# Change Log¶

Deprecated

`Generator`

and `RandomState`

were **REMOVED** in 1.23.
You should be using `numpy.random.Generator`

or
`numpy.random.RandomState`

which are maintained.

## v1.26.1¶

Initial compatability with Cython 3 and NumPy 2

## v1.26.0¶

Fixed a bug that affected the

`randomgen.xoroshiro128.Xoroshiro128.jumped()`

method of`randomgen.xoroshiro128.Xoroshiro128`

where the ** version was swapped with the standard version.Fixed a bug where

`numpy.random.SeedSequence`

was not copied when advancing generators using`jumped`

.Small compatibility fixes for change in NumPy.

Changes the documentation theme to sphinx-immaterial.

Added builds for Python 3.11.

Increased the minimum Python to 3.8.

## v1.23.1¶

Registered the bit generators included in

`randomgen`

with NumPy so that NumPy`Generator`

instances can be pickled and unpickled when using a`randomstate`

bit generator.Changed the canonical name of the bit generators to be their fully qualified name. For example,

`PCG64`

is not named`"randomgen.pcg64.PCG64"`

instead of`"PCG64"`

. This was done to avoid ambiguity with NumPy’s supplied bit generators with the same name.

## v1.23.0¶

Removed

`Generator`

and`RandomState`

.

## v1.20.2¶

Fixed a bug in

`SFC64`

the used the wrong value from the Weyl sequence. In the original implementation, the current value is added to the next random integer and then incremented. The buggy version was incrementing then adding, and so was shifted by one value. This sequence should be similarly random in appearance, but it does not match the original specification and so has been changed.Added

`mode="numpy"`

support to`PCG64`

,`MT19937`

,`Philox`

, and`SFC64`

. When using this mode, the sequence generated is guaranteed to match the sequence produced using the NumPy implementations as long as a`SeedSequence`

or`numpy.random.SeedSequence`

is used with the same initial seed values.Added

`random()`

with support for`dtype="longdouble"`

to produce extended precision random floats.

```
In [1]: import numpy as np
In [2]: from randomgen import ExtendedGenerator, PCG64
In [3]: eg = ExtendedGenerator(PCG64(20210501))
In [4]: eg.random(5, dtype=np.longdouble)
Out[4]:
array([0.66851489, 0.01769784, 0.87316102, 0.86532386, 0.85384162],
dtype=float128)
```

## v1.20.1¶

Fixed a bug that affects

`standard_gamma`

when used with`out`

and a Fortran contiguous array.Added

`standard_wishart()`

and`wishart()`

variate generators.

## v1.20.0¶

Sync upstream changes from NumPy

Added typing information

Fixed a bug in

`AESCounter`

that prevented a small number of counter values from being directly set.

## v1.19.3¶

Future proofed setup against

`setuptools`

and`distutils`

changes.Enhanced documentation for

`RDRAND`

.

## v1.19.2¶

Corrected

`RDRAND`

to retry on failures with pause between retries. Add a parameter`retry`

which allows the number of retries to be set. It defaults to the Intel recommended value of 10. Also sets an exception when the number of retries has been exhausted (very unlikely). See the`RDRAND`

docstring with unique considerations when using`RDRAND`

that do not occur with deterministic PRNGs.

## v1.19.1¶

Added

`randomgen.romu.Romu`

which is among the fastest available bit generators.Added

`weyl_increments()`

to simplify generating increments for use in parallel applications of`SFC64`

.Completed * Quality Assurance of all bit generators to at least 4TB.

## v1.19.0¶

Tested all bit generators out to at least 1TB using PractRand.

Added

`randomgen.pcg64.PCG64DXSM`

which is an alias for`randomgen.pcg64.PCG64`

with`variant="dxsm"`

and`mode="sequence"`

. This is the 2.0 version of PCG64 and will likely become the default bit generator in NumPy in the near future.Added

`randomgen.efiix64.EFIIX64`

which is both fast and high-quality.Added

`randomgen.sfc.SFC64`

which supports generating streams using distinct Weyl constants.Added a

`randomgen.pcg64.LCG128Mix`

which supports setting the LCG multiplier, changing the output function (including support for user-defined output functions) and pre- or post-state update generation.Added a

`randomgen.lxm.LXM`

which generates variates using a mix of two simple, but flawed generators: an Xorshift and a 64-bit LCG. This has been proposed for including in in Java.Added a

`randomgen.wrapper.UserBitGenerator`

which allows bit generators to be written in Python or numba.Added

`randomgen.generator.ExtendedGenerator`

which contains features not in`numpy.random.Generator`

.Added support for the

`dxsm`

and`dxsm-128`

variants of`randomgen.pcg64.PCG64`

. The`dxsm`

variant is the official PCG 2.0 generator.Added support for broadcasting inputs in

`randomgen.generator.ExtendedGenerator.multivariate_normal`

.Added support for the ++ variant of

`randomgen.xoroshiro128.Xoroshiro128`

.Fixed a bug the produced incorrect results in

`jumped()`

.Fixed multiple bugs in

`Generator`

that were fixed in`numpy.random.Generator`

.

