| <!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/analysis/score.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>score.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">//! Functions for scoring a set of predictions, i.e. evaluating |
| //! how close predictions and truth are. All functions in this |
| //! module obey the convention that higher is better. |
| |
| </span><span class="kw">use </span>libnum::{Zero, One}; |
| |
| <span class="kw">use </span>linalg::{BaseMatrix, Matrix}; |
| <span class="kw">use </span>learning::toolkit::cost_fn::{CostFunc, MeanSqError}; |
| |
| <span class="comment">// ************************************ |
| // Classification Scores |
| // ************************************ |
| |
| </span><span class="doccomment">/// Returns the fraction of outputs which match their target. |
| /// |
| /// # Arguments |
| /// |
| /// * `outputs` - Iterator of output (predicted) labels. |
| /// * `targets` - Iterator of expected (actual) labels. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::analysis::score::accuracy; |
| /// let outputs = [1, 1, 1, 0, 0, 0]; |
| /// let targets = [1, 1, 0, 0, 1, 1]; |
| /// |
| /// assert_eq!(accuracy(outputs.iter(), targets.iter()), 0.5); |
| /// ``` |
| /// |
| /// # Panics |
| /// |
| /// - outputs and targets have different length |
| </span><span class="kw">pub fn </span>accuracy<I1, I2, T>(outputs: I1, targets: I2) -> f64 |
| <span class="kw">where </span>T: PartialEq, |
| I1: ExactSizeIterator + Iterator<Item=T>, |
| I2: ExactSizeIterator + Iterator<Item=T> |
| { |
| <span class="macro">assert!</span>(outputs.len() == targets.len(), <span class="string">"outputs and targets must have the same length"</span>); |
| <span class="kw">let </span>len = outputs.len() <span class="kw">as </span>f64; |
| <span class="kw">let </span>correct = outputs |
| .zip(targets) |
| .filter(|<span class="kw-2">&</span>(<span class="kw-2">ref </span>x, <span class="kw-2">ref </span>y)| x == y) |
| .count(); |
| correct <span class="kw">as </span>f64 / len |
| } |
| |
| <span class="doccomment">/// Returns the fraction of outputs rows which match their target. |
| </span><span class="kw">pub fn </span>row_accuracy(outputs: <span class="kw-2">&</span>Matrix<f64>, targets: <span class="kw-2">&</span>Matrix<f64>) -> f64 { |
| accuracy(outputs.row_iter().map(|r| r.raw_slice()), |
| targets.row_iter().map(|r| r.raw_slice())) |
| } |
| |
| <span class="doccomment">/// Returns the precision score for 2 class classification. |
| /// |
| /// Precision is calculated with true-positive / (true-positive + false-positive), |
| /// see [Precision and Recall](https://en.wikipedia.org/wiki/Precision_and_recall) for details. |
| /// |
| /// # Arguments |
| /// |
| /// * `outputs` - Iterator of output (predicted) labels which only contains 0 or 1. |
| /// * `targets` - Iterator of expected (actual) labels which only contains 0 or 1. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::analysis::score::precision; |
| /// let outputs = [1, 1, 1, 0, 0, 0]; |
| /// let targets = [1, 1, 0, 0, 1, 1]; |
| /// |
| /// assert_eq!(precision(outputs.iter(), targets.iter()), 2.0f64 / 3.0f64); |
| /// ``` |
| /// |
| /// # Panics |
| /// |
| /// - outputs and targets have different length |
| /// - outputs or targets contains a value which is not 0 or 1 |
| </span><span class="kw">pub fn </span>precision<<span class="lifetime">'a</span>, I, T>(outputs: I, targets: I) -> f64 |
| <span class="kw">where </span>I: ExactSizeIterator<Item=<span class="kw-2">&</span><span class="lifetime">'a </span>T>, |
| T: <span class="lifetime">'a </span>+ PartialEq + Zero + One |
| { |
| <span class="macro">assert!</span>(outputs.len() == targets.len(), <span class="string">"outputs and targets must have the same length"</span>); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>tpfp = <span class="number">0.0f64</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>tp = <span class="number">0.0f64</span>; |
| |
| <span class="kw">for </span>(<span class="kw-2">ref </span>o, <span class="kw-2">ref </span>t) <span class="kw">in </span>outputs.zip(targets) { |
| <span class="kw">if </span><span class="kw-2">*</span>o == <span class="kw-2">&</span>T::one() { |
