libsvm-ts


Modern TypeScript wrapper for libsvm with WebAssembly support and an sklearn-like API. This library brings powerful Support Vector Machine capabilities to TypeScript/JavaScript with a familiar, Pythonic interface inspired by scikit-learn.

🚀 Quick Start

$3

``bash
npm install @millcrest/libsvmts
`

$3

`typescript
import { SVC } from "@millcrest/libsvmts";

// Create and configure classifier
const clf = new SVC({
kernel: 'rbf',
C: 1.0,
gamma: 'scale',
});

// Prepare data
const x_train = [[0, 0], [1, 1], [1, 0], [0, 1]];
const y_train = [0, 0, 1, 1];

// Train model
await clf.fit(x_train, y_train);

// Make predictions
const predictions = clf.predict([[0.5, 0.5]]);
console.log(predictions); // [0]

// Get accuracy
const accuracy = clf.score(x_train, y_train);
console.log(Accuracy: ${(accuracy * 100).toFixed(2)}%);

// Clean up
clf.free();
`

$3

`typescript
import { SVR } from "@millcrest/libsvmts";

// Create regressor
const regressor = new SVR({
kernel: 'rbf',
C: 1.0,
epsilon: 0.1,
});

// Prepare data
const x_train = [[0], [1], [2], [3], [4]];
const y_train = [0, 1, 4, 9, 16]; // y = x^2

// Train model
await regressor.fit(x_train, y_train);

// Make predictions
const predictions = regressor.predict([[2.5]]);
console.log(predictions); // ~6.25

// Get R² score
const r2 = regressor.score(x_train, y_train);
console.log(R² Score: ${r2.toFixed(4)});

// Clean up
regressor.free();
`

📖 Documentation

$3

#### SVC (Support Vector Classification)

Mimics sklearn.svm.SVC

Constructor Parameters:

`typescript
interface SVCParams {
C?: number; // Regularization parameter (default: 1.0)
kernel?: 'linear' | 'poly' | 'rbf' | 'sigmoid' | 'precomputed'; // default: 'rbf'
degree?: number; // Degree for poly kernel (default: 3)
gamma?: number | 'scale' | 'auto'; // Kernel coefficient (default: 'scale')
coef0?: number; // Independent term in kernel (default: 0.0)
tol?: number; // Tolerance for stopping (default: 1e-3)
shrinking?: boolean; // Use shrinking heuristic (default: true)
probability?: boolean; // Enable probability estimates (default: false)
cacheSize?: number; // Kernel cache size in MB (default: 200)
classWeight?: 'balanced' | Record | null; // Class weights
verbose?: boolean; // Verbose output (default: false)
maxIter?: number; // Max iterations, -1 for no limit (default: -1)
decisionFunctionShape?: 'ovr' | 'ovo'; // Decision function shape (default: 'ovr')
breakTies?: boolean; // Break ties by confidence (default: false)
randomState?: number | null; // Random seed (default: null)
}
`

Methods:

- async fit(X: Matrix, y: Vector): Promise - Fit the SVM model
- predict(X: Matrix): Vector - Predict class labels
- predictProba(X: Matrix): PredictionWithProba[] - Predict class probabilities (requires probability: true)
- decisionFunction(X: Matrix): Matrix - Compute decision function values
- score(X: Matrix, y: Vector): number - Return mean accuracy
- getModelInfo(): ModelInfo | null - Get model information
- free(): void - Free model memory

#### SVR (Support Vector Regression)

Mimics sklearn.svm.SVR

Constructor Parameters:

`typescript
interface SVRParams {
C?: number; // Regularization parameter (default: 1.0)
kernel?: 'linear' | 'poly' | 'rbf' | 'sigmoid' | 'precomputed'; // default: 'rbf'
degree?: number; // Degree for poly kernel (default: 3)
gamma?: number | 'scale' | 'auto'; // Kernel coefficient (default: 'scale')
coef0?: number; // Independent term in kernel (default: 0.0)
epsilon?: number; // Epsilon in epsilon-SVR (default: 0.1)
tol?: number; // Tolerance for stopping (default: 1e-3)
shrinking?: boolean; // Use shrinking heuristic (default: true)
cacheSize?: number; // Kernel cache size in MB (default: 200)
verbose?: boolean; // Verbose output (default: false)
maxIter?: number; // Max iterations, -1 for no limit (default: -1)
}
`

