Statistical Machine Intelligence & Learning Engine

SMILE (Statistical Machine Intelligence & Learning Engine) is a comprehensive, high-performance machine learning framework for the JVM. SMILE v5+ requires Java 25; v4.x requires Java 21; all previous versions require Java 8. SMILE also provides idiomatic APIs for Scala and Kotlin. With advanced data structures and algorithms, SMILE delivers state-of-the-art performance across every aspect of machine learning.

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Table of Contents

1. Features
2. Module Map
3. Installation
   • Maven
   • SBT (Scala)
   • Gradle (Kotlin)
   • Native Libraries (BLAS / LAPACK)
4. Quick Start
5. SMILE Studio & Shell
6. Model Serialization
7. Visualization
8. License
9. Issues & Discussions
10. Contributing
11. Maintainers
12. Gallery

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Features

Area	Highlights
LLM	LLaMA-3 inference, tiktoken BPE tokenizer, OpenAI-compatible REST server, SSE chat streaming
Deep Learning	LibTorch/GPU backend, EfficientNet-V2 image classification, custom layer API
Classification	SVM, Decision Trees, Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, Neural Networks, RBF Networks, MaxEnt, KNN, Naïve Bayes, LDA/QDA/RDA
Regression	SVR, Gaussian Process, Regression Trees, GBDT, Random Forest, RBF, OLS, LASSO, ElasticNet, Ridge
Clustering	BIRCH, CLARANS, DBSCAN, DENCLUE, Deterministic Annealing, K-Means, X-Means, G-Means, Neural Gas, Growing Neural Gas, Hierarchical, SIB, SOM, Spectral, Min-Entropy
Manifold Learning	IsoMap, LLE, Laplacian Eigenmap, t-SNE, UMAP, PCA, Kernel PCA, Probabilistic PCA, GHA, Random Projection, ICA
Feature Engineering	Genetic Algorithm selection, Ensemble selection, TreeSHAP, SNR, Sum-Squares ratio, data transformations, formula API
NLP	Sentence / word tokenization, Bigram test, Phrase & Keyword extraction, Stemmer, POS tagging, Relevance ranking
Association Rules	FP-growth frequent itemset mining
Sequence Learning	Hidden Markov Model, Conditional Random Field
Nearest Neighbor	BK-Tree, Cover Tree, KD-Tree, SimHash, LSH
Numerical Methods	Linear algebra, numerical optimization (BFGS, L-BFGS), interpolation, wavelets, RBF, distributions, hypothesis tests
Visualization	Swing plots (scatter, line, bar, box, histogram, surface, heatmap, contour, …) and declarative Vega-Lite charts

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Module Map

Each module has its own detailed user guide. Click the README link for the module overview, or drill into individual topic guides.

base/ — Foundation

Data structures, math, linear algebra, statistical utilities, I/O

Document	Topics
README	Module overview and dependency setup
DATA_FRAME.md	DataFrame API — creation, selection, transformation
DATA_IO.md	CSV, JSON, Parquet, Arrow, JDBC, Avro readers/writers
DATA_TRANSFORMATION.md	Scalers, encoders, imputers, feature transforms
DATASET.md	Built-in benchmark and real-world datasets
FORMULA.md	R-style formula language for model matrices
DISTRIBUTIONS.md	Probability distributions (Normal, Poisson, Beta, …)
HYPOTHESIS_TESTING.md	t-test, chi-squared, ANOVA, KS-test, …
DISTANCES.md	Euclidean, Mahalanobis, Hamming, edit distance, …
NEAREST_NEIGHBOR.md	KD-Tree, Cover Tree, BK-Tree, LSH
KERNELS.md	Gaussian, polynomial, Laplacian, and other kernel functions
RBF.md	Radial basis function networks
INTERPOLATION.md	Linear, cubic spline, bilinear, bicubic
GRAPH.md	Adjacency list/matrix graph, BFS/DFS, spanning trees
SORT.md	Quick sort, heap sort, counting sort, index sort
HASH.md	Locality-sensitive hashing, SimHash
RNG.md	Random number generators, sampling, permutations
BFGS.md	L-BFGS and BFGS numerical optimizers
ICA.md	Independent Component Analysis
TENSOR.md	N-dimensional array (CPU tensor without LibTorch)
WAVELET.md	DWT, CWT, and wavelet families
GAP.md	GAP statistic for optimal cluster count estimation
COMPRESSED_SENSING.md	Compressed sensing and basis pursuit

