haifengl/smile
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Step 1. Add the JitPack repository to your build file
Add it in your root settings.gradle at the end of repositories:
dependencyResolutionManagement {
repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
repositories {
mavenCentral()
maven { url 'https://jitpack.io' }
}
}Add it in your settings.gradle.kts at the end of repositories:
dependencyResolutionManagement {
repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
repositories {
mavenCentral()
maven { url = uri("https://jitpack.io") }
}
}Add to pom.xml
<repositories>
<repository>
<id>jitpack.io</id>
<url>https://jitpack.io</url>
</repository>
</repositories>Add it in your build.sbt at the end of resolvers:
resolvers += "jitpack" at "https://jitpack.io"
Add it in your project.clj at the end of repositories:
:repositories [["jitpack" "https://jitpack.io"]]
Step 2. Add the dependency
dependencies {
implementation 'com.github.haifengl:smile:2.6.0'
} dependencies {
implementation("com.github.haifengl:smile:2.6.0")
} <dependency>
<groupId>com.github.haifengl</groupId>
<artifactId>smile</artifactId>
<version>2.6.0</version>
</dependency>
libraryDependencies += "com.github.haifengl" % "smile" % "2.6.0"
:dependencies [[com.github.haifengl/smile "2.6.0"]]
Readme
Statistical Machine Intelligence & Learning Engine
Goal
<img align="left" width="48" src="/web/src/images/smile.jpg"> SMILE (Statistical Machine Intelligence & Learning Engine) is a fast and comprehensive machine learning framework in Java. SMILE v5.x requires Java 25, v4.x requires Java 21, and all previous versions require Java 8. SMILE also provides APIs in Scala and Kotlin with corresponding language paradigms. With advanced data structures and algorithms, SMILE delivers state-of-art performance. SMILE covers every aspect of machine learning, including deep learning, large language models, classification, regression, clustering, association rule mining, feature selection and extraction, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc. Furthermore, SMILE also provides advanced algorithms for graph, linear algebra, numerical analysis, interpolation, computer algebra system for symbolic manipulations, and data visualization.Features
SMILE implements the following major machine learning algorithms:
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LLM: Native Java implementation of Llama 3.1, tiktoken tokenizer, high performance LLM inference server with OpenAI-compatible APIs and SSE-based chat streaming, fully functional frontend.
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Deep Learning: Deep learning with CPU and GPU. EfficientNet model for image classification.
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Classification: Support Vector Machines, Decision Trees, AdaBoost, Gradient Boosting, Random Forest, Logistic Regression, Neural Networks, RBF Networks, Maximum Entropy Classifier, KNN, Naïve Bayesian, Fisher/Linear/Quadratic/Regularized Discriminant Analysis.
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Regression: Support Vector Regression, Gaussian Process, Regression Trees, Gradient Boosting, Random Forest, RBF Networks, OLS, LASSO, ElasticNet, Ridge Regression.
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Feature Selection: Genetic Algorithm based Feature Selection, Ensemble Learning based Feature Selection, TreeSHAP, Signal Noise ratio, Sum Squares ratio.
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Clustering: BIRCH, CLARANS, DBSCAN, DENCLUE, Deterministic Annealing, K-Means, X-Means, G-Means, Neural Gas, Growing Neural Gas, Hierarchical Clustering, Sequential Information Bottleneck, Self-Organizing Maps, Spectral Clustering, Minimum Entropy Clustering.
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Association Rule & Frequent Itemset Mining: FP-growth mining algorithm.
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Manifold Learning: IsoMap, LLE, Laplacian Eigenmap, t-SNE, UMAP, PCA, Kernel PCA, Probabilistic PCA, GHA, Random Projection, ICA.
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Multi-Dimensional Scaling: Classical MDS, Isotonic MDS, Sammon Mapping.
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Nearest Neighbor Search: BK-Tree, Cover Tree, KD-Tree, SimHash, LSH.
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Sequence Learning: Hidden Markov Model, Conditional Random Field.
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Natural Language Processing: Sentence Splitter and Tokenizer, Bigram Statistical Test, Phrase Extractor, Keyword Extractor, Stemmer, POS Tagging, Relevance Ranking
License
SMILE employs a dual license model designed to meet the development and distribution needs of both commercial distributors (such as OEMs, ISVs and VARs) and open source projects. For details, please see LICENSE. To acquire a commercial license, please contact smile.sales@outlook.com.
Issues/Discussions
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Discussion/Questions: If you wish to ask questions about SMILE, we're active on GitHub Discussions and Stack Overflow.