| tpfp += <span class="number">1.0f64</span>; |
| <span class="kw">if </span><span class="kw-2">*</span>t == <span class="kw-2">&</span>T::one() { |
| tp += <span class="number">1.0f64</span>; |
| } |
| } |
| <span class="kw">if </span>((<span class="kw-2">*</span>t != <span class="kw-2">&</span>T::zero()) & (<span class="kw-2">*</span>t != <span class="kw-2">&</span>T::one())) | |
| ((<span class="kw-2">*</span>o != <span class="kw-2">&</span>T::zero()) & (<span class="kw-2">*</span>o != <span class="kw-2">&</span>T::one())) { |
| <span class="macro">panic!</span>(<span class="string">"precision must be used for 2 class classification"</span>) |
| } |
| } |
| tp / tpfp |
| } |
| |
| <span class="doccomment">/// Returns the recall score for 2 class classification. |
| /// |
| /// Recall is calculated with true-positive / (true-positive + false-negative), |
| /// see [Precision and Recall](https://en.wikipedia.org/wiki/Precision_and_recall) for details. |
| /// |
| /// # Arguments |
| /// |
| /// * `outputs` - Iterator of output (predicted) labels which only contains 0 or 1. |
| /// * `targets` - Iterator of expected (actual) labels which only contains 0 or 1. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::analysis::score::recall; |
| /// let outputs = [1, 1, 1, 0, 0, 0]; |
| /// let targets = [1, 1, 0, 0, 1, 1]; |
| /// |
| /// assert_eq!(recall(outputs.iter(), targets.iter()), 0.5); |
| /// ``` |
| /// |
| /// # Panics |
| /// |
| /// - outputs and targets have different length |
| /// - outputs or targets contains a value which is not 0 or 1 |
| </span><span class="kw">pub fn </span>recall<<span class="lifetime">'a</span>, I, T>(outputs: I, targets: I) -> f64 |
| <span class="kw">where </span>I: ExactSizeIterator<Item=<span class="kw-2">&</span><span class="lifetime">'a </span>T>, |
| T: <span class="lifetime">'a </span>+ PartialEq + Zero + One |
| { |
| <span class="macro">assert!</span>(outputs.len() == targets.len(), <span class="string">"outputs and targets must have the same length"</span>); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>tpfn = <span class="number">0.0f64</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>tp = <span class="number">0.0f64</span>; |
| |
| <span class="kw">for </span>(<span class="kw-2">ref </span>o, <span class="kw-2">ref </span>t) <span class="kw">in </span>outputs.zip(targets) { |
| <span class="kw">if </span><span class="kw-2">*</span>t == <span class="kw-2">&</span>T::one() { |
| tpfn += <span class="number">1.0f64</span>; |
| <span class="kw">if </span><span class="kw-2">*</span>o == <span class="kw-2">&</span>T::one() { |
| tp += <span class="number">1.0f64</span>; |
| } |
| } |
| <span class="kw">if </span>((<span class="kw-2">*</span>t != <span class="kw-2">&</span>T::zero()) & (<span class="kw-2">*</span>t != <span class="kw-2">&</span>T::one())) | |
| ((<span class="kw-2">*</span>o != <span class="kw-2">&</span>T::zero()) & (<span class="kw-2">*</span>o != <span class="kw-2">&</span>T::one())) { |
| <span class="macro">panic!</span>(<span class="string">"recall must be used for 2 class classification"</span>) |
| } |
| } |
| tp / tpfn |
| } |
| |
| <span class="doccomment">/// Returns the f1 score for 2 class classification. |
| /// |
| /// F1-score is calculated with 2 * precision * recall / (precision + recall), |
| /// see [F1 score](https://en.wikipedia.org/wiki/F1_score) for details. |
| /// |
| /// # Arguments |
| /// |
| /// * `outputs` - Iterator of output (predicted) labels which only contains 0 or 1. |
| /// * `targets` - Iterator of expected (actual) labels which only contains 0 or 1. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::analysis::score::f1; |
| /// let outputs = [1, 1, 1, 0, 0, 0]; |
| /// let targets = [1, 1, 0, 0, 1, 1]; |
| /// |
| /// assert_eq!(f1(outputs.iter(), targets.iter()), 0.5714285714285714); |
| /// ``` |
| /// |
| /// # Panics |
| /// |
| /// - outputs and targets have different length |
| /// - outputs or targets contains a value which is not 0 or 1 |
| </span><span class="kw">pub fn </span>f1<<span class="lifetime">'a</span>, I, T>(outputs: I, targets: I) -> f64 |
| <span class="kw">where </span>I: ExactSizeIterator<Item=<span class="kw-2">&</span><span class="lifetime">'a </span>T>, |
