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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<title>Smile - Home</title>
<meta name="description" content="Statistical Machine Intelligence and Learning Engine">
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<img src="images/smile.jpg" style="height:100px; width:auto; vertical-align: bottom; margin-top: 20px; margin-right: 20px;">
<span class="tagline">Smile — Statistical Machine Intelligence and Learning Engine</span>
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<li><a href="quickstart.html">Quick Start</a></li>
<li><a href="overview.html">What's Machine Learning</a></li>
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<h5>Latest News</h5>
<ul class="list-unstyled">
<li><a href="https://github.com/haifengl/smile/releases/tag/v4.2.0">Smile 4.2.0 Released!</a>
<span class="small">(Feb 1, 2025)</span></li>
<li><a href="https://github.com/haifengl/smile/releases/tag/v4.1.0">Smile 4.1.0 Released!</a>
<span class="small">(Jan 12, 2025)</span></li>
<li><a href="https://github.com/haifengl/smile/releases/tag/v4.0.0">Smile 4.0.0 Released!</a>
<span class="small">(Nov 25, 2024)</span></li>
<li><a href="https://github.com/haifengl/smile/releases/tag/v3.1.1">Smile 3.1.1 Released!</a>
<span class="small">(May 22, 2024)</span></li>
<li><a href="https://github.com/haifengl/smile/releases/tag/v3.1.0">Smile 3.1.0 Released!</a>
<span class="small">(Apr 2, 2024)</span></li>
<li><a href="https://medium.com/@lbq999/time-series-prediction-model-in-java-using-the-smile-library-df4bcdc4cfa0">Time-Series Prediction Model in Java Using the Smile Library</a>
<span class="small">(Oct 16, 2023, by Boqiang & Henry)</span></li>
<li><a href="https://www.almabetter.com/bytes/articles/machine-learning-libraries">10 Best Machine Learning Libraries You Should Know in 2024</a>
<span class="small">(May 16, 2023, by AlmaBetter Bytes)</span></li>
<li><a href="https://magnusgunnarsson.se/offentlig/getting-started-with-smile-with-kotlin/">Getting started with SMILE with Kotlin</a>
<span class="small">(Feb 23, 2021, by Magnus MacHale-Gunnarsson)</span></li>
<li><a href="https://www.youtube.com/watch?v=aqT_0qFOBUI">Dex Machine Learning with SMILE ML</a>
<span class="small">(Mar 28, 2018, by Patrick Martin)</span></li>
<li><a href="https://www.infoworld.com/article/3220428/machine-learning/3-projects-lighting-a-fire-under-machine-learning.html">3 projects lighting a fire under machine learning</a>
<span class="small">(Aug 30, 2017, by InfoWorld)</span></li>
<li><a href="https://www.kdnuggets.com/2017/04/five-machine-learning-projects-cant-overlook-april.html">5 Machine Learning Projects You Can No Longer Overlook</a>
<span class="small">(Apr 19, 2017, by KDnuggets)</span></li>
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<p style="font-size: 16px; font-weight: 500; color: #555;">
Built-in Algorithms:
</p>
<dl>
<dt><a href="api/java/smile/classification/package-summary.html">Classification</a></dt>
<dd><p>
<a href="api/java/smile/classification/DecisionTree.html">Decision Trees</a>,
<a href="api/java/smile/classification/AdaBoost.html">AdaBoost</a>,
<a href="api/java/smile/classification/GradientTreeBoost.html">Gradient Boosting</a>,
<a href="api/java/smile/classification/RandomForest.html">Random Forest</a>,
<a href="api/java/smile/classification/LogisticRegression.html">Logistic Regression</a>,
<a href="api/java/smile/classification/MLP.html">Neural Networks</a>,
