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Merge pull request #92 from IntelPython/feature/add-numpy-random-interface
Implement `mkl_random.interfaces` and update `mkl_random`
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‎.git-blame-ignore-revs‎

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# $ git config blame.ignoreRevsFile .git-blame-ignore-revs
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# Sporadic fixes in test_random.py
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e76aa3a5a4b889c0434f0103ec102a50b93ab1ca

‎CHANGELOG.md‎

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## [dev] (MM/DD/YYYY)
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### Added
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* Added `mkl_random.interfaces` with `mkl_random.interfaces.numpy_random` interface, which aliases `mkl_random` functionality to more strictly adhere to NumPy's API (i.e., drops arguments and functions which are not part of standard NumPy) [gh-92](https://github.com/IntelPython/mkl_random/pull/92)
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### Removed
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* Dropped support for Python 3.9 [gh-81](https://github.com/IntelPython/mkl_random/pull/81)
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### Deprecated
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* Deprecated `mkl_random.RandomState` in favor of `mkl_random.MKLRandomState`, delegating `mkl_random.RandomState` to the `mkl_random.interfaces.numpy_random` interface [gh-92](https://github.com/IntelPython/mkl_random/pull/92)
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## [1.3.1] (12/02/2025)
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### Added

‎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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from . import _init_helper
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from .mklrand import *
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from .mklrand import (
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MKLRandomState,
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RandomState,
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seed,
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get_state,
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set_state,
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random_sample,
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choice,
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randint,
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bytes,
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uniform,
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rand,
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randn,
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random_integers,
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standard_normal,
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normal,
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beta,
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exponential,
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standard_exponential,
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standard_gamma,
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gamma,
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f,
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noncentral_f,
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chisquare,
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noncentral_chisquare,
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standard_cauchy,
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standard_t,
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vonmises,
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pareto,
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weibull,
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power,
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laplace,
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gumbel,
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logistic,
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lognormal,
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rayleigh,
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wald,
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triangular,
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binomial,
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negative_binomial,
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poisson,
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zipf,
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geometric,
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hypergeometric,
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logseries,
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multivariate_normal,
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multinormal_cholesky,
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multinomial,
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dirichlet,
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shuffle,
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permutation,
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)
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from ._version import __version__
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try:
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test = PytestTester(__name__)
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del PytestTester
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from mkl_random import interfaces
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__all__ = [
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"MKLRandomState",
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"RandomState",
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"seed",
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"get_state",
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"set_state",
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"random_sample",
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"choice",
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"randint",
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"bytes",
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"uniform",
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"rand",
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"randn",
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"random_integers",
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"standard_normal",
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"normal",
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"beta",
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"exponential",
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"standard_exponential",
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"standard_gamma",
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"gamma",
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"f",
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"noncentral_f",
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"chisquare",
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"noncentral_chisquare",
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"standard_cauchy",
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"standard_t",
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"vonmises",
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"pareto",
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"weibull",
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"power",
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"laplace",
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"gumbel",
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"logistic",
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"lognormal",
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"rayleigh",
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"wald",
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"triangular",
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"binomial",
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"negative_binomial",
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"poisson",
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"zipf",
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"geometric",
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"hypergeometric",
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"logseries",
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"multivariate_normal",
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"multinormal_cholesky",
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"multinomial",
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"dirichlet",
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"shuffle",
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"permutation",
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"interfaces",
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]
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del _init_helper

‎mkl_random/interfaces/README.md‎

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# Interfaces
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The `mkl_random` package provides interfaces that serve as drop-in replacements for equivalent functions in NumPy.
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---
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## NumPy interface - `mkl_random.interfaces.numpy_random`
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This interface is a drop-in replacement for the legacy portion of the [`numpy.random`](https://numpy.org/devdocs/reference/random/legacy.html) module and includes **all** classes and functions available there:
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* random generator: `RandomState`.
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* seeding and state functions: `get_state`, `set_state`, and `seed`.
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* simple random data: `rand`, `randn`, `randint`, `random_integers`, `random_sample`, `choice` and `bytes`.
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* permutations: `shuffle` and `permutation`
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* distributions: `beta`, `binomial`, `chisquare`, `dirichlet`, `exponential`, `f`, `gamma`, `geometric`, `gumbel`, `hypergeometric`, `laplace`, `logistic`, `lognormal`, `logseries`, `multinomial`, `multivariate_normal`, `negative_binomial`, `noncentral_chisquare`, `noncentral_f`, `normal`, `pareto`, `poisson`, `power`, `rayleigh`, `standard_cauchy`, `standard_exponential`, `standard_gamma`, `standard_normal`, `standard_t`, `triangular`, `uniform`, `vonmises`, `wald`, `weibull`, and `zipf`.

‎mkl_random/interfaces/__init__.py‎

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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 . import numpy_random
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# submodules
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__all__ = ["numpy_random"]

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