<p align="center"> <img src="docs/public/logo.png" width="200" height="200" alt="NDArray PHP Logo"> </p> <h1 align="center">NDArray PHP</h1> <p align="center">High-performance N-dimensional arrays for PHP, powered by Rust via FFI</p> <p align="center"> <a href="https://packagist.org/packages/phpmlkit/ndarray"><img src="https://img.shields.io/packagist/v/phpmlkit/ndarray?style=flat-square" alt="Latest Version"></a> <a href="https://github.com/phpmlkit/ndarray/actions"><img src="https://img.shields.io/github/actions/workflow/status/phpmlkit/ndarray/tests.yml?branch=main&label=tests&style=flat-square" alt="GitHub Workflow Status"></a> <a href="https://packagist.org/packages/phpmlkit/ndarray"><img src="https://img.shields.io/packagist/dt/phpmlkit/ndarray?style=flat-square" alt="Total Downloads"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License"></a> </p>

Features

Blazing Fast - Zero-copy views, Rust backend, minimal FFI overhead
NumPy Compatible - Familiar API for Python developers
Memory Safe - Automatic memory management via Rust
N-Dimensional - Support for arbitrary dimensions
Type Safe - Multiple data types with automatic inference
Scientific Computing - Complete toolkit for ML and data science

Installation

composer require phpmlkit/ndarray

Requirements: PHP 8.1+ with FFI extension enabled

Quick Example

use PhpMlKit\NDArray\NDArray;

// Create arrays
$a = NDArray::array([[1, 2], [3, 4]]);
$b = NDArray::ones([2, 2]);

// Mathematical operations
$c = $a->add($b);
$d = $a->matmul($b);
$e = $a->sum();

// Slicing with zero-copy views
$first_row = $a[0];  // View: no data copied!
$sub_matrix = $a->slice(['0:1', '0:1']);

// Linear algebra
$dot = $a->dot($b);
$trace = $a->trace();

Why NDArray?

Zero-Copy Views

Work with large datasets efficiently:

$data = NDArray::random([10000, 1000]);  // 80 MB array
$batch = $data->slice(['0:1000']);       // View: 0 bytes copied!
$mean = $batch->mean();                  // Process on view

NumPy Familiarity

Coming from Python? You'll feel at home (with PHP syntax):

| NumPy | NDArray PHP | |-------|-------------| | np.array([1, 2, 3]) | NDArray::array([1, 2, 3]) | | arr.shape | $arr->shape() | | arr.sum() | $arr->sum() | | a + b | $a->add($b) | | a[0, 0] | $a['0,0'] or $a->get(0, 0) | | a[0:5] | $a->slice(['0:5']) |

Documentation

Key Concepts

Views vs Copies

Views share memory with the parent array (zero-copy):

$arr = NDArray::array([[1, 2], [3, 4]]);
$row = $arr[0];      // View
$row[0] = 999; // Modifies $arr!

Copies are independent:

$copy = $arr->copy();
$copy->set([0, 0], 999);  // Doesn't modify $arr

Operations create copies:

$result = $arr->multiply(2);  // Copy - original unchanged

Important PHP Syntax Differences

PHP does NOT support operator overloading. Use method calls:

// ❌ This doesn't work in PHP
$c = $a + $b;
$c = $a * 2;

// ✅ Use method calls instead
$c = $a->add($b);
$c = $a->multiply(2);

Multi-dimensional indexing uses strings:

// ❌ Invalid PHP syntax
$value = $matrix[0, 0];

// ✅ Use string syntax
$value = $matrix['0,0'];
// or
$value = $matrix->get(0, 0);

Slicing uses string syntax or method calls:

// ❌ Invalid PHP syntax
$slice = $arr[0:5];

// ✅ Use string syntax
$slice = $arr['0:5'];
// or
$slice = $arr->slice(['0:5']);

Supported Data Types

Integers: Int8, Int16, Int32, Int64
Unsigned: UInt8, UInt16, UInt32, UInt64
Floats: Float32, Float64
Boolean: Bool

Supported Operations

✅ Array creation (zeros, ones, random, arange, linspace)
✅ Indexing and slicing (multi-dimensional, negative indices, steps)
✅ Views and copies (zero-copy slicing, transpose)
✅ Arithmetic operations (add, subtract, multiply, divide, mod, power)
✅ Mathematical functions (abs, sqrt, exp, log, trig, etc.)
✅ Reductions (sum, mean, std, min, max, argmin, argmax)
✅ Linear algebra (dot, matmul, trace, diagonal)
✅ Shape manipulation (reshape, transpose, squeeze, expandDims)
✅ Comparisons and boolean operations
✅ Sorting and searching

Development

# Clone repository
git clone https://github.com/phpmlkit/ndarray.git
cd ndarray

# Build the rust library
./scripts/build.sh debug

# Install dependencies
composer install

# Run tests
composer test

# Run static analysis
composer lint

# Format code
composer cs:fix

Documentation Development

# Install Node dependencies
npm install

# Start docs dev server
npm run docs:dev

# Build docs
npm run docs:build

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE file for details.

Credits

Created by CodeWithKyrian

⭐ Star this repo if you find it helpful!

Ndarray

Install / Use

README