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 <!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/registry/src/github.com-1ecc6299db9ec823/gbdt-0.1.1/src/fitness.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>fitness.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="../../gbdt/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="../../gbdt/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">//! This module implements some math functions used for gradient boosting process.

 </span><span class="attribute">#[cfg(all(feature = <span class="string">&quot;mesalock_sgx&quot;</span>, not(target_env = <span class="string">&quot;sgx&quot;</span>)))]
 </span><span class="kw">use </span>std::prelude::v1::<span class="kw-2">*</span>;

 <span class="kw">use </span><span class="kw">crate</span>::decision_tree::{DataVec, PredVec, ValueType};

 <span class="doccomment">/// Comparing two number with a costomized floating error threshold.
 ///
 /// # Example
 /// ```rust
 /// use gbdt::fitness::almost_equal_thrs;
 /// assert_eq!(true, almost_equal_thrs(1.0, 0.998, 0.01));
 /// ```
 </span><span class="attribute">#[inline(always)]
 </span><span class="kw">pub fn </span>almost_equal_thrs(a: ValueType, b: ValueType, thrs: f64) -&gt; bool {
     f64::from((a - b).abs()) &lt; thrs
 }

 <span class="doccomment">/// Comparing two number with default floating error threshold.
 ///
 /// # Example
 /// ```rust
 /// use gbdt::fitness::almost_equal;
 /// assert_eq!(false, almost_equal(1.0, 0.998));
 /// assert_eq!(true, almost_equal(1.0, 0.999998));
 /// ```
 </span><span class="kw">pub fn </span>almost_equal(a: ValueType, b: ValueType) -&gt; bool {
     f64::from((a - b).abs()) &lt; <span class="number">1.0e-5
 </span>}

 <span class="doccomment">/// Return whether the first n data in data vector have same target values.
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 </span><span class="kw">pub fn </span>same(dv: <span class="kw-2">&amp;</span>DataVec, len: usize) -&gt; bool {
     <span class="macro">assert!</span>(dv.len() &gt;= len);

     <span class="kw">if </span>len &lt; <span class="number">1 </span>{
         <span class="kw">return </span><span class="bool-val">false</span>;
     }

     <span class="kw">let </span>t: ValueType = dv[<span class="number">0</span>].target;
     <span class="kw">for </span>i <span class="kw">in </span>dv.iter().skip(<span class="number">1</span>) {
         <span class="kw">if </span>!(almost_equal(t, i.target)) {
             <span class="kw">return </span><span class="bool-val">false</span>;
         }
     }
     <span class="bool-val">true
 </span>}

 <span class="doccomment">/// Logistic value function.
 </span><span class="kw">pub fn </span>logit(f: ValueType) -&gt; ValueType {
     <span class="number">1.0 </span>/ (<span class="number">1.0 </span>+ (-<span class="number">2.0 </span>* f).exp())
 }

 <span class="doccomment">/// Negative binomial log-likelyhood loss function.
 </span><span class="kw">pub fn </span>logit_loss(y: ValueType, f: ValueType) -&gt; ValueType {
     <span class="number">2.0 </span>* (<span class="number">1.0 </span>+ (-<span class="number">2.0 </span>* y * f)).ln()
 }

 <span class="doccomment">/// Log-likelyhood gradient calculation.
 </span><span class="kw">pub fn </span>logit_loss_gradient(y: ValueType, f: ValueType) -&gt; ValueType {
     <span class="number">2.0 </span>* y / (<span class="number">1.0 </span>+ (<span class="number">2.0 </span>* y * f).exp())
 }

 <span class="doccomment">/// LAD loss function.
 </span><span class="kw">pub fn </span>lad_loss(y: ValueType, f: ValueType) -&gt; ValueType {
     (y - f).abs()
 }

 <span class="doccomment">/// LAD gradient calculation.
 </span><span class="kw">pub fn </span>lad_loss_gradient(y: ValueType, f: ValueType) -&gt; ValueType {
     <span class="kw">if </span>y - f &gt; <span class="number">0.0 </span>{
         <span class="number">1.0
     </span>} <span class="kw">else </span>{
         -<span class="number">1.0
     </span>}
 }