<a href="api/java/smile/classification/SVM.html">Support Vector Machines</a>,
<a href="api/java/smile/classification/RBFNetwork.html">RBF Networks</a>,
<a href="api/java/smile/classification/Maxent.html">Maximum Entropy Classifier</a>,
<a href="api/java/smile/classification/NaiveBayes.html">Generic Naïve Bayes Classifier</a>,
<a href="api/java/smile/classification/DiscreteNaiveBayes.html">Naïve Bayes Document Classfier</a>,
<a href="api/java/smile/classification/FLD.html">Fisher</a> /
<a href="api/java/smile/classification/LDA.html">Linear</a> /
<a href="api/java/smile/classification/QDA.html">Quadratic</a> /
<a href="api/java/smile/classification/RDA.html">Regularized Discriminant Analysis</a>,
<a href="api/java/smile/classification/PlattScaling.html">Platt Scaling</a>,
<a href="api/java/smile/classification/IsotonicRegressionScaling.html">Isotonic Regression Scaling</a>,
<a href="api/java/smile/classification/OneVersusOne.html">One vs. One</a>,
<a href="api/java/smile/classification/OneVersusRest.html">One vs. Rest</a>
</p></dd>
<dt><dt><a href="api/java/smile/regression/package-summary.html">Regression</a></dt>
<dd><p>
<a href="api/java/smile/regression/OLS.html">Linear Regression</a>,
<a href="api/java/smile/regression/LASSO.html">LASSO</a>,
<a href="api/java/smile/regression/ElasticNet.html">ElasticNet</a>,
<a href="api/java/smile/regression/RidgeRegression.html">Ridge Regression</a>,
<a href="api/java/smile/regression/RegressionTree.html">Regression Trees</a>,
<a href="api/java/smile/regression/GradientTreeBoost.html">Gradient Boosting</a>,
<a href="api/java/smile/regression/RandomForest.html">Random Forest</a>,
<a href="api/java/smile/regression/RBFNetwork.html">RBF Networks</a>,
<a href="api/java/smile/regression/MLP.html">Neural Networks</a>,
<a href="api/java/smile/regression/SVR.html">Support Vector Regression</a>,
<a href="api/java/smile/regression/GaussianProcessRegression.html">Gaussian Process</a>,
<a href="api/java/smile/glm/GLM.html">Generalized Linear Model</a>
</p></dd>
<dt><a href="api/java/smile/feature/package-summary.html">Feature Engineering and Selection</a></dt>
<dd><p>
<a href="api/java/smile/feature/Bag.html">Bag of Words</a>,
<a href="api/java/smile/feature/SparseOneHotEncoder.html">Sparse One Hot Encoding</a>,
<a href="api/java/smile/feature/Standardizer.html">Standardizer</a>,
<a href="api/java/smile/feature/RobustStandardizer.html">Robust Standardizer</a>,
<a href="api/java/smile/feature/MaxAbsScaler.html">Maximum Absolute Value Scaler</a>,
<a href="api/java/smile/feature/WinsorScaler.html">Winsor Scaler</a>,
<a href="api/java/smile/feature/Normalizer.html">Normalizer</a>,
<a href="api/java/smile/feature/GAFE.html">Genetic Algorithm based Feature Selection</a>,
<a href="api/java/smile/classification/RandomForest.html#importance--">Ensemble Learning based Feature Selection</a>,
<a href="api/java/smile/feature/TreeSHAP.html">TreeSHAP</a>,
<a href="api/java/smile/feature/SignalNoiseRatio.html">Signal Noise ratio</a>,
<a href="api/java/smile/feature/SumSquaresRatio.html">Sum Squares ratio</a>
</p></dd>
<dt><a href="api/java/smile/projection/package-summary.html">Dimension Reduction</a></dt>
<dd><p>
<a href="api/java/smile/feature/extraction/PCA.html">PCA</a>,
<a href="api/java/smile/feature/extraction/KernalPCA.html">Kernel PCA</a>,
<a href="api/java/smile/feature/extraction/ProbabilisticPCA.html">Probabilistic PCA</a>,
<a href="api/java/smile/feature/extraction/GHA.html">Generalized Hebbian Algorithm</a>,
