| Commit message (Collapse) | Author | Age | Files | Lines |
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remove files that were part of the origal repo
rework randomgen docs to integrate with numpy and fix some links
remove convenience functions, require explicit call to gen.brng
move code out of numpy.random.randomgen into numpy.random
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The transform in line 620 onwards was the polar method, not Box-Muller. See section 11.3.1 of "Introduction to probability models" by Sheldon Ross (https://fac.ksu.edu.sa/sites/default/files/introduction-to-probability-model-s.ross-math-cs.blog_.ir_.pdf).
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Removes _POSIX_C_SOURCE redefine warnings. Standard library headers
should always be included last.
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Random ndarrays of the following types can now be generated:
* np.bool,
* np.int8, np.uint8,
* np.int16, np.uint16,
* np.int32, np.uint32,
* np.int64, np.uint64,
* np.int_ (long), np.intp
The specification is by precision rather than by C type. Hence, on some
platforms np.int64 may be a `long` instead of `long long` even if the
specified dtype is `long long` because the two may have the same
precision. The resulting type depends on which c type numpy uses for the
given precision. The byteorder specification is also ignored, the
generated arrays are always in native byte order.
The dtype of the result could be made more explicit if desired without
changing the user visible results.
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have identical pickles.
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Trailing whitespace removal.
Some coding style cleanups.
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__MSVCRT_VERSION__ to avoid possible duplicate.
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are built with mingw in distutils setup.py.
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adding per-subpackage include path is a PITA with distutils.
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need to workaround mingw bug.
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import libraries.
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2**32.
For 64-bit machines, this means results will agree with 32-bit machines,
and will be faster for these interval sizes (one less rk_random evaluation).
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