diff options
Diffstat (limited to 'doc/source/reference/random')
| -rw-r--r-- | doc/source/reference/random/bit_generators/bitgenerators.rst | 11 | ||||
| -rw-r--r-- | doc/source/reference/random/bit_generators/index.rst | 43 | ||||
| -rw-r--r-- | doc/source/reference/random/bit_generators/mt19937.rst | 6 | ||||
| -rw-r--r-- | doc/source/reference/random/bit_generators/pcg64.rst | 4 | ||||
| -rw-r--r-- | doc/source/reference/random/bit_generators/philox.rst | 4 | ||||
| -rw-r--r-- | doc/source/reference/random/bit_generators/sfc64.rst | 4 | ||||
| -rw-r--r-- | doc/source/reference/random/entropy.rst | 6 | ||||
| -rw-r--r-- | doc/source/reference/random/generator.rst | 1 | ||||
| -rw-r--r-- | doc/source/reference/random/index.rst | 8 | ||||
| -rw-r--r-- | doc/source/reference/random/legacy.rst | 18 | ||||
| -rw-r--r-- | doc/source/reference/random/new-or-different.rst | 16 | ||||
| -rw-r--r-- | doc/source/reference/random/parallel.rst | 18 | ||||
| -rw-r--r-- | doc/source/reference/random/performance.rst | 20 |
13 files changed, 63 insertions, 96 deletions
diff --git a/doc/source/reference/random/bit_generators/bitgenerators.rst b/doc/source/reference/random/bit_generators/bitgenerators.rst deleted file mode 100644 index 1474f7dac..000000000 --- a/doc/source/reference/random/bit_generators/bitgenerators.rst +++ /dev/null @@ -1,11 +0,0 @@ -:orphan: - -BitGenerator ------------- - -.. currentmodule:: numpy.random.bit_generator - -.. autosummary:: - :toctree: generated/ - - BitGenerator diff --git a/doc/source/reference/random/bit_generators/index.rst b/doc/source/reference/random/bit_generators/index.rst index 35d9e5d09..94d3d8a3c 100644 --- a/doc/source/reference/random/bit_generators/index.rst +++ b/doc/source/reference/random/bit_generators/index.rst @@ -1,5 +1,3 @@ -.. _bit_generator: - .. currentmodule:: numpy.random Bit Generators @@ -35,14 +33,18 @@ The included BitGenerators are: .. _`Random123`: https://www.deshawresearch.com/resources_random123.html .. _`SFC author's page`: http://pracrand.sourceforge.net/RNG_engines.txt +.. autosummary:: + :toctree: generated/ + + BitGenerator + .. toctree:: - :maxdepth: 1 + :maxdepth: 1 - BitGenerator <bitgenerators> - MT19937 <mt19937> - PCG64 <pcg64> - Philox <philox> - SFC64 <sfc64> + MT19937 <mt19937> + PCG64 <pcg64> + Philox <philox> + SFC64 <sfc64> Seeding and Entropy ------------------- @@ -53,14 +55,14 @@ seed. All of the provided BitGenerators will take an arbitrary-sized non-negative integer, or a list of such integers, as a seed. BitGenerators need to take those inputs and process them into a high-quality internal state for the BitGenerator. All of the BitGenerators in numpy delegate that task to -`~SeedSequence`, which uses hashing techniques to ensure that even low-quality +`SeedSequence`, which uses hashing techniques to ensure that even low-quality seeds generate high-quality initial states. .. code-block:: python - from numpy.random import PCG64 + from numpy.random import PCG64 - bg = PCG64(12345678903141592653589793) + bg = PCG64(12345678903141592653589793) .. end_block @@ -75,14 +77,14 @@ user, which is up to you. .. code-block:: python - from numpy.random import PCG64, SeedSequence + from numpy.random import PCG64, SeedSequence - # Get the user's seed somehow, maybe through `argparse`. - # If the user did not provide a seed, it should return `None`. - seed = get_user_seed() - ss = SeedSequence(seed) - print('seed = {}'.format(ss.entropy)) - bg = PCG64(ss) + # Get the user's seed somehow, maybe through `argparse`. + # If the user did not provide a seed, it should return `None`. + seed = get_user_seed() + ss = SeedSequence(seed) + print('seed = {}'.format(ss.entropy)) + bg = PCG64(ss) .. end_block @@ -104,9 +106,6 @@ or using ``secrets.randbits(128)`` from the standard library are both convenient ways. .. autosummary:: - :toctree: generated/ + :toctree: generated/ SeedSequence - bit_generator.ISeedSequence - bit_generator.ISpawnableSeedSequence - bit_generator.SeedlessSeedSequence diff --git a/doc/source/reference/random/bit_generators/mt19937.rst b/doc/source/reference/random/bit_generators/mt19937.rst index 25ba1d7b5..71875db4e 100644 --- a/doc/source/reference/random/bit_generators/mt19937.rst +++ b/doc/source/reference/random/bit_generators/mt19937.rst @@ -1,9 +1,7 @@ -Mersenne Twister (MT19937) +Mersenne Twister (MT19937) -------------------------- -.. module:: numpy.random.mt19937 - -.. currentmodule:: numpy.random.mt19937 +.. currentmodule:: numpy.random .. autoclass:: MT19937 :exclude-members: diff --git a/doc/source/reference/random/bit_generators/pcg64.rst b/doc/source/reference/random/bit_generators/pcg64.rst index 7aef1e0dd..5881b7008 100644 --- a/doc/source/reference/random/bit_generators/pcg64.rst +++ b/doc/source/reference/random/bit_generators/pcg64.rst @@ -1,9 +1,7 @@ Parallel Congruent Generator (64-bit, PCG64) -------------------------------------------- -.. module:: numpy.random.pcg64 - -.. currentmodule:: numpy.random.pcg64 +.. currentmodule:: numpy.random .. autoclass:: PCG64 :exclude-members: diff --git a/doc/source/reference/random/bit_generators/philox.rst b/doc/source/reference/random/bit_generators/philox.rst index 5e581e094..8eba2d351 100644 --- a/doc/source/reference/random/bit_generators/philox.rst +++ b/doc/source/reference/random/bit_generators/philox.rst @@ -1,9 +1,7 @@ Philox Counter-based RNG ------------------------ -.. module:: numpy.random.philox - -.. currentmodule:: numpy.random.philox +.. currentmodule:: numpy.random .. autoclass:: Philox :exclude-members: diff --git a/doc/source/reference/random/bit_generators/sfc64.rst b/doc/source/reference/random/bit_generators/sfc64.rst index dc03820ae..d34124a33 100644 --- a/doc/source/reference/random/bit_generators/sfc64.rst +++ b/doc/source/reference/random/bit_generators/sfc64.rst @@ -1,9 +1,7 @@ SFC64 Small Fast Chaotic PRNG ----------------------------- -.. module:: numpy.random.sfc64 - -.. currentmodule:: numpy.random.sfc64 +.. currentmodule:: numpy.random .. autoclass:: SFC64 :exclude-members: diff --git a/doc/source/reference/random/entropy.rst b/doc/source/reference/random/entropy.rst deleted file mode 100644 index 0664da6f9..000000000 --- a/doc/source/reference/random/entropy.rst +++ /dev/null @@ -1,6 +0,0 @@ -System Entropy -============== - -.. module:: numpy.random.entropy - -.. autofunction:: random_entropy diff --git a/doc/source/reference/random/generator.rst b/doc/source/reference/random/generator.rst index 068143270..a2cbb493a 100644 --- a/doc/source/reference/random/generator.rst +++ b/doc/source/reference/random/generator.rst @@ -62,6 +62,7 @@ Distributions ~numpy.random.Generator.lognormal ~numpy.random.Generator.logseries ~numpy.random.Generator.multinomial + ~numpy.random.Generator.multivariate_hypergeometric ~numpy.random.Generator.multivariate_normal ~numpy.random.Generator.negative_binomial ~numpy.random.Generator.noncentral_chisquare diff --git a/doc/source/reference/random/index.rst b/doc/source/reference/random/index.rst index 01f9981a2..9b19620d8 100644 --- a/doc/source/reference/random/index.rst +++ b/doc/source/reference/random/index.rst @@ -123,7 +123,7 @@ The `Generator` is the user-facing object that is nearly identical to rg.random() One can also instantiate `Generator` directly with a `BitGenerator` instance. -To use the older `~mt19937.MT19937` algorithm, one can instantiate it directly +To use the older `MT19937` algorithm, one can instantiate it directly and pass it to `Generator`. .. code-block:: python @@ -151,9 +151,6 @@ What's New or Different select distributions * Optional ``out`` argument that allows existing arrays to be filled for select distributions -* `~entropy.random_entropy` provides access to the system - source of randomness that is used in cryptographic applications (e.g., - ``/dev/urandom`` on Unix). * All BitGenerators can produce doubles, uint64s and uint32s via CTypes (`~.PCG64.ctypes`) and CFFI (`~.PCG64.cffi`). This allows the bit generators to be used in numba. @@ -190,7 +187,7 @@ Concepts :maxdepth: 1 generator - legacy mtrand <legacy> + Legacy Generator (RandomState) <legacy> BitGenerators, SeedSequences <bit_generators/index> Features @@ -203,7 +200,6 @@ Features new-or-different Comparing Performance <performance> extending - Reading System Entropy <entropy> Original Source ~~~~~~~~~~~~~~~ diff --git a/doc/source/reference/random/legacy.rst b/doc/source/reference/random/legacy.rst index 04d4d3569..413a42727 100644 --- a/doc/source/reference/random/legacy.rst +++ b/doc/source/reference/random/legacy.rst @@ -4,7 +4,7 @@ Legacy Random Generation ------------------------ -The `~mtrand.RandomState` provides access to +The `RandomState` provides access to 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 @@ -12,19 +12,19 @@ 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 previous versions of NumPy. -`~mtrand.RandomState` adds additional information +`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`, and