motedb 0.1.1

AI-native embedded multimodal database for embodied intelligence (robots, AR glasses, industrial arms).
Documentation 
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//! Distance metrics for vector similarity computation
//!
//! Optimized distance functions for embedded environments.

pub mod euclidean;
pub mod cosine;

pub use euclidean::euclidean_distance;
pub use cosine::{cosine_distance, cosine_similarity};

/// Distance metric trait
pub trait DistanceMetric: Send + Sync {
    /// Compute distance between two vectors
    fn distance(&self, a: &[f32], b: &[f32]) -> f32;
}

/// Euclidean distance metric
#[derive(Debug, Clone, Copy)]
pub struct Euclidean;

impl DistanceMetric for Euclidean {
    #[inline]
    fn distance(&self, a: &[f32], b: &[f32]) -> f32 {
        euclidean_distance(a, b)
    }
}

/// Cosine distance metric (1 - cosine_similarity)
#[derive(Debug, Clone, Copy)]
pub struct Cosine;

impl DistanceMetric for Cosine {
    #[inline]
    fn distance(&self, a: &[f32], b: &[f32]) -> f32 {
        cosine_distance(a, b)
    }
}

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

    #[test]
    fn test_euclidean_metric() {
        let metric = Euclidean;
        let a = vec![1.0, 2.0, 3.0];
        let b = vec![4.0, 5.0, 6.0];
        let dist = metric.distance(&a, &b);
        assert!((dist - 5.196152).abs() < 0.001);
    }

    #[test]
    fn test_cosine_metric() {
        let metric = Cosine;
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![1.0, 0.0, 0.0];
        let dist = metric.distance(&a, &b);
        assert!(dist < 0.01); // Same vector should have ~0 distance
    }
}