| <!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Source of the Rust file `/root/.cargo/git/checkouts/incubator-teaclave-crates-c8106113f74feefc/ede1f68/rusty-machine/src/learning/knn/binary_tree.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>binary_tree.rs - source</title><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceSerif4-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../FiraSans-Regular.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../FiraSans-Medium.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceCodePro-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceSerif4-Bold.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceCodePro-Semibold.ttf.woff2"><link rel="stylesheet" href="../../../../normalize.css"><link rel="stylesheet" href="../../../../rustdoc.css" id="mainThemeStyle"><link rel="stylesheet" href="../../../../ayu.css" disabled><link rel="stylesheet" href="../../../../dark.css" disabled><link rel="stylesheet" href="../../../../light.css" id="themeStyle"><script id="default-settings" ></script><script src="../../../../storage.js"></script><script defer src="../../../../source-script.js"></script><script defer src="../../../../source-files.js"></script><script defer src="../../../../main.js"></script><noscript><link rel="stylesheet" href="../../../../noscript.css"></noscript><link rel="alternate icon" type="image/png" href="../../../../favicon-16x16.png"><link rel="alternate icon" type="image/png" href="../../../../favicon-32x32.png"><link rel="icon" type="image/svg+xml" href="../../../../favicon.svg"></head><body class="rustdoc source"><!--[if lte IE 11]><div class="warning">This old browser is unsupported and will most likely display funky things.</div><![endif]--><nav class="sidebar"><a class="sidebar-logo" href="../../../../rusty_machine/index.html"><div class="logo-container"><img class="rust-logo" src="../../../../rust-logo.svg" alt="logo"></div></a></nav><main><div class="width-limiter"><nav class="sub"><a class="sub-logo-container" href="../../../../rusty_machine/index.html"><img class="rust-logo" src="../../../../rust-logo.svg" alt="logo"></a><form class="search-form"><div class="search-container"><span></span><input class="search-input" name="search" autocomplete="off" spellcheck="false" placeholder="Click or press ‘S’ to search, ‘?’ for more options…" type="search"><div id="help-button" title="help" tabindex="-1"><a href="../../../../help.html">?</a></div><div id="settings-menu" tabindex="-1"><a href="../../../../settings.html" title="settings"><img width="22" height="22" alt="Change settings" src="../../../../wheel.svg"></a></div></div></form></nav><section id="main-content" class="content"><div class="example-wrap"><pre class="src-line-numbers"><span id="1">1</span> |
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| <span id="838">838</span> |
| </pre><pre class="rust"><code><span class="doccomment">//! Binary Tree implementations |
| </span><span class="kw">use </span>std::borrow::Borrow; |
| <span class="kw">use </span>std::collections::VecDeque; |
| |
| <span class="kw">use </span>linalg::{Matrix, BaseMatrix, Vector}; |
| <span class="kw">use </span>learning::error::Error; |
| |
| <span class="kw">use super</span>::{KNearest, KNearestSearch, get_distances, dist}; |
| |
| <span class="doccomment">/// Binary tree |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>BinaryTree<B: BinarySplit> { |
| <span class="comment">// Binary tree leaf size |
| </span>leafsize: usize, |
| <span class="comment">// Search data |
| </span>data: <span class="prelude-ty">Option</span><Matrix<f64>>, |
| <span class="comment">// Binary tree |
| </span>root: <span class="prelude-ty">Option</span><Node<B>> |
| } |
| |
| <span class="kw">impl</span><B: BinarySplit> Default <span class="kw">for </span>BinaryTree<B> { |
| <span class="doccomment">/// Constructs default binary-tree (kd-tree or ball-tree) seach. |
| /// Each leaf contains 30 elements at maximum. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::knn::{KDTree, BallTree}; |
| /// let _ = KDTree::default(); |
| /// let _ = BallTree::default(); |
| /// ``` |
| </span><span class="kw">fn </span>default() -> <span class="self">Self </span>{ |
| BinaryTree { |
| leafsize: <span class="number">30</span>, |
| data: <span class="prelude-val">None</span>, |
| root: <span class="prelude-val">None |
| </span>} |
| } |
| } |
| |
| <span class="doccomment">/// Binary splittable |
| </span><span class="kw">pub trait </span>BinarySplit: Sized { |
| |
| <span class="doccomment">/// Build branch from passed args |
| </span><span class="kw">fn </span>build(data: <span class="kw-2">&</span>Matrix<f64>, remains: Vec<usize>, |
| dim: usize, split: f64, min: Vector<f64>, max: Vector<f64>, |
| left: Node<<span class="self">Self</span>>, right: Node<<span class="self">Self</span>>) |
| -> Node<<span class="self">Self</span>>; |
| |
| <span class="doccomment">/// Return a tuple of left and right node. First node is likely to be |
| /// closer to the point |
| </span><span class="kw">unsafe fn </span>maybe_close<<span class="lifetime">'s</span>, <span class="lifetime">'p</span>>(<span class="kw-2">&</span><span class="lifetime">'s </span><span class="self">self</span>, point: <span class="kw-2">&</span><span class="lifetime">'p </span>[f64]) |
| -> (<span class="kw-2">&</span><span class="lifetime">'s </span>Node<<span class="self">Self</span>>, <span class="kw-2">&</span><span class="lifetime">'s </span>Node<<span class="self">Self</span>>); |
| |
| <span class="doccomment">/// Return distance between the point and myself |
| </span><span class="kw">fn </span>dist(<span class="kw-2">&</span><span class="self">self</span>, point: <span class="kw-2">&</span>[f64]) -> f64; |
| |
| <span class="doccomment">/// Return left node |
| </span><span class="kw">fn </span>left(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="kw-2">&</span>Node<<span class="self">Self</span>>; |
| <span class="doccomment">/// Return right node |
| </span><span class="kw">fn </span>right(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="kw-2">&</span>Node<<span class="self">Self</span>>; |
| } |
| |
| <span class="doccomment">/// Kd-tree branch |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>KDTreeBranch { |
| <span class="doccomment">/// dimension (column) to split |
| </span>dim: usize, |
| <span class="doccomment">/// split value |
| </span>split: f64, |
| |
| <span class="doccomment">/// min and max of bounding box |
| /// i.e. hyper-rectangle contained in the branch |
| </span>min: Vector<f64>, |
| max: Vector<f64>, |
| |
| <span class="comment">// link to left / right node |
| // - left node contains rows which the column specified with |
| // ``dim`` is less than ``split`` value. |
| // - right node contains greater than or equal to ``split`` value |
| </span>left: Box<Node<KDTreeBranch>>, |
| right: Box<Node<KDTreeBranch>>, |
| } |
| |
