Skip to content

Added cosine similarity #7001

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Oct 29, 2022
Merged

Added cosine similarity #7001

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f9b013e
Added cosine similarity
KushagraMakharia Oct 11, 2022
ba68fd8
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 11, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 17 additions & 0 deletions machine_learning/similarity_search.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
import math

import numpy as np
from numpy.linalg import norm


def euclidean(input_a: np.ndarray, input_b: np.ndarray) -> float:
Expand Down Expand Up @@ -135,6 +136,22 @@ def similarity_search(
return answer


def cosine_similarity(input_a: np.ndarray, input_b: np.ndarray) -> float:
"""
Calculates cosine similarity between two data.
:param input_a: ndarray of first vector.
:param input_b: ndarray of second vector.
:return: Cosine similarity of input_a and input_b. By using math.sqrt(),
result will be float.

>>> cosine_similarity(np.array([1]), np.array([1]))
1.0
>>> cosine_similarity(np.array([1, 2]), np.array([6, 32]))
0.9615239476408232
"""
return np.dot(input_a, input_b) / (norm(input_a) * norm(input_b))


if __name__ == "__main__":
import doctest

Expand Down