 <span class="doccomment">/// RMSE (Root-Mean-Square deviation) calculation for first n element in data vector.
 /// See [wikipedia](https://en.wikipedia.org/wiki/Root-mean-square_deviation) for detailed algorithm.
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 ///
 /// If the length of data vector and predicted vector is not same, it will panic.
 </span><span class="attribute">#[allow(non_snake_case)]
 </span><span class="kw">pub fn </span>RMSE(dv: <span class="kw-2">&amp;</span>DataVec, predict: <span class="kw-2">&amp;</span>PredVec, len: usize) -&gt; ValueType {
     <span class="macro">assert_eq!</span>(dv.len(), predict.len());
     <span class="macro">assert!</span>(dv.len() &gt;= len);

     <span class="kw">let </span><span class="kw-2">mut </span>s: f64 = <span class="number">0.0</span>;
     <span class="kw">let </span><span class="kw-2">mut </span>c: f64 = <span class="number">0.0</span>;

     <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..dv.len() {
         s += (f64::from(predict[i]) - f64::from(dv[i].label)).powf(<span class="number">2.0</span>) * f64::from(dv[i].weight);
         c += f64::from(dv[i].weight);
     }

     <span class="kw">if </span>c.abs() &lt; <span class="number">1e-10 </span>{
         <span class="number">0.0
     </span>} <span class="kw">else </span>{
         (s / c) <span class="kw">as </span>ValueType
     }
 }

 <span class="doccomment">/// MAE (Mean Absolute Error) calculation for first n element in data vector.
 /// See [wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_error) for detail for detailed algorithm.
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 ///
 /// If the length of data vector and predicted vector is not same, it will panic.
 </span><span class="attribute">#[allow(non_snake_case)]
 </span><span class="kw">pub fn </span>MAE(dv: <span class="kw-2">&amp;</span>DataVec, predict: <span class="kw-2">&amp;</span>PredVec, len: usize) -&gt; ValueType {
     <span class="macro">assert_eq!</span>(dv.len(), predict.len());
     <span class="macro">assert!</span>(dv.len() &gt;= len);

     <span class="kw">let </span><span class="kw-2">mut </span>s: ValueType = <span class="number">0.0</span>;
     <span class="kw">let </span><span class="kw-2">mut </span>c: ValueType = <span class="number">0.0</span>;

     <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..dv.len() {
         s += (predict[i] - dv[i].label).abs() * dv[i].weight;
         c += dv[i].weight;
     }
     s / c
 }

 <span class="kw">struct </span>AucPred {
     score: ValueType,
     label: ValueType,
 }

 <span class="doccomment">/// AUC (Area Under the Curve) calculation for first n element in data vector.
 /// See [wikipedia](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve) for detailed algorithm.
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 ///
 /// If the length of data vector and predicted vector is not same, it will panic.
 ///
 /// If the data vector contains only one class or more than two classes, it will panic.
 </span><span class="attribute">#[allow(non_snake_case)]
 </span><span class="kw">pub fn </span>AUC(dv: <span class="kw-2">&amp;</span>DataVec, predict: <span class="kw-2">&amp;</span>PredVec, len: usize) -&gt; ValueType {
     <span class="macro">assert_eq!</span>(dv.len(), predict.len());
     <span class="macro">assert!</span>(dv.len() &gt;= len);

     <span class="kw">let </span><span class="kw-2">mut </span>classes: Vec&lt;ValueType&gt; = Vec::new();
     <span class="kw">for </span>i <span class="kw">in </span>dv {
         <span class="kw">if </span>!classes.contains(<span class="kw-2">&amp;</span>i.label) {
             classes.push(i.label);
         }
     }
     <span class="macro">assert!</span>(classes.len() == <span class="number">2</span>);

     <span class="kw">let </span><span class="kw-2">mut </span>preds: Vec&lt;AucPred&gt; = Vec::new();
     <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..predict.len() {
         preds.push(AucPred {
             score: predict[i],
             label: dv[i].label,
         });
     }
     preds.sort_by(|a, b| b.score.partial_cmp(<span class="kw-2">&amp;</span>a.score).unwrap());

     <span class="kw">let </span><span class="kw-2">mut </span>tp: ValueType = <span class="number">0.0</span>;
     <span class="kw">let </span><span class="kw-2">mut </span>fp: ValueType = <span class="number">0.0</span>;
     <span class="kw">let </span>(<span class="kw-2">mut </span>tps, <span class="kw-2">mut </span>fps) = (<span class="macro">vec!</span>[], <span class="macro">vec!</span>[]);
     <span class="kw">for </span>x <span class="kw">in </span>preds.iter() {
         tps.push(tp);
         fps.push(fp);
         <span class="kw">if </span>almost_equal(x.label, <span class="number">1.0</span>) {
             tp += <span class="number">1.0</span>;
         } <span class="kw">else </span>{
             fp += <span class="number">1.0</span>;
         }
     }
     tps.push(tp);
     fps.push(fp);
     <span class="kw">let </span>true_positives = tps[tps.len() - <span class="number">1</span>];
     <span class="kw">let </span>false_positives = fps[fps.len() - <span class="number">1</span>];
     <span class="comment">// println!(&quot;tps={}, fps={}&quot;, true_positives, false_positives);

