| <!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/lib.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>lib.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">//! # The rusty-machine crate. |
| //! |
| //! A crate built for machine learning that works out-of-the-box. |
| //! |
| //! --- |
| //! |
| //! ## Structure |
| //! |
| //! The crate is made up of two primary modules: learning and linalg. |
| //! |
| //! ### learning |
| //! |
| //! The learning module contains all of the machine learning modules. |
| //! This means the algorithms, models and related tools. |
| //! |
| //! The currently supported techniques are: |
| //! |
| //! - Linear Regression |
| //! - Logistic Regression |
| //! - Generalized Linear Models |
| //! - K-Means Clustering |
| //! - Neural Networks |
| //! - Gaussian Process Regression |
| //! - Support Vector Machines |
| //! - Gaussian Mixture Models |
| //! - Naive Bayes Classifiers |
| //! - DBSCAN |
| //! - k-Nearest Neighbor Classifiers |
| //! - Principal Component Analysis |
| //! |
| //! ### linalg |
| //! |
| //! The linalg module reexports some structs and traits from the |
| //! [rulinalg](https://crates.io/crates/rulinalg) crate. This is to provide |
| //! easy access to common linear algebra tools within this library. |
| //! |
| //! --- |
| //! |
| //! ## Usage |
| //! |
| //! Specific usage of modules is described within the modules themselves. This section |
| //! will focus on the general workflow for this library. |
| //! |
| //! The models contained within the learning module should implement either |
| //! `SupModel` or `UnSupModel`. These both provide a `train` and a `predict` |
| //! function which provide an interface to the model. |
| //! |
| //! You should instantiate the model, with your chosen options and then train using |
| //! the training data. Followed by predicting with your test data. *For now* |
| //! cross-validation, data handling, and many other things are left explicitly |
| //! to the user. |
| //! |
| //! Here is an example usage for Gaussian Process Regression: |
| //! |
| //! ``` |
| //! use rusty_machine::linalg::Matrix; |
| //! use rusty_machine::linalg::Vector; |
| //! use rusty_machine::learning::gp::GaussianProcess; |
| //! use rusty_machine::learning::gp::ConstMean; |
| //! use rusty_machine::learning::toolkit::kernel; |
| //! use rusty_machine::learning::SupModel; |
| //! |
| //! // First we'll get some data. |
| //! |
| //! // Some example training data. |
| //! let inputs = Matrix::new(3,3,vec![1.,1.,1.,2.,2.,2.,3.,3.,3.]); |
| //! let targets = Vector::new(vec![0.,1.,0.]); |
| //! |
| //! // Some example test data. |
| //! let test_inputs = Matrix::new(2,3, vec![1.5,1.5,1.5,2.5,2.5,2.5]); |
| //! |
| //! // Now we'll set up our model. |
| //! // This is close to the most complicated a model in rusty-machine gets! |
| //! |
| //! // A squared exponential kernel with lengthscale 2, and amplitude 1. |
| //! let ker = kernel::SquaredExp::new(2., 1.); |
| //! |
| //! // The zero function |
| //! let zero_mean = ConstMean::default(); |
| //! |
| //! // Construct a GP with the specified kernel, mean, and a noise of 0.5. |
| //! let mut gp = GaussianProcess::new(ker, zero_mean, 0.5); |
| //! |
| //! |
| //! // Now we can train and predict from the model. |
| //! |
| //! // Train the model! |
| //! gp.train(&inputs, &targets).unwrap(); |
| //! |
| //! // Predict the output from test data. |
| //! let outputs = gp.predict(&test_inputs).unwrap(); |
| //! ``` |
| //! |
| //! This code could have been a lot simpler if we had simply adopted |
| //! `let mut gp = GaussianProcess::default();`. Conversely, you could also implement |
| //! your own kernels and mean functions by using the appropriate traits. |
| //! |
| //! Additionally you'll notice there's quite a few `use` statements at the top of this code. |
| //! We can remove some of these by utilizing the `prelude`: |
| //! |
| //! ``` |
| //! use rusty_machine::prelude::*; |
| //! |
| //! let _ = Matrix::new(2,2,vec![2.0;4]); |
| //! ``` |
| |
| </span><span class="attribute">#![deny(missing_docs)] |
| #![warn(missing_debug_implementations)] |
| |
| #[macro_use] |
| </span><span class="kw">extern crate </span>rulinalg; |
| <span class="kw">extern crate </span>num <span class="kw">as </span>libnum; |
| <span class="kw">extern crate </span>rand; |
| <span class="kw">extern crate </span>rand_distr; |
| |
| <span class="kw">pub mod </span>prelude; |
| |
| <span class="doccomment">/// The linear algebra module |
| /// |
| /// This module contains reexports of common tools from the rulinalg crate. |
| </span><span class="kw">pub mod </span>linalg { |
| <span class="kw">pub use </span>rulinalg::matrix::{Axes, Matrix, MatrixSlice, MatrixSliceMut, BaseMatrix, BaseMatrixMut}; |
| <span class="kw">pub use </span>rulinalg::vector::Vector; |
| <span class="kw">pub use </span>rulinalg::norm; |
| <span class="kw">pub use </span>rulinalg::matrix::decomposition::<span class="kw-2">*</span>; |
| } |
| |
| <span class="doccomment">/// Module for data handling |
| </span><span class="kw">pub mod </span>data { |
