# @johnhenry/math-plus-adapter-math

> Bridge between @johnhenry/math (Vector/Matrix, science/CAS side) and @johnhenry/math-plus-tensor-core (Tensor, engineering side) — Matrix/Vector Tensor conversion (issue #14); Symbolic->tensor-compile IR (issue #15) and Symbolic->frame-arrow Expr (iss

Latest version **0.0.5** (published 2026-09-24) · MIT license · 0 weekly downloads

## Install

```sh
npm install @johnhenry/math-plus-adapter-math
pnpm add @johnhenry/math-plus-adapter-math
yarn add @johnhenry/math-plus-adapter-math
bun add @johnhenry/math-plus-adapter-math
```

## Health

**Score 65/100 (B)** — status: active.

Positive: esm support; no vulnerabilities; has provenance; recently updated; high maintenance score.

Warnings: low downloads; no types; pre 1.0.

## Facts

| | |
|---|---|
| Version | 0.0.5 |
| Published | 2026-09-24 |
| First published | 2026-08-22 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | ESM |
| Node | >=24.0.0 |
| Dependencies | 4 |
| Unpacked size | 109.1 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| Provenance | attested (GitHub Actions) |
| GitHub stars | 0 |
| Maintainers | johnhenry |

## Links

- npm: https://www.npmjs.com/package/@johnhenry/math-plus-adapter-math
- Repository: https://github.com/johnhenry/math-plus
- Homepage: https://github.com/johnhenry/math-plus/tree/main/adapters/adapter-math
- Issues: https://github.com/johnhenry/math-plus/issues
- npm.io page: https://npm.io/package/@johnhenry/math-plus-adapter-math

## Dependencies (4)

- [@johnhenry/math](https://npm.io/package/@johnhenry/math.md) ^0.0.1
- [@johnhenry/math-plus-frame-arrow](https://npm.io/package/@johnhenry/math-plus-frame-arrow.md) ^0.0.5
- [@johnhenry/math-plus-tensor-core](https://npm.io/package/@johnhenry/math-plus-tensor-core.md) ^0.2.1
- [@johnhenry/math-plus-tensor-compile](https://npm.io/package/@johnhenry/math-plus-tensor-compile.md) ^0.1.3

## Recent versions

- 0.0.5 (latest) — 2026-09-24
- 0.0.4 — 2026-09-24
- 0.0.3 — 2026-09-24
- 0.0.2 — 2026-09-23
- 0.0.1 — 2026-09-23
- 0.0.0 — 2026-08-22

## README

# @johnhenry/math-plus-adapter-math

[![npm version](https://img.shields.io/npm/v/%40johnhenry%2Fmath-plus-adapter-math.svg)](https://www.npmjs.com/package/@johnhenry/math-plus-adapter-math)
[![license](https://img.shields.io/npm/l/%40johnhenry%2Fmath-plus-adapter-math.svg)](../../LICENSE)

The bridge between `@johnhenry/math` (Vector/Matrix, `Symbolic` CAS — the
science side) and `@johnhenry/math-plus-tensor-core` (`Tensor` — the
engineering side). Three jobs live here: Matrix/Vector ↔ Tensor conversion,
compiling `Symbolic` expressions into the tensor and dataframe worlds, and
the tensor-native numerics (`linalg`, stats, FFT) that sit on top of the
conversion layer.

## Install

```bash
npm install @johnhenry/math-plus-adapter-math
```

Depends on `@johnhenry/math`, `tensor-core`, `tensor-compile`, and
`frame-arrow` (all `@johnhenry/math-plus-*`).

