4672 lines
613 KiB
Plaintext
4672 lines
613 KiB
Plaintext
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Lœ H‰Þ¿ 1ÀH‹š èöôÿÿH‰�ˆ H…À„þÿÿH‹=?œ H‰ÚH‰ÞèÄóÿÿHƒøH‰ˆ ÀZ[]ÃUSH�ìÈ è5òÿÿH…À„E H‰ÃL‹òš H‹ ˜ H‰æH‹
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á— H‹2� H¿B Ì H‰$H‹E˜ H‰D$è]õÿÿH‰Ì‡ H…À„• H‹|— I‰ÙH‰æL‹Gš H‹
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ð– H‹áŒ H¿A Ï H‰$H‹<Ž H‰D$è õÿÿH‰ƒ‡ H…À„D H‹+— I‰ÙH‰æH‹
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— L‹‡™ H‹�Œ H¿B é H‰$H‰L$èÂôÿÿH‰A‡ H…À„ú H‹á– I‰ÙH‰æL‹\š H‹
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õ‘ H‹FŒ H¿b H‰$H‹“ H‰D$H‹— H‰D$èeôÿÿH‰ì† H…À„� H‹„– I‰ÙH‰æL‹g™ H‹
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ˆ– H‹é‹ H¿¢ > H‰$H‹ü– H‰D$H‹¸– H‰D$H‹D‘ H‰D$H‹x‘ H‰D$ èðóÿÿH‰† H…À„( H‹– I‰ÙH‰æL‹r™ H‹
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;• H‹t‹ H¿B ‹ H‰$H‹7– H‰D$èŸóÿÿH‰6† H…À„× H‹¾• I‰ÙH‰æL‹Y™ H‹
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Ú” H‹#‹ H¿B À H‰$H‹æ• H‰D$èNóÿÿH‰í… H…À„† H‹m• I‰ÙH‰æL‹À˜ H‹
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¡Ž H‹ÒŠ H¿„ É H‰$H‹Ý� H‰D$H‹qŽ H‰D$H‹}• H‰D$èåòÿÿH‰Œ… H…À„ H‹• I‰ÙH‰æL‹˜ H‹
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�� H‹iŠ H¿c ý H‰$H‹¼” H‰D$H‹ • H‰D$èˆòÿÿH‰7… H…À„À H‹§” I‰ÙH‰æL‹ª— H‹
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• H‹ Š H¿B J H‰$H‹Ï” H‰D$è7òÿÿH‰î„ H…À„o H‹V” I‰ÙH‰æL‹1— H‹
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fïöf/Îw¨fInÆH‹D$Hò\ÁòL,øL)ýI9ÄLOýéDÿÿÿf D I9ÔH‰ÐfïÒH‰ÖINÄI� fïÀIMôfïÛH‰ÚòH*ÐI‰÷H‰ÞH‰D$0òH*ÃH)ÎfïäH9ñòH*áI‰öH‰t$PLNñf(Êò^ÈòI*ÞL)òf(ÁòYÃòXœô òD$fïÀòH*ÂH�SÿòYÄò% ô ò\áòYÁfïÉòH*ÊòYÄò^ÁfïÉòXXô f.ȇ½ f(ÈòQÉf(ÁI�VfïäHƒÃòY¶õ òT$(M)÷òXõ òL$ ò\$XòD$fïÀòH*ÂH�PòH*âòYÄfïäòH*ãò^Äf: À òH,ØfïÀH�SòH*Â蛤ÿÿH‹T$0òD$fïÀH)ÚHƒÂòH*Âè{¤ÿÿL‰öò\$H)ÞH‰òòXØfïÀHƒÂòH*Âò\$èP¤ÿÿòXD$J�T;L‰|$8òD$fïÀòH*Âè,¤ÿÿòd$L9t$0òL$ òT$(òXàòd$@�• òY
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‚ð ò^L$fI~ÆòD$èt#ûÿf(Ðf: Ò f/Bò w°ò=hð f/úw¢f(ÇòL$òT$ò^ÂòXÇè5#ûÿòT$fInÎòt$f(Øò\ (ð òYÊò^ÆòYËf(Þò\ ð ò^Ëf/Á‚BÿÿÿHƒÄ(òH,Â[A^à D f/pð H‰ør é Éÿÿf D HƒìH‹?òD$ÿPfWpï èë"ûÿòL$ò$ò¨ï ò\ÁèŸ"ûÿò$HƒÄò^Ðf: Ò
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òH,ÂÃf D é+ßÿÿff. „ SHƒì0f.ÉòD$(òL$ŠM òíð H‰ûf(Ùf/Á‡¬ òäð f/Á†º ò"ï ò$ò^ÁòXÁòD$ ë5 D f(ÑòL$ò^Ðf(Âèñ!ûÿòX$òL$fïäò\Áf/ÄszH‹;ÿSòYrð è%"ûÿòt$ H‹;f(ÎòYÈòXÆòX $f(ùòL$ò^øò\÷ò|$òYÎòL$ÿSòL$òï fïÛò\ÑòYÑò\Ðf/Ó‚VÿÿÿH‹;ÿSò$òD$è"ûÿò$Ýð f(Èò‘î f(áfW%Õí f(ÙòÂÐf(Âf8Üf(Ëò\$(òXÙf(ÃfT™í òX™ï ò$Ý$ÙøßàžzùÝÙÝ\$òT$f.ÒŠÎ ò\lï f(ÂfïÒòÂÚf(èf(àfW-Wí HƒÄ0[f(Ãf8åf(Äà @ H‹?ÿSòXÀò\~í òY ï HƒÄ0[Ãò ï ò-`í òYÁò,$òYÁòXÅòQÀòXÅf(ÈòXÈòQÉò\Áf(ËòXËò^Áf(ÈòYÈòXÀòXÍò^ÈòL$ é4þÿÿ @ ò�î ëˆò
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¾î ò$ò\$è& ûÿò\$òT$é ÿÿÿ D SH‰ûHƒì0ò5Àì òD$ ò\ðf(Æè ûÿòD$(H‹;ÿSf/D$ ƒ» H‹;òD$ÿSòYD$(è. ûÿò vì òT$ò\Øf(ÃòYÃf/Âr~f(Âò\$òT$èI ûÿò\$òD$f(Ãè4 ûÿòL$ò^ÈòX
|
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"ì f: É òH,ÑH…ÒžÁŽdÿÿÿòT$fïöf.Ö›ÀEÁ„À…HÿÿÿHƒÄ0H‰Ð[Ãf D f/Ór
|
|||
|
|
º ëä º ëÚ ó úHƒìHƒÄà while calling a Python object NULL result without error in PyObject_Call Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling %.200s() keywords must be strings %s() got multiple values for keyword argument '%U' __int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python. __int__ returned non-int (type %.200s) %.200s does not export expected C %.8s %.200s C %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s) Interpreter change detected - this module can only be loaded into one interpreter per process. too many values to unpack (expected %zd) Failed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'. numpy.random._bounded_integers %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %s() got an unexpected keyword argument '%U' invalid vtable found for imported type base class '%.200s' is not a heap type extension type '%.200
|
|||
|
|
--
|
|||
|
|
|
|||
|
|
Container for the slow Mersenne Twister pseudo-random number generator.