| <span class="doccomment">/// Ball-tree branch |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>BallTreeBranch { |
| <span class="doccomment">/// dimension (column) to split |
| </span>dim: usize, |
| <span class="doccomment">/// split value |
| </span>split: f64, |
| |
| <span class="doccomment">/// ball centroid and its radius |
| </span>center: Vector<f64>, |
| radius: f64, |
| |
| <span class="comment">// link to left / right node, see KDTreeBranch comment |
| </span>left: Box<Node<BallTreeBranch>>, |
| right: Box<Node<BallTreeBranch>>, |
| } |
| |
| <span class="doccomment">/// Kd-tree implementation |
| </span><span class="kw">pub type </span>KDTree = BinaryTree<KDTreeBranch>; |
| |
| <span class="doccomment">/// Ball-tree implementation |
| </span><span class="kw">pub type </span>BallTree = BinaryTree<BallTreeBranch>; |
| |
| <span class="kw">impl </span>BinarySplit <span class="kw">for </span>KDTreeBranch { |
| |
| <span class="kw">fn </span>build(<span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: Vec<usize>, |
| dim: usize, split: f64, min: Vector<f64>, max: Vector<f64>, |
| left: Node<<span class="self">Self</span>>, right: Node<<span class="self">Self</span>>) -> Node<<span class="self">Self</span>> { |
| |
| <span class="kw">let </span>b = KDTreeBranch { |
| dim: dim, |
| split: split, |
| min: min, |
| max: max, |
| left: Box::new(left), |
| right: Box::new(right) |
| }; |
| Node::Branch(b) |
| } |
| |
| <span class="kw">unsafe fn </span>maybe_close<<span class="lifetime">'s</span>, <span class="lifetime">'p</span>>(<span class="kw-2">&</span><span class="lifetime">'s </span><span class="self">self</span>, point: <span class="kw-2">&</span><span class="lifetime">'p </span>[f64]) |
| -> (<span class="kw-2">&</span><span class="lifetime">'s </span>Node<<span class="self">Self</span>>, <span class="kw-2">&</span><span class="lifetime">'s </span>Node<<span class="self">Self</span>>) { |
| |
| <span class="kw">if </span><span class="kw-2">*</span>point.get_unchecked(<span class="self">self</span>.dim) < <span class="self">self</span>.split { |
| (<span class="kw-2">&</span><span class="self">self</span>.left, <span class="kw-2">&</span><span class="self">self</span>.right) |
| } <span class="kw">else </span>{ |
| (<span class="kw-2">&</span><span class="self">self</span>.right, <span class="kw-2">&</span><span class="self">self</span>.left) |
| } |
| } |
| |
| <span class="kw">fn </span>dist(<span class="kw-2">&</span><span class="self">self</span>, point: <span class="kw-2">&</span>[f64]) -> f64 { |
| <span class="kw">let </span><span class="kw-2">mut </span>d = <span class="number">0.</span>; |
| <span class="kw">for </span>((<span class="kw-2">&</span>p, <span class="kw-2">&</span>mi), <span class="kw-2">&</span>ma) <span class="kw">in </span>point.iter() |
| .zip(<span class="self">self</span>.min.iter()) |
| .zip(<span class="self">self</span>.max.iter()) { |
| <span class="kw">if </span>p < mi { |
| d += (mi - p) * (mi - p); |
| } <span class="kw">else if </span>ma < p { |
| d += (ma - p) * (ma - p); |
| } |
| <span class="comment">// otherwise included in the hyper-rectangle |
| </span>} |
| d.sqrt() |
| } |
| |
| <span class="kw">fn </span>left(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="kw-2">&</span>Node<<span class="self">Self</span>> { |
| <span class="self">self</span>.left.borrow() |
| } |
| |
| <span class="kw">fn </span>right(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="kw-2">&</span>Node<<span class="self">Self</span>> { |
| <span class="self">self</span>.right.borrow() |
| } |
| } |
| |
| <span class="kw">impl </span>BinarySplit <span class="kw">for </span>BallTreeBranch { |
| |
| <span class="kw">fn </span>build(data: <span class="kw-2">&</span>Matrix<f64>, remains: Vec<usize>, |
| dim: usize, split: f64, <span class="kw">_</span>: Vector<f64>, <span class="kw">_</span>: Vector<f64>, |
| left: Node<<span class="self">Self</span>>, right: Node<<span class="self">Self</span>>) -> Node<<span class="self">Self</span>> { |
| |
| <span class="comment">// calculate centroid (mean) |
| // TODO: cleanup using .row() |
| </span><span class="kw">let </span><span class="kw-2">mut </span>center: Vec<f64> = <span class="macro">vec!</span>[<span class="number">0.</span>; data.cols()]; |
| <span class="kw">for </span><span class="kw-2">&</span>i <span class="kw">in </span><span class="kw-2">&</span>remains { |
| <span class="kw">let </span>row: Vec<f64> = data.select_rows(<span class="kw-2">&</span>[i]).into_vec(); |
| <span class="kw">for </span>(c, r) <span class="kw">in </span>center.iter_mut().zip(row.iter()) { |
| <span class="kw-2">*</span>c += <span class="kw-2">*</span>r; |
| } |
| } |
| <span class="kw">let </span>len = remains.len() <span class="kw">as </span>f64; |
| <span class="kw">for </span>c <span class="kw">in </span><span class="kw-2">&mut </span>center { |
| <span class="kw-2">*</span>c /= len; |
| } |
| <span class="kw">let </span><span class="kw-2">mut </span>radius = <span class="number">0.</span>; |
| <span class="kw">for </span><span class="kw-2">&</span>i <span class="kw">in </span><span class="kw-2">&</span>remains { |
| <span class="kw">let </span>row: Vec<f64> = data.select_rows(<span class="kw-2">&</span>[i]).into_vec(); |
| <span class="kw">let </span>d = dist(<span class="kw-2">&</span>center, <span class="kw-2">&</span>row); |
| <span class="kw">if </span>d > radius { |
| radius = d; |
| } |
| } |
| |
| <span class="kw">let </span>b = BallTreeBranch { |
| dim: dim, |
| split: split, |
| center: Vector::new(center), |
| radius: radius, |
| left: Box::new(left), |
| right: Box::new(right) |
| }; |
| Node::Branch(b) |
| } |
| |
| <span class="kw">unsafe fn </span>maybe_close<<span class="lifetime">'s</span>, <span class="lifetime">'p</span>>(<span class="kw-2">&</span><span class="lifetime">'s </span><span class="self">self</span>, point: <span class="kw-2">&</span><span class="lifetime">'p </span>[f64]) |
| -> (<span class="kw-2">&</span><span class="lifetime">'s </span>Node<<span class="self">Self</span>>, <span class="kw-2">&</span><span class="lifetime">'s </span>Node<<span class="self">Self</span>>) { |
| |
| <span class="kw">if </span><span class="kw-2">*</span>point.get_unchecked(<span class="self">self</span>.dim) < <span class="self">self</span>.split { |
| (<span class="kw-2">&</span><span class="self">self</span>.left, <span class="kw-2">&</span><span class="self">self</span>.right) |
| } <span class="kw">else </span>{ |
| (<span class="kw-2">&</span><span class="self">self</span>.right, <span class="kw-2">&</span><span class="self">self</span>.left) |
| } |
| } |
| |
| <span class="kw">fn </span>dist(<span class="kw-2">&</span><span class="self">self</span>, point: <span class="kw-2">&</span>[f64]) -> f64 { |
| <span class="kw">let </span>d = dist(<span class="self">self</span>.center.data(), point); |
| <span class="kw">if </span>d < <span class="self">self</span>.radius { |