     </span><span class="kw">for </span>(tp, fp) <span class="kw">in </span>tps.iter_mut().zip(fps.iter_mut()) {
         <span class="kw-2">*</span>tp /= true_positives;
         <span class="kw-2">*</span>fp /= false_positives;
         <span class="comment">// println!(&quot;fp={}, tp={}&quot;, fp, tp);
     </span>}

     <span class="kw">let </span><span class="kw-2">mut </span>prev_y: ValueType = <span class="kw-2">*</span>tps.first().unwrap();
     <span class="kw">let </span><span class="kw-2">mut </span>prev_x: ValueType = <span class="kw-2">*</span>fps.first().unwrap();

     <span class="kw">let </span><span class="kw-2">mut </span>auc: ValueType = <span class="number">0.0</span>;

     <span class="kw">for </span>(<span class="kw-2">&amp;</span>x, <span class="kw-2">&amp;</span>y) <span class="kw">in </span>fps.iter().skip(<span class="number">1</span>).zip(tps.iter().skip(<span class="number">1</span>)) {
         auc += (x - prev_x) * (prev_y + y) / <span class="number">2.0</span>;
         prev_x = x;
         prev_y = y;
     }

     auc
 }

 <span class="doccomment">/// Return the weighted target average for first n data in data vector.
 ///
 /// # Example
 /// ```rust
 /// use gbdt::decision_tree::{DataVec, Data, VALUE_TYPE_UNKNOWN};
 /// use gbdt::fitness::{average, almost_equal};
 /// let mut dv: DataVec = Vec::new();
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.1,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.2,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.3,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.4,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// assert!(almost_equal(0.3, average(&amp;dv, dv.len())));
 /// ```
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 </span><span class="kw">pub fn </span>average(dv: <span class="kw-2">&amp;</span>DataVec, len: usize) -&gt; ValueType {
     <span class="macro">assert!</span>(dv.len() &gt;= len);

     <span class="kw">if </span>len == <span class="number">0 </span>{
         <span class="kw">return </span><span class="number">0.0</span>;
     }

     <span class="kw">let </span><span class="kw-2">mut </span>s: ValueType = <span class="number">0.0</span>;
     <span class="kw">let </span><span class="kw-2">mut </span>c: ValueType = <span class="number">0.0</span>;
     <span class="kw">for </span>d <span class="kw">in </span>dv {
         s += d.weight * d.target;
         c += d.weight;
     }
     s / c
 }

 <span class="doccomment">/// Return the weighted label average for first n data in data vector.
 ///
 /// # Example
 /// ```rust
 /// use gbdt::decision_tree::{DataVec, Data, VALUE_TYPE_UNKNOWN};
 /// use gbdt::fitness::{label_average, almost_equal};
 /// let mut dv: DataVec = Vec::new();
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.1,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.2,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.3,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.4,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// assert!(almost_equal(0.4, label_average(&amp;dv, dv.len())));
 /// ```
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 </span><span class="kw">pub fn </span>label_average(dv: <span class="kw-2">&amp;</span>DataVec, len: usize) -&gt; ValueType {
     <span class="macro">assert!</span>(dv.len() &gt;= len);
     <span class="kw">let </span><span class="kw-2">mut </span>s: f64 = <span class="number">0.0</span>;
     <span class="kw">let </span><span class="kw-2">mut </span>c: f64 = <span class="number">0.0</span>;
     <span class="kw">for </span>d <span class="kw">in </span>dv {
         s += f64::from(d.label) * f64::from(d.weight);
         c += f64::from(d.weight);
     }
     <span class="kw">if </span>c.abs() &lt; <span class="number">1e-10 </span>{
         <span class="number">0.0
     </span>} <span class="kw">else </span>{
         (s / c) <span class="kw">as </span>ValueType
     }
 }

