haifengl/smile

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.

SMILE Studio is an agentic IDE for data science using Python, Java, or Scala. See studio/README.md how to get your first project up and start interacting with your data with natural language in a few minutes.

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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.model.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

An agentic IDE for data science using Python or SMILE

| Document | Topics | |-------------------------------|---| | README.md | Desktop Studio UX | | CLI | CLI entry points (smile, smile shell, smile scala, smile serve) |

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

com.github.haifengl
  smile-core
  6.3.0
com.github.haifengl
  smile-deep
  6.3.0
com.github.haifengl
  smile-nlp
  6.3.0
com.github.haifengl
  smile-plot
  6.3.0

SBT (Scala)

libraryDependencies += "com.github.haifengl" %% "smile-scala" % "6.3.0"

Gradle (Kotlin)

dependencies {
    implementation("com.github.haifengl:smile-kotlin:6.3.0")
}

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-dev

macOS (Homebrew)

brew install arpack
If macOS SIP strips DYLD_LIBRARY_PATH, create a symlink to the dylib in your working dir:
ln -s /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 PATH (Windows) or LD_LIBRARY_PATH (Linux).

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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

SMILE Studio is an agentic IDE for data science using Python or SMILE on JVM. See studio/README.md for full documentation.

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

path/to/smile/bin/setup      # install required native dependencies
path/to/smile/bin/smile      # launch SMILE Studio from your project directory

Other entry points:

| Command | Description | |-----------------|------------------------------------------------| | smile | Desktop agentic 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:

path/to/smile/bin/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.

  com.github.haifengl
  smile-plot
  6.3.0

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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]](http://stackoverflow.com/questions/tagged/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