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| author | mattip <matti.picus@gmail.com> | 2019-05-13 14:17:51 -0700 |
|---|---|---|
| committer | mattip <matti.picus@gmail.com> | 2019-05-20 19:00:34 +0300 |
| commit | 17e0070df93f4262908f884dca4b08cb7d0bba7f (patch) | |
| tree | 2db0eec024d5e021a36e6dca9f4b97d118bc9444 /doc/source/reference/random | |
| parent | dd77ce3cb84986308986974acfe988575323f75a (diff) | |
| download | numpy-17e0070df93f4262908f884dca4b08cb7d0bba7f.tar.gz | |
MAINT: Implement API changes for randomgen-derived code
remove numpy.random.gen, BRNG.generator, pcg*, rand, randn
remove use_mask and Lemire's method, fix benchmarks for PCG removal
convert brng to bitgen (in C) and bit_generator (in python)
convert base R{NG,andom.*} to BitGenerator, fix last commit
randint -> integers, remove rand, randn, random_integers
RandomGenerator -> Generator, more "basic RNG" -> BitGenerator
random_sample -> random, jump -> jumped, resync with randomgen
Remove derived code from entropy
Port over changes accepted in upstream to protect log(0.0) where relevant
fix doctests for jumped, better document choice
Remove Python 2.7 shims
Use NPY_INLINE to simplify
Fix performance.py to work
Renam directory brng to bit_generators
Fix examples wiht new directory structure
Clarify relationship to historical RandomState
Remove references to .generator
Rename xoshiro256/512starstar
Diffstat (limited to 'doc/source/reference/random')
25 files changed, 308 insertions, 532 deletions
diff --git a/doc/source/reference/random/brng/dsfmt.rst b/doc/source/reference/random/bit_generators/dsfmt.rst index a47586a50..e7c6dbb31 100644 --- a/doc/source/reference/random/brng/dsfmt.rst +++ b/doc/source/reference/random/bit_generators/dsfmt.rst @@ -23,14 +23,7 @@ Parallel generation .. autosummary:: :toctree: generated/ - ~DSFMT.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~DSFMT.generator + ~DSFMT.jumped Extending ========= diff --git a/doc/source/reference/random/brng/index.rst b/doc/source/reference/random/bit_generators/index.rst index a7e5ad873..e3ca073c9 100644 --- a/doc/source/reference/random/brng/index.rst +++ b/doc/source/reference/random/bit_generators/index.rst @@ -1,12 +1,12 @@ -.. _brng: +.. _bit_generator: -Basic Random Number Generators ------------------------------- +Bit Generators +-------------- .. currentmodule:: numpy.random -The random values produced by :class:`~RandomGenerator` -are produced by a basic RNG. These basic RNGs do not directly provide +The random values produced by :class:`~Generator` +orignate in a BitGenerator. The BitGenerators do not directly provide random numbers and only contains methods used for seeding, getting or setting the state, jumping or advancing the state, and for accessing low-level wrappers for consumption by code that can efficiently @@ -20,23 +20,20 @@ Stable RNGs DSFMT <dsfmt> MT19937 <mt19937> - PCG64 <pcg64> Philox <philox> ThreeFry <threefry> XoroShiro128+ <xoroshiro128> Xorshift1024*φ <xorshift1024> - Xoshiro256** <xoshiro256starstar> - Xoshiro512** <xoshiro512starstar> + Xoshiro256** <xoshiro256> + Xoshiro512** <xoshiro512> Experimental RNGs ================= -These RNGs are currently included for testing but are may not be -permanent. +These BitGenerators are currently included but are may not be permanent. .. toctree:: :maxdepth: 1 - PCG32 <pcg32> ThreeFry32 <threefry32> diff --git a/doc/source/reference/random/brng/mt19937.rst b/doc/source/reference/random/bit_generators/mt19937.rst index 1bd3597c8..f5843ccf0 100644 --- a/doc/source/reference/random/brng/mt19937.rst +++ b/doc/source/reference/random/bit_generators/mt19937.rst @@ -22,14 +22,7 @@ Parallel generation .. autosummary:: :toctree: generated/ - ~MT19937.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~MT19937.generator + ~MT19937.jumped Extending ========= diff --git a/doc/source/reference/random/brng/philox.rst b/doc/source/reference/random/bit_generators/philox.rst index c1e047c54..7ef451d4b 100644 --- a/doc/source/reference/random/brng/philox.rst +++ b/doc/source/reference/random/bit_generators/philox.rst @@ -23,14 +23,7 @@ Parallel generation :toctree: generated/ ~Philox.advance - ~Philox.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~Philox.generator + ~Philox.jumped Extending ========= diff --git a/doc/source/reference/random/brng/threefry.rst b/doc/source/reference/random/bit_generators/threefry.rst index eefe16ea0..951108d72 100644 --- a/doc/source/reference/random/brng/threefry.rst +++ b/doc/source/reference/random/bit_generators/threefry.rst @@ -23,14 +23,7 @@ Parallel generation :toctree: generated/ ~ThreeFry.advance - ~ThreeFry.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~ThreeFry.generator + ~ThreeFry.jumped Extending ========= diff --git a/doc/source/reference/random/brng/threefry32.rst b/doc/source/reference/random/bit_generators/threefry32.rst index f0d3dc281..1af9491a6 100644 --- a/doc/source/reference/random/brng/threefry32.rst +++ b/doc/source/reference/random/bit_generators/threefry32.rst @@ -23,14 +23,7 @@ Parallel generation :toctree: generated/ ~ThreeFry32.advance - ~ThreeFry32.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~ThreeFry32.generator + ~ThreeFry32.jumped Extending ========= diff --git a/doc/source/reference/random/brng/xoroshiro128.rst b/doc/source/reference/random/bit_generators/xoroshiro128.rst index 590552236..41f5238fc 100644 --- a/doc/source/reference/random/brng/xoroshiro128.rst +++ b/doc/source/reference/random/bit_generators/xoroshiro128.rst @@ -22,14 +22,7 @@ Parallel generation .. autosummary:: :toctree: generated/ - ~Xoroshiro128.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~Xoroshiro128.generator + ~Xoroshiro128.jumped Extending ========= diff --git a/doc/source/reference/random/brng/xorshift1024.rst b/doc/source/reference/random/bit_generators/xorshift1024.rst index 24ed3df04..5d0c9048f 100644 --- a/doc/source/reference/random/brng/xorshift1024.rst +++ b/doc/source/reference/random/bit_generators/xorshift1024.rst @@ -22,14 +22,7 @@ Parallel generation .. autosummary:: :toctree: generated/ - ~Xorshift1024.