| <!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/dbscan.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>dbscan.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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| </pre><pre class="rust"><code><span class="doccomment">//! DBSCAN Clustering |
| //! |
| //! *Note: This module is likely to change dramatically in the future and |
| //! should be treated as experimental.* |
| //! |
| //! Provides an implementaton of DBSCAN clustering. The model |
| //! also implements a `predict` function which uses nearest neighbours |
| //! to classify the points. To utilize this function you must use |
| //! `self.set_predictive(true)` before training the model. |
| //! |
| //! The algorithm works by specifying `eps` and `min_points` parameters. |
| //! The `eps` parameter controls how close together points must be to be |
| //! placed in the same cluster. The `min_points` parameter controls how many |
| //! points must be within distance `eps` of eachother to be considered a cluster. |
| //! |
| //! If a point is not within distance `eps` of a cluster it will be classified |
| //! as noise. This means that it will be set to `None` in the clusters `Vector`. |
| //! |
| //! # Examples |
| //! |
| //! ``` |
| //! use rusty_machine::learning::dbscan::DBSCAN; |
| //! use rusty_machine::learning::UnSupModel; |
| //! use rusty_machine::linalg::Matrix; |
| //! |
| //! let inputs = Matrix::new(6, 2, vec![1.0, 2.0, |
| //! 1.1, 2.2, |
| //! 0.9, 1.9, |
| //! 1.0, 2.1, |
| //! -2.0, 3.0, |
| //! -2.2, 3.1]); |
| //! |
| //! let mut model = DBSCAN::new(0.5, 2); |
| //! model.train(&inputs).unwrap(); |
| //! |
| //! let clustering = model.clusters().unwrap(); |
| //! ``` |
| |
| </span><span class="kw">use </span>learning::{LearningResult, UnSupModel}; |
| <span class="kw">use </span>learning::error::{Error, ErrorKind}; |
| |
| <span class="kw">use </span>linalg::{Matrix, Vector, BaseMatrix}; |
| <span class="kw">use </span>rulinalg::utils; |
| <span class="kw">use </span>rulinalg::matrix::Row; |
| |
| <span class="doccomment">/// DBSCAN Model |
| /// |
| /// Implements clustering using the DBSCAN algorithm |
| /// via the `UnSupModel` trait. |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>DBSCAN { |
| eps: f64, |
| min_points: usize, |
| clusters: <span class="prelude-ty">Option</span><Vector<<span class="prelude-ty">Option</span><usize>>>, |
| predictive: bool, |
| _visited: Vec<bool>, |
| _cluster_data: <span class="prelude-ty">Option</span><Matrix<f64>>, |
| } |
| |
| <span class="doccomment">/// Constructs a non-predictive DBSCAN model with the |
| /// following parameters: |
| /// |
| /// - `eps` : `0.5` |
| /// - `min_points` : `5` |
| </span><span class="kw">impl </span>Default <span class="kw">for </span>DBSCAN { |
| <span class="kw">fn </span>default() -> DBSCAN { |
| DBSCAN { |
| eps: <span class="number">0.5</span>, |
| min_points: <span class="number">5</span>, |
| clusters: <span class="prelude-val">None</span>, |
| predictive: <span class="bool-val">false</span>, |
| _visited: Vec::new(), |
| _cluster_data: <span class="prelude-val">None</span>, |
| } |
| } |
| } |
| |
| <span class="kw">impl </span>UnSupModel<Matrix<f64>, Vector<<span class="prelude-ty">Option</span><usize>>> <span class="kw">for </span>DBSCAN { |
| <span class="doccomment">/// Train the classifier using input data. |
| </span><span class="kw">fn </span>train(<span class="kw-2">&mut </span><span class="self">self</span>, inputs: <span class="kw-2">&</span>Matrix<f64>) -> LearningResult<()> { |
| <span class="self">self</span>.init_params(inputs.rows()); |
| <span class="kw">let </span><span class="kw-2">mut </span>cluster = <span class="number">0</span>; |
| |
| <span class="kw">for </span>(idx, point) <span class="kw">in </span>inputs.row_iter().enumerate() { |
| <span class="kw">let </span>visited = <span class="self">self</span>._visited[idx]; |
| |
| <span class="kw">if </span>!visited { |
| <span class="self">self</span>._visited[idx] = <span class="bool-val">true</span>; |
| |
| <span class="kw">let </span>neighbours = <span class="self">self</span>.region_query(point, inputs); |
| |
| <span class="kw">if </span>neighbours.len() >= <span class="self">self</span>.min_points { |
| <span class="self">self</span>.expand_cluster(inputs, idx, neighbours, cluster); |
| cluster += <span class="number">1</span>; |
| } |
| } |
| } |
| |
| <span class="kw">if </span><span class="self">self</span>.predictive { |
| <span class="self">self</span>._cluster_data = <span class="prelude-val">Some</span>(inputs.clone()); |