| <span class="number">0. |
| </span>} <span class="kw">else </span>{ |
| d - <span class="self">self</span>.radius |
| } |
| } |
| |
| <span class="kw">fn </span>left(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="kw-2">&</span>Node<<span class="self">Self</span>> { |
| <span class="self">self</span>.left.borrow() |
| } |
| |
| <span class="kw">fn </span>right(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="kw-2">&</span>Node<<span class="self">Self</span>> { |
| <span class="self">self</span>.right.borrow() |
| } |
| } |
| |
| <span class="doccomment">/// Binary tree node (either branch or leaf) |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub enum </span>Node<B: BinarySplit> { |
| <span class="doccomment">/// Binary tree branch |
| </span>Branch(B), |
| <span class="doccomment">/// Binary tree leaf |
| </span>Leaf(Leaf) |
| } |
| |
| <span class="doccomment">/// Binary tree leaf |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>Leaf { |
| children: Vec<usize> |
| } |
| |
| <span class="kw">impl </span>Leaf { |
| <span class="kw">fn </span>new(children: Vec<usize>) -> <span class="self">Self </span>{ |
| Leaf { |
| children: children |
| } |
| } |
| } |
| |
| <span class="kw">impl</span><B: BinarySplit> BinaryTree<B> { |
| |
| <span class="doccomment">/// Constructs binary-tree (kd-tree or ball-tree) seach. |
| /// Specify leafsize which is maximum number to be contained in each leaf. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::knn::{KDTree, BallTree}; |
| /// let _ = KDTree::new(10); |
| /// let _ = BallTree::new(50); |
| /// ``` |
| </span><span class="kw">pub fn </span>new(leafsize: usize) -> <span class="self">Self </span>{ |
| BinaryTree { |
| leafsize: leafsize, |
| data: <span class="prelude-val">None</span>, |
| root: <span class="prelude-val">None |
| </span>} |
| } |
| |
| <span class="doccomment">/// Select next split dimension and value. Returns tuple with 6 elements |
| /// - split dim |
| /// - split value |
| /// - remains for left node |
| /// - remains for right node |
| /// - updated max for left node |
| /// - updated min for right node |
| </span><span class="kw">fn </span>select_split(<span class="kw-2">&</span><span class="self">self</span>, data: <span class="kw-2">&</span>Matrix<f64>, <span class="kw-2">mut </span>remains: Vec<usize>, |
| <span class="kw-2">mut </span>dmin: Vector<f64>, <span class="kw-2">mut </span>dmax: Vector<f64>) |
| -> (usize, f64, Vec<usize>, Vec<usize>, Vector<f64>, Vector<f64>){ |
| |
| <span class="comment">// avoid recursive call |
| </span><span class="kw">loop </span>{ |
| <span class="comment">// split columns which has the widest range |
| </span><span class="kw">let </span>(dim, d) = (<span class="kw-2">&</span>dmax - <span class="kw-2">&</span>dmin).argmax(); |
| |
| <span class="comment">// Use midpoint rule, see "On the Efficiency of Nearest Neighbor Searching |
| // with Data Clustered in Lower Dimensions (Maneewongvatan and Mount, 1999)" |
| // ToDo: use unsafe get (v0.4.0?) |
| // https://github.com/AtheMathmo/rulinalg/pull/104 |
| </span><span class="kw">let </span>split = <span class="kw">unsafe </span>{ |
| dmin.data().get_unchecked(dim) + d / <span class="number">2.0 |
| </span>}; |
| |
| <span class="comment">// split remains |
| </span><span class="kw">let </span><span class="kw-2">mut </span>l_remains: Vec<usize> = Vec::with_capacity(remains.len()); |
| <span class="kw">let </span><span class="kw-2">mut </span>r_remains: Vec<usize> = Vec::with_capacity(remains.len()); |
| <span class="kw">unsafe </span>{ |
| <span class="kw">for </span>r <span class="kw">in </span>remains { |
| <span class="kw">if </span><span class="kw-2">*</span>data.get_unchecked([r, dim]) < split { |
| l_remains.push(r); |
| } <span class="kw">else </span>{ |
| r_remains.push(r); |
| } |
| } |
| } |
| r_remains.shrink_to_fit(); |
| l_remains.shrink_to_fit(); |
| |
| <span class="kw">if </span>l_remains.is_empty() { |
| <span class="comment">// all rows are in r_remains. re-split r_remains |
| </span>remains = r_remains; |
| dmin[dim] = split; |
| } <span class="kw">else if </span>r_remains.is_empty() { |
| <span class="comment">// all rows are in l_remains. re-split l_remains |
| </span>remains = l_remains; |
| dmax[dim] = split; |
| } <span class="kw">else </span>{ |
| <span class="comment">// new hyper-rectangle's min / max |
| </span><span class="kw">let </span><span class="kw-2">mut </span>l_max = dmax.clone(); |
| <span class="comment">// ToDo: use unsafe mut (v0.4.0?) |
| // https://github.com/AtheMathmo/rulinalg/pull/104 |
| </span>l_max[dim] = split; |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>r_min = dmin.clone(); |
| r_min[dim] = split; |
| |
| <span class="kw">return </span>(dim, split, l_remains, r_remains, l_max, r_min); |
| } |
| }; |
| } |
| |
| <span class="doccomment">/// find next binary split |
| </span><span class="kw">fn </span>split(<span class="kw-2">&</span><span class="self">self</span>, data: <span class="kw-2">&</span>Matrix<f64>, remains: Vec<usize>, |
| dmin: Vector<f64>, dmax: Vector<f64>) -> Node<B> { |
| |
| <span class="kw">if </span>remains.len() < <span class="self">self</span>.leafsize { |
| Node::Leaf(Leaf::new(remains)) |
| } <span class="kw">else </span>{ |
| |
| <span class="comment">// ToDo: avoid this clone |
| </span><span class="kw">let </span>(dim, split, l_remains, r_remains, l_max, r_min) = |
| <span class="self">self</span>.select_split(data, remains.clone(), dmin.clone(), dmax.clone()); |
| |
| <span class="kw">let </span>l_node = <span class="self">self</span>.split(data, l_remains, dmin.clone(), l_max); |
| <span class="kw">let </span>g_node = <span class="self">self</span>.split(data, r_remains, r_min, dmax.clone()); |
| B::build(data, remains, dim, split, dmin, dmax, l_node, g_node) |
| } |
| } |
| |
| <span class="doccomment">/// find leaf contains search point |
| </span><span class="kw">fn </span>search_leaf<<span class="lifetime">'s</span>, <span class="lifetime">'p</span>>(<span class="kw-2">&</span><span class="lifetime">'s </span><span class="self">self</span>, point: <span class="kw-2">&</span><span class="lifetime">'p </span>[f64], k: usize) |
| -> <span class="prelude-ty">Result</span><(KNearest, VecDeque<<span class="kw-2">&</span><span class="lifetime">'s </span>Node<B>>), Error> { |
| |
| <span class="kw">if let </span>(<span class="kw-2">&</span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>root), <span class="kw-2">&</span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>data)) = (<span class="kw-2">&</span><span class="self">self</span>.root, <span class="kw-2">&</span><span class="self">self</span>.data) { |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>queue: VecDeque<<span class="kw-2">&</span>Node<B>> = VecDeque::new(); |
| queue.push_front(root); |
| |
| <span class="kw">loop </span>{ |
| <span class="comment">// pop first element |