|
|||
|
|
Consider using a different BitGenerator with the Generator container
|
|||
|
|
instead.
|
|||
|
|
|
|||
|
|
`RandomState` and `Generator` expose a number of methods for generating
|
|||
|
|
random numbers drawn from a variety of probability distributions. In
|
|||
|
|
addition to the distribution-specific arguments, each method takes a
|
|||
|
|
keyword argument `size` that defaults to ``None``. If `size` is ``None``,
|
|||
|
|
then a single value is generated and returned. If `size` is an integer,
|
|||
|
|
then a 1-D array filled with generated values is returned. If `size` is a
|
|||
|
|
tuple, then an array with that shape is filled and returned.
|
|||
|
|
|
|||
|
|
**Compatibility Guarantee**
|
|||
|
|
|
|||
|
|
A fixed bit generator using a fixed seed and a fixed series of calls to
|
|||
|
|
'RandomState' methods using the same parameters will always produce the
|
|||
|
|
same results up to roundoff error except when the values were incorrect.
|
|||
|
|
`RandomState` is effectively frozen and will only receive updates that
|
|||
|
|
are required by changes in the internals of Numpy. More substantial
|
|||
|
|
changes, including algorithmic improvements, are reserved for
|
|||
|
|
`Generator`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
seed : {None, int, array_like, BitGenerator}, optional
|
|||
|
|
Random seed used to initialize the pseudo-random number generator or
|
|||
|
|
an instantized BitGenerator. If an integer or array, used as a seed for
|
|||
|
|
the MT19937 BitGenerator. Values can be any integer between 0 and
|
|||
|
|
2**32 - 1 inclusive, an array (or other sequence) of such integers,
|
|||
|
|
or ``None`` (the default). If `seed` is ``None``, then the `MT19937`
|
|||
|
|
BitGenerator is initialized by reading data from ``/dev/urandom``
|
|||
|
|
(or the Windows analogue) if available or seed from the clock
|
|||
|
|
otherwise.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The Python stdlib module "random" also contains a Mersenne Twister
|
|||
|
|
pseudo-random number generator with a number of methods that are similar
|
|||
|
|
to the ones available in `RandomState`. `RandomState`, besides being
|
|||
|
|
NumPy-aware, has the advantage that it provides a much larger number
|
|||
|
|
of probability distributions to choose from.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
Generator
|
|||
|
|
MT19937
|
|||
|
|
numpy.random.BitGenerator
|
|||
|
|
|
|||
|
|
takes no arguments %.200s() %s (%zd given) takes exactly one argument Bad call flags for CyFunction takes no keyword arguments %.200s() %s needs an argument _cython_3_2_4 <cyfunction %U at %p> __pyx_capi__ __loader__ loader __file__ origin __package__ parent __path__ submodule_search_locations decompress zlib function numpy.random._common variable builtins bool complex numpy dtype flatiter broadcast ndarray generic number unsignedinteger inexact complexfloating flexible character ufunc numpy.random.bit_generator BitGenerator SeedlessSeedSequence SeedlessSequence keywords must be strings Missing type object an integer is required cannot import name %S name '%U' is not defined numpy/random/mtrand.pyx exactly __getstate__ __reduce__ at most at least random randn laplace gumbel logistic lognormal rayleigh numpy/__init__.cython-30.pxd numpy._core._multiarray_umath numpy.core._multiarray_umath _ARRAY_API _ARRAY_API is NULL pointer numpy.import_array bytes numpy.random.mtrand.ranf numpy.random.mtrand.sample logseries zipf poisson negative_binomial set_bit_generator __init__ hypergeometric random_integers get_state assignment uniform __setstate__ dirichlet multinomial random_sample choice randint triangular standard_normal standard_exponential wald vonmises noncentral_chisquare beta standard_cauchy noncentral_f power weibull pareto standard_t standard_gamma shuffle tomaxint numpy.random.mtrand.seed multivariate_normal set_state numpy.PyArray_MultiIterNew2 numpy.PyArray_MultiIterNew3 permutation cython_runtime __builtins__ does not match init numpy.random.mtrand __module__ __vectorcalloffset__ __weaklistoffset__ func_doc __doc__ func_name __name__ __qualname__ func_dict __dict__ func_globals __globals__ func_closure __closure__ func_code __code__ func_defaults __defaults__ __kwdefaults__ __annotations__ _is_coroutine get_bit_generator xÚì½ë~ÛF–/ú=OQ£ì ‘
|
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¼H”:Jÿ _w'nïØ�t�-‹ Rˆ@€
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'bæbÄAêçIꊗ§�ý•˜/³\Œœ çÐtâ> fðú·�˜„ó Î` ™ðÓ@à°ý(J΃Éó4yNÂxS˜ä—‹@œ‡ù)ü�㊹õè2’tƒ
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3j›-‹$Íá
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|
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|
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|
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|
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|
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|
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|
|||
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|
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|
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|
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r Å ? 9 f 5 Y 9 : D B 8 ¤ : : 8 s ( 8 D * ° Ë · = ! t Þ = < ; < 9 8 8 : : ; = 9 8 B $ = C ¨ 9 = å 9 ÞÐûÿÌÐûÿ¹Ðûÿ¦Ðûÿ”Ðûÿ¹/ýÿ§/ýÿ”/ýÿ�/ýÿo/ýÿ<ýÿð;ýÿÞ;ýÿÌ;ýÿº;ýÿ
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cýÿøbýÿåbýÿÒbýÿÀbýÿ>°ýÿ,°ýÿ°ýÿ°ýÿô¯ýÿ›9þÿ†9þÿp9þÿZ9þÿD9þÿ/9þÿ
|
|||
|
|
This is an alias of `random_sample`. See `random_sample` for the complete
|
|||
|
|
documentation.