<a href="api/java/smile/feature/extraction/RandomProjection.html">Random Project</a>,
<a href="api/java/smile/ica/ICA.html">ICA</a>
</p></dd>
<dt><a href="api/java/smile/validation/package-summary.html">Model Validation</a></dt>
<dd><p>
<a href="api/java/smile/validation/CrossValidation.html">Cross Validation</a>,
<a href="api/java/smile/validation/LOOCV.html">Leave-One-Out Validation</a>,
<a href="api/java/smile/validation/Bootstrap.html">Bootstrap</a>,
<a href="api/java/smile/validation/ConfusionMatrix.html">Confusion Matrix</a>,
<a href="api/java/smile/validation/Hyperparameters.html">Hyperparameter Tuning</a>,
<a href="api/java/smile/validation/metric/AUC.html">AUC</a>,
<a href="api/java/smile/validation/metric/LogLoss.html">LogLoss</a>,
<a href="api/java/smile/validation/metric/CrossEntropy.html">CrossEntropy</a>,
<a href="api/java/smile/validation/metric/Accuracy.html">Accuracy</a>,
<a href="api/java/smile/validation/metric/Error.html">Error</a>,
<a href="api/java/smile/validation/metric/Fallout.html">Fallout</a>,
<a href="api/java/smile/validation/metric/FDR.html">FDR</a>,
<a href="api/java/smile/validation/metric/FScore.html">F-Score</a>,
<a href="api/java/smile/validation/metric/Precision.html">Precision</a>,
<a href="api/java/smile/validation/metric/Recall.html">Recall</a>,
<a href="api/java/smile/validation/metric/Sensitivity.html">Sensitivity</a>,
<a href="api/java/smile/validation/metric/Specificity.html">Specificity</a>,
<a href="api/java/smile/validation/metric/MatthewsCorrelation.html">Matthews Correlation Coefficient</a>,
<a href="api/java/smile/validation/metric/MSE.html">MSE</a>,
<a href="api/java/smile/validation/metric/RMSE.html">RMSE</a>,
<a href="api/java/smile/validation/metric/RSS.html">RSS</a>,
<a href="api/java/smile/validation/metric/R2.html">R2</a>,
<a href="api/java/smile/validation/metric/MeanAbsoluteDeviation.html">Mean Absolute Deviation</a>,
<a href="api/java/smile/validation/metric/RandIndex.html">Rand Index</a>,
<a href="api/java/smile/validation/metric/AdjustedRandIndex.html">Adjusted Rand Index</a>,
<a href="api/java/smile/validation/metric/MutualInformation.html">Mutual Information Score</a>,
</p></dd>
<dt><a href="api/java/smile/clustering/package-summary.html">Clustering</a></dt>
<dd><p>
<a href="api/java/smile/clustering/HierarchicalClustering.html">Hierarchical Clustering</a>,
<a href="api/java/smile/clustering/CLARANS.html">CLARANS</a>,
<a href="api/java/smile/clustering/DBSCAN.html">DBSCAN</a>,
<a href="api/java/smile/clustering/DENCLUE.html">DENCLUE</a>,
<a href="api/java/smile/clustering/KMeans.html">K-Means</a>,
<a href="api/java/smile/clustering/XMeans.html">X-Means</a>,
<a href="api/java/smile/clustering/GMeans.html">G-Means</a>,
<a href="api/java/smile/clustering/KModes.html">K-Modes</a>,
<a href="api/java/smile/clustering/DeterministicAnnealing.html">Deterministic Annealing</a>,
<a href="api/java/smile/clustering/SIB.html">Sequential Information Bottleneck</a>,
<a href="api/java/smile/clustering/SpectralClustering.html">Spectral Clustering</a>,
<a href="api/java/smile/clustering/MEC.html">Minimum Entropy Clustering</a>
</p></dd>
<dt><a href="api/java/smile/vq/package-summary.html">Vector Quantization</a></dt>
<dd><p>
<a href="api/java/smile/vq/BIRCH.html">BIRCH</a>,
<a href="api/java/smile/vq/SOM.html">Self-Organizing Maps</a>,
<a href="api/java/smile/vq/NeuralGas.html">Neural Gas</a>,
<a href="api/java/smile/vq/GrowingNeuralGas.html">Growing Neural Gas</a>,