| </span><span class="kw">let </span>current: <span class="kw-2">&</span>Node<B> = queue.pop_front().unwrap(); |
| <span class="kw">match </span><span class="kw-2">*</span>current { |
| Node::Leaf(<span class="kw-2">ref </span>l) => { |
| <span class="kw">let </span>distances = get_distances(data, point, <span class="kw-2">&</span>l.children); |
| <span class="kw">let </span>kn = KNearest::new(k, l.children.clone(), distances); |
| <span class="kw">return </span><span class="prelude-val">Ok</span>((kn, queue)); |
| }, |
| Node::Branch(<span class="kw-2">ref </span>b) => { |
| <span class="comment">// the current branch must contains target point. |
| // store the child branch which contains target point to |
| // the front, put other side on the back. |
| </span><span class="kw">let </span>(close, far) = <span class="kw">unsafe </span>{ |
| b.maybe_close(point) |
| }; |
| queue.push_front(close); |
| queue.push_back(far); |
| } |
| } |
| } |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Err</span>(Error::new_untrained()) |
| } |
| } |
| } |
| |
| <span class="doccomment">/// Can search k-nearest items |
| </span><span class="kw">impl</span><B: BinarySplit> KNearestSearch <span class="kw">for </span>BinaryTree<B> { |
| |
| <span class="doccomment">/// build data structure for search optimization |
| </span><span class="kw">fn </span>build(<span class="kw-2">&mut </span><span class="self">self</span>, data: Matrix<f64>) { |
| <span class="kw">let </span>remains: Vec<usize> = (<span class="number">0</span>..data.rows()).collect(); |
| <span class="kw">let </span>dmin = min(<span class="kw-2">&</span>data); |
| <span class="kw">let </span>dmax = max(<span class="kw-2">&</span>data); |
| <span class="self">self</span>.root = <span class="prelude-val">Some</span>(<span class="self">self</span>.split(<span class="kw-2">&</span>data, remains, dmin, dmax)); |
| <span class="self">self</span>.data = <span class="prelude-val">Some</span>(data); |
| } |
| |
| <span class="doccomment">/// Serch k-nearest items close to the point |
| </span><span class="kw">fn </span>search(<span class="kw-2">&</span><span class="self">self</span>, point: <span class="kw-2">&</span>[f64], k: usize) -> <span class="prelude-ty">Result</span><(Vec<usize>, Vec<f64>), Error> { |
| <span class="kw">if let </span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>data) = <span class="self">self</span>.data { |
| <span class="kw">let </span>(<span class="kw-2">mut </span>query, <span class="kw-2">mut </span>queue) = <span class="self">self</span>.search_leaf(point, k)<span class="question-mark">?</span>; |
| <span class="kw">while </span>!queue.is_empty() { |
| <span class="kw">let </span>current = queue.pop_front().unwrap(); |
| |
| <span class="kw">match </span><span class="kw-2">*</span>current { |
| Node::Leaf(<span class="kw-2">ref </span>l) => { |
| <span class="kw">let </span>distances = get_distances(data, point, <span class="kw-2">&</span>l.children); |
| <span class="kw">let </span><span class="kw-2">mut </span>current_dist = query.dist(); |
| |
| <span class="kw">for </span>(<span class="kw-2">&</span>i, d) <span class="kw">in </span>l.children.iter().zip(distances.into_iter()) { |
| <span class="kw">if </span>d < current_dist { |
| current_dist = query.add(i, d); |
| } |
| } |
| }, |
| Node::Branch(<span class="kw-2">ref </span>b) => { |
| <span class="kw">let </span>d = b.dist(point); |
| <span class="kw">if </span>d < query.dist() { |
| queue.push_back(b.left()); |
| queue.push_back(b.right()); |
| } |
| } |
| } |
| } |
| <span class="prelude-val">Ok</span>(query.get_results()) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Err</span>(Error::new_untrained()) |
| } |
| } |
| } |
| |
| |
| <span class="doccomment">/// min |
| </span><span class="kw">fn </span>min(data: <span class="kw-2">&</span>Matrix<f64>) -> Vector<f64> { |
| <span class="comment">// ToDo: use rulinalg .min (v0.4.1?) |
| // https://github.com/AtheMathmo/rulinalg/pull/115 |
| </span><span class="kw">let </span><span class="kw-2">mut </span>results = Vec::with_capacity(data.cols()); |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..data.cols() { |
| results.push(data[[<span class="number">0</span>, i]]); |
| } |
| <span class="kw">for </span>row <span class="kw">in </span>data.row_iter() { |
| <span class="kw">for </span>(r, v) <span class="kw">in </span>results.iter_mut().zip(row.iter()) { |
| <span class="kw">if </span><span class="kw-2">*</span>r > <span class="kw-2">*</span>v { |
| <span class="kw-2">*</span>r = <span class="kw-2">*</span>v; |
| } |
| } |
| } |
| Vector::new(results) |
| } |
| |
| <span class="doccomment">/// max |
| </span><span class="kw">fn </span>max(data: <span class="kw-2">&</span>Matrix<f64>) -> Vector<f64> { |
| <span class="comment">// ToDo: use rulinalg .max (v0.4.1?) |
| // https://github.com/AtheMathmo/rulinalg/pull/115 |
| </span><span class="kw">let </span><span class="kw-2">mut </span>results = Vec::with_capacity(data.cols()); |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..data.cols() { |
| results.push(data[[<span class="number">0</span>, i]]); |
| } |
| <span class="kw">for </span>row <span class="kw">in </span>data.row_iter() { |
| <span class="kw">for </span>(r, v) <span class="kw">in </span>results.iter_mut().zip(row.iter()) { |
| <span class="kw">if </span><span class="kw-2">*</span>r < <span class="kw-2">*</span>v { |
| <span class="kw-2">*</span>r = <span class="kw-2">*</span>v; |
| } |
| } |
| } |
| Vector::new(results) |
| } |
| |
| <span class="attribute">#[cfg(test)] |
| </span><span class="kw">mod </span>tests { |
| |
| <span class="kw">use </span>linalg::{Vector, Matrix, BaseMatrix}; |
| <span class="kw">use </span><span class="kw">super</span>::<span class="kw">super</span>::KNearestSearch; |
| <span class="kw">use super</span>::{KDTree, BallTree, min, max}; |
| |
| <span class="kw">use super</span>::{Node, BinarySplit, Leaf}; |
| |
| <span class="comment">// return node's leaf reference, for testing purpose |
| </span><span class="kw">fn </span>as_leaf<B: BinarySplit>(n: <span class="kw-2">&</span>Node<B>) -> <span class="kw-2">&</span>Leaf { |
| <span class="kw">match </span>n { |
| <span class="kw-2">&</span>Node::Leaf(<span class="kw-2">ref </span>leaf) => leaf, |
| <span class="kw">_ </span>=> <span class="macro">panic!</span>(<span class="string">"Node is not leaf"</span>) |
| } |
| } |
| |
| <span class="comment">// return node's branch reference, for testing purpose |
| </span><span class="kw">fn </span>as_branch<B: BinarySplit>(n: <span class="kw-2">&</span>Node<B>) -> <span class="kw-2">&</span>B { |
| <span class="kw">match </span>n { |
| <span class="kw-2">&</span>Node::Branch(<span class="kw-2">ref </span>branch) => branch, |
| <span class="kw">_ </span>=> <span class="macro">panic!</span>(<span class="string">"Node is not branch"</span>) |
