| <!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/optim/fmincg.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>fmincg.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">//! Module for the fmincg optimization algorithm. |
| //! |
| //! This algorithm was taken from Andrew Ng's coursera machine |
| //! learning course. The function was translated from MATLAB into rust. |
| //! Original source code can be found [here](http://www.mathworks.com/matlabcentral/fileexchange/42770-logistic-regression-with-regularization-used-to-classify-hand-written-digits/content/Logistic%20Regression%20with%20regularisation/fmincg.m). |
| //! |
| //! The attached license permits use and modification for research |
| //! and education only. |
| //! |
| //! Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13 |
| //! |
| //! |
| //! (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen |
| //! |
| //! Permission is granted for anyone to copy, use, or modify these |
| //! programs and accompanying documents for purposes of research or |
| //! education, provided this copyright notice is retained, and note is |
| //! made of any changes that have been made. |
| //! |
| //! These programs and documents are distributed without any warranty, |
| //! express or implied. As the programs were written for research |
| //! purposes only, they have not been tested to the degree that would be |
| //! advisable in any important application. All use of these programs is |
| //! entirely at the user's own risk. |
| //! |
| //! [rusty-machine] Changes made: |
| //! |
| //! - Conversion to Rust. |
| //! - Length hard defaults to the max iterations. |
| |
| </span><span class="kw">use </span>learning::optim::{Optimizable, OptimAlgorithm}; |
| <span class="kw">use </span>linalg::Vector; |
| |
| <span class="kw">use </span>std::cmp; |
| <span class="kw">use </span>std::f64; |
| |
| |
| <span class="doccomment">/// Conjugate Gradient Descent algorithm |
| </span><span class="attribute">#[derive(Clone, Copy, Debug)] |
| </span><span class="kw">pub struct </span>ConjugateGD { |
| <span class="doccomment">/// Constant in the Wolfe-Powell conditions. |
| </span><span class="kw">pub </span>rho: f64, |
| <span class="doccomment">/// Constant in the Wolfe-Powell conditions. |
| </span><span class="kw">pub </span>sig: f64, |
| <span class="doccomment">/// Don't reevaluate within `int` of the limit of the current bracket. |
| </span><span class="kw">pub </span>int: f64, |
| <span class="doccomment">/// Extrapolate max of `ext` times the current bracket. |
| </span><span class="kw">pub </span>ext: f64, |
| <span class="doccomment">/// Max of `max` function evaluations per line search |
| </span><span class="kw">pub </span>max: usize, |
| <span class="doccomment">/// The maximum allowed slope ratio |
| </span><span class="kw">pub </span>ratio: f64, |
| |
| <span class="doccomment">/// The default number of max iterations. |
| </span><span class="kw">pub </span>iters: usize, |
| } |
| |
| <span class="doccomment">/// The default Conjugate GD algorithm. |
| /// |
| /// The defaults are: |
| /// |
| /// - rho = 0.01 |
| /// - sig = 0.5 |
| /// - int = 0.1 |
| /// - ext = 3 |
| /// - max = 20 |
| /// - ration = 100 |
| /// - iters = 100 |
| </span><span class="kw">impl </span>Default <span class="kw">for </span>ConjugateGD { |
| <span class="kw">fn </span>default() -> ConjugateGD { |
| ConjugateGD { |
| rho: <span class="number">0.01</span>, |
| sig: <span class="number">0.5</span>, |
| int: <span class="number">0.1</span>, |
| ext: <span class="number">3.0</span>, |
| max: <span class="number">20</span>, |
| ratio: <span class="number">100.0</span>, |
| iters: <span class="number">100</span>, |
| } |
| } |
| } |
| |
| <span class="kw">impl</span><M: Optimizable> OptimAlgorithm<M> <span class="kw">for </span>ConjugateGD { |
| <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">let </span><span class="kw-2">mut </span>i = <span class="number">0usize</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>ls_failed = <span class="bool-val">false</span>; |
| |
| <span class="kw">let </span>(<span class="kw-2">mut </span>f1, vec_df1) = model.compute_grad(start, inputs, targets); |