<a href="api/java/smile/vq/NeuralMap.html">Neural Map</a>
</p></dd>
<dt><a href="api/java/smile/association/package-summary.html">Association Rules</a></dt>
<dd><p>
<a href="api/java/smile/association/FPGrowth.html">Frequent Itemset Mining</a>,
<a href="api/java/smile/association/ARM.html">Association Rule Mining</a>
</p></dd>
<dt><a href="api/java/smile/manifold/package-summary.html">Manifold learning</a></dt>
<dd><p>
<a href="api/java/smile/manifold/IsoMap.html">IsoMap</a>,
<a href="api/java/smile/manifold/LLE.html">LLE</a>,
<a href="api/java/smile/manifold/LaplacianEigenmap.html">Laplacian Eigenmap</a>,
<a href="api/java/smile/manifold/TSNE.html">t-SNE</a>,
<a href="api/java/smile/manifold/UMAP.html">UMAP</a>,
<a href="api/java/smile/manifold/MDS.html">Classical MDS</a>,
<a href="api/java/smile/manifold/IsotonicMDS.html">Isotonic MDS</a>,
<a href="api/java/smile/manifold/SammonMapping.html">Sammon Mapping</a>
</p></dd>
<dt><a href="api/java/smile/neighbor/package-summary.html">Nearest Neighbor Search</a></dt>
<dd><p>
<a href="api/java/smile/neighbor/LinearSearch.html">Linear Search</a>,
<a href="api/java/smile/neighbor/BKTree.html">BK-Tree</a>,
<a href="api/java/smile/neighbor/CoverTree.html">Cover Tree</a>,
<a href="api/java/smile/neighbor/KDTree.html">KD-Tree</a>,
<a href="api/java/smile/neighbor/LSH.html">LSH</a>,
<a href="api/java/smile/neighbor/MPLSH.html">Multi-Probe LSH</a>,
<a href="api/java/smile/hash/SimHash.html">SimHash</a>
</p></dd>
<dt><a href="api/java/smile/sequence/package-summary.html">Sequence Learning</a></dt>
<dd><p>
<a href="api/java/smile/sequence/HMM.html">Hidden Markov Model</a>,
<a href="api/java/smile/sequence/CRF.html">Conditional Random Field</a>
</p></dd>
<dt><a href="api/java/smile/timeseries/package-summary.html">Time Series</a></dt>
<dd><p>
<a href="api/java/smile/timeseries/TimeSeries.html#acf-double:A-int-">ACF</a>,
<a href="api/java/smile/timeseries/TimeSeries.html#pacf-double:A-int-">PACF</a>,
<a href="api/java/smile/timeseries/BoxTest.html">Box-Pierce and Ljung-Box Test</a>,
<a href="api/java/smile/timeseries/AR.html">AR</a>,
<a href="api/java/smile/timeseries/ARMA.html">ARMA</a>
</p></dd>
<dt><a href="api/java/smile/nlp/package-summary.html">Natural Language Processing</a></dt>
<dd><p>
<a href="api/java/smile/nlp/tokenizer/SimpleSentenceSplitter.html">Sentence Splitter</a>,
<a href="api/java/smile/nlp/tokenizer/SimpleTokenizer.html">Tokenizer</a>,
<a href="api/java/smile/nlp/collocation/Bigram.html">Bigram Extractor</a>,
<a href="api/java/smile/nlp/collocation/NGram.html">Phrase Extractor</a>,
<a href="api/java/smile/nlp/keyword/CooccurrenceKeywords.html">Keyword Extractor</a>,
<a href="api/java/smile/nlp/stemmer/PorterStemmer.html">Porter Stemmer</a>,
<a href="api/java/smile/nlp/stemmer/LancasterStemmer.html">Lancaster Stemmer</a>,
<a href="api/java/smile/nlp/pos/HMMPOSTagger.html">POS Tagging</a>,
<a href="api/java/smile/nlp/relevance/BM25.html">Relevance Ranking</a>,
<a href="api/java/smile/nlp/embedding/Word2Vec.html">Word2Vec</a>
</p></dd>
<dt><a href="api/java/smile/math/package-summary.html">Mathematics</a></dt>
<dd><p>
<a href="api/java/smile/gap/package-summary.html">Genetic Algorithms</a>,
<a href="api/java/smile/graph/package-summary.html">Graph</a>,
<a href="api/java/smile/hash/package-summary.html">Hash Functions</a>,
<a href="api/java/smile/interpolation/package-summary.html">Interpolation</a>,
<a href="api/java/smile/sort/package-summary.html">Sort Algorithms</a>,
