https://github.com/ghostjat/np
A Lite & Memory Efficient PHP Library for Scientific Computing
https://github.com/ghostjat/np
blas calculus computing ffi lapack libblas linear-algebra lite math matrix memory np numphp php php-ffi php8 scientific-computing statstics trignometry vector
Last synced: 6 months ago
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A Lite & Memory Efficient PHP Library for Scientific Computing
- Host: GitHub
- URL: https://github.com/ghostjat/np
- Owner: ghostjat
- License: mit
- Created: 2021-04-11T17:36:11.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-21T13:24:32.000Z (about 1 year ago)
- Last Synced: 2024-11-14T21:38:16.471Z (6 months ago)
- Topics: blas, calculus, computing, ffi, lapack, libblas, linear-algebra, lite, math, matrix, memory, np, numphp, php, php-ffi, php8, scientific-computing, statstics, trignometry, vector
- Language: PHP
- Homepage: https://ghostjat.github.io/Np/
- Size: 1.38 MB
- Stars: 9
- Watchers: 3
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
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## Description
-----------
Lite, Fast & Memory Efficient *Mathematical PHP library for scientific computing*
Np(numphp) is a library that provides objects for computing large sets of numbers in [PHP](https://php.net).
## Installation
Install [Np](https://packagist.org/packages/ghostjat/np) into your project with [Composer](https://getcomposer.org/):
```sh
$ composer require ghostjat/np
```
##Sample Code
```php
require __DIR__ . '/../vendor/autoload.php';
use Np\matrix;
$ta = matrix::randn(1000, 1000);
$tb = matrix::randn(1000, 1000); // to generate random 2d matrix
$ta->dot($tb); // do a dot operation on given matrix
$ta->getMemory(); // get memory use
$ta->time(); // get time
/**
* 7.7mb
* Time-Consumed:- 0.18390893936157
*/
```
*Synopsis*
--------
WARNING:
This module is in its early stages and should be considered a Work in Progress.The interface is not final and may change in the future.
*Requirements*
------------
- [PHP](https://php.net) 8+ 64bit with ffi & #libblas, #liblapacke
Make sure you have all the necessary tools installed such as FFI, libblas, liblapacke.
*Performance*
-----------
System Conf:- Intel(R) Core(TM) i3-2370M CPU @ 2.40GHz 64bit
Memory:- 8GB
php:- 8.0.5 64bit
*Current Benchmarks of this library*
-----------------------------------
Data Size :- [500x500] Revolutions:- 5 Iterations:- 5
| subject | mem_peak | best | mode | mean | worst | stdev |
|----------|----------|--------|--------|--------|--------|-------|
| sum | 3.606mb | 0.014s | 0.014s | 0.015s | 0.015s | 0.000s|
| multiply | 8.589mb | 0.070s | 0.071s | 0.071s | 0.071s | 0.000s|
| lu | 4.648mb | 0.064s | 0.065s | 0.065s | 0.068s | 0.001s|
| eign | 2.801mb | 0.085s | 0.086s | 0.086s | 0.088s | 0.001s|
| cholesky | 1.621mb | 0.001s | 0.001s | 0.001s | 0.001s | 0.000s|
| svd | 3.706mb | 0.126s | 0.126s | 0.127s | 0.133s | 0.002s|
| normL2 | 1.621mb | 0.003s | 0.003s | 0.003s | 0.003s | 0.000s|
| Pinverse | 4.903mb | 0.156s | 0.156s | 0.158s | 0.163s | 0.003s|
| inverse | 1.819mb | 0.016s | 0.016s | 0.016s | 0.017s | 0.000s|
| normL1 | 1.621mb | 0.001s | 0.001s | 0.001s | 0.001s | 0.000s|
| dotMatrix| 3.769mb | 0.006s | 0.006s | 0.006s | 0.006s | 0.000s|
| det | 4.662mb | 0.066s | 0.066s | 0.067s | 0.067s | 0.000s|
| rref | 1.529mb | 9.227s | 9.271s | 9.309s | 9.427s | 0.072s|
| ref | 1.818mb | 0.007s | 0.008s | 0.008s | 0.008s | 0.000s|
| clip | 8.516mb | 0.073s | 0.076s | 0.075s | 0.077s | 0.002s|
| clipUpper| 8.516mb | 0.055s | 0.056s | 0.057s | 0.059s | 0.002s|
| clipLower| 8.516mb | 0.055s | 0.058s | 0.057s | 0.059s | 0.002s|
| joinBelow| 4.517mb | 0.027s | 0.027s | 0.027s | 0.028s | 0.000s|
| transpose| 8.504mb | 0.057s | 0.057s | 0.058s | 0.059s | 0.001s|
| joinLeft | 4.511mb | 0.025s | 0.025s | 0.026s | 0.027s | 0.001s|
| poisson | 1.590mb | 0.029s | 0.029s | 0.029s | 0.030s | 0.000s|
| gaussian | 20.203mb | 0.056s | 0.056s | 0.056s | 0.056s | 0.000s|
| randn | 1.528mb | 0.017s | 0.017s | 0.017s | 0.017s | 0.000s|
| uniform | 1.528mb | 0.021s | 0.021s | 0.021s | 0.022s | 0.000s|
| multiply | 4.507mb | 0.042s | 0.042s | 0.043s | 0.045s | 0.001s|
Previous BenchMark
| benchmark | subject | set | revs | its | mem_peak | mode | rstdev |
|---------------------------|-----------|-----|------|-----|----------|---------|----------|
| eignBench | eign | 0 | 1 | 5 | 2.699mb | 0.309s | ±4.51% |
| svdBench | svd | 0 | 1 | 5 | 3.604mb | 0.148s | ±3.60% |
| poissonMatrixBench | poisson | 0 | 1 | 5 | 11.738mb | 0.105s | ±7.07% |
| gaussianMatrixBench | gaussian | 0 | 1 | 5 | 11.738mb | 0.112s | ±17.12% |
| randMatrixBench | randn | 0 | 1 | 5 | 1.429mb | 0.048s | ±2.37% |
| uniformMatrixBench | uniform | 0 | 1 | 5 | 1.429mb | 0.063s | ±8.16% |
| matrixTransposeBench | transpose | 0 | 1 | 5 | 8.431mb | 0.120s | ±1.32% |
| rrefBench | rref | 0 | 1 | 5 | 1.501mb | 28.513s | ±1.90% |
| refBench | ref | 0 | 1 | 5 | 1.731mb | 0.023s | ±7.24% |
| sumMatrixBench | sum | 0 | 1 | 5 | 2.434mb | 0.051s | ±3.59% |
| matrixPseudoInverseBench | inverse | 0 | 1 | 5 | 4.775mb | 0.222s | ±13.76% |
| matrixInverseBench | inverse | 0 | 1 | 5 | 1.731mb | 0.032s | ±127.50% |
| dotMatrixBench | dotMatrix | 0 | 1 | 5 | 3.656mb | 0.013s | ±27.94% |
| matrixL1NormBench | normL1 | 0 | 1 | 10 | 1.525mb | 0.001s | ±0.80% |
| matrixL2NormBench | normL2 | 0 | 1 | 10 | 1.525mb | 0.003s | ±1.63% |
License
-------
The code is licensed [MIT](LICENSE) and the documentation is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
Author
------
Shubham Chaudhary