|
|||
|
|
|
|||
|
|
This is an alias of `random_sample`. See `random_sample` for the complete
|
|||
|
|
documentation.
|
|||
|
|
|
|||
|
|
Sets the singleton RandomState's bit generator
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
bitgen
|
|||
|
|
A bit generator instance
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The singleton RandomState provides the random variate generators in the
|
|||
|
|
``numpy.random``namespace. This function, and its counterpart get method,
|
|||
|
|
provides a path to hot-swap the default MT19937 bit generator with a
|
|||
|
|
user provided alternative. These function are intended to provide
|
|||
|
|
a continuous path where a single underlying bit generator can be
|
|||
|
|
used both with an instance of ``Generator`` and with the singleton
|
|||
|
|
instance of RandomState.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
get_bit_generator
|
|||
|
|
numpy.random.Generator
|
|||
|
|
|
|||
|
|
Returns the singleton RandomState's bit generator
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
BitGenerator
|
|||
|
|
The bit generator that underlies the singleton RandomState instance
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The singleton RandomState provides the random variate generators in the
|
|||
|
|
``numpy.random`` namespace. This function, and its counterpart set method,
|
|||
|
|
provides a path to hot-swap the default MT19937 bit generator with a
|
|||
|
|
user provided alternative. These function are intended to provide
|
|||
|
|
a continuous path where a single underlying bit generator can be
|
|||
|
|
used both with an instance of ``Generator`` and with the singleton
|
|||
|
|
instance of RandomState.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
set_bit_generator
|
|||
|
|
numpy.random.Generator
|
|||
|
|
|
|||
|
|
seed(seed=None)
|
|||
|
|
|
|||
|
|
Reseed the singleton RandomState instance.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
This is a convenience, legacy function that exists to support
|
|||
|
|
older code that uses the singleton RandomState. Best practice
|
|||
|
|
is to use a dedicated ``Generator`` instance rather than
|
|||
|
|
the random variate generation methods exposed directly in
|
|||
|
|
the random module.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
numpy.random.Generator
|
|||
|
|
|
|||
|
|
permutation(x)
|
|||
|
|
|
|||
|
|
Randomly permute a sequence, or return a permuted range.
|
|||
|
|
|
|||
|
|
If `x` is a multi-dimensional array, it is only shuffled along its
|
|||
|
|
first index.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the
|
|||
|
|
`~numpy.random.Generator.permutation`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
x : int or array_like
|
|||
|
|
If `x` is an integer, randomly permute ``np.arange(x)``.
|
|||
|
|
If `x` is an array, make a copy and shuffle the elements
|
|||
|
|
randomly.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray
|
|||
|
|
Permuted sequence or array range.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.permutation: which should be used for new code.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> np.random.permutation(10)
|
|||
|
|
array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) # random
|
|||
|
|
|
|||
|
|
>>> np.random.permutation([1, 4, 9, 12, 15])
|
|||
|
|
array([15, 1, 9, 4, 12]) # random
|
|||
|
|
|
|||
|
|
>>> arr = np.arange(9).reshape((3, 3))
|
|||
|
|
>>> np.random.permutation(arr)
|
|||
|
|
array([[6, 7, 8], # random
|
|||
|
|
[0, 1, 2],
|
|||
|
|
[3, 4, 5]])
|
|||
|
|
|
|||
|
|
|
|||
|
|
shuffle(x)
|
|||
|
|
|
|||
|
|
Modify a sequence in-place by shuffling its contents.
|
|||
|
|
|
|||
|
|
This function only shuffles the array along the first axis of a
|
|||
|
|
multi-dimensional array. The order of sub-arrays is changed but
|
|||
|
|
their contents remains the same.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.shuffle`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
x : ndarray or MutableSequence
|
|||
|
|
The array, list or mutable sequence to be shuffled.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
None
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.shuffle: which should be used for new code.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> arr = np.arange(10)
|
|||
|
|
>>> np.random.shuffle(arr)
|
|||
|
|
>>> arr
|
|||
|
|
[1 7 5 2 9 4 3 6 0 8] # random
|
|||
|
|
|
|||
|
|
Multi-dimensional arrays are only shuffled along the first axis:
|
|||
|
|
|
|||
|
|
>>> arr = np.arange(9).reshape((3, 3))
|
|||
|
|
>>> np.random.shuffle(arr)
|
|||
|
|
>>> arr
|
|||
|
|
array([[3, 4, 5], # random
|
|||
|
|
[6, 7, 8],
|
|||
|
|
[0, 1, 2]])
|
|||
|
|
|
|||
|
|
|
|||
|
|
dirichlet(alpha, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from the Dirichlet distribution.
|
|||
|
|
|
|||
|
|
Draw `size` samples of dimension k from a Dirichlet distribution. A
|
|||
|
|
Dirichlet-distributed random variable can be seen as a multivariate
|
|||
|
|
generalization of a Beta distribution. The Dirichlet distribution
|
|||
|
|
is a conjugate prior of a multinomial distribution in Bayesian
|
|||
|
|
inference.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.dirichlet`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
alpha : sequence of floats, length k
|
|||
|
|
Parameter of the distribution (length ``k`` for sample of
|
|||
|
|
length ``k``).