## v1.18.0¶

`choice`

pulled in upstream performance improvement that use a hash set when choosing without replacement and without user-provided probabilities.Added support for

`SeedSequence`

(and NumPy’s`SeedSequence`

).Fixed a bug that affected both

`randint`

in`Generator`

and`randint`

in`RandomState`

when`high=2**32`

. This value is inbounds for a 32-bit unsigned closed interval generator, and so should have been redirected to a 32-bit generator. It was erroneously sent to the 64-bit path. The random values produced are fully random but inefficient. This fix breaks the stream in`Generator is the value for ``high``

is used. The fix restores`RandomState`

to NumPy 1.16 compatibility. only affects the output if`dtype`

is`'int64'`

This release brings many breaking changes. Most of these have been implemented using

`DeprecationWarnings`

. This has been done to bring`randomgen`

in-line with the API changes of the version going into NumPy.Two changes that are more abrupt are:

The

`.generator`

method of the bit generators raise`NotImplementedError`

The internal structures that is used in C have been renamed. The main rename is

`brng_t`

to`bitgen_t`

The other key changes are:

Rename

`RandomGenerator`

to`Generator`

.Rename

`randint`

to`integers`

.Rename

`random_integers`

to`integers`

.Rename

`random_sample`

to`random`

.Change

`jump`

which operated in-place to`jumped()`

which returns a new`BitGenerator`

.Rename Basic RNG to bit generator, which has been consistently applied across the docs and references

Add the integer-based SIMD-based Fast Mersenne Twister (SFMT) generator

`SFMT`

.Add the 64-bit Mersenne Twister (MT64) generator

`MT64`

.Renamed Xoshiro256StarStar to

`Xoshiro256`

and Xoshiro512StarStar to`Xoshiro512`

## v1.17.0¶

This release was skipped

## v1.16.6¶

Changed the default jump step size to phi times the period of the generator for

`PCG32`

and`PCG64`

.Improved the performance of

`PCG64`

on Windows.Improves backward compatibility of

`RandomState`

## v1.16.5¶

Fixed bugs in

`laplace`

,`gumbel`

,`logseries`

,`normal`

,`standard_normal`

,`standard_exponential`

,`exponential`

, and`logistic`

that could result in`nan`

values in rare circumstances (about 1 in \(10^{53}\) draws).Added keyword

`closed`

to`randint`

which changes sampling from the half-open interval`[low, high)`

to the closed interval`[low, high]`

.Fixed a bug in

`random_integers`

that could lead to valid values being treated as invalid.

## v1.16.4¶

Add a fast path for broadcasting

`randint`

when using`uint64`

or`int64`

.Refactor PCG64 so that it does not rely on Cython conditional compilation.

Add

`brng`

to access the basic RNG.Allow multidimensional arrays in

`choice`

.Speed-up

`choice`

when not replacing. The gains can be very large (1000x or more) when the input array is large but the sample size is small.Add parameter checks in

`multinomial`

.Fix an edge-case bug in

`zipf`

.Allow 0 for sample in

`hypergeometric`

.Add broadcasting to

`multinomial`

(see NumPy issue 9710)

## v1.16.3¶

Release fixing Python 2.7 issues

## v1.16.2¶

Updated Xoroshiro120 to use Author’s latest parametrization

Closely synchronized with the version of randomgen being integrated into NumPy, including removing:

`random_raw`

, which have been moved to the individual bit generators`random_uintegers`

, which can be replaced with`randint`

.

Added

`RandomState`

as a clone of NumPy’s RandomState.Removed

`LegacyGenerator`

since this is no longer neededFixed many small bugs, including in cffi and ctype interfaces

## v1.16.1¶

Synchronized with upstream changes.

Fixed a bug in gamma generation if the shape parameters is 0.0.

## v1.16.0¶

Fixed a bug that affected

`DSFMT`

when calling`jump()`

or`seed()`

that failed to reset the buffer. This resulted in up to 381 values from the previous state being used before the buffer was refilled at the new state.Fixed bugs in

`Xoshiro512`

and`Xorshift1024`

where the fallback entropy initialization used too few bytes. This bug is unlikely to be encountered since this path is only encountered if the system random number generator fails.Synchronized with upstream changes.

## v1.15.1¶

Added Xoshiro256** and Xoshiro512**, the preferred generators of this class.

Fixed bug in jump method of Random123 generators which did not specify a default value.

Added support for generating bounded uniform integers using Lemire’s method.

Synchronized with upstream changes, which requires moving the minimum supported NumPy to 1.13.

## v1.15¶

Synced empty choice changes

Synced upstream docstring changes

Synced upstream changes in permutation

Synced upstream doc fixes

Added absolute_import to avoid import noise on Python 2.7

Add legacy generator which allows NumPy replication

Improve type handling of integers

Switch to array-fillers for 0 parameter distribution to improve performance

Small changes to build on manylinux

Build wheels using multibuild