Methods:

- async fit(X: Matrix, y: Vector): Promise - Fit the SVM model
- predict(X: Matrix): Vector - Predict target values
- score(X: Matrix, y: Vector): number - Return R² score
- getModelInfo(): ModelInfo | null - Get model information
- free(): void - Free model memory

$3

`typescript
type Matrix = number[][]; // 2D array for features
type Vector = number[]; // 1D array for labels/targets

interface PredictionWithProba {
label: number;
probabilities: Record;
}

interface ModelInfo {
nClasses: number;
classes?: number[];
nSupportPerClass?: number[];
nSupport: number;
supportVectorIndices: number[];
isFitted: boolean;
}
`

🏗️ Building from Source

$3

- Node.js 16+
- Emscripten (for building WASM)

$3

`bash


Clone with submodules

git clone --recursive git@github.com:millcrest/libsvmts.git
cd libsvmts

Or if already cloned

git submodule update --init --recursive

Install dependencies

npm install

Build everything

npm run build
`

Note: The first npm install will warn that WASM isn't built yet - that's expected.

$3

Choose one of these methods:

Option 1: System-wide (recommended)
`bash


Follow official guide: https://emscripten.org/docs/getting_started/downloads.html


`

Option 2: Using Docker
`bash
docker run -v $(pwd):/src -w /src emscripten/emsdk make build
`

Option 3: Local emsdk (if you prefer)
`bash
git clone https://github.com/emscripten-core/emsdk.git
cd emsdk
./emsdk install latest
./emsdk activate latest
source ./emsdk_env.sh
cd ..
`

$3

`bash
npm run build # Build WASM + TypeScript
make build # Build WASM only (requires emcc in PATH)
npm run build:ts # Build TypeScript only
`
npm run build:ts
`

$3

`bash
npm run dev # Watch mode for TypeScript
npm run build # Build everything (WASM + TS)
npm run build:wasm # Build WASM only
npm run build:ts # Build TypeScript only
npm run test # Run tests
npm run test:watch # Watch mode for tests
npm run lint # Lint code
npm run format # Format code
npm run typecheck # Type check without building
`

🔬 Advanced Usage

$3

`typescript
const clf = new SVC({
kernel: 'rbf',
probability: true, // Enable probability estimates
});

await clf.fit(x_train, y_train);

// Get predictions with probabilities
const predictions = clf.predictProba(X_test);
predictions.forEach(pred => {
console.log(Predicted: ${pred.label});
console.log(Probabilities:, pred.probabilities);
});
`

$3

`typescript
// Balanced class weights
const clf = new SVC({
classWeight: 'balanced'
});

// Manual class weights
const clf = new SVC({
classWeight: {
0: 1.0,
1: 2.0 // Give class 1 twice the weight
}
});
`

$3

`typescript
const clf = new SVC({
decisionFunctionShape: 'ovr' // One-vs-Rest
});

await clf.fit(x_train, y_train);

// Get decision function values
const decisionValues = clf.decisionFunction(X_test);
`

$3

`typescript
// Get model information
const modelInfo = clf.getModelInfo();
console.log('Support vectors:', modelInfo.nSupport);
console.log('Classes:', modelInfo.classes);

// Serialize model (TODO: implementation pending)
// const serialized = clf.serializeModel();
`

🧪 Testing

This project uses Vitest for both unit and integration tests.

`bash


Run all tests (unit + integration)

npm test

Run tests in watch mode

npm run test:watch

Run tests with UI

npm run test:ui

Run with coverage

npm run test:coverage
``

📝 License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

libsvm is also licensed under the BSD 3-Clause License. See the libsvm license for details.

🙏 Acknowledgments

- libsvm by Chih-Chung Chang and Chih-Jen Lin
- scikit-learn for API inspiration
- libsvm-ts (original implementation) for initial inspiration

📚 References

- Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
- libsvm website
- Support Vector Machines on Wikipedia