<a href="api/java/smile/taxonomy/package-summary.html">Taxonomy</a>,
<a href="api/java/smile/wavelet/package-summary.html">Wavelet</a>
</p></dd>
<dt><a href="api/java/smile/math/matrix/package-summary.html">Linear Algebra</a></dt>
<dd><p>
<a href="api/java/smile/math/matrix/Matrix.html">Dense Matrix</a>,
<a href="api/java/smile/math/matrix/BandMatrix.html">Band Matrix</a>,
<a href="api/java/smile/math/matrix/SparseMatrix.html">Sparse Matrix</a>,
<a href="api/java/smile/math/matrix/Matrix.LU.html">LU</a>,
<a href="api/java/smile/math/matrix/Matrix.Cholesky.html">Cholesky</a>,
<a href="api/java/smile/math/matrix/Matrix.QR.html">QR</a>,
<a href="api/java/smile/math/matrix/Matrix.EVD.html">EVD</a>,
<a href="api/java/smile/math/matrix/Matrix.SVD.html">SVD</a>,
<a href="api/java/smile/math/matrix/BiconjugateGradient.html">Biconjugate Gradient</a>,
<a href="api/java/smile/math/BFGS.html">BFGS</a>,
<a href="api/scala/smile/cas/index.html">Computer Algebra System</a>
</p></dd>
<dt><a href="api/java/smile/stat/package-summary.html">Statistics</a></dt>
<dd><p>
<a href="api/java/smile/stat/distribution/package-summary.html">Distributions</a>,
<a href="api/java/smile/math/random/package-summary.html">Random Number Generators</a>,
<a href="api/java/smile/stat/Hypothesis.html">Hypothesis Tests</a>
</p></dd>
</dl>
</div>
</div>
<div class="col-md-9 col-md-pull-3">
<div class="jumbotron">
<p><b>Smile</b> is a fast and comprehensive machine learning engine.</p>
<div id="testimonial-slider" class="owl-carousel">
<div class="testimonial">
<div class="testimonial-content">
<p class="testimonial-description">
Smile now seems to be the go-to general-purpose machine learning library
for those working in the Java and Scala worlds — a JVM Scikit-learn, if you will.
I would actually find it hard to believe that you are working in that ecosystem
and are unaware of the project.
</p>
<div class="testimonial-review">
<h3 class="testimonial-title">
- KDnuggets
</h3>
</div>
</div>
</div>
<div class="testimonial">
<div class="testimonial-content">
<p class="testimonial-description">
Smile gives you a broad range of algorithms out of the box, ranging from simple
functions like classification and regression to sophisticated offerings like
natural language processing. And all you need is Java, or any JVM language.
</p>
<div class="testimonial-review">
<h3 class="testimonial-title">
- InfoWorld
</h3>
</div>
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<div class="testimonial">
<div class="testimonial-content">
<p class="testimonial-description">
Smile will amaze you with fast and extensive applications,
efficient memory usage and a large set of machine learning algorithms for Classification,
Regression, Nearest Neighbor Search, Feature Selection, etc.
</p>
<div class="testimonial-review">
<h3 class="testimonial-title">
- ActiveWizards
</h3>
</div>
</div>
</div>
<div class="testimonial">
<div class="testimonial-content">
<p class="testimonial-description">
To say that I am satisfied with SMILE would be an understatement. It's truly one of the
hidden gems in the Java framework ecosystem today.
</p>
<div class="testimonial-review">
<h3 class="testimonial-title">
- Patrick Martin,
<small>Principal Architect at Citi</small>
</h3>
</div>
</div>
</div>
<div class="testimonial">
<div class="testimonial-content">
<p class="testimonial-description">
LinkedIn used Smile to train its workforce on machine learning for its AI Academy.