core/ — Machine Learning Algorithms

Classification, regression, clustering, manifold learning, and more

Document	Topics
README	Module overview
CLASSIFICATION.md	SVM, Random Forest, AdaBoost, GBDT, KNN, Naïve Bayes, LDA, …
REGRESSION.md	SVR, Gaussian Process, LASSO, Ridge, ElasticNet, GBDT, …
CLUSTERING.md	K-Means, DBSCAN, BIRCH, SOM, Spectral Clustering, …
FEATURE_ENGINEERING.md	Feature selection, PCA, ICA, projection, encoding
MANIFOLD.md	t-SNE, UMAP, IsoMap, LLE, Laplacian Eigenmap
ANOMALY_DETECTION.md	IsolationForest, one-class SVM, local outlier factor
ASSOCIATION_RULE_MINING.md	FP-growth, association rules, frequent itemsets
SEQUENCE.md	HMM (Baum-Welch, Viterbi), CRF
TIME_SERIES.md	ARIMA, box-plots, autocorrelation
REGRESSION.md	Full regression API reference
TRAINING.md	Cross-validation, bootstrap, hyper-parameter search
VALIDATION.md	Hold-out, k-fold, leave-one-out evaluation
VALIDATION_METRICS.md	Accuracy, AUC, F1, RMSE, MAE, confusion matrix
HYPER_PARAMETER_OPTIMIZATION.md	Grid search, random search, Bayesian optimization
VECTOR_QUANTIZATION.md	LVQ, Neural Gas, SOM as vector quantizers
ONNX.md	Exporting and importing models via ONNX

deep/ — Deep Learning & LLMs

LibTorch-backed GPU/CPU tensor operations, neural network layers, LLaMA-3 inference, EfficientNet

Document	Topics
README	Full deep-learning & LLM user guide (tensors, layers, loss, optimizer, EfficientNet, LLaMA)

The deep/README.md covers:

• smile.deep.tensor — Tensor factory, indexing, arithmetic, AutoScope memory management, dtype/device
• smile.deep.layer — Linear, Conv2d, pooling, normalization (BN/GN/RMS), dropout, embedding, sequential blocks
• smile.deep.activation — ReLU, GELU, SiLU, Tanh, Sigmoid, Softmax, GLU, HardShrink, …
• smile.deep.Loss — MSE, cross-entropy, BCE, Huber, KL, hinge, and more
• smile.deep.Optimizer — SGD, Adam, AdamW, RMSprop
• smile.deep.Model — Abstract base class + training loop
• smile.deep.metric — Accuracy, Precision, Recall, F1Score with macro/micro/weighted averaging
• smile.llm — Message, Role, FinishReason, ChatCompletion records; sinusoidal & RoPE positional encodings
• smile.llm.tokenizer — Tokenizer interface, Tiktoken BPE implementation (LLaMA-3 compatible)
• smile.llm.llama — Full LLaMA-3 stack: Llama.build(), generate(), chat(), streaming via SubmissionPublisher
• smile.vision — VisionModel, ImageDataset, EfficientNet.V2S/M/L() pretrained models, ImageNet labels
• smile.vision.transform — Transform interface, ImageClassification pipeline, resize/crop/toTensor helpers

nlp/ — Natural Language Processing

Text normalization, tokenization, POS tagging, stemming, relevance ranking

Document	Topics
README	Module overview
TOKENIZER.md	Sentence splitter, word tokenizer, regex tokenizer
POS.md	Part-of-speech tagging (Brill tagger, HMM tagger)
STEM.md	Porter, Lancaster, Lovins stemmers; lemmatization
COLLOCATION.md	Bigram/trigram statistical tests, phrase extraction
RELEVANCE.md	TF-IDF, BM25, keyword extraction
TAXONOMY.md	WordNet integration, synsets, hypernyms

plot/ — Data Visualization

Swing-based interactive plots and declarative Vega-Lite charts

Document	Topics
README	Swing plotting API — scatter, line, bar, box, histogram, heatmap, surface, contour, wireframe
VEGA.md	Declarative smile.plot.vega (Vega-Lite) — JSON spec generation, web/Jupyter rendering

serve/ — Inference Server

Quarkus-based REST inference service with OpenAI-compatible API and SSE streaming

Document	Topics
README	Building and running the server, /chat/completions endpoint, SSE streaming, configuration

studio/ — Interactive Shell & Desktop IDE

REPL / notebook environment for Java, Scala, and Kotlin

Document	Topics
README.md	Desktop Studio notebook UI, cell types, output rendering
CLI	CLI entry points (smile, smile shell, smile scala, smile kotlin, smile server)

scala/ — Scala API

Idiomatic Scala shim — concise wrappers, symbolic operators, Scala collections integration

Document	Topics
README	API overview, smile.classification, smile.regression, smile.clustering, smile.plot in Scala

kotlin/ — Kotlin API

Idiomatic Kotlin shim — extension functions, named parameters, builder DSLs

Document	Topics
README	API overview, extension functions, Kotlin-style builders
packages.md	Full package-by-package listing of all Kotlin extension functions

json/ — JSON Library (Scala)

