Ruvector Math Wasm

ruvector-math-wasm

WebAssembly-compiled mathematical primitives for Optimal Transport, Information Geometry, and Product Manifold operations. Runs in browsers, Node.js, and edge runtimes with near-native performance.

Quick Reference

Installation

npx ruvector-math-wasm@latest

Node.js Usage

import init, {
  WassersteinDistance,
  FisherMetric,
  ProductManifold,
  SinkhornSolver,
} from 'ruvector-math-wasm';

await init();

// Optimal Transport: Wasserstein distance between distributions
const p = new Float64Array([0.2, 0.3, 0.5]);
const q = new Float64Array([0.1, 0.4, 0.5]);
const distance = WassersteinDistance.compute(p, q);
console.log(`W2 distance: ${distance}`);

// Sinkhorn approximation for large distributions
const solver = new SinkhornSolver({ epsilon: 0.01, maxIter: 100 });
const transport = solver.solve(costMatrix, p, q);
console.log(`Transport plan cost: ${transport.cost}`);

// Information Geometry: Fisher-Rao distance
const dist1 = new Float64Array([0.3, 0.7]);  // Bernoulli p=0.3
const dist2 = new Float64Array([0.6, 0.4]);  // Bernoulli p=0.6
const fisherDist = FisherMetric.distance(dist1, dist2);
console.log(`Fisher-Rao distance: ${fisherDist}`);

// Product Manifold: geodesic interpolation
const pointA = new Float64Array([1.0, 0.0, 0.5]);
const pointB = new Float64Array([0.0, 1.0, 0.8]);
const midpoint = ProductManifold.geodesic(pointA, pointB, 0.5);

Browser Usage

<script type="module">
  import init, { WassersteinDistance, FisherMetric } from 'ruvector-math-wasm';
  await init();

  const p = new Float64Array([0.25, 0.25, 0.25, 0.25]);
  const q = new Float64Array([0.1, 0.2, 0.3, 0.4]);
  const dist = WassersteinDistance.compute(p, q);
  document.getElementById('result').textContent = `Distance: ${dist}`;
</script>

Key API

WassersteinDistance

Compute Earth Mover's Distance (Wasserstein-p) between probability distributions.

WassersteinDistance.compute(p: Float64Array, q: Float64Array, order?: number): number
WassersteinDistance.computeWithCost(p: Float64Array, q: Float64Array, costMatrix: Float64Array): number

Parameters:

SinkhornSolver

Entropic-regularized optimal transport for large-scale problems.

const solver = new SinkhornSolver(config?: SinkhornConfig);
const result = solver.solve(costMatrix: Float64Array, p: Float64Array, q: Float64Array): TransportResult;

SinkhornConfig:

TransportResult:

FisherMetric

Information-geometric distance on statistical manifolds.

FisherMetric.distance(p: Float64Array, q: Float64Array): number
FisherMetric.matrix(params: Float64Array, family: string): Float64Array
FisherMetric.naturalGradient(params: Float64Array, gradient: Float64Array): Float64Array

Parameters:

ProductManifold

Operations on product spaces of Riemannian manifolds.

ProductManifold.geodesic(a: Float64Array, b: Float64Array, t: number): Float64Array
ProductManifold.distance(a: Float64Array, b: Float64Array): number
ProductManifold.expMap(point: Float64Array, tangent: Float64Array): Float64Array
ProductManifold.logMap(base: Float64Array, target: Float64Array): Float64Array
ProductManifold.parallelTransport(v: Float64Array, from: Float64Array, to: Float64Array): Float64Array

Parameters:

References

• API Reference
• npm