| } |
| |
| <span class="prelude-val">Ok</span>(()) |
| } |
| |
| <span class="kw">fn </span>predict(<span class="kw-2">&</span><span class="self">self</span>, inputs: <span class="kw-2">&</span>Matrix<f64>) -> LearningResult<Vector<<span class="prelude-ty">Option</span><usize>>> { |
| <span class="kw">if </span><span class="self">self</span>.predictive { |
| <span class="kw">if let </span>(<span class="kw-2">&</span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>cluster_data), <span class="kw-2">&</span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>clusters)) = (<span class="kw-2">&</span><span class="self">self</span>._cluster_data, |
| <span class="kw-2">&</span><span class="self">self</span>.clusters) { |
| <span class="kw">let </span><span class="kw-2">mut </span>classes = Vec::with_capacity(inputs.rows()); |
| |
| <span class="kw">for </span>input_point <span class="kw">in </span>inputs.row_iter() { |
| <span class="kw">let </span><span class="kw-2">mut </span>distances = Vec::with_capacity(cluster_data.rows()); |
| |
| <span class="kw">for </span>cluster_point <span class="kw">in </span>cluster_data.row_iter() { |
| <span class="kw">let </span>point_distance = |
| utils::vec_bin_op(input_point.raw_slice(), cluster_point.raw_slice(), |x, y| x - y); |
| distances.push(utils::dot(<span class="kw-2">&</span>point_distance, <span class="kw-2">&</span>point_distance).sqrt()); |
| } |
| |
| <span class="kw">let </span>(closest_idx, closest_dist) = utils::argmin(<span class="kw-2">&</span>distances); |
| <span class="kw">if </span>closest_dist < <span class="self">self</span>.eps { |
| classes.push(clusters[closest_idx]); |
| } <span class="kw">else </span>{ |
| classes.push(<span class="prelude-val">None</span>); |
| } |
| } |
| |
| <span class="prelude-val">Ok</span>(Vector::new(classes)) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Err</span>(Error::new_untrained()) |
| } |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Err</span>(Error::new(ErrorKind::InvalidState, |
| <span class="string">"Model must be set to predictive. Use `self.set_predictive(true)`."</span>)) |
| } |
| } |
| } |
| |
| <span class="kw">impl </span>DBSCAN { |
| <span class="doccomment">/// Create a new DBSCAN model with a given |
| /// distance episilon and minimum points per cluster. |
| </span><span class="kw">pub fn </span>new(eps: f64, min_points: usize) -> DBSCAN { |
| <span class="macro">assert!</span>(eps > <span class="number">0f64</span>, <span class="string">"The model epsilon must be positive."</span>); |
| |
| DBSCAN { |
| eps: eps, |
| min_points: min_points, |
| clusters: <span class="prelude-val">None</span>, |
| predictive: <span class="bool-val">false</span>, |
| _visited: Vec::new(), |
| _cluster_data: <span class="prelude-val">None</span>, |
| } |
| } |
| |
| <span class="doccomment">/// Set predictive to true if the model is to be used |
| /// to classify future points. |
| /// |
| /// If the model is set as predictive then the input data |
| /// will be cloned during training. |
| </span><span class="kw">pub fn </span>set_predictive(<span class="kw-2">&mut </span><span class="self">self</span>, predictive: bool) { |
| <span class="self">self</span>.predictive = predictive; |
| } |
| |
| <span class="doccomment">/// Return an Option pointing to the model clusters. |
| </span><span class="kw">pub fn </span>clusters(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="prelude-ty">Option</span><<span class="kw-2">&</span>Vector<<span class="prelude-ty">Option</span><usize>>> { |
| <span class="self">self</span>.clusters.as_ref() |
| } |
| |
| <span class="kw">fn </span>expand_cluster(<span class="kw-2">&mut </span><span class="self">self</span>, |
| inputs: <span class="kw-2">&</span>Matrix<f64>, |
| point_idx: usize, |
| neighbour_pts: Vec<usize>, |
| cluster: usize) { |
| <span class="macro">debug_assert!</span>(point_idx < inputs.rows(), |
| <span class="string">"Point index too large for inputs"</span>); |
| <span class="macro">debug_assert!</span>(neighbour_pts.iter().all(|x| <span class="kw-2">*</span>x < inputs.rows()), |
| <span class="string">"Neighbour indices too large for inputs"</span>); |
| |
| <span class="self">self</span>.clusters.as_mut().map(|x| x.mut_data()[point_idx] = <span class="prelude-val">Some</span>(cluster)); |
| |
| <span class="kw">for </span>data_point_idx <span class="kw">in </span><span class="kw-2">&</span>neighbour_pts { |
| <span class="kw">let </span>visited = <span class="self">self</span>._visited[<span class="kw-2">*</span>data_point_idx]; |