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n)``, then
|
|||
|
|
``m * n * k`` samples are drawn. Default is None, in which case a
|
|||
|
|
vector of length ``k`` is returned.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
samples : ndarray,
|
|||
|
|
The drawn samples, of shape ``(size, k)``.
|
|||
|
|
|
|||
|
|
Raises
|
|||
|
|
------
|
|||
|
|
ValueError
|
|||
|
|
If any value in ``alpha`` is less than or equal to zero
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.dirichlet: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The Dirichlet distribution is a distribution over vectors
|
|||
|
|
:math:`x` that fulfil the conditions :math:`x_i>0` and
|
|||
|
|
:math:`\sum_{i=1}^k x_i = 1`.
|
|||
|
|
|
|||
|
|
The probability density function :math:`p` of a
|
|||
|
|
Dirichlet-distributed random vector :math:`X` is
|
|||
|
|
proportional to
|
|||
|
|
|
|||
|
|
.. math:: p(x) \propto \prod_{i=1}^{k}{x^{\alpha_i-1}_i},
|
|||
|
|
|
|||
|
|
where :math:`\alpha` is a vector containing the positive
|
|||
|
|
concentration parameters.
|
|||
|
|
|
|||
|
|
The method uses the following property for computation: let :math:`Y`
|
|||
|
|
be a random vector which has components that follow a standard gamma
|
|||
|
|
distribution, then :math:`X = \frac{1}{\sum_{i=1}^k{Y_i}} Y`
|
|||
|
|
is Dirichlet-distributed
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] David McKay, "Information Theory, Inference and Learning
|
|||
|
|
Algorithms," chapter 23,
|
|||
|
|
https://www.inference.org.uk/mackay/itila/
|
|||
|
|
.. [2] Wikipedia, "Dirichlet distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Dirichlet_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Taking an example cited in Wikipedia, this distribution can be used if
|
|||
|
|
one wanted to cut strings (each of initial length 1.0) into K pieces
|
|||
|
|
with different lengths, where each piece had, on average, a designated
|
|||
|
|
average length, but allowing some variation in the relative sizes of
|
|||
|
|
the pieces.
|
|||
|
|
|
|||
|
|
>>> s = np.random.dirichlet((10, 5, 3), 20).transpose()
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> plt.barh(range(20), s[0])
|
|||
|
|
>>> plt.barh(range(20), s[1], left=s[0], color='g')
|
|||
|
|
>>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')
|
|||
|
|
>>> plt.title("Lengths of Strings")
|
|||
|
|
|
|||
|
|
|
|||
|
|
multinomial(n, pvals, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a multinomial distribution.
|
|||
|
|
|
|||
|
|
The multinomial distribution is a multivariate generalization of the
|
|||
|
|
binomial distribution. Take an experiment with one of ``p``
|
|||
|
|
possible outcomes. An example of such an experiment is throwing a dice,
|
|||
|
|
where the outcome can be 1 through 6. Each sample drawn from the
|
|||
|
|
distribution represents `n` such experiments. Its values,
|
|||
|
|
``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the
|
|||
|
|
outcome was ``i``.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.multinomial`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
.. warning::
|
|||
|
|
This function defaults to the C-long dtype, which is 32bit on windows
|
|||
|
|
and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones).
|
|||
|
|
Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms
|
|||
|
|
and 64bit on 64bit platforms.
|
|||
|
|
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
n : int
|
|||
|
|
Number of experiments.
|
|||
|
|
pvals : sequence of floats, length p
|
|||
|
|
Probabilities of each of the ``p`` different outcomes. These
|
|||
|
|
must sum to 1 (however, the last element is always assumed to
|
|||
|
|
account for the remaining probability, as long as
|
|||
|
|
``sum(pvals[:-1]) <= 1)``.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. Default is None, in which case a
|
|||
|
|
single value is returned.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray
|
|||
|
|
The drawn samples, of shape *size*, if that was provided. If not,
|
|||
|
|
the shape is ``(N,)``.
|
|||
|
|
|
|||
|
|
In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
|
|||
|
|
value drawn from the distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.multinomial: which should be used for new code.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Throw a dice 20 times:
|
|||
|
|
|
|||
|
|
>>> np.random.multinomial(20, [1/6.]*6, size=1)
|
|||
|
|
array([[4, 1, 7, 5, 2, 1]]) # random
|
|||
|
|
|
|||
|
|
It landed 4 times on 1, once on 2, etc.
|
|||
|
|
|
|||
|
|
Now, throw the dice 20 times, and 20 times again:
|
|||
|
|
|
|||
|
|
>>> np.random.multinomial(20, [1/6.]*6, size=2)
|
|||
|
|
array([[3, 4, 3, 3, 4, 3], # random
|
|||
|
|
[2, 4, 3, 4, 0, 7]])
|
|||
|
|
|
|||
|
|
For the first run, we threw 3 times 1, 4 times 2, etc. For the second,
|
|||
|
|
we threw 2 times 1, 4 times 2, etc.
|
|||
|
|
|
|||
|
|
A loaded die is more likely to land on number 6:
|
|||
|
|
|
|||
|
|
>>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
|
|||
|
|
array([11, 16, 14, 17, 16, 26]) # random
|
|||
|
|
|
|||
|
|
The probability inputs should be normalized. As an implementation
|
|||
|
|
detail, the value of the last entry is ignored and assumed to take
|
|||
|
|
up any leftover probability mass, but this should not be relied on.
