Implement cosine similarity function in Python

machine_learning/cosine_similarity.py

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+import math
+
+def cosine_similarity(vector_a: list[float], vector_b: list[float]) -> float:
+    """
+    Finds the cosine similarity between two multi-dimensional vectors.
+    The result ranges from -1 (exactly opposite) to 1 (exactly the same).
+
+    https://en.wikipedia.org/wiki/Cosine_similarity
+
+    >>> cosine_similarity([1, 2, 3], [1, 2, 3])
+    1.0
+    >>> cosine_similarity([1, 0], [0, 1])
+    0.0
+    >>> cosine_similarity([1, 2, 3], [-1, -2, -3])
+    -1.0
+    """
+    if len(vector_a) != len(vector_b):
+        raise ValueError("Vectors must be of the same length")
+
+    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("Cannot compute similarity with a zero-vector")
+
+    return dot_product / (magnitude_a * magnitude_b)
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()