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‎LICENSE.txt

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Copyright (c) 2017, Intel Corporation
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice,
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this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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* Neither the name of Intel Corporation nor the names of its contributors
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may be used to endorse or promote products derived from this software
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without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎README.md

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##``mkl_random`` -- a NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
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`mkl_random` has started as Intel (R) Distribution for Python optimizations for NumPy.
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Per NumPy's community suggestions, voiced in https://github.com/numpy/numpy/pull/8209, it is being released as a
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stand-alone package.
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Prebuilt `mkl_random` can be installed into conda environment from Intel's channel on Anaconda cloud:
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```
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conda install -c intel mkl_random
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```
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`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`.
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For distributions directly supported in Intel (R) Math Kernel Library (MKL), `method` keyword is supported:
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```
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mkl_random.standard_normal(size=(10**5, 10**3), method='BoxMuller')
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```
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Additionally, `mkl_random` exposes different basic random number generation algorithms available in MKL. For example to use `SFMT19937` use
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```
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mkl_random.RandomState(77777, brng='SFMT19937')
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```
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For generator families, such that `MT2203` and Wichmann-Hill, a particular member of the family can be chosen by specifying ``brng=('WH', 3)``, etc.
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See MKL reference guide for more details:
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https://software.intel.com/en-us/mkl-developer-reference-c-random-number-generators

‎conda-recipe/bld.bat

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@rem Remember to source Intel (R) Compiler
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set CC=icl
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set LD=xilink
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%PYTHON% setup.py config --compiler=intelemw install --old-and-unmanageable
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if errorlevel 1 exit 1

‎conda-recipe/build.sh

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#!/bin/bash -x
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if [ `uname` == Darwin ]; then
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export MACOSX_DEPLOYMENT_TARGET=10.10
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fi
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export CFLAGS="-I$PREFIX/include $CFLAGS"
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export CC=icc
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export LDSHARED="icc -shared"
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$PYTHON setup.py config --compiler=intelem --fcompiler=intelem build install --old-and-unmanageable

‎conda-recipe/meta.yaml

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{% set version = "1.0.0" %}
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{% set buildnumber = 5 %}
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### If you change the iccver here, you must also set the path correctly in build.sh / bld.bat!!!
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{% set iccver = "16.0.3" %} [unix or py3k]
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{% set iccver = "13.1.5" %} [win and py27]
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package:
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name: mkl_random
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version: {{ version }}
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source:
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git_url: http://github.com/IntelPython/mkl_random
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git_branch: v{{version}}
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build:
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number: {{buildnumber}}
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features:
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- intel
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always_include_files:
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- {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_random/__init__.py
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- {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_random/mklrand.*
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- {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_random/setup.py
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- {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_random/tests/test_random.py
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- {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_random/tests/test_regressions.py
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- {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_random/__pycache__/*
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- {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_random/tests/__pycache__/*
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requirements:
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build:
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- python
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- setuptools
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- intelpython
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- mkl-devel [not nomkl]
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- icc_rt {{iccver}}
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- cython
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- numpy x.x
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run:
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- python
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- mkl [not nomkl]
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- icc_rt >={{iccver}}
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- intelpython
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- numpy x.x
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test:
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commands:
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- nosetests -v mkl_random
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requires:
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- nose
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imports:
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- mkl_random
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- mkl_random.mklrand
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about:
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home: http://github.com/IntelPython/mkl_random
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license: BSD
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license_file: LICENSE.txt
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summary: NumPy-based implementation of random number generation sampling using Intel (R) Math Kernel Library, mirroring numpy.random, but exposing all choices of sampling algorithms available in MKL.

‎mkl_random/__init__.py

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#!/usr/bin/env python
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# Copyright (c) 2017, Intel Corporation
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of Intel Corporation nor the names of its contributors
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# may be used to endorse or promote products derived from this software
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# without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from __future__ import division, absolute_import, print_function
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from .mklrand import *
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from numpy.testing.nosetester import _numpy_tester
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test = _numpy_tester().test
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bench = _numpy_tester().bench

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