|
|||
|
|
A biased coin which has twice as much weight on one side as on the
|
|||
|
|
other should be sampled like so:
|
|||
|
|
|
|||
|
|
>>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3]) # RIGHT
|
|||
|
|
array([38, 62]) # random
|
|||
|
|
|
|||
|
|
not like:
|
|||
|
|
|
|||
|
|
>>> np.random.multinomial(100, [1.0, 2.0]) # WRONG
|
|||
|
|
Traceback (most recent call last):
|
|||
|
|
ValueError: pvals < 0, pvals > 1 or pvals contains NaNs
|
|||
|
|
|
|||
|
|
|
|||
|
|
multivariate_normal(mean, cov, size=None, check_valid='warn', tol=1e-8)
|
|||
|
|
|
|||
|
|
Draw random samples from a multivariate normal distribution.
|
|||
|
|
|
|||
|
|
The multivariate normal, multinormal or Gaussian distribution is a
|
|||
|
|
generalization of the one-dimensional normal distribution to higher
|
|||
|
|
dimensions. Such a distribution is specified by its mean and
|
|||
|
|
covariance matrix. These parameters are analogous to the mean
|
|||
|
|
(average or "center") and variance (standard deviation, or "width,"
|
|||
|
|
squared) of the one-dimensional normal distribution.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the
|
|||
|
|
`~numpy.random.Generator.multivariate_normal`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
mean : 1-D array_like, of length N
|
|||
|
|
Mean of the N-dimensional distribution.
|
|||
|
|
cov : 2-D array_like, of shape (N, N)
|
|||
|
|
Covariance matrix of the distribution. It must be symmetric and
|
|||
|
|
positive-semidefinite for proper sampling.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are
|
|||
|
|
generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because
|
|||
|
|
each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.
|
|||
|
|
If no shape is specified, a single (`N`-D) sample is returned.
|
|||
|
|
check_valid : { 'warn', 'raise', 'ignore' }, optional
|
|||
|
|
Behavior when the covariance matrix is not positive semidefinite.
|
|||
|
|
tol : float, optional
|
|||
|
|
Tolerance when checking the singular values in covariance matrix.
|
|||
|
|
cov is cast to double before the check.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray
|
|||
|
|
The drawn samples, of shape *size*, if that was provided. If not,
|
|||
|
|
the shape is ``(N,)``.
|
|||
|
|
|
|||
|
|
In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
|
|||
|
|
value drawn from the distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.multivariate_normal: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The mean is a coordinate in N-dimensional space, which represents the
|
|||
|
|
location where samples are most likely to be generated. This is
|
|||
|
|
analogous to the peak of the bell curve for the one-dimensional or
|
|||
|
|
univariate normal distribution.
|
|||
|
|
|
|||
|
|
Covariance indicates the level to which two variables vary together.
|
|||
|
|
From the multivariate normal distribution, we draw N-dimensional
|
|||
|
|
samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix
|
|||
|
|
element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.
|
|||
|
|
The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its
|
|||
|
|
"spread").
|
|||
|
|
|
|||
|
|
Instead of specifying the full covariance matrix, popular
|
|||
|
|
approximations include:
|
|||
|
|
|
|||
|
|
- Spherical covariance (`cov` is a multiple of the identity matrix)
|
|||
|
|
- Diagonal covariance (`cov` has non-negative elements, and only on
|
|||
|
|
the diagonal)
|
|||
|
|
|
|||
|
|
This geometrical property can be seen in two dimensions by plotting
|
|||
|
|
generated data-points:
|
|||
|
|
|
|||
|
|
>>> mean = [0, 0]
|
|||
|
|
>>> cov = [[1, 0], [0, 100]] # diagonal covariance
|
|||
|
|
|
|||
|
|
Diagonal covariance means that the variables are independent, and the
|
|||
|
|
probability density contours have their axes aligned with the coordinate axes:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> x, y = np.random.multivariate_normal(mean, cov, 5000).T
|
|||
|
|
>>> plt.plot(x, y, 'x')
|
|||
|
|
>>> plt.axis('equal')
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
Note that the covariance matrix must be positive semidefinite (a.k.a.
|
|||
|
|
nonnegative-definite). Otherwise, the behavior of this method is
|
|||
|
|
undefined and backwards compatibility is not guaranteed.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Papoulis, A., "Probability, Random Variables, and Stochastic
|
|||
|
|
Processes," 3rd ed., New York: McGraw-Hill, 1991.
|
|||
|
|
.. [2] Duda, R. O., Hart, P. E., and Stork, D. G., "Pattern
|
|||
|
|
Classification," 2nd ed., New York: Wiley, 2001.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> mean = (1, 2)
|
|||
|
|
>>> cov = [[1, 0], [0, 1]]
|
|||
|
|
>>> x = np.random.multivariate_normal(mean, cov, (3, 3))
|
|||
|
|
>>> x.shape
|
|||
|
|
(3, 3, 2)
|
|||
|
|
|
|||
|
|
Here we generate 800 samples from the bivariate normal distribution
|
|||
|
|
with mean [0, 0] and covariance matrix [[6, -3], [-3, 3.5]]. The
|
|||
|
|
expected variances of the first and second components of the sample
|
|||
|
|
are 6 and 3.5, respectively, and the expected correlation
|
|||
|
|
coefficient is -3/sqrt(6*3.5) ≈ -0.65465.
|
|||
|
|
|
|||
|
|
>>> cov = np.array([[6, -3], [-3, 3.5]])
|
|||
|
|
>>> pts = np.random.multivariate_normal([0, 0], cov, size=800)
|
|||
|
|
|
|||
|
|
Check that the mean, covariance, and correlation coefficient of the
|
|||
|
|
sample are close to the expected values:
|
|||
|
|
|
|||
|
|
>>> pts.mean(axis=0)
|
|||
|
|
array([ 0.0326911 , -0.01280782]) # may vary
|
|||
|
|
>>> np.cov(pts.T)
|
|||
|
|
array([[ 5.96202397, -2.85602287],
|
|||
|
|
[-2.85602287, 3.47613949]]) # may vary
|
|||
|
|
>>> np.corrcoef(pts.T)[0, 1]
|
|||
|
|
-0.6273591314603949 # may vary
|
|||
|
|
|
|||
|
|
We can visualize this data with a scatter plot. The orientation
|
|||
|
|
of the point cloud illustrates the negative correlation of the
|
|||
|
|
components of this sample.
