| <!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/pca.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>pca.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">//! Principal Component Analysis Module |
| //! |
| //! Contains implementation of PCA. |
| //! |
| //! # Examples |
| //! |
| //! ``` |
| //! use rusty_machine::learning::pca::PCA; |
| //! use rusty_machine::learning::UnSupModel; |
| //! |
| //! use rusty_machine::linalg::Matrix; |
| //! let mut pca = PCA::default(); |
| //! |
| //! let inputs = Matrix::new(3, 2, vec![1., 0.1, |
| //! 3., 0.2, |
| //! 4., 0.2]); |
| //! // Train the model |
| //! pca.train(&inputs).unwrap(); |
| //! |
| //! // Mapping a new point to principal component space |
| //! let new_data = Matrix::new(1, 2, vec![2., 0.1]); |
| //! let output = pca.predict(&new_data).unwrap(); |
| //! |
| //! assert_eq!(output, Matrix::new(1, 2, vec![-0.6686215718235227, 0.042826190364433595])); |
| //! ``` |
| |
| </span><span class="kw">use </span>linalg::{Matrix, BaseMatrix, Axes}; |
| <span class="kw">use </span>linalg::Vector; |
| |
| <span class="kw">use </span>learning::{LearningResult, UnSupModel}; |
| <span class="kw">use </span>learning::error::{Error, ErrorKind}; |
| |
| <span class="doccomment">/// Principal Component Analysis |
| /// |
| /// - PCA uses rulinalg SVD which is experimental (not yet work for large data) |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>PCA { |
| <span class="doccomment">/// number of componentsc considered |
| </span>n: <span class="prelude-ty">Option</span><usize>, |
| <span class="doccomment">/// Flag whether to centering inputs |
| </span>center: bool, |
| |
| <span class="comment">// Number of original input |
| </span>n_features: <span class="prelude-ty">Option</span><usize>, |
| <span class="comment">// Center of inputs |
| </span>centers: <span class="prelude-ty">Option</span><Vector<f64>>, |
| <span class="comment">// Principal components |
| </span>components: <span class="prelude-ty">Option</span><Matrix<f64>>, |
| <span class="comment">// Whether components is inversed (trained with number of rows < cols data) |
| </span>inv: bool |
| } |
| |
| <span class="kw">impl </span>PCA { |
| |
| <span class="doccomment">/// Constructs untrained PCA model. |
| /// |
| /// # Parameters |
| /// |
| /// - `n` : number of principal components |
| /// - `center` : flag whether centering inputs to be specified. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::pca::PCA; |
| /// |
| /// let model = PCA::new(3, true); |
| /// ``` |
| </span><span class="kw">pub fn </span>new(n: usize, center: bool) -> PCA { |
| |
| PCA { |
| <span class="comment">// accept n as usize, user should know the number of columns |
| </span>n: <span class="prelude-val">Some</span>(n), |
| center: center, |
| |
| n_features: <span class="prelude-val">None</span>, |
| centers: <span class="prelude-val">None</span>, |
| components: <span class="prelude-val">None</span>, |
| inv: <span class="bool-val">false |
| </span>} |
| } |
| |
| <span class="doccomment">/// Returns principal components (matrix which contains eigenvectors as columns) |
| </span><span class="kw">pub fn </span>components(<span class="kw-2">&</span><span class="self">self</span>) -> LearningResult<<span class="kw-2">&</span>Matrix<f64>> { |
| <span class="kw">match </span><span class="self">self</span>.components { |
| <span class="prelude-val">None </span>=> <span class="prelude-val">Err</span>(Error::new_untrained()), |
| <span class="prelude-val">Some</span>(<span class="kw-2">ref </span>rot) => { <span class="prelude-val">Ok</span>(rot) } |
| } |
| } |
| } |
| |
| <span class="doccomment">/// The default PCA. |
| /// |
| /// Parameters: |
| /// |
| /// - `n` = `None` (keep all components) |
| /// - `center` = `true` |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::pca::PCA; |
| /// |
| /// let model = PCA::default(); |
| /// ``` |
| </span><span class="kw">impl </span>Default <span class="kw">for </span>PCA { |
| <span class="kw">fn </span>default() -> <span class="self">Self </span>{ |
| PCA { |
| <span class="comment">// because number of columns is unknown, |
| // return all components by default |
| </span>n: <span class="prelude-val">None</span>, |
| center: <span class="bool-val">true</span>, |
| |
| n_features: <span class="prelude-val">None</span>, |
| centers: <span class="prelude-val">None</span>, |
| components: <span class="prelude-val">None</span>, |
| inv: <span class="bool-val">false |
| </span>} |
| } |
| } |
| |
| <span class="doccomment">/// Train the model and predict the model output from new data. |
| </span><span class="kw">impl </span>UnSupModel<Matrix<f64>, Matrix<f64>> <span class="kw">for </span>PCA { |
| |
| <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<Matrix<f64>> { |
| |
| <span class="kw">match </span><span class="self">self</span>.n_features { |
| <span class="prelude-val">None </span>=> { <span class="kw">return </span><span class="prelude-val">Err</span>(Error::new_untrained()); }, |
| <span class="prelude-val">Some</span>(f) => { |
| <span class="kw">if </span>f != inputs.cols() { |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::new(ErrorKind::InvalidData, |
| <span class="string">"Input data must have the same number of columns as training data"</span>)); |
| } |
| } |
| }; |
| |
| <span class="kw">match </span><span class="self">self</span>.components { |
| <span class="comment">// this can't happen |
