SMILE — Statistical Machine Intelligence and Learning Engine

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Built-in Algorithms:

Large Language Models

Language Model, Chat Completion, Message, Llama, Qwen, Inference Engine, Tiktoken, Grouped-Query Attention, RoPE, SwiGLU Feed-Forward, KV Cache

Deep Learning

Model, Layers, Sequential Block, Linear, Conv2d, Embedding, Batch Norm, RMS Norm, Dropout, Tensor, Device, Optimizer, Loss, Dataset, EfficientNet

Classification

Decision Trees, AdaBoost, Gradient Boosting, Random Forest, Logistic Regression, Neural Networks, Support Vector Machines, RBF Networks, Maximum Entropy Classifier, Generic Naïve Bayes Classifier, Naïve Bayes Document Classfier, Fisher / Linear / Quadratic / Regularized Discriminant Analysis, Platt Scaling, Isotonic Regression Scaling, One vs. One, One vs. Rest

Regression

Linear Regression, LASSO, ElasticNet, Ridge Regression, Regression Trees, Gradient Boosting, Random Forest, RBF Networks, Neural Networks, Support Vector Regression, Gaussian Process, Generalized Linear Models, Generalized Additive Models

Feature Engineering and Selection

Bag of Words, Sparse One Hot Encoding, Standardizer, Robust Standardizer, Maximum Absolute Value Scaler, Winsor Scaler, Normalizer, Genetic Algorithm based Feature Selection, Ensemble Learning based Feature Selection, TreeSHAP, Signal Noise ratio, Sum Squares ratio

Dimension Reduction

PCA, Kernel PCA, Probabilistic PCA, Generalized Hebbian Algorithm, Random Project, ICA

Model Validation

Cross Validation, Leave-One-Out Validation, Bootstrap, Confusion Matrix, Hyperparameter Tuning, AUC, LogLoss, CrossEntropy, Accuracy, Error, Fallout, FDR, F-Score, Precision, Recall, Sensitivity, Specificity, Matthews Correlation Coefficient, MSE, RMSE, RSS, R2, Mean Absolute Deviation, Rand Index, Adjusted Rand Index, Mutual Information Score,

Clustering

Hierarchical Clustering, CLARANS, DBSCAN, DENCLUE, K-Means, X-Means, G-Means, K-Modes, Deterministic Annealing, Sequential Information Bottleneck, Spectral Clustering, Minimum Entropy Clustering

Vector Quantization

BIRCH, Self-Organizing Maps, Neural Gas, Growing Neural Gas, Neural Map

Association Rules

Frequent Itemset Mining, Association Rule Mining

Manifold learning

IsoMap, LLE, Laplacian Eigenmap, t-SNE, UMAP, Classical MDS, Isotonic MDS, Sammon Mapping

Nearest Neighbor Search

Linear Search, BK-Tree, Cover Tree, KD-Tree, LSH, Multi-Probe LSH, SimHash

Sequence Learning

Hidden Markov Model, Conditional Random Field

Time Series

ACF, PACF, Box-Pierce and Ljung-Box Test, AR, ARMA

Natural Language Processing

Sentence Splitter, Tokenizer, Bigram Extractor, Phrase Extractor, Keyword Extractor, Porter Stemmer, Lancaster Stemmer, POS Tagging, Relevance Ranking, Word2Vec

Mathematics

Genetic Algorithms, Graph, Hash Functions, Interpolation, Sort Algorithms, Taxonomy, Wavelet

Linear Algebra

Dense Matrix, Band Matrix, Sparse Matrix, LU, Cholesky, QR, EVD, SVD, Biconjugate Gradient, BFGS, Computer Algebra System

Statistics

Distributions, Random Number Generators, Hypothesis Tests

SMILE is a comprehensive, high-performance machine learning engine for the JVM. From classic algorithms to GPU-accelerated deep learning and LLM inference — all in pure Java, Scala, or Kotlin. Get started in 5 minutes →

Speed

Advanced data structures and algorithms deliver state-of-the-art performance.

Compared to a third-party benchmark, SMILE outperforms R, Python, Spark, H2O, and XGBoost significantly — often by several times while using far less memory. If you can train advanced models on a laptop, why buy a cluster?

Training Time (seconds)

Ease of Use

Write applications quickly in Java, Scala, Kotlin, Clojure, or Groovy. Data scientists and engineers can now speak the same language.