| <span class="kw">if </span>!visited { |
| <span class="self">self</span>._visited[<span class="kw-2">*</span>data_point_idx] = <span class="bool-val">true</span>; |
| <span class="kw">let </span>data_point_row = <span class="kw">unsafe </span>{ inputs.row_unchecked(<span class="kw-2">*</span>data_point_idx) }; |
| <span class="kw">let </span>sub_neighbours = <span class="self">self</span>.region_query(data_point_row, inputs); |
| |
| <span class="kw">if </span>sub_neighbours.len() >= <span class="self">self</span>.min_points { |
| <span class="self">self</span>.expand_cluster(inputs, <span class="kw-2">*</span>data_point_idx, sub_neighbours, cluster); |
| } |
| } |
| } |
| } |
| |
| |
| <span class="kw">fn </span>region_query(<span class="kw-2">&</span><span class="self">self</span>, point: Row<f64>, inputs: <span class="kw-2">&</span>Matrix<f64>) -> Vec<usize> { |
| <span class="macro">debug_assert!</span>(point.cols() == inputs.cols(), |
| <span class="string">"point must be of same dimension as inputs"</span>); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>in_neighbourhood = Vec::new(); |
| <span class="kw">for </span>(idx, data_point) <span class="kw">in </span>inputs.row_iter().enumerate() { |
| <span class="comment">//TODO: Use `MatrixMetric` when rulinalg#154 is fixed. |
| </span><span class="kw">let </span>point_distance = utils::vec_bin_op(data_point.raw_slice(), point.raw_slice(), |x, y| x - y); |
| <span class="kw">let </span>dist = utils::dot(<span class="kw-2">&</span>point_distance, <span class="kw-2">&</span>point_distance).sqrt(); |
| |
| <span class="kw">if </span>dist < <span class="self">self</span>.eps { |
| in_neighbourhood.push(idx); |
| } |
| } |
| |
| in_neighbourhood |
| } |
| |
| <span class="kw">fn </span>init_params(<span class="kw-2">&mut </span><span class="self">self</span>, total_points: usize) { |
| <span class="kw">unsafe </span>{ |
| <span class="self">self</span>._visited.reserve(total_points); |
| <span class="self">self</span>._visited.set_len(total_points); |
| } |
| |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..total_points { |
| <span class="self">self</span>._visited[i] = <span class="bool-val">false</span>; |
| } |
| |
| <span class="self">self</span>.clusters = <span class="prelude-val">Some</span>(Vector::new(<span class="macro">vec!</span>[<span class="prelude-val">None</span>; total_points])); |
| } |
| } |
| |
| <span class="attribute">#[cfg(test)] |
| </span><span class="kw">mod </span>tests { |
| <span class="kw">use </span><span class="kw">super</span>::DBSCAN; |
| <span class="kw">use </span>linalg::{Matrix, BaseMatrix}; |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_region_query() { |
| <span class="kw">let </span>model = DBSCAN::new(<span class="number">1.0</span>, <span class="number">3</span>); |
| |
| <span class="kw">let </span>inputs = Matrix::new(<span class="number">3</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.0</span>, <span class="number">1.0</span>, <span class="number">1.1</span>, <span class="number">1.9</span>, <span class="number">3.0</span>, <span class="number">3.0</span>]); |
| |
| <span class="kw">let </span>m = <span class="macro">matrix!</span>[<span class="number">1.0</span>, <span class="number">1.0</span>]; |
| <span class="kw">let </span>row = m.row(<span class="number">0</span>); |
| <span class="kw">let </span>neighbours = model.region_query(row, <span class="kw-2">&</span>inputs); |
| |
| <span class="macro">assert!</span>(neighbours.len() == <span class="number">2</span>); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_region_query_small_eps() { |
| <span class="kw">let </span>model = DBSCAN::new(<span class="number">0.01</span>, <span class="number">3</span>); |
| |
| <span class="kw">let </span>inputs = Matrix::new(<span class="number">3</span>, <span class="number">2</span>, <span class="macro">vec!</span>[<span class="number">1.0</span>, <span class="number">1.0</span>, <span class="number">1.1</span>, <span class="number">1.9</span>, <span class="number">1.1</span>, <span class="number">1.1</span>]); |
| |
| <span class="kw">let </span>m = <span class="macro">matrix!</span>[<span class="number">1.0</span>, <span class="number">1.0</span>]; |
| <span class="kw">let </span>row = m.row(<span class="number">0</span>); |
| <span class="kw">let </span>neighbours = model.region_query(row, <span class="kw-2">&</span>inputs); |
| |
| <span class="macro">assert!</span>(neighbours.len() == <span class="number">1</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> |