| <span class="kw">let </span><span class="kw-2">mut </span>df1 = Vector::new(vec_df1); |
| |
| <span class="comment">// The reduction in the function. Can also be specified as part of length |
| </span><span class="kw">let </span>red = <span class="number">1f64</span>; |
| |
| <span class="kw">let </span>length = <span class="self">self</span>.iters <span class="kw">as </span>i32; |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>s = -df1.clone(); |
| <span class="kw">let </span><span class="kw-2">mut </span>d1 = -s.dot(<span class="kw-2">&</span>s); |
| <span class="kw">let </span><span class="kw-2">mut </span>z1 = red / (<span class="number">1f64 </span>- d1); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>x = Vector::new(start.to_vec()); |
| |
| <span class="kw">let </span>(<span class="kw-2">mut </span>f2, <span class="kw-2">mut </span>df2): (f64, Vector<f64>); |
| |
| <span class="kw">while </span>(i <span class="kw">as </span>i32) < length.abs() { |
| <span class="kw">if </span>length > <span class="number">0 </span>{ |
| i += <span class="number">1</span>; |
| } |
| |
| <span class="kw">let </span>(x0, f0) = (x.clone(), f1); |
| |
| x += <span class="kw-2">&</span>s * z1; |
| |
| <span class="kw">let </span>cost = model.compute_grad(x.data(), inputs, targets); |
| f2 = cost.<span class="number">0</span>; |
| df2 = Vector::new(cost.<span class="number">1</span>); |
| |
| <span class="kw">if </span>length < <span class="number">0 </span>{ |
| i += <span class="number">1</span>; |
| } |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>d2 = df2.dot(<span class="kw-2">&</span>s); |
| |
| <span class="kw">let </span>(<span class="kw-2">mut </span>f3, <span class="kw-2">mut </span>d3, <span class="kw-2">mut </span>z3) = (f1, d1, -z1); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>m = <span class="kw">if </span>length > <span class="number">0 </span>{ |
| <span class="self">self</span>.max <span class="kw">as </span>i32 |
| } <span class="kw">else </span>{ |
| cmp::min(<span class="self">self</span>.max <span class="kw">as </span>i32, -length - (i <span class="kw">as </span>i32)) |
| }; |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>success = <span class="bool-val">false</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>limit = -<span class="number">1f64</span>; |
| |
| <span class="kw">loop </span>{ |
| <span class="kw">let </span><span class="kw-2">mut </span>z2: f64; |
| |
| <span class="kw">while </span>((f2 > (f1 + z1 * <span class="self">self</span>.rho * d1)) || (d2 > -<span class="self">self</span>.sig * d1)) && (m > <span class="number">0i32</span>) { |
| |
| limit = z1; |
| |
| <span class="kw">if </span>f2 > f1 { |
| z2 = z3 - (<span class="number">0.5 </span>* d3 * z3 * z3) / (d3 * z3 + f2 - f3); |
| } <span class="kw">else </span>{ |
| <span class="kw">let </span>a = <span class="number">6f64 </span>* (f2 - f3) / z3 + <span class="number">3f64 </span>* (d2 + d3); |
| <span class="kw">let </span>b = <span class="number">3f64 </span>* (f3 - f2) - z3 * (<span class="number">2f64 </span>* d2 + d3); |
| z2 = ((b * b - a * d2 * z3 * z3).sqrt() - b) / a; |
| } |
| |
| <span class="kw">if </span>z2.is_nan() || z2.is_infinite() { |
| z2 = z3 / <span class="number">2f64</span>; |
| } |
| |
| <span class="kw">if </span>z2 <= <span class="self">self</span>.int * z3 { |
| <span class="kw">if </span>z2 <= (<span class="number">1f64 </span>- <span class="self">self</span>.int) * z3 { |
| z2 = (<span class="number">1f64 </span>- <span class="self">self</span>.int) * z3; |
| } |
| } <span class="kw">else if </span><span class="self">self</span>.int * z3 <= (<span class="number">1f64 </span>- <span class="self">self</span>.int) * z3 { |
| z2 = (<span class="number">1f64 </span>- <span class="self">self</span>.int) * z3; |
| } <span class="kw">else </span>{ |
| z2 = <span class="self">self</span>.int * z3; |
| } |
| |
| z1 += z2; |
| x += <span class="kw-2">&</span>s * z2; |
| <span class="kw">let </span>cost_grad = model.compute_grad(x.data(), inputs, targets); |
| f2 = cost_grad.<span class="number">0</span>; |
| df2 = Vector::new(cost_grad.<span class="number">1</span>); |
