nowrap

Author: haifengl

classification.html

@@ -959,7 +959,7 @@ <h2 id="logit">Logistic Regression</h2>
         </div>
         <div class="tab-pane active" id="java_14">
             <div class="code" style="text-align: left;">
-          <pre class="prettyprint lang-java"><code>
+          <pre class="prettyprint lang-java"><code style="white-space: preserve nowrap;">
     public class LogisticRegression {
         public static LogisticRegression fit(double[][] x, int[] y, double lambda, double tol, int maxIter);
     }
@@ -1296,7 +1296,7 @@ <h2 id="neural-network">Multilayer Perceptron Neural Network</h2>
         significantly exceeds the needed free parameters. There are two general
         approaches for avoiding this problem: The first is to use cross-validation
         and similar techniques to check for the presence of over-fitting and
-        optimally select hyper-parameters such as to minimize the generalization
+        optimally select hyperparameters such as to minimize the generalization
         error. The second is to use some form of regularization, which emerges
         naturally in a Bayesian framework, where the regularization can be
         performed by selecting a larger prior probability over simpler models;
@@ -1576,7 +1576,7 @@ <h2 id="svm">Support Vector Machines</h2>
     var x = zip.drop(0).toArray();
     var y = zip.column(0).toIntArray();
     var kernel = new GaussianKernel(8.0);
-    var model = OneVersusRest.fit(x, y, (x, y) -> SVM.fit(x, y, kernel, 5, 1E-3));
+    var model = OneVersusRest.fit(x, y, (x, y) -> SVM.fit(x, y, kernel, new SVM.Options(5)));
           </code></pre>
             </div>
         </div>
@@ -1658,7 +1658,7 @@ <h2 id="cart">Decision Trees</h2>
         </div>
         <div class="tab-pane active" id="java_24">
             <div class="code" style="text-align: left;">
-          <pre class="prettyprint lang-java"><code>
+          <pre class="prettyprint lang-java"><code style="white-space: preserve nowrap;">
     public class DecisionTree {
          public static DecisionTree fit(Formula formula, DataFrame data, SplitRule rule, int maxDepth, int maxNodes, int nodeSize);
     }
@@ -1700,7 +1700,7 @@ <h2 id="cart">Decision Trees</h2>
     <div class="tab-content">
         <div class="tab-pane" id="scala_25">
             <div class="code" style="text-align: left;">
-    <pre class="prettyprint lang-scala"><code>
+    <pre class="prettyprint lang-scala"><code style="white-space: preserve nowrap;">
     smile> cart("V1" ~, zip)
     [main] INFO smile.util.package$ - Decision Tree runtime: 0:00:00.754375
     res22: DecisionTree = n=7291
@@ -1734,7 +1734,7 @@ <h2 id="cart">Decision Trees</h2>
         </div>
         <div class="tab-pane active" id="java_25">
             <div class="code" style="text-align: left;">
-          <pre class="prettyprint lang-java"><code>
+          <pre class="prettyprint lang-java"><code style="white-space: preserve nowrap;">
     smile> import smile.data.formula.*
     smile> DecisionTree.fit(Formula.lhs("V1"), zip)
     $232 ==> n=7291
@@ -1768,7 +1768,7 @@ <h2 id="cart">Decision Trees</h2>
         </div>
         <div class="tab-pane" id="kotlin_25">
             <div class="code" style="text-align: left;">
-          <pre class="prettyprint lang-kotlin"><code>
+          <pre class="prettyprint lang-kotlin"><code style="white-space: preserve nowrap;">
     >>> import smile.data.formula.*
     >>> cart(Formula.lhs("V1"), zip)
     res160: smile.classification.DecisionTree = n=7291

faq.html

@@ -292,7 +292,7 @@ <h2 class="question" id="link-with-smile">Link with Smile</h2>
         some clustering algorithms, Gaussian Process regression, MLP, etc.).
         By default, Smile includes OpenBLAS for macOS, Windows, and Linux:</p>
     <div class="code" style="text-align: left;">
-    <pre class="prettyprint"><code>
+    <pre class="prettyprint"><code style="white-space: preserve nowrap;">
     libraryDependencies ++= Seq(
       "org.bytedeco" % "javacpp"   % "1.5.11"        classifier "macosx-arm64" classifier "macosx-x86_64" classifier "windows-x86_64" classifier "linux-x86_64",
       "org.bytedeco" % "openblas"  % "0.3.28-1.5.11" classifier "macosx-arm64" classifier "macosx-x86_64" classifier "windows-x86_64" classifier "linux-x86_64",

feature.html

@@ -614,8 +614,8 @@ <h3 id="tree-shap">TreeSHAP</h3>
 smile&gt; smile.sort.QuickSort.sort(importance, fields);
 
 smile&gt; for (int i = 0; i &lt; importance.length; i++) {
-   ...&gt;     System.out.format("%-15s %12.4f%n", fields[i], importance[i]);
-   ...&gt; }
+           System.out.format("%-15s %12.4f%n", fields[i], importance[i]);
+       }
 CHAS              86870.7977
 ZN               140436.2962
 RAD              150921.6370
@@ -636,8 +636,8 @@ <h3 id="tree-shap">TreeSHAP</h3>
 smile&gt; smile.sort.QuickSort.sort(shap, fields);
 
 smile&gt; for (int i = 0; i &lt; shap.length; i++) {
-   ...&gt;     System.out.format("%-15s %12.4f%n", fields[i], shap[i]);
-   ...&gt; }
+           System.out.format("%-15s %12.4f%n", fields[i], shap[i]);
+       }
 CHAS                  0.0486
 ZN                    0.0535
 RAD                   0.0622

llm.html

@@ -248,12 +248,12 @@ <h2 id="llama" class="title">Llama 3</h2>
         call <code>chat()</code> method to generate conversation responses.</p>
 
     <ul class="nav nav-tabs">
-        <li class="active"><a href="#java_1" data-toggle="tab">Java</a></li>
+        <li class="active"><a href="#java_2" data-toggle="tab">Java</a></li>
     </ul>
     <div class="tab-content">
-        <div class="tab-pane active" id="java_1">
+        <div class="tab-pane active" id="java_2">
             <div class="code" style="text-align: left;">
-          <pre class="prettyprint lang-java"><code>
+          <pre class="prettyprint lang-java"><code style="white-space: preserve nowrap;">
     // List of conversational dialogs, where each dialog is a list of messages.
     Message[][] dialogs = {
           {