| } |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_kdtree_construct() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">5</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">2.</span>, |
| <span class="number">8.</span>, <span class="number">0.</span>, |
| <span class="number">6.</span>, <span class="number">10.</span>, |
| <span class="number">3.</span>, <span class="number">6.</span>, |
| <span class="number">0.</span>, <span class="number">3.</span>]); |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = KDTree::new(<span class="number">3</span>); |
| tree.build(m); |
| |
| <span class="comment">// split to [0, 1, 4] and [2, 3] with columns #1 |
| </span><span class="kw">let </span>root = tree.root.unwrap(); |
| <span class="kw">let </span>b = as_branch(<span class="kw-2">&</span>root); |
| <span class="macro">assert_eq!</span>(b.dim, <span class="number">1</span>); |
| <span class="macro">assert_eq!</span>(b.split, <span class="number">5.</span>); |
| <span class="macro">assert_eq!</span>(b.min, Vector::new(<span class="macro">vec!</span>[<span class="number">0.</span>, <span class="number">0.</span>])); |
| <span class="macro">assert_eq!</span>(b.max, Vector::new(<span class="macro">vec!</span>[<span class="number">8.</span>, <span class="number">10.</span>])); |
| |
| <span class="comment">// split to [0, 4] and [1] with columns #0 |
| </span><span class="kw">let </span>bl = as_branch(b.left()); |
| <span class="kw">let </span>br = as_leaf(b.right()); |
| <span class="macro">assert_eq!</span>(bl.dim, <span class="number">0</span>); |
| <span class="macro">assert_eq!</span>(bl.split, <span class="number">4.</span>); |
| <span class="macro">assert_eq!</span>(bl.min, Vector::new(<span class="macro">vec!</span>[<span class="number">0.</span>, <span class="number">0.</span>])); |
| <span class="macro">assert_eq!</span>(bl.max, Vector::new(<span class="macro">vec!</span>[<span class="number">8.</span>, <span class="number">5.</span>])); |
| <span class="macro">assert_eq!</span>(br.children, <span class="macro">vec!</span>[<span class="number">2</span>, <span class="number">3</span>]); |
| |
| <span class="kw">let </span>bll = as_leaf(bl.left()); |
| <span class="kw">let </span>blr = as_leaf(bl.right()); |
| <span class="macro">assert_eq!</span>(bll.children, <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">4</span>]); |
| <span class="macro">assert_eq!</span>(blr.children, <span class="macro">vec!</span>[<span class="number">1</span>]); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_kdtree_search() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">5</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">2.</span>, |
| <span class="number">8.</span>, <span class="number">0.</span>, |
| <span class="number">6.</span>, <span class="number">10.</span>, |
| <span class="number">3.</span>, <span class="number">6.</span>, |
| <span class="number">0.</span>, <span class="number">3.</span>]); |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = KDTree::new(<span class="number">3</span>); |
| tree.build(m); |
| |
| <span class="comment">// search first leaf |
| </span><span class="kw">let </span>(kn, <span class="kw">_</span>) = tree.search_leaf(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(kn.pairs, <span class="macro">vec!</span>[(<span class="number">0</span>, (<span class="number">2.0f64 </span>* <span class="number">2.0f64 </span>+ <span class="number">2.9f64 </span>* <span class="number">2.9f64</span>).sqrt())]); |
| |
| <span class="comment">// search tree |
| </span><span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">3</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.0999999999999996</span>]); |
| |
| <span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">3</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">0</span>, <span class="number">4</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.0999999999999996</span>, <span class="number">3.5227829907617076</span>, <span class="number">3.551056180912941</span>]); |
| |
| <span class="comment">// search first leaf |
| </span><span class="kw">let </span>(kn, <span class="kw">_</span>) = tree.search_leaf(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">2</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(kn.pairs, <span class="macro">vec!</span>[(<span class="number">0</span>, (<span class="number">2.0f64 </span>* <span class="number">2.0f64 </span>+ <span class="number">2.9f64 </span>* <span class="number">2.9f64</span>).sqrt()), |
| (<span class="number">4</span>, (<span class="number">3.0f64 </span>* <span class="number">3.0f64 </span>+ (<span class="number">4.9f64 </span>- <span class="number">3.0f64</span>) * (<span class="number">4.9f64 </span>- <span class="number">3.0f64</span>)).sqrt())]); |
| <span class="comment">// search tree |
| </span><span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">2</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">0</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.0999999999999996</span>, <span class="number">3.5227829907617076</span>]); |
| } |
| |
| <span class="attribute">#[cfg(feature = <span class="string">"datasets"</span>)] |
| #[test] |
| </span><span class="kw">fn </span>test_kdtree_search_iris_2cols() { |
| <span class="kw">use </span><span class="kw">super</span>::<span class="kw">super</span>::<span class="kw">super</span>::<span class="kw">super</span>::datasets::iris; |
| |
| <span class="kw">let </span>dataset = iris::load(); |
| <span class="kw">let </span>data = dataset.data().select_cols(<span class="kw-2">&</span>[<span class="number">0</span>, <span class="number">1</span>]); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = KDTree::new(<span class="number">10</span>); |
| tree.build(data); |
| |
| <span class="comment">// search tree |
| </span><span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">5.8</span>, <span class="number">3.6</span>], <span class="number">4</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">18</span>, <span class="number">85</span>, <span class="number">36</span>, <span class="number">14</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">0.22360679774997858</span>, <span class="number">0.2828427124746193</span>, <span class="number">0.31622776601683783</span>, <span class="number">0.3999999999999999</span>]); |
| |
| <span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">7.0</span>, <span class="number">2.6</span>], <span class="number">4</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">76</span>, <span class="number">108</span>, <span class="number">102</span>, <span class="number">107</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">0.28284271247461895</span>, <span class="number">0.31622776601683783</span>, <span class="number">0.41231056256176585</span>, <span class="number">0.4242640687119283</span>]); |
| } |
| |
| <span class="attribute">#[cfg(feature = <span class="string">"datasets"</span>)] |
| #[test] |
