scirs2_spatial/distance/
euclideandistance_traits.rs

1//! # EuclideanDistance - Trait Implementations
2//!
3//! This module contains trait implementations for `EuclideanDistance`.
4//!
5//! ## Implemented Traits
6//!
7//! - `Default`
8//! - `Distance`
9//!
10//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
11
12use scirs2_core::numeric::Float;
13
14use super::functions::prefetch_read;
15use super::functions::Distance;
16use super::types::EuclideanDistance;
17
18impl<T: Float> Default for EuclideanDistance<T> {
19    fn default() -> Self {
20        Self::new()
21    }
22}
23
24impl<T: Float + Send + Sync> Distance<T> for EuclideanDistance<T> {
25    fn distance(&self, a: &[T], b: &[T]) -> T {
26        if a.len() != b.len() {
27            return T::nan();
28        }
29        if let Some(result) = Self::try_simd_f64(a, b) {
30            return result;
31        }
32        if let Some(result) = Self::try_simd_f32(a, b) {
33            return result;
34        }
35        let len = a.len();
36        let mut sum = T::zero();
37        let chunks = len / 4;
38        #[allow(clippy::needless_range_loop)]
39        for i in 0..chunks {
40            let base = i * 4;
41            if base + 8 < len {
42                let end_idx = (base + 8).min(len);
43                prefetch_read(&a[base + 4..end_idx]);
44                prefetch_read(&b[base + 4..end_idx]);
45                if base + 16 < len {
46                    let far_end = (base + 16).min(len);
47                    prefetch_read(&a[base + 8..far_end]);
48                    prefetch_read(&b[base + 8..far_end]);
49                }
50            }
51            let diff0 = a[base] - b[base];
52            let diff1 = a[base + 1] - b[base + 1];
53            let diff2 = a[base + 2] - b[base + 2];
54            let diff3 = a[base + 3] - b[base + 3];
55            let sq0 = diff0 * diff0;
56            let sq1 = diff1 * diff1;
57            let sq2 = diff2 * diff2;
58            let sq3 = diff3 * diff3;
59            let pair_sum0 = sq0 + sq1;
60            let pair_sum1 = sq2 + sq3;
61            let chunk_sum = pair_sum0 + pair_sum1;
62            sum = sum + chunk_sum;
63        }
64        for i in (chunks * 4)..len {
65            let diff = a[i] - b[i];
66            sum = sum + diff * diff;
67        }
68        sum.sqrt()
69    }
70    fn min_distance_point_rectangle(&self, point: &[T], mins: &[T], maxes: &[T]) -> T {
71        let mut sum = T::zero();
72        for i in 0..point.len() {
73            let coord = point[i];
74            let min_val = mins[i];
75            let max_val = maxes[i];
76            let clamped = coord.max(min_val).min(max_val);
77            let diff = coord - clamped;
78            sum = sum + diff * diff;
79        }
80        sum.sqrt()
81    }
82}
scirs2_spatial/distance/euclideandistance_traits.rs

scirs2_spatial/distance/
euclideandistance_traits.rs
