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‎.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: ea1dff499fc8e995abc2adf43ef4b04f
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config: e598c293df90486acc6b4a8d8021316d
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎_sources/library/random.rst.txt

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@@ -34,10 +34,8 @@ instance of the :class:`random.Random` class. You can instantiate your own
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instances of :class:`Random` to get generators that don't share state.
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Class :class:`Random` can also be subclassed if you want to use a different
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basic generator of your own devising: in that case, override the :meth:`~Random.random`,
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:meth:`~Random.seed`, :meth:`~Random.getstate`, and :meth:`~Random.setstate` methods.
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Optionally, a new generator can supply a :meth:`~Random.getrandbits` method --- this
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allows :meth:`randrange` to produce selections over an arbitrarily large range.
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basic generator of your own devising: see the documentation on that class for
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more details.
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The :mod:`random` module also provides the :class:`SystemRandom` class which
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uses the system function :func:`os.urandom` to generate random numbers
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.. versionchanged:: 3.11
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The *seed* must be one of the following types:
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*NoneType*, :class:`int`, :class:`float`, :class:`str`,
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``None``, :class:`int`, :class:`float`, :class:`str`,
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:class:`bytes`, or :class:`bytearray`.
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.. function:: getstate()
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``None``, :class:`int`, :class:`float`, :class:`str`,
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:class:`bytes`, or :class:`bytearray`.
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Subclasses of :class:`!Random` should override the following methods if they
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wish to make use of a different basic generator:
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.. method:: Random.seed(a=None, version=2)
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Override this method in subclasses to customise the :meth:`~random.seed`
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behaviour of :class:`!Random` instances.
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.. method:: Random.getstate()
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Override this method in subclasses to customise the :meth:`~random.getstate`
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behaviour of :class:`!Random` instances.
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.. method:: Random.setstate(state)
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Override this method in subclasses to customise the :meth:`~random.setstate`
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behaviour of :class:`!Random` instances.
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.. method:: Random.random()
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Override this method in subclasses to customise the :meth:`~random.random`
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behaviour of :class:`!Random` instances.
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Optionally, a custom generator subclass can also supply the following method:
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.. method:: Random.getrandbits(k)
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Override this method in subclasses to customise the
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:meth:`~random.getrandbits` behaviour of :class:`!Random` instances.
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.. class:: SystemRandom([seed])
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Class that uses the :func:`os.urandom` function for generating random numbers
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Basic examples::
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>>> random() # Random float: 0.0 <= x < 1.0
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>>> random() # Random float: 0.0 <= x < 1.0
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0.37444887175646646
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>>> uniform(2.5, 10.0) # Random float: 2.5 <= x <= 10.0
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>>> uniform(2.5, 10.0) # Random float: 2.5 <= x <= 10.0
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3.1800146073117523
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>>> expovariate(1 / 5) # Interval between arrivals averaging 5 seconds
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>>> expovariate(1 / 5) # Interval between arrivals averaging 5 seconds
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5.148957571865031
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>>> randrange(10) # Integer from 0 to 9 inclusive
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>>> randrange(10) # Integer from 0 to 9 inclusive
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7
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>>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
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>>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
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>>> choice(['win', 'lose', 'draw']) # Single random element from a sequence
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>>> choice(['win', 'lose', 'draw']) # Single random element from a sequence
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'draw'
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>>> deck = 'ace two three four'.split()
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>>> shuffle(deck) # Shuffle a list
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>>> shuffle(deck) # Shuffle a list
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>>> deck
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['four', 'two', 'ace', 'three']
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>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
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>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
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[40, 10, 50, 30]
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Simulations::
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including simulation, sampling, shuffling, and cross-validation.
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`Economics Simulation
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<https://nbviewer.jupyter.org/url/norvig.com/ipython/Economics.ipynb>`_
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<https://nbviewer.org/url/norvig.com/ipython/Economics.ipynb>`_
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a simulation of a marketplace by
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`Peter Norvig <https://norvig.com/bio.html>`_ that shows effective
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use of many of the tools and distributions provided by this module
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(gauss, uniform, sample, betavariate, choice, triangular, and randrange).
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`A Concrete Introduction to Probability (using Python)
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<https://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb>`_
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<https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb>`_
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a tutorial by `Peter Norvig <https://norvig.com/bio.html>`_ covering
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the basics of probability theory, how to write simulations, and
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how to perform data analysis using Python.

‎about.html

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最後更新於 Dec 28, 2023 (15:24 UTC)。
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<a href="/bugs.html">Found a bug</a>?
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‎bugs.html

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‎c-api/abstract.html

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‎c-api/allocation.html

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‎c-api/apiabiversion.html

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‎c-api/arg.html

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‎c-api/bool.html

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‎c-api/buffer.html

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