| T: <span class="lifetime">'a </span>+ PartialEq + Zero + One |
| { |
| <span class="macro">assert!</span>(outputs.len() == targets.len(), <span class="string">"outputs and targets must have the same length"</span>); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>tpos = <span class="number">0.0f64</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>fpos = <span class="number">0.0f64</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>fneg = <span class="number">0.0f64</span>; |
| |
| <span class="kw">for </span>(<span class="kw-2">ref </span>o, <span class="kw-2">ref </span>t) <span class="kw">in </span>outputs.zip(targets) { |
| <span class="kw">if </span>(<span class="kw-2">*</span>o == <span class="kw-2">&</span>T::one()) & (<span class="kw-2">*</span>t == <span class="kw-2">&</span>T::one()) { |
| tpos += <span class="number">1.0f64</span>; |
| } <span class="kw">else if </span><span class="kw-2">*</span>t == <span class="kw-2">&</span>T::one() { |
| fpos += <span class="number">1.0f64</span>; |
| } <span class="kw">else if </span><span class="kw-2">*</span>o == <span class="kw-2">&</span>T::one() { |
| fneg += <span class="number">1.0f64</span>; |
| } |
| <span class="kw">if </span>((<span class="kw-2">*</span>t != <span class="kw-2">&</span>T::zero()) & (<span class="kw-2">*</span>t != <span class="kw-2">&</span>T::one())) | |
| ((<span class="kw-2">*</span>o != <span class="kw-2">&</span>T::zero()) & (<span class="kw-2">*</span>o != <span class="kw-2">&</span>T::one())) { |
| <span class="macro">panic!</span>(<span class="string">"f1-score must be used for 2 class classification"</span>) |
| } |
| } |
| <span class="number">2.0f64 </span>* tpos / (<span class="number">2.0f64 </span>* tpos + fneg + fpos) |
| } |
| |
| <span class="comment">// ************************************ |
| // Regression Scores |
| // ************************************ |
| |
| // TODO: generalise to accept arbitrary iterators of diff-able things |
| </span><span class="doccomment">/// Returns the additive inverse of the mean-squared-error of the |
| /// outputs. So higher is better, and the returned value is always |
| /// negative. |
| </span><span class="kw">pub fn </span>neg_mean_squared_error(outputs: <span class="kw-2">&</span>Matrix<f64>, targets: <span class="kw-2">&</span>Matrix<f64>) -> f64 |
| { |
| <span class="comment">// MeanSqError divides the actual mean squared error by two. |
| </span>-<span class="number">2f64 </span>* MeanSqError::cost(outputs, targets) |
| } |
| |
| <span class="attribute">#[cfg(test)] |
| </span><span class="kw">mod </span>tests { |
| <span class="kw">use </span>linalg::Matrix; |
| <span class="kw">use super</span>::{accuracy, precision, recall, f1, neg_mean_squared_error}; |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_accuracy() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">5</span>, <span class="number">6</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">3</span>, <span class="number">5</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(accuracy(outputs.iter(), targets.iter()), <span class="number">2f64</span>/<span class="number">3f64</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(accuracy(outputs.iter(), targets.iter()), <span class="number">5.0f64 </span>/ <span class="number">6.0f64</span>); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_precision() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(precision(outputs.iter(), targets.iter()), <span class="number">2.0f64 </span>/ <span class="number">3.0f64</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(precision(outputs.iter(), targets.iter()), <span class="number">0.8</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>]; |
| <span class="macro">assert_eq!</span>(precision(outputs.iter(), targets.iter()), <span class="number">2.0f64 </span>/ <span class="number">3.0f64</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(precision(outputs.iter(), targets.iter()), <span class="number">0.4</span>); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_precision_outputs_not_2class() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| precision(outputs.iter(), targets.iter()); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_precision_targets_not_2class() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| precision(outputs.iter(), targets.iter()); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_recall() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(recall(outputs.iter(), targets.iter()), <span class="number">0.5</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(recall(outputs.iter(), targets.iter()), <span class="number">1.0</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>]; |
