No project description provided
Project description
mkl_random
-- a NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
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
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.
See MKL reference guide for more details: https://software.intel.com/en-us/mkl-developer-reference-c-random-number-generators
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for mkl_random-1.2.0-10-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f81ea6ebe61e79ec22015f2272d463001cc239aa1e1fb5c89e8171e12ee64dc7 |
|
MD5 | 887ed733b8ecb7d2438c12189060a187 |
|
BLAKE2b-256 | 0ff067d8e81c6f8fcd5ecc029262e48d3a6e32e537447110930ac93b7ef21c96 |
Hashes for mkl_random-1.2.0-10-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 05c4e0154de123fcbdca836fe47a40e78fc758b2d6b6c91b416b8df29fdda5a3 |
|
MD5 | 718a3987299c954c6f7b2190626cdf4e |
|
BLAKE2b-256 | fb0860d148cb443a53cc5e70f1c51d833082287c9b16a03bbe3e737d830de886 |