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项目作者： ghostjat

项目描述 ：
A Lite & Memory Efficient PHP Library for Scientific Computing
高级语言： PHP
项目地址: git://github.com/ghostjat/Np.git
创建时间： 2021-04-11T17:36:11Z
项目社区：https://github.com/ghostjat/Np
开源协议：MIT License
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Np

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.

Installation

Install Np into your project with Composer:

  1. $ composer require ghostjat/np

Sample Code

  1. require __DIR__ . '/../vendor/autoload.php';
  2. use Np\matrix;
  4. $ta = matrix::randn(1000, 1000);    
  5. $tb = matrix::randn(1000, 1000); // to generate random 2d matrix
  6. $ta->dot($tb);                  // do a dot operation on given matrix
  7. $ta->getMemory();              // get memory use
  8. $ta->time();                  // get time
  9. /**
  10.  * 7.7mb
  11.  * Time-Consumed:- 0.18390893936157
  12.  */

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 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

Benckmark

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 and the documentation is licensed CC BY-NC 4.0.

Author

Shubham Chaudhary ghost.jat@gmail.com