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:

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 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 and the documentation is licensed CC BY-NC 4.0.

Author

Shubham Chaudhary ghost.jat@gmail.com