From 9ce077c0c567d43396e08e27fcf5f42834ee3e3a Mon Sep 17 00:00:00 2001
From: Anand Mani Tiwari <24646755+anandmt@users.noreply.github.com>
Date: Thu, 28 May 2026 12:47:08 +0400
Subject: [PATCH] Add cosine similarity algorithm

---
 maths/cosine_similarity.py | 79 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 maths/cosine_similarity.py

diff --git a/maths/cosine_similarity.py b/maths/cosine_similarity.py
new file mode 100644
index 000000000000..ab4ed0542288
--- /dev/null
+++ b/maths/cosine_similarity.py
@@ -0,0 +1,79 @@
+"""
+Cosine similarity is a metric used to measure how similar two vectors are,
+regardless of their magnitude. It calculates the cosine of the angle between
+two vectors projected in a multi-dimensional space.
+
+Widely used in information retrieval, text mining, and recommendation systems.
+In NLP and AI, cosine similarity is fundamental for comparing document embeddings,
+word vectors, and semantic similarity.
+
+Reference: https://en.wikipedia.org/wiki/Cosine_similarity
+
+Cosine similarity = (A · B) / (||A|| * ||B||)
+
+Where:
+    A · B is the dot product of vectors A and B
+    ||A|| is the magnitude (L2 norm) of vector A
+    ||B|| is the magnitude (L2 norm) of vector B
+"""
+
+import math
+
+
+def cosine_similarity(vector_a: list[float], vector_b: list[float]) -> float:
+    """
+    Calculates the cosine similarity between two vectors.
+
+    Parameters:
+        vector_a: A non-empty numerical vector.
+        vector_b: A non-empty numerical vector of the same dimension as vector_a.
+
+    Returns:
+        The cosine similarity between the two vectors, a float in [-1, 1].
+        1 means identical direction, 0 means orthogonal, -1 means opposite.
+
+    >>> cosine_similarity([1, 2, 3], [1, 2, 3])
+    1.0
+    >>> cosine_similarity([1, 2, 3], [-1, -2, -3])
+    -1.0
+    >>> cosine_similarity([1, 0, 0], [0, 1, 0])
+    0.0
+    >>> cosine_similarity([1, 1], [1, 1])
+    1.0
+    >>> round(cosine_similarity([1, 2, 3], [4, 5, 6]), 6)
+    0.974632
+    >>> round(cosine_similarity([3, 45, 7, 2], [2, 54, 13, 15]), 6)
+    0.972583
+    >>> cosine_similarity([], [1, 2])
+    Traceback (most recent call last):
+        ...
+    ValueError: Vectors must not be empty.
+    >>> cosine_similarity([1, 2], [1, 2, 3])
+    Traceback (most recent call last):
+        ...
+    ValueError: Vectors must have the same dimension.
+    >>> cosine_similarity([0, 0, 0], [1, 2, 3])
+    Traceback (most recent call last):
+        ...
+    ValueError: At least one vector has zero magnitude.
+    """
+    if not vector_a or not vector_b:
+        raise ValueError("Vectors must not be empty.")
+
+    if len(vector_a) != len(vector_b):
+        raise ValueError("Vectors must have the same dimension.")
+
+    dot_product = sum(a * b for a, b in zip(vector_a, vector_b))
+    magnitude_a = math.sqrt(sum(a * a for a in vector_a))
+    magnitude_b = math.sqrt(sum(b * b for b in vector_b))
+
+    if magnitude_a == 0 or magnitude_b == 0:
+        raise ValueError("At least one vector has zero magnitude.")
+
+    return dot_product / (magnitude_a * magnitude_b)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()