Package Exports
- tensorly
Readme
tensorly
NumPy, but for TypeScript. No Python runtime, no WASM blobs, no nonsense — just typed arrays and math.
If you've ever wished you could np.linalg.solve() in a Node script without spinning up a Jupyter notebook, this is for you.
Install
npm install tensorlyWorks in Node 16+, Bun, Deno, and any bundler (Vite, webpack, esbuild). Ships ESM + CJS + full .d.ts types.
Quick Start
import np, { array, zeros, arange, linspace } from "tensorly";
// vectors & matrices
const v = array([1, 2, 3, 4, 5]);
const m = arange(0, 12).reshape([3, 4]);
// element-wise math — broadcasting just works
const doubled = np.multiply(v, 2);
const normed = np.divide(v, np.sqrt(np.sum(np.power(v, 2))));
// linear algebra
const A = array([
[2, 1],
[5, 7],
]);
const b = array([11, 13]);
const x = np.linalg.solve(A, b); // solves Ax = b
// statistics
np.mean(v); // 3
np.std(v); // 1.414...
np.corrcoef(v, v); // [[1, 1], [1, 1]]
// random (seeded for reproducibility)
np.random.seed(42);
const noise = np.random.randn(100, 100); // 100x100 gaussian noiseWhat's Inside
| Module | Highlights |
|---|---|
| Core | N-dim typed arrays, strides, broadcasting, slicing with steps |
| Creation | zeros, ones, full, arange, linspace, eye, diag, tile |
| Math | All the trig, exp/log, floor/ceil, clip, element-wise comparisons |
| Linalg | det, inv, solve, eig, svd, qr, cholesky, norm |
| Stats | mean, median, std, var, corrcoef, cov, histogram |
| Random | Xorshift128+ PRNG, normal, uniform, poisson, gamma, beta, choice |
| FFT | Cooley-Tukey radix-2: fft, ifft, fft2, fftfreq, fftshift |
| Manipulation | concat, stack, split, flip, rot90, unique, where, meshgrid |
API Cheat Sheet
Array Creation
np.array([
[1, 2],
[3, 4],
]); // from nested arrays
np.zeros([3, 3]); // 3×3 of zeros
np.ones([2, 4], "float32"); // with explicit dtype
np.arange(0, 10, 0.5); // [0, 0.5, 1, ..., 9.5]
np.linspace(0, 1, 50); // 50 points from 0 to 1
np.eye(4); // 4×4 identity
np.diag([1, 2, 3]); // diagonal matrixMath (element-wise)
np.add(a, b) np.subtract(a, b)
np.multiply(a, b) np.divide(a, b)
np.power(a, 2) np.sqrt(a)
np.sin(a) np.exp(a)
np.log(a) np.clip(a, 0, 1)
np.abs(a) np.round(a)Linear Algebra
np.dot(a, b); // dot product / matmul
np.linalg.det(A); // determinant
np.linalg.inv(A); // inverse
np.linalg.solve(A, b); // solve linear system
np.linalg.eig(A); // eigenvalues & vectors
np.linalg.svd(A); // singular value decomposition
np.linalg.norm(v); // L2 norm
np.linalg.qr(A); // QR decomposition
np.linalg.cholesky(A); // Cholesky factorizationStatistics
np.mean(a) np.median(a)
np.std(a) np.var_(a)
np.min(a) np.max(a)
np.sum(a, axis) np.prod(a, axis)
np.corrcoef(x, y) np.cov(x, y)
np.percentile(a, 75)
np.histogram(a, 10)Random
np.random.seed(123); // reproducible
np.random.rand(3, 3); // uniform [0, 1)
np.random.randn(3, 3); // standard normal
np.random.randint(0, 100, [5]); // random ints
np.random.normal(0, 1, [1000]); // gaussian
np.random.choice(a, 5); // sample
np.random.shuffle(a); // in-place shuffleManipulation
a.reshape([2, 6]) a.transpose()
a.flatten() a.slice([...])
np.concatenate([a, b]) np.stack([a, b])
np.flip(a) np.rot90(a)
np.unique(a) np.where(cond, x, y)
np.diff(a) np.cumsum(a)
np.meshgrid(x, y) np.outer(a, b)Dtypes
Backed by real TypedArrays — no boxing overhead.
float64 (default) · float32 · int32 · int16 · int8 · uint32 · uint16 · uint8 · bool
const a = np.zeros([1000, 1000], "float32"); // ~4MB instead of ~8MB
const b = a.astype("int32");Why This Exists
Python's NumPy is fantastic but sometimes you just need to crunch numbers in a TypeScript codebase without crossing language boundaries. This library gives you the same mental model — shapes, broadcasting, axis-based reductions — in a package you can import and go.
Not a 1:1 port (no C extensions here), but covers the 90% of NumPy that most people actually use day-to-day.
Contributing
PRs welcome. Run the test suite before submitting:
npm test # 68 tests via vitest
npm run lint # eslint
npm run build # tsup → cjs + esm + .d.tsLicense
MIT