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//! # kdtree //! K-dimensional tree for Rust(bucket point-region implementation) //! //! ## Usage //! ``` //! use kdtree::KdTree; //! use kdtree::ErrorKind; //! use kdtree::distance::squared_euclidean; //! //! let a: ([f64; 2], usize) = ([0f64, 0f64], 0); //! let b: ([f64; 2], usize) = ([1f64, 1f64], 1); //! let c: ([f64; 2], usize) = ([2f64, 2f64], 2); //! let d: ([f64; 2], usize) = ([3f64, 3f64], 3); //! //! let dimensions = 2; //! let mut kdtree = KdTree::new(dimensions); //! //! kdtree.add(&a.0, a.1).unwrap(); //! kdtree.add(&b.0, b.1).unwrap(); //! kdtree.add(&c.0, c.1).unwrap(); //! kdtree.add(&d.0, d.1).unwrap(); //! //! assert_eq!(kdtree.size(), 4); //! assert_eq!( //! kdtree.nearest(&a.0, 0, &squared_euclidean).unwrap(), //! vec![] //! ); //! assert_eq!( //! kdtree.nearest(&a.0, 1, &squared_euclidean).unwrap(), //! vec![(0f64, &0)] //! ); //! assert_eq!( //! kdtree.nearest(&a.0, 2, &squared_euclidean).unwrap(), //! vec![(0f64, &0), (2f64, &1)] //! ); //! assert_eq!( //! kdtree.nearest(&a.0, 3, &squared_euclidean).unwrap(), //! vec![(0f64, &0), (2f64, &1), (8f64, &2)] //! ); //! assert_eq!( //! kdtree.nearest(&a.0, 4, &squared_euclidean).unwrap(), //! vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)] //! ); //! assert_eq!( //! kdtree.nearest(&a.0, 5, &squared_euclidean).unwrap(), //! vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)] //! ); //! assert_eq!( //! kdtree.nearest(&b.0, 4, &squared_euclidean).unwrap(), //! vec![(0f64, &1), (2f64, &0), (2f64, &2), (8f64, &3)] //! ); //! ``` pub mod kdtree; pub mod distance; mod heap_element; mod util; pub use kdtree::KdTree; pub use kdtree::ErrorKind;