| </span><span class="prelude-val">None </span>=> { <span class="kw">return </span><span class="prelude-val">Err</span>(Error::new_untrained()); }, |
| <span class="prelude-val">Some</span>(<span class="kw-2">ref </span>comp) => { |
| <span class="kw">if </span><span class="self">self</span>.center == <span class="bool-val">true </span>{ |
| <span class="kw">match </span><span class="self">self</span>.centers { |
| <span class="comment">// this can't happen |
| </span><span class="prelude-val">None </span>=> <span class="kw">return </span><span class="prelude-val">Err</span>(Error::new_untrained()), |
| <span class="prelude-val">Some</span>(<span class="kw-2">ref </span>centers) => { |
| <span class="kw">let </span>data = <span class="kw">unsafe </span>{ centering(inputs, <span class="kw-2">&</span>centers) }; |
| <span class="kw">if </span><span class="self">self</span>.inv == <span class="bool-val">true </span>{ |
| <span class="prelude-val">Ok</span>(data * comp.transpose()) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Ok</span>(data * comp) |
| } |
| } |
| } |
| } <span class="kw">else </span>{ |
| <span class="kw">if </span><span class="self">self</span>.inv == <span class="bool-val">true </span>{ |
| <span class="prelude-val">Ok</span>(inputs * comp.transpose()) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Ok</span>(inputs * comp) |
| } |
| } |
| } |
| } |
| } |
| |
| <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="kw">match </span><span class="self">self</span>.n { |
| <span class="prelude-val">None </span>=> {}, |
| <span class="prelude-val">Some</span>(n) => { |
| <span class="kw">if </span>n > inputs.cols() { |
| <span class="kw">return </span><span class="prelude-val">Err</span>(Error::new(ErrorKind::InvalidData, |
| <span class="string">"Input data must have equal or larger number of columns than n"</span>)); |
| } |
| } |
| } |
| |
| <span class="kw">let </span>data = <span class="kw">if </span><span class="self">self</span>.center == <span class="bool-val">true </span>{ |
| <span class="kw">let </span>centers = inputs.mean(Axes::Row); |
| <span class="kw">let </span>m = <span class="kw">unsafe </span>{ centering(inputs, <span class="kw-2">&</span>centers) }; |
| <span class="self">self</span>.centers = <span class="prelude-val">Some</span>(centers); |
| m |
| } <span class="kw">else </span>{ |
| inputs.clone() |
| }; |
| <span class="kw">let </span>(<span class="kw">_</span>, <span class="kw">_</span>, <span class="kw-2">mut </span>v) = data.svd().unwrap(); |
| <span class="kw">if </span>inputs.cols() > inputs.rows() { |
| v = v.transpose(); |
| <span class="self">self</span>.inv = <span class="bool-val">true</span>; |
| } |
| |
| <span class="self">self</span>.components = <span class="kw">match </span><span class="self">self</span>.n { |
| <span class="prelude-val">Some</span>(c) => { |
| <span class="kw">let </span>slicer: Vec<usize> = (<span class="number">0</span>..c).collect(); |
| <span class="prelude-val">Some</span>(v.select_cols(<span class="kw-2">&</span>slicer)) |
| }, |
| <span class="prelude-val">None </span>=> <span class="prelude-val">Some</span>(v) |
| }; |
| <span class="self">self</span>.n_features = <span class="prelude-val">Some</span>(inputs.cols()); |
| <span class="prelude-val">Ok</span>(()) |
| } |
| } |
| |
| <span class="doccomment">/// Subtract center Vector from each rows |
| </span><span class="kw">unsafe fn </span>centering(inputs: <span class="kw-2">&</span>Matrix<f64>, centers: <span class="kw-2">&</span>Vector<f64>) -> Matrix<f64> { |
| <span class="comment">// Number of inputs columns and centers length must be the same |
| </span>Matrix::from_fn(inputs.rows(), inputs.cols(), |
| |c, r| inputs.get_unchecked([r, c]) - centers.data().get_unchecked(c)) |
| } |
| |
| <span class="attribute">#[cfg(test)] |
| </span><span class="kw">mod </span>tests { |
| |
| <span class="kw">use </span>linalg::{Matrix, Axes, Vector}; |
| <span class="kw">use </span><span class="kw">super</span>::centering; |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_centering() { |
| <span class="kw">let </span>m = Matrix::new(<span class="number">2</span>, <span class="number">3</span>, <span class="macro">vec!</span>[<span class="number">1.</span>, <span class="number">2.</span>, <span class="number">3.</span>, |
| <span class="number">2.</span>, <span class="number">4.</span>, <span class="number">4.</span>]); |
| <span class="kw">let </span>centers = m.mean(Axes::Row); |
| <span class="macro">assert_vector_eq!</span>(centers, Vector::new(<span class="macro">vec!</span>[<span class="number">1.5</span>, <span class="number">3.</span>, <span class="number">3.5</span>]), comp=abs, tol=<span class="number">1e-8</span>); |
| |
| <span class="kw">let </span>centered = <span class="kw">unsafe </span>{ centering(<span class="kw-2">&</span>m, <span class="kw-2">&</span>centers) }; |
| <span class="kw">let </span>exp = Matrix::new(<span class="number">2</span>, <span class="number">3</span>, <span class="macro">vec!</span>[-<span class="number">0.5</span>, -<span class="number">1.</span>, -<span class="number">0.5</span>, |
| <span class="number">0.5</span>, <span class="number">1.</span>, <span class="number">0.5</span>]); |
| <span class="macro">assert_matrix_eq!</span>(centered, exp, comp=abs, tol=<span class="number">1e-8</span>); |
| } |
| }</code></pre></div> |
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