Smile was chosen because it's a Java library with a friendly open source license
and supports a wide range of common algorithms.
</p>
<div class="testimonial-review">
<h3 class="testimonial-title">
- Ben McCann, <small>Head of Hire Matching at LinkedIn</small>
</h3>
</div>
</div>
</div>
<div class="testimonial">
<div class="testimonial-content">
<p class="testimonial-description">
We leverage Smile's impressive capability in various machine
learning tasks: feature engineering, modeling, visualization,
benchmark test, etc. Thanks Smile and strongly recommend it
to every engineer who is interested in machine learning.
</p>
<div class="testimonial-review">
<h3 class="testimonial-title">
- Ray Ma,
<small>Technology Manager at moKredit</small>
</h3>
</div>
</div>
</div>
<div class="testimonial">
<div class="testimonial-content">
<p class="testimonial-description">
SMILE is a great Java library for a wide range of AI tasks. Building bespoke methods atop
SMILE run considerably faster than implementations in other languages more
associated with data science.
</p>
<div class="testimonial-review">
<h3 class="testimonial-title">
- Shantanu Lodh,
<small>Senior Data Scientist at Hidden Depth AI, UK</small>
</h3>
</div>
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</div>
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<a class="github-button" href="https://github.com/haifengl/smile" data-icon="octicon-star" data-size="large" data-show-count="true" aria-label="Star haifengl/smile on GitHub">Star</a>
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<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Speed</h2>
<p class="lead">
With advanced data structures and algorithms, Smile delivers the state-of-art performance.
</p>
<p>Compared to this third-party <a href="https://github.com/szilard/benchm-ml">benchmark</a>,
Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is several times
faster than the closest competitor. The memory usage is also very efficient. If we can train
advanced machine learning models on a PC, why buy a cluster?
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="width: 100%; display: inline-block; text-align: center;">
<img src="images/benchm-ml.png" style="width: 100%;" />
<div class="caption" style="min-width: 272px;">Training Time (seconds)</div>
</div>
</div>
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Ease of Use</h2>
<p class="lead">
Write applications quickly in Java, Scala, or any JVM languages.
Data scientists and developers can speak the same language now!
</p>
<p>Smile provides hundreds advanced algorithms with clean interface. Scala/Kotlin
API also offers high-level operators that make it easy to build machine learning apps.
And you can use it interactively from the shell, embedded in Scala.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<ul class="nav nav-tabs">
<li class="active"><a href="#java_1" data-toggle="tab">Java</a></li>
<li><a href="#scala_1" data-toggle="tab">Scala</a></li>
<li><a href="#kotlin_1" data-toggle="tab">Kotlin</a></li>
<li><a href="#clojure_1" data-toggle="tab">Clojure</a></li>
<li><a href="#groovy_1" data-toggle="tab">Groovy</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="java_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
var iris = Read.arff("iris.arff");
var model = RandomForest.fit(Formula.lhs("class"), iris);
println(model.metrics());
</code></pre>
</div>
<div class="caption">DataFrame, Model Fitting, and Metrics</div>
</div>
<div class="tab-pane" id="scala_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-scala"><code>
val iris = read.arff("iris.arff")
val model = randomForest("class" ~, iris)
println(model.metrics)
</code></pre>
</div>
<div class="caption">DataFrame, Model Fitting, and Metrics</div>
</div>
<div class="tab-pane" id="kotlin_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-kotlin"><code>
val iris = read.arff("iris.arff")
val model = randomForest(Formula.lhs("class"), iris)
println(model.metrics())
</code></pre>
</div>
<div class="caption">DataFrame, Model Fitting, and Metrics</div>
</div>
<div class="tab-pane" id="clojure_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-clj"><code>
(let [iris (read-arff
"data/weka/iris.arff")
model (random-forest
(Formula/lhs "class") iris)]
(.metrics model))
</code></pre>
</div>
<div class="caption">DataFrame, Model Fitting, and Metrics</div>
</div>
<div class="tab-pane" id="groovy_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-groovy"><code>
var iris = Read.arff("iris.arff")
var model = RandomForest.fit(Formula.lhs("class"), iris)
println model.metrics()
</code></pre>
</div>
<div class="caption">DataFrame, Model Fitting, and Metrics</div>
</div>
</div>
</div>
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Comprehensive</h2>
<p class="lead">
The most complete machine learning engine. Smile covers every aspect of machine learning.