| </span><span class="kw">fn </span>test_kdtree_search_iris() { |
| <span class="kw">use </span><span class="kw">super</span>::<span class="kw">super</span>::<span class="kw">super</span>::<span class="kw">super</span>::datasets::iris; |
| |
| <span class="kw">let </span>dataset = iris::load(); |
| <span class="kw">let </span>data = dataset.data(); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = KDTree::new(<span class="number">10</span>); |
| tree.build(data.clone()); |
| |
| <span class="comment">// search tree |
| </span><span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">5.8</span>, <span class="number">3.1</span>, <span class="number">3.8</span>, <span class="number">1.2</span>], <span class="number">8</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">64</span>, <span class="number">88</span>, <span class="number">82</span>, <span class="number">95</span>, <span class="number">99</span>, <span class="number">96</span>, <span class="number">71</span>, <span class="number">61</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">0.360555127546399</span>, <span class="number">0.3872983346207417</span>, <span class="number">0.41231056256176596</span>, |
| <span class="number">0.4242640687119288</span>, <span class="number">0.4472135954999579</span>, <span class="number">0.4690415759823433</span>, |
| <span class="number">0.4795831523312721</span>, <span class="number">0.5196152422706636</span>]); |
| |
| <span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">6.5</span>, <span class="number">3.5</span>, <span class="number">3.2</span>, <span class="number">1.3</span>], <span class="number">10</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">71</span>, <span class="number">64</span>, <span class="number">74</span>, <span class="number">82</span>, <span class="number">79</span>, <span class="number">61</span>, <span class="number">65</span>, <span class="number">97</span>, <span class="number">75</span>, <span class="number">51</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.1357816691600549</span>, <span class="number">1.1532562594670799</span>, <span class="number">1.2569805089976533</span>, |
| <span class="number">1.2767145334803702</span>, <span class="number">1.2767145334803702</span>, <span class="number">1.284523257866513</span>, |
| <span class="number">1.2845232578665131</span>, <span class="number">1.2884098726725122</span>, <span class="number">1.3076696830622023</span>, |
| <span class="number">1.352774925846868</span>]); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_kdtree_dim_selection() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">5</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">2.</span>, |
| <span class="number">3.</span>, <span class="number">0.</span>, |
| <span class="number">2.</span>, <span class="number">10.</span>, |
| <span class="number">3.</span>, <span class="number">6.</span>, |
| <span class="number">1.</span>, <span class="number">3.</span>]); |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = KDTree::new(<span class="number">3</span>); |
| tree.build(m); |
| |
| <span class="comment">// split to [0, 1, 4] and [2, 3] with columns #1 |
| </span><span class="kw">let </span>root = tree.root.unwrap(); |
| <span class="kw">let </span>b = as_branch(<span class="kw-2">&</span>root); |
| <span class="macro">assert_eq!</span>(b.dim, <span class="number">1</span>); |
| <span class="macro">assert_eq!</span>(b.split, <span class="number">5.</span>); |
| <span class="macro">assert_eq!</span>(b.min, Vector::new(<span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">0.</span>])); |
| <span class="macro">assert_eq!</span>(b.max, Vector::new(<span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">10.</span>])); |
| |
| <span class="comment">// split to [0, 1] and [4] with columns #1 |
| </span><span class="kw">let </span>bl = as_branch(b.left()); |
| <span class="macro">assert_eq!</span>(bl.dim, <span class="number">1</span>); |
| <span class="macro">assert_eq!</span>(bl.split, <span class="number">2.5</span>); |
| <span class="macro">assert_eq!</span>(bl.min, Vector::new(<span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">0.</span>])); |
| <span class="macro">assert_eq!</span>(bl.max, Vector::new(<span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">5.</span>])); |
| |
| <span class="kw">let </span>br = as_leaf(b.right()); |
| <span class="macro">assert_eq!</span>(br.children, <span class="macro">vec!</span>[<span class="number">2</span>, <span class="number">3</span>]); |
| |
| <span class="kw">let </span>bll = as_leaf(bl.left()); |
| <span class="kw">let </span>blr = as_leaf(bl.right()); |
| <span class="macro">assert_eq!</span>(bll.children, <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">1</span>]); |
| <span class="macro">assert_eq!</span>(blr.children, <span class="macro">vec!</span>[<span class="number">4</span>]); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_kdtree_dim_selection_biased() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">5</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">0.</span>, |
| <span class="number">3.</span>, <span class="number">0.</span>, |
| <span class="number">2.</span>, <span class="number">20.</span>, |
| <span class="number">3.</span>, <span class="number">0.</span>, |
| <span class="number">1.</span>, <span class="number">0.</span>]); |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = KDTree::new(<span class="number">3</span>); |
| tree.build(m); |
| |
| <span class="comment">// split to [0, 1, 3, 4] and [2] with columns #1 |
| </span><span class="kw">let </span>root = tree.root.unwrap(); |
| <span class="kw">let </span>b = as_branch(<span class="kw-2">&</span>root); |
| <span class="macro">assert_eq!</span>(b.dim, <span class="number">1</span>); |
| <span class="macro">assert_eq!</span>(b.split, <span class="number">10.</span>); |
| <span class="macro">assert_eq!</span>(b.min, Vector::new(<span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">0.</span>])); |
| <span class="macro">assert_eq!</span>(b.max, Vector::new(<span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">20.</span>])); |
| |
| <span class="comment">// split to [0, 4] and [1, 3] with columns #0 |
| </span><span class="kw">let </span>bl = as_branch(b.left()); |
| <span class="macro">assert_eq!</span>(bl.dim, <span class="number">0</span>); |
| <span class="macro">assert_eq!</span>(bl.split, <span class="number">2.</span>); |
| <span class="macro">assert_eq!</span>(bl.min, Vector::new(<span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">0.</span>])); |
| <span class="macro">assert_eq!</span>(bl.max, Vector::new(<span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">10.</span>])); |
| |
| <span class="kw">let </span>br = as_leaf(b.right()); |
| <span class="macro">assert_eq!</span>(br.children, <span class="macro">vec!</span>[<span class="number">2</span>]); |
| |
| <span class="kw">let </span>bll = as_leaf(bl.left()); |