| <span class="macro">assert_eq!</span>(recall(outputs.iter(), targets.iter()), <span class="number">0.4</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(recall(outputs.iter(), targets.iter()), <span class="number">2.0f64 </span>/ <span class="number">3.0f64</span>); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_recall_outputs_not_2class() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| recall(outputs.iter(), targets.iter()); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_recall_targets_not_2class() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| recall(outputs.iter(), targets.iter()); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_f1() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(f1(outputs.iter(), targets.iter()), <span class="number">0.5714285714285714</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(f1(outputs.iter(), targets.iter()), <span class="number">0.8888888888888888</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>]; |
| <span class="macro">assert_eq!</span>(f1(outputs.iter(), targets.iter()), <span class="number">0.5</span>); |
| |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| <span class="macro">assert_eq!</span>(f1(outputs.iter(), targets.iter()), <span class="number">0.5</span>); |
| } |
| |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_f1_outputs_not_2class() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| f1(outputs.iter(), targets.iter()); |
| } |
| |
| <span class="attribute">#[test] |
| #[should_panic] |
| </span><span class="kw">fn </span>test_f1_targets_not_2class() { |
| <span class="kw">let </span>outputs = [<span class="number">1</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">0</span>]; |
| <span class="kw">let </span>targets = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">0</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">1</span>]; |
| f1(outputs.iter(), targets.iter()); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_neg_mean_squared_error_1d() { |
| <span class="kw">let </span>outputs = Matrix::new(<span class="number">3</span>, <span class="number">1</span>, <span class="macro">vec!</span>[<span class="number">1f64</span>, <span class="number">2f64</span>, <span class="number">3f64</span>]); |
| <span class="kw">let </span>targets = Matrix::new(<span class="number">3</span>, <span class="number">1</span>, <span class="macro">vec!</span>[<span class="number">2f64</span>, <span class="number">4f64</span>, <span class="number">3f64</span>]); |
| <span class="macro">assert_eq!</span>(neg_mean_squared_error(<span class="kw-2">&</span>outputs, <span class="kw-2">&</span>targets), -<span class="number">5f64</span>/<span class="number">3f64</span>); |
| } |
| |
| <span class="attribute">#[test] |
| </span><span class="kw">fn </span>test_neg_mean_squared_error_2d() { |
| <span class="kw">let </span>outputs = Matrix::new(<span class="number">3</span>, <span class="number">2</span>, <span class="macro">vec!</span>[ |
| <span class="number">1f64</span>, <span class="number">2f64</span>, |
| <span class="number">3f64</span>, <span class="number">4f64</span>, |
| <span class="number">5f64</span>, <span class="number">6f64 |
| </span>]); |
| <span class="kw">let </span>targets = Matrix::new(<span class="number">3</span>, <span class="number">2</span>, <span class="macro">vec!</span>[ |
| <span class="number">1.5f64</span>, <span class="number">2.5f64</span>, |
| <span class="number">5f64</span>, <span class="number">6f64</span>, |
| <span class="number">5.5f64</span>, <span class="number">6.5f64 |
| </span>]); |
| <span class="macro">assert_eq!</span>(neg_mean_squared_error(<span class="kw-2">&</span>outputs, <span class="kw-2">&</span>targets), -<span class="number">3f64</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> |
