diskann-utils 0.50.1

/*
 * Copyright (c) Microsoft Corporation.
 * Licensed under the MIT license.
 */

use rand::{rngs::StdRng, Rng};
use rand_distr::StandardNormal;

pub trait RoundFromf32 {
    fn round_from_f32(x: f32) -> Self;
}

impl RoundFromf32 for f32 {
    fn round_from_f32(x: f32) -> Self {
        x
    }
}
impl RoundFromf32 for i8 {
    fn round_from_f32(x: f32) -> Self {
        x.round() as i8
    }
}
impl RoundFromf32 for u8 {
    fn round_from_f32(x: f32) -> Self {
        x.round() as u8
    }
}
impl RoundFromf32 for half::f16 {
    fn round_from_f32(x: f32) -> Self {
        half::f16::from_f32(x)
    }
}

pub trait WithApproximateNorm: Sized {
    fn with_approximate_norm(dim: usize, norm: f32, rng: &mut StdRng) -> Vec<Self>;
}

impl WithApproximateNorm for f32 {
    fn with_approximate_norm(dim: usize, norm: f32, rng: &mut StdRng) -> Vec<Self> {
        generate_random_vector_with_norm_signed(dim, norm, true, rng, |x: f32| x)
    }
}

impl WithApproximateNorm for half::f16 {
    fn with_approximate_norm(dim: usize, norm: f32, rng: &mut StdRng) -> Vec<Self> {
        // Small QOL improvement, `diskann_wide::cast_f32_to_f16` works under `Miri` while `half::f16::from_f32`
        // does not.
        generate_random_vector_with_norm_signed(dim, norm, true, rng, diskann_wide::cast_f32_to_f16)
    }
}

impl WithApproximateNorm for u8 {
    fn with_approximate_norm(dim: usize, norm: f32, rng: &mut StdRng) -> Vec<Self> {
        generate_random_vector_with_norm_signed(dim, norm, false, rng, |x| x as u8)
    }
}

impl WithApproximateNorm for i8 {
    fn with_approximate_norm(dim: usize, norm: f32, rng: &mut StdRng) -> Vec<Self> {
        generate_random_vector_with_norm_signed(dim, norm, true, rng, |x| x as i8)
    }
}

// This function uses StandardNormal distribution. StandardNormal creates uniformly
// distributed points on sphere surface, making the graph easier to navigate.
fn generate_random_vector_with_norm_signed<T, F>(
    dim: usize,
    norm: f32,
    signed: bool,
    rng: &mut StdRng,
    f: F,
) -> Vec<T>
where
    F: Fn(f32) -> T,
{
    let mut vec: Vec<f32> = (0..dim).map(|_| rng.sample(StandardNormal)).collect();
    let current_norm: f32 = vec.iter().map(|x| x * x).sum::<f32>().sqrt();
    let scale = norm / current_norm;
    if signed {
        vec.iter_mut().for_each(|x| *x *= scale);
    } else {
        vec.iter_mut().for_each(|x| *x = (*x * scale).abs());
    };
    vec.into_iter().map(f).collect()
}

#[cfg(test)]
mod tests {
    use super::*;
    use rand::SeedableRng;
    use rstest::rstest;

    #[rstest]
    #[case(1, 0.01)]
    #[case(100, 0.01)]
    #[case(171, 5.0)]
    #[case(1024, 100.7)]
    fn test_generate_random_vector_with_norm_f32(#[case] dim: usize, #[case] norm: f32) {
        let seed = 42;
        let mut rng = StdRng::seed_from_u64(seed);
        let vec: Vec<f32> = WithApproximateNorm::with_approximate_norm(dim, norm, &mut rng);
        let computed_norm: f32 = vec.iter().map(|x| x * x).sum::<f32>().sqrt();
        let tolerance = 1e-5;
        assert!((computed_norm - norm).abs() / norm < tolerance);
    }

    #[rstest]
    #[case(1, 0.01)]
    #[case(100, 0.01)]
    #[case(171, 5.0)]
    #[case(1024, 100.7)]
    fn test_generate_random_vector_with_norm_half_f16(#[case] dim: usize, #[case] norm: f32) {
        let seed = 42;
        let mut rng = StdRng::seed_from_u64(seed);
        let vec: Vec<half::f16> = WithApproximateNorm::with_approximate_norm(dim, norm, &mut rng);
        let computed_norm: f32 = vec
            .iter()
            .map(|x| {
                let val: f32 = x.to_f32();
                val * val
            })
            .sum::<f32>()
            .sqrt();
        let tolerance = 1e-2; // half precision
        assert!((computed_norm - norm).abs() / norm < tolerance);
    }

    #[rstest]
    #[case(17, 50.0)]
    #[case(1024, 1007.0)]
    fn test_generate_random_vector_with_norm_u8(#[case] dim: usize, #[case] norm: f32) {
        let seed = 42;
        let mut rng = StdRng::seed_from_u64(seed);
        let vec: Vec<u8> = WithApproximateNorm::with_approximate_norm(dim, norm, &mut rng);
        let computed_norm: f32 = vec
            .iter()
            .map(|&x| {
                let val: f32 = x as f32;
                val * val
            })
            .sum::<f32>()
            .sqrt();
        let tolerance = 1e-1; // due to quantization
        assert!((computed_norm - norm).abs() / norm < tolerance);
    }