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~Xorshift1024.generator + ~Xorshift1024.jumped Extending ========= diff --git a/doc/source/reference/random/bit_generators/xoshiro256.rst b/doc/source/reference/random/bit_generators/xoshiro256.rst new file mode 100644 index 000000000..fedc61b33 --- /dev/null +++ b/doc/source/reference/random/bit_generators/xoshiro256.rst @@ -0,0 +1,35 @@ +Xoshiro256** +------------ + +.. module:: numpy.random.xoshiro256 + +.. currentmodule:: numpy.random.xoshiro256 + +.. autoclass:: Xoshiro256 + :exclude-members: + +Seeding and State +================= + +.. autosummary:: + :toctree: generated/ + + ~Xoshiro256.seed + ~Xoshiro256.state + +Parallel generation +=================== +.. autosummary:: + :toctree: generated/ + + ~Xoshiro256.jumped + +Extending +========= +.. autosummary:: + :toctree: generated/ + + ~Xoshiro256.cffi + ~Xoshiro256.ctypes + + diff --git a/doc/source/reference/random/bit_generators/xoshiro512.rst b/doc/source/reference/random/bit_generators/xoshiro512.rst new file mode 100644 index 000000000..e39346cd6 --- /dev/null +++ b/doc/source/reference/random/bit_generators/xoshiro512.rst @@ -0,0 +1,35 @@ +Xoshiro512** +------------ + +.. module:: numpy.random.xoshiro512 + +.. currentmodule:: numpy.random.xoshiro512 + +.. autoclass:: Xoshiro512 + :exclude-members: + +Seeding and State +================= + +.. autosummary:: + :toctree: generated/ + + ~Xoshiro512.seed + ~Xoshiro512.state + +Parallel generation +=================== +.. autosummary:: + :toctree: generated/ + + ~Xoshiro512.jumped + +Extending +========= +.. autosummary:: + :toctree: generated/ + + ~Xoshiro512.cffi + ~Xoshiro512.ctypes + + diff --git a/doc/source/reference/random/brng/pcg32.rst b/doc/source/reference/random/brng/pcg32.rst deleted file mode 100644 index f079f5a8c..000000000 --- a/doc/source/reference/random/brng/pcg32.rst +++ /dev/null @@ -1,43 +0,0 @@ -Parallel Congruent Generator (32-bit, PCG32) --------------------------------------------- - -.. module:: numpy.random.pcg32 - -.. currentmodule:: numpy.random.pcg32 - -.. autoclass:: PCG32 - :exclude-members: - -Seeding and State -================= - -.. autosummary:: - :toctree: generated/ - - ~PCG32.seed - ~PCG32.state - -Parallel generation -=================== -.. autosummary:: - :toctree: generated/ - - ~PCG32.advance - ~PCG32.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~PCG32.generator - -Extending -========= -.. autosummary:: - :toctree: generated/ - - ~PCG32.cffi - ~PCG32.ctypes - - diff --git a/doc/source/reference/random/brng/pcg64.rst b/doc/source/reference/random/brng/pcg64.rst deleted file mode 100644 index 93f026fcb..000000000 --- a/doc/source/reference/random/brng/pcg64.rst +++ /dev/null @@ -1,43 +0,0 @@ -Parallel Congruent Generator (64-bit, PCG64) --------------------------------------------- - -.. module:: numpy.random.pcg64 - -.. currentmodule:: numpy.random.pcg64 - -.. autoclass:: PCG64 - :exclude-members: - -Seeding and State -================= - -.. autosummary:: - :toctree: generated/ - - ~PCG64.seed - ~PCG64.state - -Parallel generation -=================== -.. autosummary:: - :toctree: generated/ - - ~PCG64.advance - ~PCG64.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~PCG64.generator - -Extending -========= -.. autosummary:: - :toctree: generated/ - - ~PCG64.cffi - ~PCG64.ctypes - - diff --git a/doc/source/reference/random/brng/xoshiro256starstar.rst b/doc/source/reference/random/brng/xoshiro256starstar.rst deleted file mode 100644 index 85c445666..000000000 --- a/doc/source/reference/random/brng/xoshiro256starstar.rst +++ /dev/null @@ -1,42 +0,0 @@ -Xoshiro256** ------------- - -.. module:: numpy.random.xoshiro256starstar - -.. currentmodule:: numpy.random.xoshiro256starstar - -.. autoclass:: Xoshiro256StarStar - :exclude-members: - -Seeding and State -================= - -.. autosummary:: - :toctree: generated/ - - ~Xoshiro256StarStar.seed - ~Xoshiro256StarStar.state - -Parallel generation -=================== -.. autosummary:: - :toctree: generated/ - - ~Xoshiro256StarStar.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~Xoshiro256StarStar.generator - -Extending -========= -.. autosummary:: - :toctree: generated/ - - ~Xoshiro256StarStar.cffi - ~Xoshiro256StarStar.ctypes - - diff --git a/doc/source/reference/random/brng/xoshiro512starstar.rst b/doc/source/reference/random/brng/xoshiro512starstar.rst deleted file mode 100644 index 0c008d56e..000000000 --- a/doc/source/reference/random/brng/xoshiro512starstar.rst +++ /dev/null @@ -1,42 +0,0 @@ -Xoshiro512** ------------- - -.. module:: numpy.random.xoshiro512starstar - -.. currentmodule:: numpy.random.xoshiro512starstar - -.. autoclass:: Xoshiro512StarStar - :exclude-members: - -Seeding and State -================= - -.. autosummary:: - :toctree: generated/ - - ~Xoshiro512StarStar.seed - ~Xoshiro512StarStar.state - -Parallel generation -=================== -.. autosummary:: - :toctree: generated/ - - ~Xoshiro512StarStar.jump - -Random Generator -================ -.. autosummary:: - :toctree: generated/ - - ~Xoshiro512StarStar.generator - -Extending -========= -.. autosummary:: - :toctree: generated/ - - ~Xoshiro512StarStar.cffi - ~Xoshiro512StarStar.ctypes - - diff --git a/doc/source/reference/random/change-log.rst b/doc/source/reference/random/change-log.rst deleted file mode 100644 index 1d9f2fefc..000000000 --- a/doc/source/reference/random/change-log.rst +++ /dev/null @@ -1,39 +0,0 @@ -Change Log for the original bashtage/randomgen repo ---------------------------------------------------- -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 ``randomgen.dsfmt.DSFMT`` when calling - ``~randomgen.dsfmt.DSFMT.jump`` or ``randomgen.dsfmt.DSFMT.seed`` - that failed to reset the buffer. This resulted in upto 381 values from the - previous state being used before the buffer was refilled at the new state. -- Fixed bugs in ``randomgen.xoshiro512starstar.Xoshiro512StarStar`` - and ``randomgen.xorshift1024.