Module generic_algorithms 

Generic implementations of spatial algorithms

This module provides generic implementations of common spatial algorithms that can work with different numeric types and point representations. These algorithms leverage the traits defined in the generic_traits module to provide flexible, reusable implementations.

Features

• Generic KD-Tree: Works with any SpatialPoint implementation
• Generic distance calculations: Support for different metrics and types
• Generic convex hull: Templated hull algorithms
• Generic clustering: K-means and other clustering algorithms
• Type safety: Compile-time dimension and type checking where possible

Examples

use scirs2_spatial::generic_algorithms::{GenericKDTree, GenericDistanceMatrix};
use scirs2_spatial::generic_traits::{Point, EuclideanMetric};

// Create points with different numeric types
let points_f32 = vec![
    Point::new_2d(1.0f32, 2.0f32),
    Point::new_2d(3.0f32, 4.0f32),
];

let points_f64 = vec![
    Point::new_2d(1.0f64, 2.0f64),
    Point::new_2d(3.0f64, 4.0f64),
];

// Both work with the same algorithm
let kdtree_f32 = GenericKDTree::new(&points_f32);
let kdtree_f64 = GenericKDTree::new(&points_f64);

Structs

DBSCANResult

Result of DBSCAN clustering

GMMResult

Result of GMM fitting

GenericConvexHull

Generic convex hull computation using Graham scan

GenericDBSCAN

Generic DBSCAN clustering implementation

GenericDistanceMatrix

Generic distance matrix computation with SIMD optimizations

GenericGMM

Generic Gaussian Mixture Model clustering

GenericKDTree

Generic KD-Tree implementation with memory optimizations

GenericKMeans

Generic K-means clustering implementation

KMeansResult

Result of K-means clustering