Metadata-Version: 2.1
Name: mkl-random
Version: 1.2.4
Summary: NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
Home-page: http://github.com/IntelPython/mkl_random
Download-URL: http://github.com/IntelPython/mkl_random
Author: Intel Corporation
Maintainer: Intel Corp.
Maintainer-email: scripting@intel.com
License: BSD
Keywords: MKL,VSL,true randomness,pseudorandomness,Philox,MT-19937,SFMT-19937,MT-2203,ARS-5,R-250,MCG-31
Platform: Windows
Platform: Linux
Platform: Mac OS-X
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy (>=1.16)

## ``mkl_random`` -- a NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
[![Build Status](https://travis-ci.com/IntelPython/mkl_random.svg?branch=master)](https://travis-ci.com/IntelPython/mkl_random)

`mkl_random` has started as Intel (R) Distribution for Python optimizations for NumPy.

Per NumPy's community suggestions, voiced in https://github.com/numpy/numpy/pull/8209, it is being released as a
stand-alone package.

Prebuilt `mkl_random` can be installed into conda environment from Intel's channel on Anaconda cloud:

```
  conda install -c intel mkl_random
```

---

To install mkl_random Pypi package please use following command:

```
   python -m pip install --i https://pypi.anaconda.org/intel/simple -extra-index-url https://pypi.org/simple mkl_random
```

If command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Anaconda Cloud:

```
   python -m pip install --i https://pypi.anaconda.org/intel/simple -extra-index-url https://pypi.org/simple mkl_random numpy==<numpy_version>
```

Where `<numpy_version>` should be the latest version from https://anaconda.org/intel/numpy

---

`mkl_random` is not fixed-seed backward compatible drop-in replacement for `numpy.random`, meaning that it implements sampling from the same distributions as `numpy.random`.

For distributions directly supported in Intel (R) Math Kernel Library (MKL), `method` keyword is supported:

```
   mkl_random.standard_normal(size=(10**5, 10**3), method='BoxMuller')
```

Additionally, `mkl_random` exposes different basic random number generation algorithms available in MKL. For example to use `SFMT19937` use

```
   mkl_random.RandomState(77777, brng='SFMT19937')
```

For generator families, such that `MT2203` and Wichmann-Hill, a particular member of the family can be chosen by specifying ``brng=('WH', 3)``, etc.

The list of supported by `mkl_random.RandomState` constructor `brng` keywords is as follows:

  * 'MT19937'
  * 'SFMT19937'
  * 'WH' or ('WH', id)
  * 'MT2203' or ('MT2203', id)
  * 'MCG31'
  * 'R250'
  * 'MRG32K3A'
  * 'MCG59'
  * 'PHILOX4X32X10'
  * 'NONDETERM'
  * 'ARS5'