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 nto 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 mtrand 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 diff --git a/doc/source/reference/random/extending.rst b/doc/source/reference/random/extending.rst index f76e3984f..f65d7708f 100644 --- a/doc/source/reference/random/extending.rst +++ b/doc/source/reference/random/extending.rst @@ -2,16 +2,15 @@ Extending --------- -The basic RNGs have been designed to be extendable using standard tools for -high-performance Python -- numba and Cython. -The `~RandomGenerator` object can also be used with -user-provided basic RNGs as long as these export a small set of required -functions. +The BitGenerators have been designed to be extendable using standard tools for +high-performance Python -- numba and Cython. The `~Generator` object can also +be used with user-provided BitGenerators as long as these export a small set of +required functions. Numba ===== Numba can be used with either CTypes or CFFI. The current iteration of the -basic RNGs all export a small set of functions through both interfaces. +BitGenerators all export a small set of functions through both interfaces. This example shows how numba can be used to produce Box-Muller normals using a pure Python implementation which is then compiled. The random numbers are @@ -66,7 +65,7 @@ examples folder. Cython ====== -Cython can be used to unpack the ``PyCapsule`` provided by a basic RNG. +Cython can be used to unpack the ``PyCapsule`` provided by a BitGenerator. This example uses `~xoroshiro128.Xoroshiro128` and ``random_gauss_zig``, the Ziggurat-based generator for normals, to fill an array. The usual caveats for writing high-performance code using Cython -- @@ -81,33 +80,29 @@ removing bounds checks and wrap around, providing array alignment information from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer from numpy.random.common cimport * from numpy.random.distributions cimport random_gauss_zig - from numpy.random.xoroshiro128 import Xoroshiro128 + from numpy.random import Xoroshiro128 @cython.boundscheck(False) @cython.wraparound(False) def normals_zig(Py_ssize_t n): cdef Py_ssize_t i - cdef brng_t *rng - cdef const char *capsule_name = "BasicRNG" + cdef bitgen_t *rng + cdef const char *capsule_name = "BitGenerator" cdef double[::1] random_values x = Xoroshiro128() capsule = x.capsule - # Optional check that the capsule if from a Basic RNG if not PyCapsule_IsValid(capsule, capsule_name): raise ValueError("Invalid pointer to anon_func_state") - # Cast the pointer - rng = <brng_t *> PyCapsule_GetPointer(capsule, capsule_name) + rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name) random_values = np.empty(n) for i in range(n): - # Call the function random_values[i] = random_gauss_zig(rng) randoms = np.asarray(random_values) return randoms - -The basic RNG can also be directly accessed using the members of the basic +The BitGenerator can also be directly accessed using the members of the basic RNG structure. .. code-block:: cython @@ -116,8 +111,8 @@ RNG structure. @cython.wraparound(False) def uniforms(Py_ssize_t n): cdef Py_ssize_t i - cdef brng_t *rng - cdef const char *capsule_name = "BasicRNG" + cdef bitgen_t *rng + cdef const char *capsule_name = "BitGenerator" cdef double[::1] random_values x = Xoroshiro128() @@ -126,7 +121,7 @@ RNG structure. if not PyCapsule_IsValid(capsule, capsule_name): raise ValueError("Invalid pointer to anon_func_state") # Cast the pointer - rng = <brng_t *> PyCapsule_GetPointer(capsule, capsule_name) + rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name) random_values = np.empty(n) for i in range(n): # Call the function @@ -139,29 +134,29 @@ examples folder. New Basic RNGs ============== -`~RandomGenerator` can be used with other -user-provided basic RNGs. The simplest way to write a new basic RNG is to -examine the pyx file of one of the existing basic RNGs. The key structure -that must be provided is the ``capsule`` which contains a ``PyCapsule`` to a -struct pointer of type ``brng_t``, +`~Generator` can be used with other user-provided BitGenerators. The simplest +way to write a new BitGenerator is to examine the pyx file of one of the +existing BitGenerators. The key structure that must be provided is the +``capsule`` which contains a ``PyCapsule`` to a struct pointer of type +``bitgen_t``, .. code-block:: c - typedef struct brng { + typedef struct bitgen { void *state; uint64_t (*next_uint64)(void *st); uint32_t (*next_uint32)(void *st); double (*next_double)(void *st); uint64_t (*next_raw)(void *st); - } brng_t; + } bitgen_t; which provides 5 pointers. The first is an opaque pointer to the data structure -used by the basic RNG. The next three are function pointers which return the -next 64- and 32-bit unsigned integers, the next random double and the next +used by the BitGenerators. The next three are function pointers which return +the next 64- and 32-bit unsigned integers, the next random double and the next raw value. This final function is used for testing and so can be set to the next 64-bit unsigned integer function if not needed. Functions inside -``RandomGenerator`` use this structure as in +``Generator`` use this structure as in .. code-block:: c - brng_state->next_uint64(brng_state->state) + bitgen_state->next_uint64(bitgen_state->state) diff --git a/doc/source/reference/random/generator.rst b/doc/source/reference/random/generator.rst index 9d248732f..ee70725e7 100644 --- a/doc/source/reference/random/generator.rst +++ b/doc/source/reference/random/generator.rst @@ -2,84 +2,81 @@ Random