| <span class="kw">let </span>blr = as_leaf(bl.right()); |
| <span class="macro">assert_eq!</span>(bll.children, <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">4</span>]); |
| <span class="macro">assert_eq!</span>(blr.children, <span class="macro">vec!</span>[<span class="number">1</span>, <span class="number">3</span>]); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_kdtree_untrained() { |
| <span class="kw">let </span>tree = KDTree::default(); |
| |
| <span class="kw">let </span>e = tree.search_leaf(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>); |
| <span class="macro">assert!</span>(e.is_err()); |
| |
| <span class="kw">let </span>e = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>); |
| <span class="macro">assert!</span>(e.is_err()); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_balltree_construct() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">5</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">2.</span>, |
| <span class="number">8.</span>, <span class="number">0.</span>, |
| <span class="number">6.</span>, <span class="number">10.</span>, |
| <span class="number">3.</span>, <span class="number">6.</span>, |
| <span class="number">0.</span>, <span class="number">3.</span>]); |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = BallTree::new(<span class="number">3</span>); |
| tree.build(m); |
| |
| <span class="comment">// split to [0, 1, 4] and [2, 3] with columns #1 |
| </span><span class="kw">let </span>root = tree.root.unwrap(); |
| <span class="kw">let </span>b = as_branch(<span class="kw-2">&</span>root); |
| <span class="macro">assert_eq!</span>(b.dim, <span class="number">1</span>); |
| <span class="macro">assert_eq!</span>(b.split, <span class="number">5.</span>); |
| <span class="macro">assert_eq!</span>(b.center, Vector::new(<span class="macro">vec!</span>[<span class="number">18. </span>/ <span class="number">5.</span>, <span class="number">21. </span>/ <span class="number">5.</span>])); |
| <span class="comment">// distance between the center and [2] |
| </span><span class="kw">let </span>exp_d: f64 = (<span class="number">6. </span>- <span class="number">3.6</span>) * (<span class="number">6. </span>- <span class="number">3.6</span>) + (<span class="number">10. </span>- <span class="number">4.2</span>) * (<span class="number">10. </span>- <span class="number">4.2</span>); |
| <span class="macro">assert_eq!</span>(b.radius, exp_d.sqrt()); |
| |
| <span class="comment">// split to [0, 4] and [1] with columns #0 |
| </span><span class="kw">let </span>bl = as_branch(b.left()); |
| <span class="kw">let </span>br = as_leaf(b.right()); |
| <span class="macro">assert_eq!</span>(bl.dim, <span class="number">0</span>); |
| <span class="macro">assert_eq!</span>(bl.split, <span class="number">4.</span>); |
| <span class="macro">assert_eq!</span>(bl.center, Vector::new(<span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">5. </span>/ <span class="number">3.</span>])); |
| <span class="comment">// distance between the center and [1] |
| </span><span class="kw">let </span>exp_d: f64 = (<span class="number">3. </span>- <span class="number">8.</span>) * (<span class="number">3. </span>- <span class="number">8.</span>) + <span class="number">5. </span>/ <span class="number">3. </span>* <span class="number">5. </span>/ <span class="number">3.</span>; |
| <span class="macro">assert_eq!</span>(bl.radius, exp_d.sqrt()); |
| <span class="macro">assert_eq!</span>(br.children, <span class="macro">vec!</span>[<span class="number">2</span>, <span class="number">3</span>]); |
| |
| <span class="kw">let </span>bll = as_leaf(bl.left()); |
| <span class="kw">let </span>blr = as_leaf(bl.right()); |
| <span class="macro">assert_eq!</span>(bll.children, <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">4</span>]); |
| <span class="macro">assert_eq!</span>(blr.children, <span class="macro">vec!</span>[<span class="number">1</span>]); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_balltree_search() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">5</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">2.</span>, |
| <span class="number">8.</span>, <span class="number">0.</span>, |
| <span class="number">6.</span>, <span class="number">10.</span>, |
| <span class="number">3.</span>, <span class="number">6.</span>, |
| <span class="number">0.</span>, <span class="number">3.</span>]); |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = BallTree::new(<span class="number">3</span>); |
| tree.build(m); |
| |
| <span class="comment">// search first leaf |
| </span><span class="kw">let </span>(kn, <span class="kw">_</span>) = tree.search_leaf(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(kn.pairs, <span class="macro">vec!</span>[(<span class="number">0</span>, (<span class="number">2.0f64 </span>* <span class="number">2.0f64 </span>+ <span class="number">2.9f64 </span>* <span class="number">2.9f64</span>).sqrt())]); |
| |
| <span class="comment">// search tree |
| </span><span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">3</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.0999999999999996</span>]); |
| |
| <span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">3</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">0</span>, <span class="number">4</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.0999999999999996</span>, <span class="number">3.5227829907617076</span>, <span class="number">3.551056180912941</span>]); |
| |
| <span class="comment">// search first leaf |
| </span><span class="kw">let </span>(kn, <span class="kw">_</span>) = tree.search_leaf(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">2</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(kn.pairs, <span class="macro">vec!</span>[(<span class="number">0</span>, (<span class="number">2.0f64 </span>* <span class="number">2.0f64 </span>+ <span class="number">2.9f64 </span>* <span class="number">2.9f64</span>).sqrt()), |
| (<span class="number">4</span>, (<span class="number">3.0f64 </span>* <span class="number">3.0f64 </span>+ (<span class="number">4.9f64 </span>- <span class="number">3.0f64</span>) * (<span class="number">4.9f64 </span>- <span class="number">3.0f64</span>)).sqrt())]); |
| <span class="comment">// search tree |
| </span><span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">2</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">3</span>, <span class="number">0</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.0999999999999996</span>, <span class="number">3.5227829907617076</span>]); |
| } |
| |
| <span class="attribute">#[cfg(feature = <span class="string">"datasets"</span>)] |
| #[test] |
| </span><span class="kw">fn </span>test_balltree_search_iris() { |
| <span class="kw">use </span><span class="kw">super</span>::<span class="kw">super</span>::<span class="kw">super</span>::<span class="kw">super</span>::datasets::iris; |
