@wlearn/libsvm

LIBSVM v3.37 compiled to WebAssembly. Kernel SVM classification, regression, and novelty detection in browsers and Node.js.

Based on LIBSVM v3.37 (BSD-3-Clause). Zero dependencies. ESM.

Install

npm install @wlearn/libsvm

Quick start

import { SVMModel } from '@wlearn/libsvm'

const model = await SVMModel.create({
  svmType: 'C_SVC',
  kernel: 'RBF',
  C: 1.0,
  gamma: 0.5
})

// Train -- accepts number[][] or { data: Float64Array, rows, cols }
model.fit(
  [[1, 2], [3, 4], [5, 6], [7, 8]],
  [0, 0, 1, 1]
)

// Predict
const preds = model.predict([[2, 3], [6, 7]])  // Float64Array

// Score
const accuracy = model.score([[2, 3], [6, 7]], [0, 1])

// Save / load
const buf = model.save()  // Uint8Array
const model2 = await SVMModel.load(buf)

// Clean up -- required, WASM memory is not garbage collected
model.dispose()
model2.dispose()

Typed matrix input (fast path)

const X = {
  data: new Float64Array([1, 2, 3, 4, 5, 6, 7, 8]),
  rows: 4,
  cols: 2
}
model.fit(X, new Float64Array([0, 0, 1, 1]))

Input coercion policy

const model = await SVMModel.create({ coerce: 'auto' })   // convert silently (default)
const model = await SVMModel.create({ coerce: 'warn' })    // warn once per instance
const model = await SVMModel.create({ coerce: 'error' })   // throw on non-typed input

API

SVMModel.create(params?)

Async factory. Loads WASM module, returns ready-to-use model.

Parameters:

• svmType -- 'C_SVC' | 'NU_SVC' | 'ONE_CLASS' | 'EPSILON_SVR' | 'NU_SVR' (default: 'C_SVC')
• kernel -- 'LINEAR' | 'POLY' | 'RBF' | 'SIGMOID' (default: 'RBF')
• C -- regularization (default: 1.0)
• gamma -- kernel coefficient, <= 0 means 1/n_features (default: 0)
• degree -- polynomial degree (default: 3)
• coef0 -- independent term in kernel (default: 0)
• nu -- for NU_SVC/NU_SVR/ONE_CLASS (default: 0.5)
• eps -- stopping tolerance (default: 0.001)
• p -- epsilon in SVR loss (default: 0.1)
• shrinking -- use shrinking heuristic (default: 1)
• probability -- enable probability estimates (default: 0)
• cacheSize -- kernel cache in MB (default: 100)
• coerce -- 'auto' | 'warn' | 'error' (default: 'auto')

model.fit(X, y)

Train on data. Returns this.

model.predict(X)

Returns Float64Array of predicted labels.

model.predictProba(X)

Returns Float64Array of shape nrow * nclass (row-major probabilities). Requires probability: 1 in constructor params.

model.decisionFunction(X)

Returns Float64Array of decision values.

• Binary classification: nrow values
• Multi-class: nrow * nr_class*(nr_class-1)/2 pairwise margins

model.score(X, y)

Returns accuracy (classification) or R-squared (regression).

model.save() / SVMModel.load(buffer)

Save to / load from Uint8Array (native LIBSVM format).

model.dispose()

Free WASM memory. Required. Idempotent.

model.getParams() / model.setParams(p)

Get/set hyperparameters.

SVMModel.defaultSearchSpace()

Returns default hyperparameter search space for AutoML.

SVM types

| Name | Code | Task | |------|------|------| | C_SVC | 0 | C-support vector classification | | NU_SVC | 1 | nu-support vector classification | | ONE_CLASS | 2 | One-class SVM (novelty detection) | | EPSILON_SVR | 3 | epsilon-support vector regression | | NU_SVR | 4 | nu-support vector regression |

Kernels

| Name | Code | Formula | |------|------|---------| | LINEAR | 0 | u'v | | POLY | 1 | (gammau'v + coef0)^degree | | RBF | 2 | exp(-gamma|u-v|^2) | | SIGMOID | 3 | tanh(gamma*u'*v + coef0) |

Resource management

WASM heap memory is not garbage collected. Call .dispose() on every model when done.

Build from source

Requires Emscripten (emsdk) activated.

git clone --recurse-submodules https://github.com/wlearn-org/libsvm-wasm
cd libsvm-wasm
npm run build
npm test

If you already cloned without --recurse-submodules:

git submodule update --init

License

BSD-3-Clause (same as upstream LIBSVM)