Generator ---------------- -The `~RandomGenerator` provides access to +The `~Generator` provides access to a wide range of distributions, and served as a replacement for :class:`~numpy.random.RandomState`. The main difference between -the two is that ``RandomGenerator`` relies on an additional basic RNG to +the two is that ``Generator`` relies on an additional BitGenerator to manage state and generate the random bits, which are then transformed into -random values from useful distributions. The default basic RNG used by -``RandomGenerator`` is :class:`~xoroshiro128.Xoroshiro128`. The basic RNG can be -changed by passing an instantized basic RNG to ``RandomGenerator``. +random values from useful distributions. The default BitGenerator used by +``Generator`` is :class:`~xoroshiro128.Xoroshiro128`. The BitGenerator +can be changed by passing an instantized BitGenerator to ``Generator``. -.. autoclass:: RandomGenerator +.. autoclass:: Generator :exclude-members: -Accessing the RNG -================= +Accessing the BitGenerator +========================== .. autosummary:: :toctree: generated/ - ~RandomGenerator.brng + ~Generator.bit_generator Simple random data ================== .. autosummary:: :toctree: generated/ - ~RandomGenerator.rand - ~RandomGenerator.randn - ~RandomGenerator.randint - ~RandomGenerator.random_integers - ~RandomGenerator.random_sample - ~RandomGenerator.choice - ~RandomGenerator.bytes + ~Generator.integers + ~Generator.random + ~Generator.choice + ~Generator.bytes Permutations ============ .. autosummary:: :toctree: generated/ - ~RandomGenerator.shuffle - ~RandomGenerator.permutation + ~Generator.shuffle + ~Generator.permutation Distributions ============= .. autosummary:: :toctree: generated/ - ~RandomGenerator.beta - ~RandomGenerator.binomial - ~RandomGenerator.chisquare - ~RandomGenerator.dirichlet - ~RandomGenerator.exponential - ~RandomGenerator.f - ~RandomGenerator.gamma - ~RandomGenerator.geometric - ~RandomGenerator.gumbel - ~RandomGenerator.hypergeometric - ~RandomGenerator.laplace - ~RandomGenerator.logistic - ~RandomGenerator.lognormal - ~RandomGenerator.logseries - ~RandomGenerator.multinomial - ~RandomGenerator.multivariate_normal - ~RandomGenerator.negative_binomial - ~RandomGenerator.noncentral_chisquare - ~RandomGenerator.noncentral_f - ~RandomGenerator.normal - ~RandomGenerator.pareto - ~RandomGenerator.poisson - ~RandomGenerator.power - ~RandomGenerator.rayleigh - ~RandomGenerator.standard_cauchy - ~RandomGenerator.standard_exponential - ~RandomGenerator.standard_gamma - ~RandomGenerator.standard_normal - ~RandomGenerator.standard_t - ~RandomGenerator.triangular - ~RandomGenerator.uniform - ~RandomGenerator.vonmises - ~RandomGenerator.wald - ~RandomGenerator.weibull - ~RandomGenerator.zipf + ~Generator.beta + ~Generator.binomial + ~Generator.chisquare + ~Generator.dirichlet + ~Generator.exponential + ~Generator.f + ~Generator.gamma + ~Generator.geometric + ~Generator.gumbel + ~Generator.hypergeometric + ~Generator.laplace + ~Generator.logistic + ~Generator.lognormal + ~Generator.logseries + ~Generator.multinomial + ~Generator.multivariate_normal + ~Generator.negative_binomial + ~Generator.noncentral_chisquare + ~Generator.noncentral_f + ~Generator.normal + ~Generator.pareto + ~Generator.poisson + ~Generator.power + ~Generator.rayleigh + ~Generator.standard_cauchy + ~Generator.standard_exponential + ~Generator.standard_gamma + ~Generator.standard_normal + ~Generator.standard_t + ~Generator.triangular + ~Generator.uniform + ~Generator.vonmises + ~Generator.wald + ~Generator.weibull + ~Generator.zipf diff --git a/doc/source/reference/random/index.rst b/doc/source/reference/random/index.rst index 9c8c99c38..032db8562 100644 --- a/doc/source/reference/random/index.rst +++ b/doc/source/reference/random/index.rst @@ -39,28 +39,28 @@ which will be faster than the legacy methods in `RandomState` from numpy import random random.standard_normal() -`Generator` can be used as a direct replacement for `RandomState`, -although the random values are generated by `~xoroshiro128.Xoroshiro128`. The +`Generator` can be used as a direct replacement for `~RandomState`, although +the random values are generated by `~xoroshiro128.Xoroshiro128`. The `Generator` holds an instance of a BitGenerator. It is accessable as ``gen.bit_generator``. .. code-block:: python # As replacement for RandomState() - from numpy.random import RandomGenerator - rg = RandomGenerator() + from numpy.random import Generator + rg = Generator() rg.standard_normal() rg.bit_generator -Seeds can be passed to any of the basic RNGs. Here `mt19937.MT19937` is used -and the ``RandomGenerator`` is accessed via the attribute `mt19937.MT19937. -generator`. +Seeds can be passed to any of the BitGenerators. Here `mt19937.MT19937` is used +and is the wrapped with a `~.Generator`. + .. code-block:: python - from numpy.random import MT19937 - rg = MT19937(12345).generator + from numpy.random import Generator, MT19937 + rg = Generator(MT19937(12345)) rg.standard_normal() @@ -70,33 +70,32 @@ RandomGen takes a different approach to producing random numbers from the `RandomState` object. Random number generation is separated into two components, a bit generator and a random generator. -The basic RNG has a limited set of responsibilities. It manages the -underlying RNG state and provides functions to produce random doubles and -random unsigned 32- and 64-bit values. The basic random generator also handles -all seeding since this varies when using alternative basic RNGs. +The bit generator has a limited set of responsibilities. It manages state +and provides functions to produce random doubles and random unsigned 32- and +64-bit values. The bit generator also handles all seeding which varies with +different bit generators. The `random generator <Generator>` takes the bit generator-provided stream and transforms them into more useful