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

<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-core</artifactId>
  <version>6.3.0</version>
</dependency>

<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-deep</artifactId>
  <version>6.3.0</version>
</dependency>

<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-nlp</artifactId>
  <version>6.3.0</version>
</dependency>

<dependency>
  <groupId>com.github.haifengl</groupId>
  <artifactId>smile-plot</artifactId>
  <version>6.3.0</version>
</dependency>

SBT (Scala)

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

Gradle (Kotlin)

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