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Docs: SMILE is well documented and our docs are available online, where you can find tutorial, programming guides, and more information. If you'd like to help improve the docs, they're part of this repository in the
web/srcdirectory. Java Docs, Scala Docs, Kotlin Docs, and Clojure Docs are also available. -
Issues/Feature Requests: Finally, any bugs or features, please report to our issue tracker.
Installation
You can use the libraries through Maven central repository by adding the following to your project pom.xml file.
<dependency>
<groupId>com.github.haifengl</groupId>
<artifactId>smile-core</artifactId>
<version>5.0.0</version>
</dependency>
For deep learning and NLP, use the artifactId smile-deep and smile-nlp, respectively.
For Scala API, please add the below into your sbt script.
libraryDependencies += "com.github.haifengl" %% "smile-scala" % "5.0.0"
For Kotlin API, add the below into the dependencies section
of Gradle build script.
implementation("com.github.haifengl:smile-kotlin:5.0.0")
Some algorithms rely on BLAS and LAPACK (e.g. manifold learning,
some clustering algorithms, Gaussian Process regression, MLP, etc.).
To use these algorithms in SMILE v5.x, you should install OpenBLAS and ARPACK
for optimized matrix computation. For Windows, you can find the pre-built
DLL files from the bin directory of release packages. Make sure to add this
directory to PATH environment variable.
To install on Linux (e.g., Ubuntu), run
sudo apt update
sudo apt install libopenblas-dev libarpack2
On Mac, we use the BLAS library from the Accelerate framework provided by macOS. But you should install ARPACK by running
brew install arpack
However, macOS System Integrity Protection (SIP) significantly impacts how JVM handles dynamic library loading by purging dynamic linker (DYLD) environment variables like DYLD_LIBRARY_PATH when launching protected processes. A simple workaround is to copy /opt/homebrew/lib/libarpack.dylib to your working directory so that JVM can successfully load it.
For SMILE v4.x, OpenBLAS and ARPACK libraries can be added to your project with the following dependencies.
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",
"org.bytedeco" % "arpack-ng" % "3.9.1-1.5.11" classifier "macosx-x86_64" classifier "windows-x86_64" classifier "linux-x86_64"
)
In this example, we include all supported 64-bit platforms and filter out 32-bit platforms. The user should include only the needed platforms to save spaces.
Shell
SMILE comes with interactive shells for Java, Scala and Kotlin. Download pre-packaged SMILE from the releases page. After unziping the package and cd into the home directory of SMILE in a terminal, type
./bin/jshell.sh
to enter SMILE shell in Java, which pre-imports all major SMILE packages. You can run any valid Java expressions in the shell. In the simplest case, you can use it as a calculator.
To enter the shell in Scala, type
./bin/smile
Similar to the shell in Java, all major SMILE packages are pre-imported. Besides, all high-level SMILE operators are predefined in the shell.
By default, the shell uses up to 75% memory. If you need more memory
to handle large data, use the option -J-Xmx or -XX:MaxRAMPercentage.
For example,
./bin/smile -J-Xmx30G
You can also modify the configuration file ./conf/smile.ini for the
memory and other JVM settings.
To use SMILE shell in Kotlin, type
./bin/kotlin.sh
Unfortunately, Kotlin shell doesn't support pre-import packages.
Model Serialization
Most models support the Java Serializable interface (all classifiers
do support Serializable interface) so that you can serialze a model
and ship it to a production environment for inference. You may also
use serialized models in other systems such as Spark.
Visualization
A picture is worth a thousand words. In machine learning, we usually handle
high-dimensional data, which is impossible to draw on display directly.
But a variety of statistical plots are tremendously valuable for us to grasp
the characteristics of many data points. SMILE provides data visualization tools
such as plots and maps for researchers to understand information more easily and quickly.
To use smile-plot, add the following to dependencies
<dependency>
<groupId>com.github.haifengl</groupId>
<artifactId>smile-plot</artifactId>
<version>5.0.0</version>
</dependency>
On Swing-based systems, the user may leverage smile.plot.swing package to
create a variety of plots such as scatter plot, line plot, staircase plot,
bar plot, box plot, histogram, 3D histogram, dendrogram, heatmap, hexmap,
QQ plot, contour plot, surface, and wireframe.
This library also support data visualization in declarative approach.
With smile.plot.vega package, we can create a specification
that describes visualizations as mappings from data to properties
of graphical marks (e.g., points or bars). The specification is
based on Vega-Lite. In a web browser,
the Vega-Lite compiler automatically produces visualization components
including axes, legends, and scales. It then determines properties
of these components based on a set of carefully designed rules.
Contributing
Please read the contributing.md on how to build and test SMILE.
Maintainers
- Haifeng Li (@haifengl)
- Karl Li (@kklioss)