| <span class="kw">pub mod </span>transforms; |
| } |
| |
| <span class="doccomment">/// Module for machine learning. |
| </span><span class="kw">pub mod </span>learning { |
| <span class="kw">pub mod </span>dbscan; |
| <span class="kw">pub mod </span>glm; |
| <span class="kw">pub mod </span>gmm; |
| <span class="kw">pub mod </span>lin_reg; |
| <span class="kw">pub mod </span>logistic_reg; |
| <span class="kw">pub mod </span>k_means; |
| <span class="kw">pub mod </span>nnet; |
| <span class="kw">pub mod </span>gp; |
| <span class="kw">pub mod </span>svm; |
| <span class="kw">pub mod </span>naive_bayes; |
| <span class="kw">pub mod </span>knn; |
| <span class="kw">pub mod </span>pca; |
| |
| <span class="kw">pub mod </span>error; |
| |
| <span class="doccomment">/// A new type which provides clean access to the learning errors |
| </span><span class="kw">pub type </span>LearningResult<T> = <span class="prelude-ty">Result</span><T, error::Error>; |
| |
| <span class="doccomment">/// Trait for supervised model. |
| </span><span class="kw">pub trait </span>SupModel<T, U> { |
| <span class="doccomment">/// Predict output from inputs. |
| </span><span class="kw">fn </span>predict(<span class="kw-2">&</span><span class="self">self</span>, inputs: <span class="kw-2">&</span>T) -> LearningResult<U>; |
| |
| <span class="doccomment">/// Train the model using inputs and targets. |
| </span><span class="kw">fn </span>train(<span class="kw-2">&mut </span><span class="self">self</span>, inputs: <span class="kw-2">&</span>T, targets: <span class="kw-2">&</span>U) -> LearningResult<()>; |
| } |
| |
| <span class="doccomment">/// Trait for unsupervised model. |
| </span><span class="kw">pub trait </span>UnSupModel<T, U> { |
| <span class="doccomment">/// Predict output from inputs. |
| </span><span class="kw">fn </span>predict(<span class="kw-2">&</span><span class="self">self</span>, inputs: <span class="kw-2">&</span>T) -> LearningResult<U>; |
| |
| <span class="doccomment">/// Train the model using inputs. |
| </span><span class="kw">fn </span>train(<span class="kw-2">&mut </span><span class="self">self</span>, inputs: <span class="kw-2">&</span>T) -> LearningResult<()>; |
| } |
| |
| <span class="doccomment">/// Module for optimization in machine learning setting. |
| </span><span class="kw">pub mod </span>optim { |
| |
| <span class="doccomment">/// Trait for models which can be gradient-optimized. |
| </span><span class="kw">pub trait </span>Optimizable { |
| <span class="doccomment">/// The input data type to the model. |
| </span><span class="kw">type </span>Inputs; |
| <span class="doccomment">/// The target data type to the model. |
| </span><span class="kw">type </span>Targets; |
| |
| <span class="doccomment">/// Compute the gradient for the model. |
| </span><span class="kw">fn </span>compute_grad(<span class="kw-2">&</span><span class="self">self</span>, |
| params: <span class="kw-2">&</span>[f64], |
| inputs: <span class="kw-2">&</span><span class="self">Self</span>::Inputs, |
| targets: <span class="kw-2">&</span><span class="self">Self</span>::Targets) |
| -> (f64, Vec<f64>); |
| } |
| |
| <span class="doccomment">/// Trait for optimization algorithms. |
| </span><span class="kw">pub trait </span>OptimAlgorithm<M: Optimizable> { |
| <span class="doccomment">/// Return the optimized parameter using gradient optimization. |
| /// |
| /// Takes in a set of starting parameters and related model data. |
| </span><span class="kw">fn </span>optimize(<span class="kw-2">&</span><span class="self">self</span>, |
| model: <span class="kw-2">&</span>M, |
| start: <span class="kw-2">&</span>[f64], |
| inputs: <span class="kw-2">&</span>M::Inputs, |
| targets: <span class="kw-2">&</span>M::Targets) |
| -> Vec<f64>; |
| } |
| |
| <span class="kw">pub mod </span>grad_desc; |
| <span class="kw">pub mod </span>fmincg; |
| } |
| |
| <span class="doccomment">/// Module for learning tools. |
| </span><span class="kw">pub mod </span>toolkit { |
| <span class="kw">pub mod </span>activ_fn; |
| <span class="kw">pub mod </span>cost_fn; |
| <span class="kw">pub mod </span>kernel; |
| <span class="kw">pub mod </span>rand_utils; |
| <span class="kw">pub mod </span>regularization; |
| } |
| } |
| |
| <span class="attribute">#[cfg(feature = <span class="string">"stats"</span>)] |
| </span><span class="doccomment">/// Module for computational statistics |
| </span><span class="kw">pub mod </span>stats { |
| |
| <span class="doccomment">/// Module for statistical distributions. |
| </span><span class="kw">pub mod </span>dist; |
| } |
| |
| <span class="doccomment">/// Module for evaluating models. |
| </span><span class="kw">pub mod </span>analysis { |
| <span class="kw">pub mod </span>confusion_matrix; |
| <span class="kw">pub mod </span>cross_validation; |
| <span class="kw">pub mod </span>score; |
| } |
| |
| <span class="attribute">#[cfg(feature = <span class="string">"datasets"</span>)] |
| </span><span class="doccomment">/// Module for datasets. |
| </span><span class="kw">pub mod </span>datasets; |
| </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> |