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> plt.plot(pts[:, 0], pts[:, 1], '.', alpha=0.5)
|
|||
|
|
>>> plt.axis('equal')
|
|||
|
|
>>> plt.grid()
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
logseries(p, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a logarithmic series distribution.
|
|||
|
|
|
|||
|
|
Samples are drawn from a log series distribution with specified
|
|||
|
|
shape parameter, 0 <= ``p`` < 1.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.logseries`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
p : float or array_like of floats
|
|||
|
|
Shape parameter for the distribution. Must be in the range [0, 1).
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``p`` is a scalar. Otherwise,
|
|||
|
|
``np.array(p).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized logarithmic series distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.logser : probability density function, distribution or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.logseries: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density for the Log Series distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(k) = \frac{-p^k}{k \ln(1-p)},
|
|||
|
|
|
|||
|
|
where p = probability.
|
|||
|
|
|
|||
|
|
The log series distribution is frequently used to represent species
|
|||
|
|
richness and occurrence, first proposed by Fisher, Corbet, and
|
|||
|
|
Williams in 1943 [2]. It may also be used to model the numbers of
|
|||
|
|
occupants seen in cars [3].
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional
|
|||
|
|
species diversity through the log series distribution of
|
|||
|
|
occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,
|
|||
|
|
Volume 5, Number 5, September 1999 , pp. 187-195(9).
|
|||
|
|
.. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The
|
|||
|
|
relation between the number of species and the number of
|
|||
|
|
individuals in a random sample of an animal population.
|
|||
|
|
Journal of Animal Ecology, 12:42-58.
|
|||
|
|
.. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small
|
|||
|
|
Data Sets, CRC Press, 1994.
|
|||
|
|
.. [4] Wikipedia, "Logarithmic distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Logarithmic_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> a = .6
|
|||
|
|
>>> s = np.random.logseries(a, 10000)
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, ignored = plt.hist(s)
|
|||
|
|
|
|||
|
|
# plot against distribution
|
|||
|
|
|
|||
|
|
>>> def logseries(k, p):
|
|||
|
|
... return -p**k/(k*np.log(1-p))
|
|||
|
|
>>> plt.plot(bins, logseries(bins, a)*count.max()/
|
|||
|
|
... logseries(bins, a).max(), 'r')
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
hypergeometric(ngood, nbad, nsample, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Hypergeometric distribution.
|
|||
|
|
|
|||
|
|
Samples are drawn from a hypergeometric distribution with specified
|
|||
|
|
parameters, `ngood` (ways to make a good selection), `nbad` (ways to make
|
|||
|
|
a bad selection), and `nsample` (number of items sampled, which is less
|
|||
|
|
than or equal to the sum ``ngood + nbad``).
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the
|
|||
|
|
`~numpy.random.Generator.hypergeometric`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
ngood : int or array_like of ints
|
|||
|
|
Number of ways to make a good selection. Must be nonnegative.
|
|||
|
|
nbad : int or array_like of ints
|
|||
|
|
Number of ways to make a bad selection. Must be nonnegative.
|
|||
|
|
nsample : int or array_like of ints
|
|||
|
|
Number of items sampled. Must be at least 1 and at most
|
|||
|
|
``ngood + nbad``.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if `ngood`, `nbad`, and `nsample`
|
|||
|
|
are all scalars. Otherwise, ``np.broadcast(ngood, nbad, nsample).size``
|
|||
|
|
samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized hypergeometric distribution. Each
|
|||
|
|
sample is the number of good items within a randomly selected subset of
|
|||
|
|
size `nsample` taken from a set of `ngood` good items and `nbad` bad items.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.hypergeom : probability density function, distribution or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.hypergeometric: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability mass function (PMF) for the Hypergeometric distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x) = \frac{\binom{g}{x}\binom{b}{n-x}}{\binom{g+b}{n}},
|
|||
|
|
|
|||
|
|
where :math:`0 \le x \le n` and :math:`n-b \le x \le g`
|
|||
|
|
|
|||
|
|
for P(x) the probability of ``x`` good results in the drawn sample,
|
|||
|
|
g = `ngood`, b = `nbad`, and n = `nsample`.
|
|||
|
|
|
|||
|
|
Consider an urn with black and white marbles in it, `ngood` of them
|
|||
|
|
are black and `nbad` are white. If you draw `nsample` balls without
|
|||
|
|
replacement, then the hypergeometric distribution describes the
|
|||
|
|
distribution of black balls in the drawn sample.
|
|||
|
|
|
|||
|
|
Note that this distribution is very similar to the binomial
|
|||
|
|
distribution, except that in this case, samples are drawn without
|
|||
|
|
replacement, whereas in the Binomial case samples are drawn with
|
|||
|
|
replacement (or the sample space is infinite). As the sample space
|
|||
|
|
becomes large, this distribution approaches the binomial.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Lentner, Marvin, "Elementary Applied Statistics", Bogden
|
|||
|
|
and Quigley, 1972.
|
|||
|
|
.. [2] Weisstein, Eric W. "Hypergeometric Distribution." From
|
|||
|
|
MathWorld--A Wolfram Web Resource.