| |
| <span class="kw">let </span>dataset = iris::load(); |
| <span class="kw">let </span>data = dataset.data(); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = BallTree::new(<span class="number">10</span>); |
| tree.build(data.clone()); |
| |
| <span class="comment">// search tree |
| </span><span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">5.8</span>, <span class="number">3.1</span>, <span class="number">3.8</span>, <span class="number">1.2</span>], <span class="number">8</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">64</span>, <span class="number">88</span>, <span class="number">82</span>, <span class="number">95</span>, <span class="number">99</span>, <span class="number">96</span>, <span class="number">71</span>, <span class="number">61</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">0.360555127546399</span>, <span class="number">0.3872983346207417</span>, <span class="number">0.41231056256176596</span>, |
| <span class="number">0.4242640687119288</span>, <span class="number">0.4472135954999579</span>, <span class="number">0.4690415759823433</span>, |
| <span class="number">0.4795831523312721</span>, <span class="number">0.5196152422706636</span>]); |
| |
| <span class="kw">let </span>(ind, dist) = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">6.5</span>, <span class="number">3.5</span>, <span class="number">3.2</span>, <span class="number">1.3</span>], <span class="number">10</span>).unwrap(); |
| <span class="macro">assert_eq!</span>(ind, <span class="macro">vec!</span>[<span class="number">71</span>, <span class="number">64</span>, <span class="number">74</span>, <span class="number">82</span>, <span class="number">79</span>, <span class="number">61</span>, <span class="number">65</span>, <span class="number">97</span>, <span class="number">75</span>, <span class="number">51</span>]); |
| <span class="macro">assert_eq!</span>(dist, <span class="macro">vec!</span>[<span class="number">1.1357816691600549</span>, <span class="number">1.1532562594670799</span>, <span class="number">1.2569805089976533</span>, |
| <span class="number">1.2767145334803702</span>, <span class="number">1.2767145334803702</span>, <span class="number">1.284523257866513</span>, |
| <span class="number">1.2845232578665131</span>, <span class="number">1.2884098726725122</span>, <span class="number">1.3076696830622023</span>, |
| <span class="number">1.352774925846868</span>]); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_balltree_dim_selection_biased() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">5</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">0.</span>, |
| <span class="number">3.</span>, <span class="number">0.</span>, |
| <span class="number">2.</span>, <span class="number">20.</span>, |
| <span class="number">3.</span>, <span class="number">0.</span>, |
| <span class="number">1.</span>, <span class="number">0.</span>]); |
| <span class="kw">let </span><span class="kw-2">mut </span>tree = BallTree::new(<span class="number">3</span>); |
| tree.build(m); |
| |
| <span class="comment">// split to [0, 1, 3, 4] and [2] with columns #1 |
| </span><span class="kw">let </span>root = tree.root.unwrap(); |
| <span class="kw">let </span>b = as_branch(<span class="kw-2">&</span>root); |
| <span class="macro">assert_eq!</span>(b.dim, <span class="number">1</span>); |
| <span class="macro">assert_eq!</span>(b.split, <span class="number">10.</span>); |
| <span class="macro">assert_eq!</span>(b.center, Vector::new(<span class="macro">vec!</span>[<span class="number">10. </span>/ <span class="number">5.</span>, <span class="number">20. </span>/ <span class="number">5.</span>])); |
| <span class="comment">// distance between the center and [2] |
| </span><span class="kw">let </span>exp_d: f64 = (<span class="number">2. </span>- <span class="number">2.</span>) * (<span class="number">2. </span>- <span class="number">2.</span>) + (<span class="number">4. </span>- <span class="number">20.</span>) * (<span class="number">4. </span>- <span class="number">20.</span>); |
| <span class="macro">assert_eq!</span>(b.radius, exp_d.sqrt()); |
| |
| <span class="comment">// split to [0, 4] and [1, 3] with columns #0 |
| </span><span class="kw">let </span>bl = as_branch(b.left()); |
| <span class="macro">assert_eq!</span>(bl.dim, <span class="number">0</span>); |
| <span class="macro">assert_eq!</span>(bl.split, <span class="number">2.</span>); |
| <span class="macro">assert_eq!</span>(bl.center, Vector::new(<span class="macro">vec!</span>[<span class="number">8. </span>/ <span class="number">4.</span>, <span class="number">0.</span>])); |
| <span class="comment">// distance between the center and [0] |
| </span><span class="kw">let </span>exp_d: f64 = (<span class="number">2. </span>- <span class="number">1.</span>) * (<span class="number">2. </span>- <span class="number">1.</span>); |
| <span class="macro">assert_eq!</span>(bl.radius, exp_d.sqrt()); |
| |
| <span class="kw">let </span>br = as_leaf(b.right()); |
| <span class="macro">assert_eq!</span>(br.children, <span class="macro">vec!</span>[<span class="number">2</span>]); |
| |
| <span class="kw">let </span>bll = as_leaf(bl.left()); |
| <span class="kw">let </span>blr = as_leaf(bl.right()); |
| <span class="macro">assert_eq!</span>(bll.children, <span class="macro">vec!</span>[<span class="number">0</span>, <span class="number">4</span>]); |
| <span class="macro">assert_eq!</span>(blr.children, <span class="macro">vec!</span>[<span class="number">1</span>, <span class="number">3</span>]); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_balltree_untrained() { |
| <span class="kw">let </span>tree = BallTree::default(); |
| |
| <span class="kw">let </span>e = tree.search_leaf(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>); |
| <span class="macro">assert!</span>(e.is_err()); |
| |
| <span class="kw">let </span>e = tree.search(<span class="kw-2">&</span><span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.9</span>], <span class="number">1</span>); |
| <span class="macro">assert!</span>(e.is_err()); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_min_max() { |
| <span class="kw">let </span>data = Matrix::new(<span class="number">3</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">2.</span>, |
| <span class="number">2.</span>, <span class="number">4.</span>, |
| <span class="number">3.</span>, <span class="number">1.</span>]); |
| <span class="macro">assert_eq!</span>(min(<span class="kw-2">&</span>data), Vector::new(<span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">1.</span>])); |
| <span class="macro">assert_eq!</span>(max(<span class="kw-2">&</span>data), Vector::new(<span class="macro">vec!</span>[<span class="number">3.</span>, <span class="number">4.</span>])); |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../../../" data-current-crate="rusty_machine" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
