la: add vector cosine similarity function and test

[suleyman-kaya]

This PR adds a new function vector_cosine_similarity to calculate the cosine similarity between two vectors, along with corresponding tests.

The new function is documented and implemented as follows:

// vector_cosine_similarity calculates the cosine similarity between two vectors
pub fn vector_cosine_similarity(a []f64, b []f64) f64 {
    if a.len != b.len {
        errors.vsl_panic('Vectors must have the same length', .efailed)
    }

    dot_product := vector_dot(a, b)
    norm_a := vector_norm(a)
    norm_b := vector_norm(b)

    if norm_a == 0 || norm_b == 0 {
        return 0
    }

    return dot_product / (norm_a * norm_b)
}

Tests have been added to verify the correctness of the new function:

fn test_vector_cosine_similarity() {
    a := [1.0, 2.0, 3.0]
    b := [4.0, 5.0, 6.0]
    c := [1.0, 0.0, 0.0]
    d := [0.0, 1.0, 0.0]

    // Cosine similarity of a and b (should be close to 0.974631846)
    assert float64.tolerance(vector_cosine_similarity(a, b), 0.974631846, 1e-8)

    // Cosine similarity of a with itself (should be exactly 1)
    assert float64.close(vector_cosine_similarity(a, a), 1.0)

    // Cosine similarity of perpendicular vectors (should be 0)
    assert float64.close(vector_cosine_similarity(c, d), 0.0)

    // Cosine similarity of a zero vector with another vector (should be 0)
    zero_vector := [0.0, 0.0, 0.0]
    assert float64.close(vector_cosine_similarity(zero_vector, a), 0.0)
}

This new function enhances the vector operations capabilities of the la module by providing a way to measure the similarity between two vectors, which is useful in various applications such as text analysis, recommendation systems, and machine learning.

Summary by CodeRabbit

• New Features

  • Introduced a new function for calculating cosine similarity between two vectors, enhancing mathematical and machine learning operations.

• Tests

  • Added a comprehensive test suite for the cosine similarity function to ensure accurate calculations across various scenarios.

[coderabbitai]

Walkthrough

The updates introduce a new function, vector_cosine_similarity, to the la module, designed to compute the cosine similarity between two f64 vectors. It includes checks for vector length and norm conditions to prevent errors. Additionally, a corresponding test function, test_vector_cosine_similarity, has been added to ensure the accuracy of the new function through various test cases.

Changes

Files	Change Summary
la/vector.v	Added pub fn vector_cosine_similarity(a []f64, b []f64) f64 for cosine similarity calculation.
la/vector_test.v	Introduced test_vector_cosine_similarity to validate the functionality of vector_cosine_similarity.

Poem

🐰 In a world of numbers, so bright and fair,
Cosine similarity dances in the air.
With vectors aligned, we measure the way,
A hop and a skip, oh what a grand play!
Accuracy assured, our tests bring delight,
Celebrating changes, everything feels right! 🌟

---

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Actionable comments posted: 0

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between bdb5c39 and a4f23cb.

Files selected for processing (2)

• la/vector.v (2 hunks)
• la/vector_test.v (1 hunks)

Additional comments not posted (6)

la/vector_test.v (4)

80-81: Verify the expected result for cosine similarity.

The expected result for the cosine similarity between vectors a and b is checked against 0.974631846 with a tolerance of 1e-8. Ensure that this expected value is accurate.

---

83-84: LGTM! Cosine similarity of a vector with itself.

The assertion correctly checks that the cosine similarity of a vector with itself is 1.

---

86-87: LGTM! Cosine similarity of perpendicular vectors.

The assertion correctly checks that the cosine similarity of perpendicular vectors is 0.

---

89-91: LGTM! Cosine similarity involving a zero vector.

The assertion correctly checks that the cosine similarity of a zero vector with another vector is 0.

la/vector.v (2)

87-88: Ensure the panic message is informative.

The panic message "Vectors must have the same length" is clear and informative. Ensure that the error handling aligns with the rest of the codebase.

---

85-99: LGTM! Cosine similarity calculation is correctly implemented.

The function correctly calculates the cosine similarity, handling cases where vector norms are zero to avoid division by zero.

[suleyman-kaya]

@ulises-jeremias