 <span class="doccomment">/// Return the weighted label median for first n data in data vector.
 ///
 /// # Example
 /// ```rust
 /// use gbdt::decision_tree::{DataVec, Data, VALUE_TYPE_UNKNOWN};
 /// use gbdt::fitness::{weighted_label_median, almost_equal};
 /// let mut dv: DataVec = Vec::new();
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.1,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.2,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.3,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.4,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// assert!(almost_equal(0.0, weighted_label_median(&amp;dv, dv.len())));
 /// ```
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 </span><span class="kw">pub fn </span>weighted_label_median(dv: <span class="kw-2">&amp;</span>DataVec, len: usize) -&gt; ValueType {
     <span class="macro">assert!</span>(dv.len() &gt;= len);
     <span class="kw">let </span><span class="kw-2">mut </span>dv_copy = dv.to_vec();
     dv_copy.sort_by(|a, b| a.label.partial_cmp(<span class="kw-2">&amp;</span>b.label).unwrap());
     <span class="kw">let </span><span class="kw-2">mut </span>all_weight: f64 = <span class="number">0.0</span>;
     <span class="kw">for </span>d <span class="kw">in </span><span class="kw-2">&amp;</span>dv_copy {
         all_weight += f64::from(d.weight);
     }

     <span class="kw">let </span><span class="kw-2">mut </span>weighted_median: ValueType = <span class="number">0.0</span>;
     <span class="kw">let </span><span class="kw-2">mut </span>weight: f64 = <span class="number">0.0</span>;

     <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..len {
         weight += f64::from(dv_copy[i].weight);
         <span class="kw">if </span>weight * <span class="number">2.0 </span>&gt; all_weight {
             <span class="kw">if </span>i - <span class="number">1 </span>&gt; <span class="number">0 </span>{
                 weighted_median = (dv_copy[i].label + dv_copy[i - <span class="number">1</span>].label) / <span class="number">2.0</span>;
             } <span class="kw">else </span>{
                 weighted_median = dv_copy[i].label;
             }
             <span class="kw">break</span>;
         }
     }
     weighted_median
 }

 <span class="doccomment">/// Return the weighted residual median for first n data in data vector.
 ///
 /// # Example
 /// ```rust
 /// use gbdt::decision_tree::{DataVec, Data, VALUE_TYPE_UNKNOWN};
 /// use gbdt::fitness::{weighted_residual_median, almost_equal};
 /// let mut dv: DataVec = Vec::new();
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.1,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 1.0,
 ///     weight: 0.2,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.3,
 ///     label: 1.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// dv.push(Data {
 ///     feature: Vec::new(),
 ///     target: 0.0,
 ///     weight: 0.4,
 ///     label: 0.0,
 ///     residual: 0.5,
 ///     initial_guess: VALUE_TYPE_UNKNOWN,
 /// });
 /// assert!(almost_equal(0.5, weighted_residual_median(&amp;dv, dv.len())));
 /// ```
 ///
 /// # Panic
 /// If the specified length is greater than the length of data vector, it will panic.
 </span><span class="kw">pub fn </span>weighted_residual_median(dv: <span class="kw-2">&amp;</span>DataVec, len: usize) -&gt; ValueType {
     <span class="macro">assert!</span>(dv.len() &gt;= len);
     <span class="kw">let </span><span class="kw-2">mut </span>dv_copy = dv.to_vec();
     dv_copy.sort_by(|a, b| a.residual.partial_cmp(<span class="kw-2">&amp;</span>b.residual).unwrap());
     <span class="kw">let </span><span class="kw-2">mut </span>all_weight: ValueType = <span class="number">0.0</span>;
     <span class="kw">for </span>d <span class="kw">in </span><span class="kw-2">&amp;</span>dv_copy {
         all_weight += d.weight;
     }

     <span class="kw">let </span><span class="kw-2">mut </span>weighted_median: ValueType = <span class="number">0.0</span>;
     <span class="kw">let </span><span class="kw-2">mut </span>weight: ValueType = <span class="number">0.0</span>;

     <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..len {
         weight += dv_copy[i].weight;
         <span class="kw">if </span>weight * <span class="number">2.0 </span>&gt; all_weight {
             <span class="kw">if </span>i - <span class="number">1 </span>&gt; <span class="number">0 </span>{
                 weighted_median = (dv_copy[i].residual + dv_copy[i - <span class="number">1</span>].residual) / <span class="number">2.0</span>;
             } <span class="kw">else </span>{
                 weighted_median = dv_copy[i].residual;
             }
             <span class="kw">break</span>;
         }
     }
     weighted_median
 }
 </code></pre></div>
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