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Merge pull request #77 from IntelPython/prepare-for-1.3.0-release
Prepare for `1.3.0` release
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CHANGELOG.md

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# changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [1.3.0] (10/06/2025)
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### Changed
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* Used `GIT_DESCRIBE_TAG` and `GIT_DESCRIBE_NUMBER` in `meta.yaml` instead of manual stepping the numbers [gh-75](https://github.com/IntelPython/mkl_random/pull/75)
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* Extended conda build scripts with the use of `WHEELS_OUTPUT_FOLDER` variable to build wheel packages [gh-74](https://github.com/IntelPython/mkl_random/pull/74)
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* Updated `meta.yaml` to have a run dependency on `numpy-base` package [gh-73](https://github.com/IntelPython/mkl_random/pull/73)

conda-recipe-cf/meta.yaml

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{% set version = "1.3.0dev0" %}
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{% set version = "1.3.0" %}
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{% set buildnumber = 0 %}
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package:

docs/source/conf.py

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project = 'mkl_random'
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copyright = '2017-2025, Intel Corp.'
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author = 'Intel Corp.'
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release = '1.3.0dev0'
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release = '1.3.0'
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# -- General configuration ---------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/how_to.rst

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Initialize the state of the generator using leap-frog method, or raise :code:`ValueError`
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exception if not supported.
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The leap-frog method partitions state tragectory into :code:`nstream` interleaved non-overlapping
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The leap-frog method partitions state trajectory into :code:`nstream` interleaved non-overlapping
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sub-sequences, and argument :code:`k` identifies the subsequence.
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The method is supported for :ref:`"mcg31m1" <mcg31m1_brng>`, :ref:`"mcg59" <mcg59_brng>`, and :ref:`"wh" <wh_brng>`

docs/source/reference/ars5.rst

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rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="ars5")
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# Use random state instance to generate 1000 random numbers from
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# Gamma(3, 1) distibution
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# Gamma(3, 1) distribution
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gsample = rs_vec.gamma(3, 1, size=1000)
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When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mcg31.rst

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rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MCG31")
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# Use random state instance to generate 1000 random numbers from
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# Gamma(3, 1) distibution
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# Gamma(3, 1) distribution
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gsample = rs_vec.gamma(3, 1, size=1000)
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When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mcg59.rst

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rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MCG59")
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# Use random state instance to generate 1000 random numbers from
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# Gamma(3, 1) distibution
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# Gamma(3, 1) distribution
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gsample = rs_vec.gamma(3, 1, size=1000)
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When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mrg32k3a.rst

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rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MRG32k3a")
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# Use random state instance to generate 1000 random numbers from
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# Gamma(3, 1) distibution
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# Gamma(3, 1) distribution
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gsample = rs_vec.gamma(3, 1, size=1000)
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When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mt2203.rst

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rs = mkl_random.RandomState([1234, 567, 89, 0], brng=("MT2203", 6023))
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# Use random state instance to generate 1000 random numbers from
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# Gamma(3, 1) distibution
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# Gamma(3, 1) distribution
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gsample = rs_vec.gamma(3, 1, size=1000)
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When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/philox4x32x10.rst

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rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="philox4x32x10")
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# Use random state instance to generate 1000 random numbers from
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# Gamma(3, 1) distibution
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# Gamma(3, 1) distribution
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gsample = rs_vec.gamma(3, 1, size=1000)
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When seed is not specified, the generator is initialized using system clock, e.g.:

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