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aaronreidsmith / numpy   python

Repository URL to install this package:

Version: 1.17.4 

/ random / pcg64.pypy3-71-x86_64-linux-gnu.so 

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    PCG64(seed_seq=None)

    BitGenerator for the PCG-64 pseudo-random number generator.

    Parameters
    ----------
    seed : {None, int, array_like[ints], ISeedSequence}, optional
        A seed to initialize the `BitGenerator`. If None, then fresh,
        unpredictable entropy will be pulled from the OS. If an ``int`` or
        ``array_like[ints]`` is passed, then it will be passed to
        `SeedSequence` to derive the initial `BitGenerator` state. One may also
        pass in an implementor of the `ISeedSequence` interface like
        `SeedSequence`.

    Notes
    -----
    PCG-64 is a 128-bit implementation of O'Neill's permutation congruential
    generator ([1]_, [2]_). PCG-64 has a period of :math:`2^{128}` and supports
    advancing an arbitrary number of steps as well as :math:`2^{127}` streams.
    The specific member of the PCG family that we use is PCG XSL RR 128/64
    as described in the paper ([2]_).

    ``PCG64`` provides a capsule containing function pointers that produce
    doubles, and unsigned 32 and 64- bit integers. These are not
    directly consumable in Python and must be consumed by a ``Generator``
    or similar object that supports low-level access.

    Supports the method :meth:`advance` to advance the RNG an arbitrary number of
    steps. The state of the PCG-64 RNG is represented by 2 128-bit unsigned
    integers.

    **State and Seeding**

    The ``PCG64`` state vector consists of 2 unsigned 128-bit values,
    which are represented externally as Python ints. One is the state of the
    PRNG, which is advanced by a linear congruential generator (LCG). The
    second is a fixed odd increment used in the LCG.

    The input seed is processed by `SeedSequence` to generate both values. The
    increment is not independently settable.

    **Parallel Features**

    The preferred way to use a BitGenerator in parallel applications is to use
    the `SeedSequence.spawn` method to obtain entropy values, and to use these
    to generate new BitGenerators:

    >>> from numpy.random import Generator, PCG64, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(PCG64(s)) for s in sg.spawn(10)]

    **Compatibility Guarantee**

    ``PCG64`` makes a guarantee that a fixed seed and will always produce
    the same random integer stream.

    References
    ----------
    .. [1] `"PCG, A Family of Better Random Number Generators"
           <http://www.pcg-random.org/>`_
    .. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient
           Statistically Good Algorithms for Random Number Generation"
           <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>`_
    
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        __pyx_capi__name '%U' is not definednumpy/random/pcg64.c%s (%s:%d)an integer is requiredstringsourcepcg64.pyxMissing type objectat mostat leastjumped__init____del__%d.%d%sbuiltinscython_runtime__builtins__18446744073709551616__init__.pxdBitGeneratornumpy.random.bit_generatortype.pxdnumpydtypeflatiterbroadcastndarrayufuncbit_generator.pxdSeedSequenceSeedlessSequencedoubleLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEwrap_intinit numpy.random.pcg64numpy.random.commonnumpy.random.pcg64.PCG64stateadvance__reduce_cython____setstate_cython__Format string allocated too short.self._bitgen,self.rng_state cannot be converted to a Python object for picklingnumpy.core.umath failed to importndarray is not Fortran contiguousNon-native byte order not supportedFormat string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to importndarray is not C contiguousstate must be for a {0} RNGstate must be a dictcline_in_traceback__setstate_cython__generate_state__reduce_cython__bit_generatorRuntimeErrorImportError__pyx_vtable__has_uint32ValueError__reduce_ex__TypeErroruinteger__setstate____getstate__advanceuint64__reduce____import__formatstaterangenumpyjumpsemptydtype__class__PCG64__test__seed__name____main____init__incget__all__np<;$#pw@|h|4}8,~-@ Ppp`@У00` p@@(PhzRx$(v`FJw?;*3$"D`{\Ot{Xjy &Da$zTAAG HAATzBBE A(D0PIgAX0D(A BBBDl{BBE B(D0A8Q@8C0A(B BBB4pUAAJ X
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        advance(delta)

        Advance the underlying RNG as-if delta draws have occurred.

        Parameters
        ----------
        delta : integer, positive
            Number of draws to advance the RNG. Must be less than the
            size state variable in the underlying RNG.

        Returns
        -------
        self : PCG64
            RNG advanced delta steps

        Notes
        -----
        Advancing a RNG updates the underlying RNG state as-if a given
        number of calls to the underlying RNG have been made. In general
        there is not a one-to-one relationship between the number output
        random values from a particular distribution and the number of
        draws from the core RNG.  This occurs for two reasons:

        * The random values are simulated using a rejection-based method
          and so, on average, more than one value from the underlying
          RNG is required to generate an single draw.
        * The number of bits required to generate a simulated value
          differs from the number of bits generated by the underlying
          RNG.  For example, two 16-bit integer values can be simulated
          from a single draw of a 32-bit RNG.

        Advancing the RNG state resets any pre-computed random numbers.
        This is required to ensure exact reproducibility.
        
        jumped(jumps=1)

        Returns a new bit generator with the state jumped.

        Jumps the state as-if jumps * 210306068529402873165736369884012333109
        random numbers have been generated.

        Parameters
        ----------
        jumps : integer, positive
            Number of times to jump the state of the bit generator returned

        Returns
        -------
        bit_generator : PCG64
            New instance of generator jumped iter times

        Notes
        -----
        The step size is phi-1 when multiplied by 2**128 where phi is the
        golden ratio.
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