|
|||
|
|
https://mathworld.wolfram.com/HypergeometricDistribution.html
|
|||
|
|
.. [3] Wikipedia, "Hypergeometric distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Hypergeometric_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> ngood, nbad, nsamp = 100, 2, 10
|
|||
|
|
# number of good, number of bad, and number of samples
|
|||
|
|
>>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)
|
|||
|
|
>>> from matplotlib.pyplot import hist
|
|||
|
|
>>> hist(s)
|
|||
|
|
# note that it is very unlikely to grab both bad items
|
|||
|
|
|
|||
|
|
Suppose you have an urn with 15 white and 15 black marbles.
|
|||
|
|
If you pull 15 marbles at random, how likely is it that
|
|||
|
|
12 or more of them are one color?
|
|||
|
|
|
|||
|
|
>>> s = np.random.hypergeometric(15, 15, 15, 100000)
|
|||
|
|
>>> sum(s>=12)/100000. + sum(s<=3)/100000.
|
|||
|
|
# answer = 0.003 ... pretty unlikely!
|
|||
|
|
|
|||
|
|
|
|||
|
|
geometric(p, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from the geometric distribution.
|
|||
|
|
|
|||
|
|
Bernoulli trials are experiments with one of two outcomes:
|
|||
|
|
success or failure (an example of such an experiment is flipping
|
|||
|
|
a coin). The geometric distribution models the number of trials
|
|||
|
|
that must be run in order to achieve success. It is therefore
|
|||
|
|
supported on the positive integers, ``k = 1, 2, ...``.
|
|||
|
|
|
|||
|
|
The probability mass function of the geometric distribution is
|
|||
|
|
|
|||
|
|
.. math:: f(k) = (1 - p)^{k - 1} p
|
|||
|
|
|
|||
|
|
where `p` is the probability of success of an individual trial.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.geometric`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
p : float or array_like of floats
|
|||
|
|
The probability of success of an individual trial.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``p`` is a scalar. Otherwise,
|
|||
|
|
``np.array(p).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized geometric distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.geometric: which should be used for new code.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw ten thousand values from the geometric distribution,
|
|||
|
|
with the probability of an individual success equal to 0.35:
|
|||
|
|
|
|||
|
|
>>> z = np.random.geometric(p=0.35, size=10000)
|
|||
|
|
|
|||
|
|
How many trials succeeded after a single run?
|
|||
|
|
|
|||
|
|
>>> (z == 1).sum() / 10000.
|
|||
|
|
0.34889999999999999 #random
|
|||
|
|
|
|||
|
|
|
|||
|
|
zipf(a, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Zipf distribution.
|
|||
|
|
|
|||
|
|
Samples are drawn from a Zipf distribution with specified parameter
|
|||
|
|
`a` > 1.
|
|||
|
|
|
|||
|
|
The Zipf distribution (also known as the zeta distribution) is a
|
|||
|
|
discrete probability distribution that satisfies Zipf's law: the
|
|||
|
|
frequency of an item is inversely proportional to its rank in a
|
|||
|
|
frequency table.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.zipf`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : float or array_like of floats
|
|||
|
|
Distribution parameter. Must be greater than 1.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``a`` is a scalar. Otherwise,
|
|||
|
|
``np.array(a).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Zipf distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.zipf : probability density function, distribution, or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.zipf: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability mass function (PMF) for the Zipf distribution is
|
|||
|
|
|
|||
|
|
.. math:: p(k) = \frac{k^{-a}}{\zeta(a)},
|
|||
|
|
|
|||
|
|
for integers :math:`k \geq 1`, where :math:`\zeta` is the Riemann Zeta
|
|||
|
|
function.
|
|||
|
|
|
|||
|
|
It is named for the American linguist George Kingsley Zipf, who noted
|
|||
|
|
that the frequency of any word in a sample of a language is inversely
|
|||
|
|
proportional to its rank in the frequency table.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Zipf, G. K., "Selected Studies of the Principle of Relative
|
|||
|
|
Frequency in Language," Cambridge, MA: Harvard Univ. Press,
|
|||
|
|
1932.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> a = 4.0
|
|||
|
|
>>> n = 20000
|
|||
|
|
>>> s = np.random.zipf(a, n)
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with
|
|||
|
|
the expected histogram based on the probability
|
|||
|
|
density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> from scipy.special import zeta # doctest: +SKIP
|
|||
|
|
|
|||
|
|
`bincount` provides a fast histogram for small integers.
|
|||
|
|
|
|||
|
|
>>> count = np.bincount(s)
|
|||
|
|
>>> k = np.arange(1, s.max() + 1)
|
|||
|
|
|
|||
|
|
>>> plt.bar(k, count[1:], alpha=0.5, label='sample count')
|
|||
|
|
>>> plt.plot(k, n*(k**-a)/zeta(a), 'k.-', alpha=0.5,
|
|||
|
|
... label='expected count') # doctest: +SKIP
|
|||
|
|
>>> plt.semilogy()
|
|||
|
|
>>> plt.grid(alpha=0.4)
|
|||
|
|
>>> plt.legend()
|
|||
|
|
>>> plt.title(f'Zipf sample, a={a}, size={n}')
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
poisson(lam=1.0, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Poisson distribution.
|
|||
|
|
|
|||
|
|
The Poisson distribution is the limit of the binomial distribution
|
|||
|
|
for large N.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.poisson`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
lam : float or array_like of floats
|
|||
|
|
Expected number of events occurring in a fixed-time interval,
|
|||
|
|
must be >= 0. A sequence must be broadcastable over the requested
|
|||
|
|
size.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``lam`` is a scalar. Otherwise,
|
|||
|
|
``np.array(lam).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Poisson distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.poisson: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability mass function (PMF) of Poisson distribution is
|
|||
|
|
|
|||
|
|
.. math:: f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}
|
|||
|
|
|
|||
|
|
For events with an expected separation :math:`\lambda` the Poisson
|
|||
|
|
distribution :math:`f(k; \lambda)` describes the probability of
|
|||
|
|
:math:`k` events occurring within the observed
|
|||
|
|
interval :math:`\lambda`.