## Quick start

```js
import { fromMatrix, toMatrix, compileExpr, compileFrameExpr, linalg } from "@johnhenry/math-plus-adapter-math";
import { Symbolic } from "@johnhenry/math";
import { Tensor } from "@johnhenry/math-plus-tensor-core";

// Matrix/Vector <-> Tensor — plain arrays or @johnhenry/math values
const t = fromMatrix([[1, 2, 3], [4, 5, 6]]); // 2x3 f32 Tensor
toMatrix(t.transpose());                      // [[1,4],[2,5],[3,6]] — views handled

// Compile a CAS expression to run over tensor batches
const expr = Symbolic.parse("sin(x) * y + sqrt(x^2 + 1)");
const compiled = compileExpr(expr, { variables: ["x", "y"] });
compiled.forward(Tensor.from([0.1, 1.7]), Tensor.from([1.0, 0.3]));
const { localGrads } = compiled.forwardWithGrad(Tensor.from([0.3]), Tensor.from([1]));
// gradients — tested against Symbolic.differentiate as an independent oracle

// Same Symbolic expression as a frame-arrow computed column
const formula = Symbolic.parse("sin(x) * x");
const derivative = Symbolic.differentiate(formula, "x");
frame.withColumns({
  y: compileFrameExpr(formula),        // free variables become col() refs
  dy_dx: compileFrameExpr(derivative), // differentiate symbolically, run columnar
});

// Tensor-native linear algebra
linalg.solve(a, b);
linalg.svd(a);
```

## API surface

- **Conversion:** `fromMatrix` / `fromVector` (plain arrays *or*
  `@johnhenry/math` `Matrix`/`Vector`; optional `{ dtype }`), `toMatrix` /
  `toVector` (correct through non-contiguous views). Ragged input throws
  `RangeError`.
- **`compileExpr(expr, opts?)`:** `Symbolic` → tensor-compile IR. Returns a
  `CompiledFn` with `forward(...tensors)` and `forwardWithGrad(...)`;
  default variable order is `Symbolic.freeVariables` (alphabetical) —
  pass `variables` to pin positions. `piecewise` compiles to select
  chains. Unsupported nodes throw `UnsupportedExprError`.
- **`compileFrameExpr(expr)`:** `Symbolic` → frame-arrow `Expr`. Function
  names map 1:1 (frame-arrow's `fn.*` was spelled to match `Symbolic`);
  unknown columns surface as frame-arrow's own error.
- **`linalg`:** `lu`/`solve`/`det`/`inv`/`rref`/`rank`/`nullSpace`, `qr`,
  `cholesky`, `eigSymmetric` (Jacobi) and `eigGeneral` (complex
  eigenvalues, returned as `@johnhenry/math` `ComplexNumber`s),
  `powerIteration`, `svd`, `leastSquares`, `pseudoInverse`, norms and
  `conditionNumber`.
- **Stats:** `mean`/`median`/`percentile`/`variance`/`standardDeviation`
  (+ population variants), `correlation`, `linearRegression`,
  `Distributions`, `HypothesisTests`, `SpecialFunctions`.
- **FFT:** `fft`/`ifft`/`fftPadded`/`convolve`/`realSignal` over
  `Float64Array` pairs.
- **Graphs:** `toCSR`/`toDense` for `@johnhenry/math` graph values.
- **`./test-utils`:** `dualGrad`/`dualGradN` — the `DualNumber`
  forward-mode gradient oracle, exported so *other* packages can check
  their gradients against an independent implementation.

## Traps

- **`compileExpr` renames `ln` → `log`** internally to match
  tensor-compile's IR; `compileFrameExpr` needs no rename table — if you
  extend one bridge, don't assume the other spells functions the same way.
- **Variable order is alphabetical unless you pass `variables`.**
  `Symbolic.parse("b - a")` compiled without options takes `(a, b)` — in
  that order.
- **Conversion defaults to `f32`.** Round-tripping f64 data through
  `fromMatrix` without `{ dtype: "f64" }` quietly drops precision.

Part of the [math-plus](https://github.com/johnhenry/math-plus) family —
docs at [opensource.johnhenry.me/math/](https://opensource.johnhenry.me/math/).

---
_Source: https://npm.io/package/@johnhenry/math-plus-adapter-math · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._