    #[rstest]
    #[case(17, 50.0)]
    #[case(1024, 1007.0)]
    fn test_generate_random_vector_with_norm_i8(#[case] dim: usize, #[case] norm: f32) {
        let seed = 42;
        let mut rng = StdRng::seed_from_u64(seed);
        let vec: Vec<i8> = WithApproximateNorm::with_approximate_norm(dim, norm, &mut rng);
        let computed_norm: f32 = vec
            .iter()
            .map(|&x| {
                let val: f32 = x as f32;
                val * val
            })
            .sum::<f32>()
            .sqrt();
        let tolerance = 1e-1; // due to quantization
        assert!((computed_norm - norm).abs() / norm < tolerance);
    }

    #[rstest]
    #[case(3.6f32, 4i8)]
    #[case(2.3f32, 2i8)]
    #[case(-1.5f32, -2i8)]
    fn test_round_f32_to_i8(#[case] input: f32, #[case] expected: i8) {
        let result: i8 = RoundFromf32::round_from_f32(input);
        assert_eq!(result, expected);
    }

    #[rstest]
    #[case(3.6f32, 4u8)]
    #[case(2.3f32, 2u8)]
    #[case(-1.5f32, 0u8)]
    fn test_round_f32_to_u8(#[case] input: f32, #[case] expected: u8) {
        let result: u8 = RoundFromf32::round_from_f32(input);
        assert_eq!(result, expected);
    }

    #[rstest]
    #[case(3.6f32, half::f16::from_f32(3.6f32))]
    #[case(2.3f32, half::f16::from_f32(2.3f32))]
    #[case(-1.5f32, half::f16::from_f32(-1.5f32))]
    fn test_round_f32_to_f16(#[case] input: f32, #[case] expected: half::f16) {
        let result: half::f16 = RoundFromf32::round_from_f32(input);
        assert_eq!(result, expected);
    }

    #[rstest]
    #[case(3.6f32, 3.6f32)]
    #[case(2.3f32, 2.3f32)]
    #[case(-1.5f32, -1.5f32)]
    fn test_round_f32_to_f32(#[case] input: f32, #[case] expected: f32) {
        let result: f32 = RoundFromf32::round_from_f32(input);
        assert_eq!(result, expected);
    }

    /// Test that generated points are evenly distributed on a circle.
    ///
    /// **Testing methodology:**
    /// 1. Split the circle into 36 buckets (signed) or 9 buckets (unsigned), each covering 10 degrees
    /// 2. Generate points and count how many fall into each angular bucket
    /// 3. Check that each bucket's count is within `tolerance_sigmas × σ` of the expected count,
    ///    where σ = sqrt(expected) is the statistical noise for random sampling
    /// 4. Fail if any bucket deviates too much (indicates clustering instead of uniform distribution)
    ///
    /// **Tolerance levels:**
    ///   - tolerance_sigmas = 1.0 → Very strict, only allows ±1σ deviation (about 68% of buckets would naturally fall within this)
    ///   - tolerance_sigmas = 3.0 → Moderate, allows ±3σ deviation (99.7% would naturally fall within this)
    ///   - tolerance_sigmas = 6.0 → Very lenient, allows ±6σ deviation (99.9997% would naturally fall within this)
    #[rstest]
    #[case(true, 500, 3.0, 42)]
    #[case(true, 500, 3.0, 43)]
    #[case(true, 500, 3.0, 44)]
    #[case(false, 500, 3.0, 42)]
    #[case(false, 500, 3.0, 43)]
    #[case(false, 500, 3.0, 44)]
    fn test_generate_random_vector_with_norm_signed_produces_uniform_distribution_on_circle(
        #[case] signed: bool,
        #[case] expected_per_bucket: usize,
        #[case] tolerance_sigmas: f32,
        #[case] seed: u64,
    ) {
        let dim = 2;
        let norm = 1.0;
        let mut rng = StdRng::seed_from_u64(seed);

        // Step 1: Pick number of buckets and calculate samples
        let num_buckets = if signed { 36 } else { 9 };
        let num_samples = num_buckets * expected_per_bucket;

        // Generate samples
        let samples: Vec<Vec<f32>> = (0..num_samples)
            .map(|_| generate_random_vector_with_norm_signed(dim, norm, signed, &mut rng, |x| x))
            .collect();

        // Step 2: Count hits per bucket
        let mut counts = vec![0usize; num_buckets];

        for sample in &samples {
            let theta = sample[1].atan2(sample[0]); // atan2(y, x) returns [-π, π]

            // Map to bucket: floor(θ / 2π × buckets)
            let bucket = if signed {
                // Full circle [0, 2π) → [0, 36)
                let normalized_theta = if theta < 0.0 {
                    theta + 2.0 * std::f32::consts::PI
                } else {
                    theta
                };
                ((normalized_theta / (2.0 * std::f32::consts::PI)) * num_buckets as f32).floor()
                    as usize
                    % num_buckets
            } else {
                // First quadrant [0, π/2) → [0, 9)
                ((theta / (std::f32::consts::PI / 2.0)) * num_buckets as f32).floor() as usize
            };

            counts[bucket] += 1;
        }

        // Step 3: Check each bucket is within tolerance_sigmas × σ
        // Noise per bucket: σ ≈ sqrt(expected)
        // Threshold: |observed - expected| / expected > tolerance_sigmas / sqrt(expected)
        let sigma = (expected_per_bucket as f32).sqrt();
        let threshold = tolerance_sigmas / sigma;

        let failed_count = counts
            .iter()
            .filter(|&&observed| {
                let deviation = (observed as f32 - expected_per_bucket as f32).abs()
                    / expected_per_bucket as f32;
                deviation > threshold
            })
            .count();

        assert_eq!(
            failed_count,
            0,
            "Distribution not uniform: {} out of {} bucket(s) had point counts that deviated more than {}σ from expected. \
             This indicates the generator is producing clustered points instead of evenly distributed points on the circle surface.",
            failed_count,
            num_buckets,
            tolerance_sigmas
        );
    }
}