SMILE provides hundreds of algorithms behind a clean, consistent API. The Scala and Kotlin bindings add high-level operators and DSL builders. Use it interactively from the SMILE shell or embed it in any JVM application. 


var iris = Read.arff("iris.arff");

var model = RandomForest.fit(Formula.lhs("class"), iris);

println(model.metrics());
DataFrame, Model Fitting, and Metrics

val iris = read.arff("iris.arff")

val model = randomForest("class" ~, iris)

println(model.metrics)
DataFrame, Model Fitting, and Metrics

val iris = read.arff("iris.arff")

val model = randomForest(Formula.lhs("class"), iris)

println(model.metrics())
DataFrame, Model Fitting, and Metrics

(let [iris (read-arff
            "data/weka/iris.arff")
      model (random-forest
             (Formula/lhs "class") iris)]
  (.metrics model))
DataFrame, Model Fitting, and Metrics

var iris = Read.arff("iris.arff")

var model = RandomForest.fit(Formula.lhs("class"), iris)

println model.metrics()
DataFrame, Model Fitting, and Metrics

Large Language Models

Run LLM inference natively on the JVM — no Python bridge required.

SMILE ships a complete LLM stack backed by LibTorch: tiktoken BPE tokenizer, grouped-query attention (GQA), rotary positional encoding (RoPE), SwiGLU feed-forward, and KV-cache. An OpenAI-compatible REST server with Server-Sent Events (SSE) streaming is included for production deployment. See Large Language Models. 


var llama = Llama.build(
    "model/Meta-Llama-3-8B-Instruct",
    "model/.../tokenizer.model",
    4, 2048, (byte) 0);

int[][] prompts = llama.tokenizer.encode(
        "Once upon a time", true, false);

var result = llama.generate(prompts, 200,
    0.6, 0.9, false, 42L, null);
Text Generation

var reply = llama.chat(
    new Message[]{
        Message.system("Be concise."),
        Message.user(
            "What is RoPE?")
    },
    128, 0.7, 0.9, false, 0L, null);

System.out.println(reply.content());
Chat Completion

Deep Learning & Computer Vision

GPU-accelerated neural networks — build, train, and deploy on LibTorch.

The smile-deep module exposes LibTorch tensors, all standard layer types (linear, Conv2d, pooling, BN/GN/RMS norm, dropout, embedding), loss functions, and optimizers (SGD, Adam, AdamW, RMSprop) through a clean Java API. Pretrained EfficientNet-V2 (S/M/L) models for ImageNet classification are available with a single method call. See Deep Learning. 


// Pretrained EfficientNet-V2-S
var model = EfficientNet.V2S();
var img = ImageIO.read(
    new File("dog.jpg"));

// Auto-preprocesses the image
try (var logits = model.forward(img)) {
    var probs = logits.softmax(1);
    int cls = probs.argmax(1, false)
        .intValue();
}
ImageNet Classification

var mlp = new SequentialBlock(
    Layer.relu(784, 256),
    Layer.relu(256, 128),
    Layer.logSoftmax(128, 10));

var opt = Optimizer.adam(
    mlp.asTorch().parameters(), 1e-3);

mlp.train(10, dataset,
    Loss.nll(), opt,
    new Accuracy(), testDataset);
Training Loop

Comprehensive

The most complete machine learning engine on the JVM.

SMILE covers every aspect of machine learning — LLM, computer vision, deep learning, classification, regression, clustering, association rule mining, manifold learning, nearest-neighbor search, feature engineering, missing-value imputation, time series, NLP, and more. See the sidebar for a full list of algorithms.

Natural Language Processing

From classic text processing to state-of-the-art LLM inference.

SMILE includes classic NLP building blocks — sentence splitter, word tokenizer, Porter/Lancaster stemmers, HMM POS tagger, bigram/phrase extraction, keyword detection, BM25 relevance ranking, and Word2Vec embeddings — alongside the full LLM inference stack. See NLP and LLM.

Mathematics & Statistics

A complete numerical computing environment inside the JVM.

Dense, band, and sparse matrices; LU, Cholesky, QR, EVD, SVD decompositions; BFGS / L-BFGS optimizers; wavelets; interpolation (linear, cubic spline, bilinear, bicubic); probability distributions; hypothesis tests (t-test, chi-squared, ANOVA, KS); and even a Scala-based computer algebra system with symbolic differentiation. See Linear Algebra and Statistics. 


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);
Linear Algebra

int[] bins1 = {8, 13, 16, 10, 3};

int[] bins2 = {4,  9, 14, 16, 7};

Hypothesis.chisq.test(bins1, bins2);
Statistics

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)
Computer Algebra System

Data Visualization

Interactive 2D/3D Swing plots and declarative Vega-Lite charts.

Scatter plot, line plot, bar plot, box plot, heatmap, hexmap, histogram, QQ plot, surface, contour, dendrogram, wireframe, and more. SMILE also supports declarative visualization that compiles to Vega-Lite for browser and Jupyter rendering. See Visualization and Declarative Visualization.