</p>
<p>LLM, computer vision, deep learning, classification, regression, clustering, association rule mining, feature selection,
manifold learning, multidimensional scaling, genetic algorithm, missing value imputation,
efficient nearest neighbor search, etc. See the sidebar for a list of available algorithms.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="width: 100%; display: inline-block; text-align: center;">
<img src="images/brain.png" style="width: 100%;" />
</div>
</div>
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Natural Language Processing</h2>
<p class="lead">
Understanding human language, and the intent behind our words.
</p>
<p>GenAI with Llama 3 on JVM (more coming). Smile also includes many classic
NLP algorithms such as tokenizers, stemming, word2vec, phrase detection,
part-of-speech tagging, keyword extraction, named entity recognition,
sentiment analysis, relevance ranking, taxomony, etc.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="width: 100%; display: inline-block; text-align: center;">
<img src="images/ml-word-cloud.jpg" style="width: 100%;" />
</div>
</div>
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Mathematics and Statistics</h2>
<p class="lead">
Hidden gems in Smile.
</p>
<p>From special functions, linear algebra, to random number generators,
statistical distributions and hypothesis tests, Smile provides an
advanced numerical computing environment. In additions, graph,
wavlets, and a variety of interpolation algorithms are implemented.
Smile even includes a computer algerbra system.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<ul class="nav nav-tabs">
<li class="active"><a href="#scala_2" data-toggle="tab">Matrix</a></li>
<li><a href="#scala_3" data-toggle="tab">Statistics</a></li>
<li><a href="#scala_4" data-toggle="tab">CAS</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="scala_2">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
var A = Matrix.randn(3, 3);
double[] x = {1.0, 2.0, 3.0};
var lu = A.lu();
lu.solve(x);
lu.inverse().mm(A);
</code></pre>
</div>
<div class="caption">Linear Algebra</div>
</div>
<div class="tab-pane" id="scala_3">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
int[] bins1 = {8, 13, 16, 10, 3};
int[] bins2 = {4, 9, 14, 16, 7};
Hypothesis.chisq.test(bins1, bins2);
</code></pre>
</div>
<div class="caption">Statistics</div>
</div>
<div class="tab-pane" id="scala_4">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-scala"><code>
val x = Var("x")
val y = Var("y")
val e = x**2 + y**3 + x**2 * cot(y**3)
val dx = e.d(x)
println(dx)
</code></pre>
</div>
<div class="caption">Computer Algebra System</div>
</div>
</div>
</div>
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Data Visualization</h2>
<p class="lead">
Interactive 2D/3D math plot.
</p>
<p>Scatter plot, line plot, staircase plot, bar plot, box plot, heatmap, hexmap, histogram, qq plot, surface,
grid, contour, dendrogram, sparse matrix visualization, wireframe, etc. Smile also supports declarative
data visualization that compiles to <a href="https://vega.github.io/vega-lite/">Vega-Lite</a>.
</p>
<div id="vegalite"></div>
<script type="text/javascript">
var spec = {
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
"width": 400,
"height": 300,
"title": "Choropleth of Unemployment Rate per County",
"data": {
"url": "https://vega.github.io/vega-lite/examples/data/us-10m.json",
"format": {
"type": "topojson",
"feature": "counties"
}
},
"transform": [{
"lookup": "id",
"from": {
"data": {
"url": "https://vega.github.io/vega-lite/examples/data/unemployment.tsv"
},
"key": "id",
"fields": ["rate"]
}
}],
"projection": {
"type": "albersUsa"
},
"mark": "geoshape",
"encoding": {
"color": {
"field": "rate",
"type": "quantitative"
}
}
};
vegaEmbed('#vegalite', spec);
</script>
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<img src="images/math.png" style="width: 100%;" />
<img src="gallery/smile-demo-ann.png" style="width: 100%;" />
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</div>
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