distributions, e.g., simulated normal random values. This structure allows -alternative basic RNGs to be used without code duplication. +alternative bit generators to be used with little code duplication. The `Generator` is the user-facing object that is nearly identical to `RandomState`. The canonical method to initialize a generator passes a `~mt19937.MT19937` bit generator, the underlying bit generator in Python -- as -the sole argument. Note that the bit generator must be instantiated. - +the sole argument. Note that the BitGenerator must be instantiated. .. code-block:: python - rg = RandomGenerator(MT19937(12345)) - rg.random_sample() + from numpy.random import Generator, MT19937 + rg = Generator(MT19937()) + rg.random() -A shorthand method is also available which uses the `~mt19937.MT19937. -generator` property from a basic RNG to access an embedded random generator. +Seed information is directly passed to the bit generator. .. code-block:: python - rg = MT19937(12345).generator - rg.random_sample() + rg = Generator(MT19937(12345)) + rg.random() What's New or Different ~~~~~~~~~~~~~~~~~~~~~~~ @@ -136,7 +135,7 @@ What's New or Different is consistent with Python's `random.random`. See :ref:`new-or-different` for a complete list of improvements and -differences. +differences from the traditional ``Randomstate``. Parallel Generation ~~~~~~~~~~~~~~~~~~~ @@ -156,33 +155,32 @@ The included BitGenerators are: that returns a new generator with state as-if ``2**128`` draws have been made. * dSFMT - SSE2 enabled versions of the MT19937 generator. Theoretically the same, but with a different state and so it is not possible to produce a - sequence identical to MT19937. Supports ``jump`` and so can + sequence identical to MT19937. Supports ``jumped`` and so can be used in parallel applications. See the `dSFMT authors' page`_. * XoroShiro128+ - Improved version of XorShift128+ with better performance and statistical quality. Like the XorShift generators, it can be jumped to produce multiple streams in parallel applications. See - `~xoroshiro128.Xoroshiro128.jump` for details. - More information about this PRNG is available at the + `~xoroshiro128.Xoroshiro128.jumped` for details. + More information about this bit generator is available at the `xorshift, xoroshiro and xoshiro authors' page`_. * XorShift1024*φ - Fast fast generator based on the XSadd - generator. Supports ``jump`` and so can be used in + generator. Supports ``jumped`` and so can be used in parallel applications. See the documentation for `~xorshift1024.Xorshift1024.jumped` for details. More information about these bit generators is available at the `xorshift, xoroshiro and xoshiro authors' page`_. * Xorshiro256** and Xorshiro512** - The most recently introduced XOR, - shift, and rotate generator. Supports ``jump`` and so can be used in + shift, and rotate generator. Supports ``jumped`` and so can be used in parallel applications. See the documentation for `~xoshiro256.Xoshirt256.jumped` for details. More information about these bit generators is available at the `xorshift, xoroshiro and xoshiro authors' page`_. * ThreeFry and Philox - counter-based generators capable of being advanced an arbitrary number of steps or generating independent streams. See the - `Random123`_ page for more details about this class of PRNG. + `Random123`_ page for more details about this class of bit generators. .. _`dSFMT authors' page`: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/ .. _`xorshift, xoroshiro and xoshiro authors' page`: http://xoroshiro.di.unimi.it/ -.. _`PCG author's page`: http://www.pcg-random.org/ .. _`Random123`: https://www.deshawresearch.com/resources_random123.html Generator @@ -201,8 +199,8 @@ BitGenerators BitGenerators <bit_generators/index> -New Features ------------- +Features +-------- .. toctree:: :maxdepth: 2 @@ -212,16 +210,9 @@ New Features Comparing Performance <performance> extending Reading System Entropy <entropy> - references -Changes -~~~~~~~ +Original Source +~~~~~~~~~~~~~~~ This package was developed independently of NumPy and was integrated in version 1.17.0. The original repo is at https://github.com/bashtage/randomgen. - -.. toctree:: - :maxdepth: 2 - - Change Log <change-log> - diff --git a/doc/source/reference/random/legacy.rst b/doc/source/reference/random/legacy.rst index 3bf7569b0..c5b92f1bb 100644 --- a/doc/source/reference/random/legacy.rst +++ b/doc/source/reference/random/legacy.rst @@ -3,16 +3,18 @@ Legacy Random Generation ------------------------ The `~mtrand.RandomState` provides access to -legacy generators. These all depend on Box-Muller normals or -inverse CDF exponentials or gammas. This class should only be used +legacy generators. This generator is considered frozen and will have +no further improvements. It is guaranteed to produce the same values +as the final point release of NumPy v1.16. These all depend on Box-Muller +normals or inverse CDF exponentials or gammas. This class should only be used if it is essential to have randoms that are identical to what would have been produced by NumPy. `~mtrand.RandomState` adds additional information to the state which is required when using Box-Muller normals since these are produced in pairs. It is important to use -`~mtrand.RandomState.get_state` -when accessing the state so that these extra values are saved. +`~mtrand.RandomState.get_state`, and not the underlying bit generators +`state`, when accessing the state so that these extra values are saved. .. warning:: @@ -30,20 +32,19 @@ when accessing the state so that these extra values are saved. .. code-block:: python from numpy.random import MT19937 - from numpy.random._mtrand import RandomState as LegacyGenerator from numpy.random import RandomState - # Use same seed + + # Use same seed rs = RandomState(12345) mt19937 = MT19937(12345) - rg = RandomGenerator(mt19937) - lg = LegacyGenerator(mt19937) + lg = RandomState(mt19937) # Identical output rs.standard_normal() lg.standard_normal() - rs.random_sample() - rg.random_sample() + rs.random() + lg.random() rs.standard_exponential() lg.standard_exponential() diff --git a/doc/source/reference/random/multithreading.rst b/doc/source/reference/random/multithreading.rst index d35cafe06..718fe05f1 100644 --- a/doc/source/reference/random/multithreading.rst +++ b/doc/source/reference/random/multithreading.rst @@ -17,7 +17,7 @@ seed will produce the same outputs. .. code-block:: ipython - from numpy.random import Xorshift1024 + from numpy.random import Generator, Xorshift1024 import multiprocessing import concurrent.futures import numpy as np @@ -29,14 +29,13 @@ seed will produce the same outputs. threads = multiprocessing.cpu_count() self.threads = threads - self._random_generators = [] + self._random_generators = [rg] + last_rg = rg for _ in range(0, threads-1): - _rg = Xorshift1024() - _rg.state = rg.state - self._random_generators.append(_rg.generator) - rg.jump() - self._random_generators.append(rg.generator) - + new_rg = last_rg.jumped() + self._random_generators.append(new_rg) + last_rg = new_rg + self.n = n self.executor = concurrent.futures.ThreadPoolExecutor(threads) self.values = np.empty(n) @@ -90,7 +89,7 @@ The single threaded call directly uses the BitGenerator. .. code-block:: ipython In [5]: values = np.empty(10000000) - ...: rg = Xorshift1024().generator + ...: rg = Generator(Xorshift1024()) ...: %timeit rg.standard_normal(out=values) 99.6 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) @@ -101,7 +100,7 @@ that does not use an existing array due to array creation overhead. .. code-block:: ipython - In [6]: rg = Xorshift1024().generator + In [6]: rg = Generator(Xorshift1024()) ...: %timeit rg.standard_normal(10000000) 125 ms ± 309 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) diff --git a/doc/source/reference/random/new-or-different.rst b/doc/source/reference/random/new-or-different.rst index 4922b6762..906cceceb 100644 --- a/doc/source/reference/random/new-or-different.rst +++ b/doc/source/reference/random/new-or-different.rst @@ -35,7 +35,11 @@ Feature Older Equivalent Notes Many other distributions are also supported. -=================== =================== ============= +------------------ -------------------- ------------- +``Generator().`` ``randint``, Use the ``endpoint`` kwarg to adjust +``integers()`` ``random_integers`` the inclusion or exclution of the + ``high`` interval endpoint +================== ==================== ============= And in more detail: @@ -43,26 +47,29 @@ And in more detail: source of randomness that is used in cryptographic applications (e.g., ``/dev/urandom`` on Unix). * Simulate from the complex normal distribution - (`~.RandomGenerator.complex_normal`) + (`~.Generator.complex_normal`) * The normal, exponential and gamma generators use 256-step Ziggurat methods which are 2-10 times faster than NumPy's default implementation in - `~.RandomGenerator.standard_normal`, - `~.RandomGenerator.standard_exponential` or - `~.RandomGenerator.standard_gamma`. + `~.Generator.standard_normal`, `~.Generator.standard_exponential` or + `~.Generator.standard_gamma`. +* `~.Generator.integers` is now the canonical way to generate integer + random numbers from a discrete uniform distribution. The ``rand`` and + ``randn`` methods are only availabe through the legacy `~.RandomState`. + This replaces both ``randint`` and the deprecated ``random_integers``. * The Box-Muller used to produce NumPy's normals is no longer available. -* All basic random generators functions to produce doubles, uint64s and +* All bit generators can produce doubles, uint64s and uint32s via CTypes (`~.xoroshiro128.Xoroshiro128. ctypes`) and CFFI (`~.xoroshiro128.Xoroshiro128.cffi`). - This allows these basic RNGs to be used in numba. -* The basic random number generators can be used in downstream projects via + This allows these bit generators to be used in numba. +* The bit generators can be used in downstream projects via Cython. .. ipython:: python - from numpy.random import Xoroshiro128 + from numpy.random import Generator, Xoroshiro128 import numpy.random - rg = Xoroshiro128().generator + rg = Generator(Xoroshiro128()) %timeit rg.standard_normal(100000) %timeit numpy.random.standard_normal(100000) @@ -80,27 +87,25 @@ And in more detail: to produce either single or double prevision uniform random variables for select distributions - * Uniforms (`~.RandomGenerator.random_sample` and - `~.RandomGenerator.rand`) - * Normals (`~.RandomGenerator.standard_normal` and - `~.RandomGenerator.randn`) - * Standard Gammas (`~.RandomGenerator.standard_gamma`) - * Standard Exponentials (`~.RandomGenerator.standard_exponential`) + * Uniforms (`~.Generator.random` and `~.Generator.integers`) + * Normals (`~.Generator.standard_normal`) + * Standard Gammas (`~.Generator.standard_gamma`) + * Standard Exponentials (`~.Generator.standard_exponential`) .. ipython:: python - rg.brng.seed(0) - rg.random_sample(3, dtype='d') - rg.brng.seed(0) - rg.random_sample(3, dtype='f') + rg.bit_generator.seed(0) + rg.random(3, dtype='d') + rg.bit_generator.seed(0) + rg.random(3, dtype='f') * Optional ``out`` argument that allows existing arrays to be filled for select distributions - * Uniforms (`~.RandomGenerator.random_sample`) - * Normals (`~.RandomGenerator.standard_normal`) - * Standard Gammas (`~.RandomGenerator.standard_gamma`) - * Standard Exponentials (`~.RandomGenerator.standard_exponential`) + * Uniforms (`~.Generator.random`) + * Normals (`~.Generator.standard_normal`) + * Standard Gammas (`~.Generator.standard_gamma`) + * Standard Exponentials (`~.Generator.standard_exponential`) This allows multithreading to fill large arrays in chunks using suitable BitGenerators in parallel. @@ -108,16 +113,6 @@ And in more detail: .. ipython:: python existing = np.zeros(4) - rg.random_sample(out=existing[:2]) + rg.random(out=existing[:2]) print(existing) -.. * For changes since the previous release, see the :ref:`change-log` - -* Support for Lemire’s method of generating uniform integers on an - arbitrary interval by setting ``use_masked=True`` in - (`~.RandomGenerator.randint`). - -.. ipython:: python - - %timeit rg.randint(0, 1535, use_masked=False) - %timeit numpy.random.randint(0, 1535) diff --git a/doc/source/reference/random/parallel.rst b/doc/source/reference/random/parallel.rst index c831effa2..8bdc3f23f 100644 --- a/doc/source/reference/random/parallel.rst +++ b/doc/source/reference/random/parallel.rst @@ -12,20 +12,20 @@ or distributed). Independent Streams ------------------- -:class:`~pcg64.PCG64`, :class:`~threefry.ThreeFry` -and :class:`~philox.Philox` support independent streams. This -example shows how many streams can be created by passing in different index -values in the second input while using the same seed in the first. +:class:`~threefry.ThreeFry` and :class:`~philox.Philox` support independent +streams. This example shows how many streams can be created by passing in +different index values in the second input while using the same seed in the +first. .. code-block:: python from numpy.random.entropy import random_entropy - from numpy.random import PCG64 + from numpy.random import ThreeFry entropy = random_entropy(4) # 128-bit number as a seed seed = sum([int(entropy[i]) * 2 ** (32 * i) for i in range(4)]) - streams = [PCG64(seed, stream) for stream in range(10)] + streams = [ThreeFry(seed, stream) for stream in range(10)] :class:`~philox.Philox` and :class:`~threefry.ThreeFry` are @@ -49,8 +49,8 @@ to produce independent streams. Jump/Advance the BitGenerator state ----------------------------------- -Jump -**** +Jumped +****** ``jumped`` advances the state of the BitGenerator *as-if* a large number of random numbers have been drawn, and returns a new instance with this state. @@ -68,8 +68,6 @@ are listed below. +-----------------+-------------------------+-------------------------+-------------------------+ | MT19937 | :math:`2^{19937}` | :math:`2^{128}` | 32 | +-----------------+-------------------------+-------------------------+-------------------------+ -| PCG64 | :math:`2^{128}` | :math:`2^{64}` | 64 | -+-----------------+-------------------------+-------------------------+-------------------------+ | Philox | :math:`2^{256}` | :math:`2^{128}` | 64 | +-----------------+-------------------------+-------------------------+-------------------------+ | ThreeFry | :math:`2^{256}` | :math:`2^{128}` | 64 | @@ -78,6 +76,10 @@ are listed below. +-----------------+-------------------------+-------------------------+-------------------------+ | Xorshift1024 | :math:`2^{1024}` | :math:`2^{512}` | 64 | +-----------------+-------------------------+-------------------------+-------------------------+ +| Xoshiro256** | :math:`2^{256}` | :math:`2^{128}` | 64 | ++-----------------+-------------------------+-------------------------+-------------------------+ +| Xoshiro512** | :math:`2^{512}` | :math:`2^{256}` | 64 | ++-----------------+-------------------------+-------------------------+-------------------------+ ``jumped`` can be used to produce long blocks which should be long enough to not overlap. @@ -93,16 +95,14 @@ overlap. blocked_rng = [] rng = Xorshift1024(seed) for i in range(10): - blocked_rng.append(rng.jumped()) - + blocked_rng.append(rng.jumped(i)) Advance ******* ``advance`` can be used to jump the state an arbitrary number of steps, and so -is a more general approach than ``jump``. :class:`~pcg64.PCG64`, -:class:`~threefry.ThreeFry` and :class:`~philox.Philox` -support ``advance``, and since these also support independent -streams, it is not usually necessary to use ``advance``. +is a more general approach than ``jumped``. :class:`~threefry.ThreeFry` and +:class:`~philox.Philox` support ``advance``, and since these also support +independent streams, it is not usually necessary to use ``advance``. Advancing a BitGenerator updates the underlying state as-if a given number of calls to the BitGenerator have been made. In general there is not a @@ -120,23 +120,23 @@ This occurs for two reasons: Advancing the BitGenerator state resets any pre-computed random numbers. This is required to ensure exact reproducibility. -This example uses ``advance`` to advance a :class:`~pcg64.PCG64` +This example uses ``advance`` to advance a :class:`~threefry.ThreeFry` generator 2 ** 127 steps to set a sequence of random number generators. .. code-block:: python - from numpy.random import PCG64 - brng = PCG64() - brng_copy = PCG64() - brng_copy.state = brng.state + from numpy.random import ThreeFry + bit_generator = ThreeFry() + bit_generator_copy = ThreeFry() + bit_generator_copy.state = bit_generator.state advance = 2**127 - brngs = [brng] + bit_generators = [bit_generator] for _ in range(9): - brng_copy.advance(advance) - brng = PCG64() - brng.state = brng_copy.state - brngs.append(brng) + bit_generator_copy.advance(advance) + bit_generator = ThreeFry() + bit_generator.state = bit_generator_copy.state + bit_generators.append(bit_generator) .. end block diff --git a/doc/source/reference/random/performance.py b/doc/source/reference/random/performance.py index 12cbbc5d3..a29e09c41 100644 --- a/doc/source/reference/random/performance.py +++ b/doc/source/reference/random/performance.py @@ -4,16 +4,15 @@ from timeit import repeat import numpy as np import pandas as pd -from randomgen import MT19937, DSFMT, ThreeFry, PCG64, Xoroshiro128, \ - Xorshift1024, Philox, Xoshiro256StarStar, Xoshiro512StarStar +from numpy.random import MT19937, DSFMT, ThreeFry, Xoroshiro128, \ + Xorshift1024, Philox, Xoshiro256, Xoshiro512 -PRNGS = [DSFMT, MT19937, Philox, PCG64, ThreeFry, Xoroshiro128, Xorshift1024, - Xoshiro256StarStar, Xoshiro512StarStar] +PRNGS = [DSFMT, MT19937, Philox, ThreeFry, Xoroshiro128, Xorshift1024, + Xoshiro256, Xoshiro512] -funcs = {'32-bit Unsigned Ints': 'random_uintegers(size=1000000,bits=32)', - '64-bit Unsigned Ints': 'random_uintegers(size=1000000,bits=32)', - 'Uniforms': 'random_sample(size=1000000)', - 'Complex Normals': 'complex_normal(size=1000000)', +funcs = {'32-bit Unsigned Ints': 'integers(0, 2**32,size=1000000, dtype="uint32")', + '64-bit Unsigned Ints': 'integers(0, 2**64,size=1000000, dtype="uint64")', + 'Uniforms': 'random(size=1000000)', 'Normals': 'standard_normal(size=1000000)', 'Exponentials': 'standard_exponential(size=1000000)', 'Gammas': 'standard_gamma(3.0,size=1000000)', @@ -22,8 +21,8 @@ funcs = {'32-bit Unsigned Ints': 'random_uintegers(size=1000000,bits=32)', 'Poissons': 'poisson(3.0, size=1000000)', } setup = """ -from randomgen import {prng} -rg = {prng}().generator +from numpy.random import {prng}, Generator +rg = Generator({prng}()) """ test = "rg.{func}" @@ -43,7 +42,6 @@ npfuncs = OrderedDict() npfuncs.update(funcs) npfuncs['32-bit Unsigned Ints'] = 'randint(2**32,dtype="uint32",size=1000000)' npfuncs['64-bit Unsigned Ints'] = 'tomaxint(size=1000000)' -del npfuncs['Complex Normals'] setup = """ from numpy.random import RandomState rg = RandomState() diff --git a/doc/source/reference/random/performance.rst b/doc/source/reference/random/performance.rst index 321d49454..61898ac89 100644 --- a/doc/source/reference/random/performance.rst +++ b/doc/source/reference/random/performance.rst @@ -10,21 +10,19 @@ Recommendation The recommended generator for single use is :class:`~xoroshiro128.Xoroshiro128`. The recommended generator for use in large-scale parallel applications is -:class:`~xorshift1024.Xorshift1024` -where the `jump` method is used to advance the state. For very large scale +:class:`~.xoshiro256.Xoshiro256` +where the `jumped` method is used to advance the state. For very large scale applications -- requiring 1,000+ independent streams, -:class:`~pcg64.PCG64` or :class:`~threefry.ThreeFry` are -the best choices. +:class:`~.philox.Philox` is the best choice. Timings ******* -The timings below are the time in ms to produce 1,000,000 random values from a +The timings below are the time in ns to produce 1 random value from a specific distribution. :class:`~xoroshiro128.Xoroshiro128` is the -fastest, followed by :class:`~xorshift1024.Xorshift1024` and -:class:`~pcg64.PCG64`. The original :class:`~mt19937.MT19937` -generator is much slower since it requires 2 32-bit values to equal the output -of the faster generators. +fastest, followed by :class:`~xorshift1024.Xorshift1024`. The original +:class:`~mt19937.MT19937` generator is much slower since it requires 2 32-bit +values to equal the output of the faster generators. Integer performance has a similar ordering although `dSFMT` is slower since it generates 53-bit floating point values rather than integer values. On the @@ -37,40 +35,38 @@ performance gap for Exponentials is also large due to the cost of computing the log function to invert the CDF. .. csv-table:: - :header: ,Xoroshiro128,Xorshift1024,PCG64,DSFMT,MT19937,Philox,ThreeFry,NumPy - :widths: 14,14,14,14,14,14,14,14,14 + :header: ,Xoroshiro128,Xoshiro256**,Xorshift1024,MT19937,Philox,ThreeFry,NumPy + :widths: 14,14,14,14,14,14,14,14 - 32-bit Unsigned Ints,3.0,3.0,3.0,3.5,3.7,6.8,6.6,3.3 - 64-bit Unsigned Ints,2.6,3.0,3.1,3.4,3.8,6.9,6.6,8.8 - Uniforms,3.2,3.8,4.4,5.0,7.4,8.9,9.9,8.8 - Normals,11.0,13.9,13.7,15.8,16.9,17.8,18.8,63.0 - Exponentials,7.0,8.4,9.0,11.2,12.5,14.1,15.0,102.2 - Binomials,20.9,22.6,22.0,21.2,26.7,27.7,29.2,26.5 - Complex Normals,23.2,28.7,29.1,33.2,35.4,37.6,38.6, - Gammas,35.3,38.6,39.2,41.3,46.7,49.4,51.2,98.8 - Laplaces,97.8,99.9,99.8,96.2,104.1,104.6,104.8,104.1 - Poissons,104.8,113.2,113.3,107.6,129.7,135.6,138.1,131.9 + 64-bit Unsigned Ints,11.9,13.6,14.9,18.0,22.0,25.9,42.0 + Uniforms,16.3,15.6,16.0,19.1,23.5,25.5,44.1 + 32-bit Unsigned Ints,21.6,23.7,23.1,23.6,27.9,32.3,17.9 + Exponentials,21.2,22.4,23.8,26.7,30.8,33.0,115.3 + Normals,25.1,26.9,26.2,31.7,32.6,37.8,106.8 + Binomials,72.4,73.0,71.9,77.4,80.0,83.1,101.9 + Complex Normals,80.4,86.4,81.1,93.4,96.3,105.5, + Laplaces,97.0,97.4,99.6,109.8,102.3,105.1,125.3 + Gammas,91.3,91.2,94.8,101.7,108.7,113.8,187.9 + Poissons,136.7,137.6,139.7,161.9,171.0,181.0,265.1 The next table presents the performance relative to `xoroshiro128+` in percentage. The overall performance was computed using a geometric mean. .. csv-table:: - :header: ,Xorshift1024,PCG64,DSFMT,MT19937,Philox,ThreeFry,NumPy - :widths: 14,14,14,14,14,14,14,14 + :header: ,Xoroshiro128,Xoshiro256**,Xorshift1024,MT19937,Philox,ThreeFry + :widths: 14,14,14,14,14,14,14 - 32-bit Unsigned Ints,102,99,118,125,229,221,111 - 64-bit Unsigned Ints,114,116,129,143,262,248,331 - Uniforms,116,137,156,231,275,306,274 - Normals,126,124,143,153,161,170,572 - Exponentials,121,130,161,179,203,215,1467 - Binomials,108,105,101,128,133,140,127 - Complex Normals,124,125,143,153,162,166, - Gammas,109,111,117,132,140,145,280 - Laplaces,102,102,98,106,107,107,106 - Poissons,108,108,103,124,129,132,126 - Overall,113,115,125,144,172,177,251 - + 64-bit Unsigned Ints,353,309,283,233,191,162 + Uniforms,271,283,276,232,188,173 + 32-bit Unsigned Ints,83,76,78,76,64,56 + Exponentials,544,514,485,432,375,350 + Normals,425,397,407,337,328,283 + Binomials,141,140,142,132,127,123 + Laplaces,129,129,126,114,123,119 + Gammas,206,206,198,185,173,165 + Poissons,194,193,190,164,155,146 + Overall,223,215,210,186,170,156 .. note:: diff --git a/doc/source/reference/random/references.rst b/doc/source/reference/random/references.rst deleted file mode 100644 index 0dc99868f..000000000 --- a/doc/source/reference/random/references.rst +++ /dev/null @@ -1,5 +0,0 @@ -References ----------- - -.. [Lemire] Daniel Lemire., "Fast Random Integer Generation in an Interval", - CoRR, Aug. 13, 2018, http://arxiv.org/abs/1805.10941. |