| |
| m -= <span class="number">1i32</span>; |
| <span class="kw">if </span>length < <span class="number">0 </span>{ |
| i += <span class="number">1</span>; |
| } |
| |
| d2 = df2.dot(<span class="kw-2">&</span>s); |
| z3 -= z2; |
| } |
| |
| <span class="kw">if </span>f2 > f1 + z1 * <span class="self">self</span>.rho * d1 || d2 > -<span class="self">self</span>.sig * d1 { |
| <span class="kw">break</span>; |
| } <span class="kw">else if </span>d2 > <span class="self">self</span>.sig * d1 { |
| success = <span class="bool-val">true</span>; |
| <span class="kw">break</span>; |
| } <span class="kw">else if </span>m == <span class="number">0i32 </span>{ |
| <span class="kw">break</span>; |
| } |
| |
| <span class="kw">let </span>a = <span class="number">6f64 </span>* (f2 - f3) / z3 + <span class="number">3f64 </span>* (d2 + d3); |
| <span class="kw">let </span>b = <span class="number">3f64 </span>* (f3 - f2) - z3 * (<span class="number">2f64 </span>* d2 + d3); |
| z2 = -d2 * z3 * z3 / (b + (b * b - a * d2 * z3 * z3).sqrt()); |
| |
| <span class="kw">if </span>z2.is_nan() || z2.is_infinite() || z2 < <span class="number">0f64 </span>{ |
| <span class="kw">if </span>limit < -<span class="number">0.5 </span>{ |
| z2 = z1 * (<span class="self">self</span>.ext - <span class="number">1f64</span>); |
| } <span class="kw">else </span>{ |
| z2 = (limit - z1) / <span class="number">2f64</span>; |
| } |
| } <span class="kw">else if </span>(limit > -<span class="number">0.5</span>) && (z2 + z1 > limit) { |
| z2 = (limit - z1) / <span class="number">2f64</span>; |
| } <span class="kw">else if </span>(limit < -<span class="number">0.5</span>) && (z2 + z1 > z1 * <span class="self">self</span>.ext) { |
| z2 = z1 * (<span class="self">self</span>.ext - <span class="number">1f64</span>); |
| } <span class="kw">else if </span>z2 < -z3 * <span class="self">self</span>.int { |
| z2 = -z3 * <span class="self">self</span>.int; |
| } <span class="kw">else if </span>(limit > -<span class="number">0.5</span>) && (z2 < (limit - z1) * (<span class="number">1f64 </span>- <span class="self">self</span>.int)) { |
| z2 = (limit - z1) * (<span class="number">1f64 </span>- <span class="self">self</span>.int); |
| } |
| |
| f3 = f2; |
| d3 = d2; |
| z3 = -z2; |
| z1 += z2; |
| x += <span class="kw-2">&</span>s * z2; |
| |
| <span class="kw">let </span>cost_grad = model.compute_grad(x.data(), inputs, targets); |
| f2 = cost_grad.<span class="number">0</span>; |
| df2 = Vector::new(cost_grad.<span class="number">1</span>); |
| |
| m -= <span class="number">1</span>; |
| <span class="kw">if </span>length < <span class="number">0 </span>{ |
| i += <span class="number">1</span>; |
| } |
| |
| d2 = df2.dot(<span class="kw-2">&</span>s); |
| } |
| |
| <span class="kw">if </span>success { |
| f1 = f2; |
| s = s * (<span class="kw-2">&</span>df2 - <span class="kw-2">&</span>df1).dot(<span class="kw-2">&</span>df2) / df1.dot(<span class="kw-2">&</span>df1) - <span class="kw-2">&</span>df2; |
| |
| df1 = df2; |
| |
| d2 = df1.dot(<span class="kw-2">&</span>s); |
| |
| <span class="kw">if </span>d2 > <span class="number">0f64 </span>{ |
| s = -<span class="kw-2">&</span>df1; |
| d2 = -s.dot(<span class="kw-2">&</span>s); |
| } |
| |
| <span class="kw">let </span>ratio = d1 / (d2 - f64::MIN_POSITIVE); |
| <span class="kw">if </span><span class="self">self</span>.ratio < ratio { |
| z1 <span class="kw-2">*</span>= <span class="self">self</span>.ratio; |
| } <span class="kw">else </span>{ |
| z1 <span class="kw-2">*</span>= ratio; |
| } |
| |
| d1 = d2; |
| ls_failed = <span class="bool-val">false</span>; |
| } <span class="kw">else </span>{ |
| x = x0; |
| f1 = f0; |
| |
| <span class="kw">if </span>ls_failed || i <span class="kw">as </span>i32 > length.abs() { |
| <span class="kw">break</span>; |
| } |
| |
| df1 = df2; |
| |
| s = -<span class="kw-2">&</span>df1; |
| d1 = -s.dot(<span class="kw-2">&</span>s); |
| |
| z1 = <span class="number">1f64 </span>/ (<span class="number">1f64 </span>- d1); |
| ls_failed = <span class="bool-val">true</span>; |
| } |
| |
| } |
| x.into_vec() |
| } |
| } |
| </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> |