Lightweight zero-dependency JSON library for Scala with a clean DSL

Document	Topics
README	Parsing, building, pattern matching, path navigation, serialization

spark/ — Apache Spark Integration

Use SMILE models inside Spark ML pipelines

Document	Topics
README	SmileTransformer, SmileClassifier, SmileRegressor; training and scoring in Spark DataFrames

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Installation

Maven

<!-- Core ML algorithms -->
<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-core</artifactId>
  <version>6.0.0</version>
</dependency>

<!-- Deep learning + LLMs (requires LibTorch) -->
<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-deep</artifactId>
  <version>6.0.0</version>
</dependency>

<!-- Natural language processing -->
<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-nlp</artifactId>
  <version>6.0.0</version>
</dependency>

<!-- Data visualization -->
<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-plot</artifactId>
  <version>6.0.0</version>
</dependency>

SBT (Scala)

libraryDependencies += "com.github.haifengl" %% "smile-scala" % "6.0.0"

Gradle (Kotlin)

dependencies {
    implementation("com.github.haifengl:smile-kotlin:6.0.0")
}

Native Libraries (BLAS / LAPACK)

Several algorithms (manifold learning, Gaussian Process, MLP, some clustering) require BLAS and LAPACK.

Linux (Ubuntu / Debian)

sudo apt update
sudo apt install libopenblas-dev libarpack2

macOS (Homebrew)

brew install arpack
# If macOS SIP strips DYLD_LIBRARY_PATH, copy the dylib to your working dir:
cp /opt/homebrew/lib/libarpack.dylib .

Windows — pre-built DLLs are included in the bin/ directory of the release package. Add that directory to PATH.

GPU (CUDA) — make sure the LibTorch CUDA native libraries are on java.library.path and that your Bytedeco pytorch classifier matches your CUDA version (e.g., linux-x86_64-gpu-cuda12.4).

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Quick Start

import smile.classification.RandomForest;
import smile.data.formula.Formula;
import smile.io.Read;

// Load data
var data = Read.csv("src/test/resources/iris.csv");

// Train a random forest
var forest = RandomForest.fit(Formula.lhs("species"), data);

// Predict
int label = forest.predict(data.get(0));
System.out.println("Predicted class: " + label);

For deep learning and LLM examples, see deep/README.md. For visualization examples, see plot/README.md.

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SMILE Studio & Shell

SMILE ships with an interactive desktop Studio (notebook-style) and a set of CLI shells. See studio/README.md for full documentation.

Download a pre-packaged release from the releases page, then:

cd bin
./setup      # install required native dependencies
./smile      # launch SMILE Studio (desktop GUI)

Other entry points:

Command	Description
./smile	Desktop notebook IDE
./smile shell	Java REPL with all SMILE packages pre-imported
./smile scala	Scala REPL
./smile train	Train a supervised learning model
./smile predict	Predict on a file using a saved model
./smile serve	Start the LLM inference server

To increase the JVM heap:

./smile -J-Xmx30G

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Model Serialization

Most SMILE models implement java.io.Serializable. You can serialize a trained model to disk and load it in a production environment or inside a Spark job:

// Save
try (var out = new ObjectOutputStream(new FileOutputStream("model.ser"))) {
    out.writeObject(forest);
}

// Load
try (var in = new ObjectInputStream(new FileInputStream("model.ser"))) {
    var loaded = (RandomForest) in.readObject();
}

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Visualization

SMILE provides two visualization layers:

• smile.plot.swing — Swing-based interactive 2D/3D plots. See plot/README.md.
• smile.plot.vega — Declarative Vega-Lite charts for browsers and Jupyter. See plot/VEGA.md.

<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-plot</artifactId>
  <version>6.0.0</version>
</dependency>

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License

SMILE employs a dual license model designed to meet the development and distribution needs of both commercial distributors (OEMs, ISVs, VARs) and open source projects. For details, see LICENSE. To acquire a commercial license, contact smile.sales@outlook.com.

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Issues & Discussions

Channel	Purpose
GitHub Discussions	Questions, ideas, show-and-tell
Stack Overflow [smile]	Technical Q&A
Issue Tracker	Bug reports and feature requests
Online Docs	Tutorials and programming guides
Java API · Scala API · Kotlin API · Clojure API	API Javadoc

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Contributing

Please read CONTRIBUTING.md for build and test instructions.

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Maintainers

• Haifeng Li (@haifengl)
• Karl Li (@kklioss)

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Gallery

Scatterplot Matrix
Scatter Plot	Line Plot	Surface Plot
Bar Plot	Box Plot	Histogram Heatmap
Rolling Average	Geo Map	UMAP
Text Plot	Heatmap with Contour	Hexmap
IsoMap	LLE	Kernel PCA
Neural Network	SVM	Hierarchical Clustering
SOM	DBSCAN	Neural Gas
Wavelet	Exponential Family Mixture	Teapot Wireframe
Grid Interpolation