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@sroussey/simsimd

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Fastest SIMD-Accelerated Vector Similarity Functions for x86 and Arm

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import test from "node:test"; import assert from "node:assert"; import * as simsimd from "./dist/esm/simsimd.js"; import * as fallback from "./dist/esm/fallback.js"; function assertAlmostEqual(actual, expected, tolerance = 1e-6) { const lowerBound = expected - tolerance; const upperBound = expected + tolerance; assert( actual >= lowerBound && actual <= upperBound, `Expected ${actual} to be almost equal to ${expected}` ); } test("Distance from itself", () => { const f32s = new Float32Array([1.0, 2.0, 3.0]); assertAlmostEqual(simsimd.sqeuclidean(f32s, f32s), 0.0, 0.01); assertAlmostEqual(simsimd.cosine(f32s, f32s), 0.0, 0.01); const f32sNormalized = new Float32Array([ 1.0 / Math.sqrt(14), 2.0 / Math.sqrt(14), 3.0 / Math.sqrt(14), ]); assertAlmostEqual(simsimd.inner(f32sNormalized, f32sNormalized), 0.0, 0.01); const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]); assertAlmostEqual( simsimd.kullbackleibler(f32sDistribution, f32sDistribution), 0.0, 0.01 ); assertAlmostEqual( simsimd.jensenshannon(f32sDistribution, f32sDistribution), 0.0, 0.01 ); const u8s = new Uint8Array([1, 2, 3]); assertAlmostEqual(simsimd.hamming(u8s, u8s), 0.0, 0.01); assertAlmostEqual(simsimd.jaccard(u8s, u8s), 0.0, 0.01); }); test("Distance from itself JS fallback", () => { const f32s = new Float32Array([1.0, 2.0, 3.0]); assertAlmostEqual(fallback.sqeuclidean(f32s, f32s), 0.0, 0.01); assertAlmostEqual(fallback.cosine(f32s, f32s), 0.0, 0.01); const arrNormalized = new Float32Array([ 1.0 / Math.sqrt(14), 2.0 / Math.sqrt(14), 3.0 / Math.sqrt(14), ]); assertAlmostEqual(fallback.inner(arrNormalized, arrNormalized), 0.0, 0.01); const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]); assertAlmostEqual( fallback.kullbackleibler(f32sDistribution, f32sDistribution), 0.0, 0.01 ); assertAlmostEqual( fallback.jensenshannon(f32sDistribution, f32sDistribution), 0.0, 0.01 ); const u8s = new Uint8Array([1, 2, 3]); assertAlmostEqual(fallback.hamming(u8s, u8s), 0.0, 0.01); assertAlmostEqual(fallback.jaccard(u8s, u8s), 0.0, 0.01); }); const f32Array1 = new Float32Array([1.0, 2.0, 3.0]); const f32Array2 = new Float32Array([4.0, 5.0, 6.0]); test("Squared Euclidean Distance", () => { const result = simsimd.sqeuclidean(f32Array1, f32Array2); assertAlmostEqual(result, 27.0, 0.01); }); test("Inner Product", () => { const result = simsimd.inner(f32Array1, f32Array2); assertAlmostEqual(result, -31.0, 0.01); }); test("Cosine Similarity", () => { const result = simsimd.cosine(f32Array1, f32Array2); assertAlmostEqual(result, 0.029, 0.01); }); test("Squared Euclidean Distance JS", () => { const result = fallback.sqeuclidean(f32Array1, f32Array2); assertAlmostEqual(result, 27.0, 0.01); }); test("Inner Product JS", () => { const result = fallback.inner(f32Array1, f32Array2); assertAlmostEqual(result, -31.0, 0.01); }); test("Cosine Similarity JS", () => { const result = fallback.cosine(f32Array1, f32Array2); assertAlmostEqual(result, 0.029, 0.01); }); test("Squared Euclidean Distance C vs JS", () => { const result = simsimd.sqeuclidean(f32Array1, f32Array2); const resultjs = fallback.sqeuclidean(f32Array1, f32Array2); assertAlmostEqual(resultjs, result, 0.01); }); test("Inner Distance C vs JS", () => { const result = simsimd.inner(f32Array1, f32Array2); const resultjs = fallback.inner(f32Array1, f32Array2); assertAlmostEqual(resultjs, result, 0.01); }); test("Cosine Similarity C vs JS", () => { const result = simsimd.cosine(f32Array1, f32Array2); const resultjs = fallback.cosine(f32Array1, f32Array2); assertAlmostEqual(resultjs, result, 0.01); }); test("Hamming C vs JS", () => { const u8s = new Uint8Array([1, 2, 3]); const result = simsimd.hamming(u8s, u8s); const resultjs = fallback.cosine(u8s, u8s); assertAlmostEqual(resultjs, result, 0.01); }); test("Jaccard C vs JS", () => { const u8s = new Uint8Array([1, 2, 3]); const result = simsimd.jaccard(u8s, u8s); const resultjs = fallback.jaccard(u8s, u8s); assertAlmostEqual(resultjs, result, 0.01); }); test("Kullback-Leibler C vs JS", () => { const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]); const result = simsimd.kullbackleibler(f32sDistribution, f32sDistribution); const resultjs = fallback.kullbackleibler(f32sDistribution, f32sDistribution); assertAlmostEqual(resultjs, result, 0.01); }); test("Jensen-Shannon C vs JS", () => { const f32sDistribution = new Float32Array([1.0 / 6, 2.0 / 6, 3.0 / 6]); const result = simsimd.jensenshannon(f32sDistribution, f32sDistribution); const resultjs = fallback.jensenshannon(f32sDistribution, f32sDistribution); assertAlmostEqual(resultjs, result, 0.01); });