A · B) / (||A|| × ||B||) using dot product and L2 norms
• Full type hints (list[float] input, float output)
• Comprehensive doctests:
  • Identical vectors → 1.0
  • Opposite vectors → -1.0
  • Orthogonal vectors → 0.0
  • Arbitrary vectors with verified results
  • Error cases: empty vectors, dimension mismatch, zero magnitude
• Follows existing code style from jaccard_similarity.py

Reference

https://en.wikipedia.org/wiki/Cosine_similarity

[algorithms-keeper]

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