not the underlying bit generators +`RandomState.get_state`, and not the underlying bit generators `state`, when accessing the state so that these extra values are saved. -Although we provide the `~mt19937.MT19937` BitGenerator for use independent of -`~mtrand.RandomState`, note that its default seeding uses `~SeedSequence` -rather than the legacy seeding algorithm. `~mtrand.RandomState` will use the +Although we provide the `MT19937` BitGenerator for use independent of +`RandomState`, note that its default seeding uses `SeedSequence` +rather than the legacy seeding algorithm. `RandomState` will use the legacy seeding algorithm. The methods to use the legacy seeding algorithm are currently private as the main reason to use them is just to implement -`~mtrand.RandomState`. However, one can reset the state of `~mt19937.MT19937` -using the state of the `~mtrand.RandomState`: +`RandomState`. However, one can reset the state of `MT19937` +using the state of the `RandomState`: .. code-block:: python @@ -47,8 +47,6 @@ using the state of the `~mtrand.RandomState`: rs2.standard_exponential() -.. currentmodule:: numpy.random.mtrand - .. autoclass:: RandomState :exclude-members: diff --git a/doc/source/reference/random/new-or-different.rst b/doc/source/reference/random/new-or-different.rst index 5442f46c9..b3bddb443 100644 --- a/doc/source/reference/random/new-or-different.rst +++ b/doc/source/reference/random/new-or-different.rst @@ -10,9 +10,10 @@ What's New or Different The Box-Muller method used to produce NumPy's normals is no longer available in `Generator`. It is not possible to reproduce the exact random values using ``Generator`` for the normal distribution or any other - distribution that relies on the normal such as the `gamma` or - `standard_t`. If you require bitwise backward compatible - streams, use `RandomState`. + distribution that relies on the normal such as the `Generator.gamma` or + `Generator.standard_t`. If you require bitwise backward compatible + streams, use `RandomState`, i.e., `RandomState.gamma` or + `RandomState.standard_t`. Quick comparison of legacy `mtrand <legacy>`_ to the new `Generator` @@ -20,9 +21,9 @@ Quick comparison of legacy `mtrand <legacy>`_ to the new `Generator` Feature Older Equivalent Notes ------------------ -------------------- ------------- `~.Generator` `~.RandomState` ``Generator`` requires a stream - source, called a `BitGenerator - <bit_generators>` A number of these - are provided. ``RandomState`` uses + source, called a `BitGenerator` + A number of these are provided. + ``RandomState`` uses the Mersenne Twister `~.MT19937` by default, but can also be instantiated with any BitGenerator. @@ -45,9 +46,6 @@ Feature Older Equivalent Notes And in more detail: -* `~.entropy.random_entropy` provides access to the system - source of randomness that is used in cryptographic applications (e.g., - ``/dev/urandom`` on Unix). * Simulate from the complex normal distribution (`~.Generator.complex_normal`) * The normal, exponential and gamma generators use 256-step Ziggurat diff --git a/doc/source/reference/random/parallel.rst b/doc/source/reference/random/parallel.rst index 2f79f22d8..721584014 100644 --- a/doc/source/reference/random/parallel.rst +++ b/doc/source/reference/random/parallel.rst @@ -18,10 +18,10 @@ a `~BitGenerator`. It uses hashing techniques to ensure that low-quality seeds are turned into high quality initial states (at least, with very high probability). -For example, `~mt19937.MT19937` has a state consisting of 624 +For example, `MT19937` has a state consisting of 624 `uint32` integers. A naive way to take a 32-bit integer seed would be to just set the last element of the state to the 32-bit seed and leave the rest 0s. This is -a valid state for `~mt19937.MT19937`, but not a good one. The Mersenne Twister +a valid state for `MT19937`, but not a good one. The Mersenne Twister algorithm `suffers if there are too many 0s`_. Similarly, two adjacent 32-bit integer seeds (i.e. ``12345`` and ``12346``) would produce very similar streams. @@ -91,15 +91,15 @@ territory ([2]_). .. [2] In this calculation, we can ignore the amount of numbers drawn from each stream. Each of the PRNGs we provide has some extra protection built in that avoids overlaps if the `~SeedSequence` pools differ in the - slightest bit. `~pcg64.PCG64` has :math:`2^{127}` separate cycles + slightest bit. `PCG64` has :math:`2^{127}` separate cycles determined by the seed in addition to the position in the :math:`2^{128}` long period for each cycle, so one has to both get on or near the same cycle *and* seed a nearby position in the cycle. - `~philox.Philox` has completely independent cycles determined by the seed. - `~sfc64.SFC64` incorporates a 64-bit counter so every unique seed is at + `Philox` has completely independent cycles determined by the seed. + `SFC64` incorporates a 64-bit counter so every unique seed is at least :math:`2^{64}` iterations away from any other seed. And - finally, `~mt19937.MT19937` has just an unimaginably huge period. Getting - a collision internal to `~SeedSequence` is the way a failure would be + finally, `MT19937` has just an unimaginably huge period. Getting + a collision internal to `SeedSequence` is the way a failure would be observed. .. _`implements an algorithm`: http://www.pcg-random.org/posts/developing-a-seed_seq-alternative.html @@ -113,10 +113,10 @@ territory ([2]_). Independent Streams ------------------- -:class:`~philox.Philox` is a counter-based RNG based which generates values by +`Philox` is a counter-based RNG based which generates values by encrypting an incrementing counter using weak cryptographic primitives. The seed determines the key that is used for the encryption. Unique keys create -unique, independent streams. :class:`~philox.Philox` lets you bypass the +unique, independent streams. `Philox` lets you bypass the seeding algorithm to directly set the 128-bit key. Similar, but different, keys will still create independent streams. diff --git a/doc/source/reference/random/performance.rst b/doc/source/reference/random/performance.rst index 2d5fca496..d70dd064a 100644 --- a/doc/source/reference/random/performance.rst +++ b/doc/source/reference/random/performance.rst @@ -5,21 +5,21 @@ Performance Recommendation ************** -The recommended generator for general use is :class:`~pcg64.PCG64`. It is +The recommended generator for general use is `PCG64`. It is statistically high quality, full-featured, and fast on most platforms, but somewhat slow when compiled for 32-bit processes. -:class:`~philox.Philox` is fairly slow, but its statistical properties have +`Philox` is fairly slow, but its statistical properties have very high quality, and it is easy to get assuredly-independent stream by using unique keys. If that is the style you wish to use for parallel streams, or you are porting from another system that uses that style, then -:class:`~philox.Philox` is your choice. +`Philox` is your choice. -:class:`~sfc64.SFC64` is statistically high quality and very fast. However, it +`SFC64` is statistically high quality and very fast. However, it lacks jumpability. If you are not using that capability and want lots of speed, even on 32-bit processes, this is your choice. -:class:`~mt19937.MT19937` `fails some statistical tests`_ and is not especially +`MT19937` `fails some statistical tests`_ and is not especially fast compared to modern PRNGs. For these reasons, we mostly do not recommend using it on its own, only through the legacy `~.RandomState` for reproducing old results. That said, it has a very long history as a default in @@ -31,20 +31,20 @@ Timings ******* The timings below are the time in ns to produce 1 random value from a -specific distribution. The original :class:`~mt19937.MT19937` generator is +specific distribution. The original `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. The pattern is similar for other, more complex generators. The normal -performance of the legacy :class:`~.RandomState` generator is much +performance of the legacy `RandomState` generator is much lower than the other since it uses the Box-Muller transformation rather than the Ziggurat generator. The performance gap for Exponentials is also large due to the cost of computing the log function to invert the CDF. The column labeled MT19973 is used the same 32-bit generator as -:class:`~.RandomState` but produces random values using -:class:`~Generator`. +`RandomState` but produces random values using +`Generator`. .. csv-table:: :header: ,MT19937,PCG64,Philox,SFC64,RandomState @@ -61,7 +61,7 @@ The column labeled MT19973 is used the same 32-bit generator as Poissons,67.6,52.4,69.2,46.4,78.1 The next table presents the performance in percentage relative to values -generated by the legacy generator, `RandomState(MT19937())`. The overall +generated by the legacy generator, ``RandomState(MT19937())``. The overall performance was computed using a geometric mean. .. csv-table:: |