|
|||
|
|
|
|||
|
|
Because the output is limited to the range of the C int64 type, a
|
|||
|
|
ValueError is raised when `lam` is within 10 sigma of the maximum
|
|||
|
|
representable value.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Weisstein, Eric W. "Poisson Distribution."
|
|||
|
|
From MathWorld--A Wolfram Web Resource.
|
|||
|
|
https://mathworld.wolfram.com/PoissonDistribution.html
|
|||
|
|
.. [2] Wikipedia, "Poisson distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Poisson_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> import numpy as np
|
|||
|
|
>>> s = np.random.poisson(5, 10000)
|
|||
|
|
|
|||
|
|
Display histogram of the sample:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, ignored = plt.hist(s, 14, density=True)
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
Draw each 100 values for lambda 100 and 500:
|
|||
|
|
|
|||
|
|
>>> s = np.random.poisson(lam=(100., 500.), size=(100, 2))
|
|||
|
|
|
|||
|
|
|
|||
|
|
negative_binomial(n, p, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a negative binomial distribution.
|
|||
|
|
|
|||
|
|
Samples are drawn from a negative binomial distribution with specified
|
|||
|
|
parameters, `n` successes and `p` probability of success where `n`
|
|||
|
|
is > 0 and `p` is in the interval [0, 1].
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the
|
|||
|
|
`~numpy.random.Generator.negative_binomial`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
n : float or array_like of floats
|
|||
|
|
Parameter of the distribution, > 0.
|
|||
|
|
p : float or array_like of floats
|
|||
|
|
Parameter of the distribution, >= 0 and <=1.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``n`` and ``p`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(n, p).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized negative binomial distribution,
|
|||
|
|
where each sample is equal to N, the number of failures that
|
|||
|
|
occurred before a total of n successes was reached.
|
|||
|
|
|
|||
|
|
.. warning::
|
|||
|
|
This function returns the C-long dtype, which is 32bit on windows
|
|||
|
|
and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones).
|
|||
|
|
Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms
|
|||
|
|
and 64bit on 64bit platforms.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.negative_binomial: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability mass function of the negative binomial distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(N;n,p) = \frac{\Gamma(N+n)}{N!\Gamma(n)}p^{n}(1-p)^{N},
|
|||
|
|
|
|||
|
|
where :math:`n` is the number of successes, :math:`p` is the
|
|||
|
|
probability of success, :math:`N+n` is the number of trials, and
|
|||
|
|
:math:`\Gamma` is the gamma function. When :math:`n` is an integer,
|
|||
|
|
:math:`\frac{\Gamma(N+n)}{N!\Gamma(n)} = \binom{N+n-1}{N}`, which is
|
|||
|
|
the more common form of this term in the pmf. The negative
|
|||
|
|
binomial distribution gives the probability of N failures given n
|
|||
|
|
successes, with a success on the last trial.
|
|||
|
|
|
|||
|
|
If one throws a die repeatedly until the third time a "1" appears,
|
|||
|
|
then the probability distribution of the number of non-"1"s that
|
|||
|
|
appear before the third "1" is a negative binomial distribution.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Weisstein, Eric W. "Negative Binomial Distribution." From
|
|||
|
|
MathWorld--A Wolfram Web Resource.
|
|||
|
|
https://mathworld.wolfram.com/NegativeBinomialDistribution.html
|
|||
|
|
.. [2] Wikipedia, "Negative binomial distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Negative_binomial_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
A real world example. A company drills wild-cat oil
|
|||
|
|
exploration wells, each with an estimated probability of
|
|||
|
|
success of 0.1. What is the probability of having one success
|
|||
|
|
for each successive well, that is what is the probability of a
|
|||
|
|
single success after drilling 5 wells, after 6 wells, etc.?
|
|||
|
|
|
|||
|
|
>>> s = np.random.negative_binomial(1, 0.1, 100000)
|
|||
|
|
>>> for i in range(1, 11): # doctest: +SKIP
|
|||
|
|
... probability = sum(s<i) / 100000.
|
|||
|
|
... print(i, "wells drilled, probability of one success =", probability)
|
|||
|
|
|
|||
|
|
|
|||
|
|
binomial(n, p, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a binomial distribution.
|
|||
|
|
|
|||
|
|
Samples are drawn from a binomial distribution with specified
|
|||
|
|
parameters, n trials and p probability of success where
|
|||
|
|
n an integer >= 0 and p is in the interval [0,1]. (n may be
|
|||
|
|
input as a float, but it is truncated to an integer in use)
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.binomial`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
n : int or array_like of ints
|
|||
|
|
Parameter of the distribution, >= 0. Floats are also accepted,
|
|||
|
|
but they will be truncated to integers.
|
|||
|
|
p : float or array_like of floats
|
|||
|
|
Parameter of the distribution, >= 0 and <=1.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``n`` and ``p`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(n, p).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized binomial distribution, where
|
|||
|
|
each sample is equal to the number of successes over the n trials.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.binom : probability density function, distribution or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.binomial: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability mass function (PMF) for the binomial distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(N) = \binom{n}{N}p^N(1-p)^{n-N},
|
|||
|
|
|
|||
|
|
where :math:`n` is the number of trials, :math:`p` is the probability
|
|||
|
|
of success, and :math:`N` is the number of successes.
|
|||
|
|
|
|||
|
|
When estimating the standard error of a proportion in a population by
|
|||
|
|
using a random sample, the normal distribution works well unless the
|
|||
|
|
product p*n <=5, where p = population proportion estimate, and n =
|
|||
|
|
number of samples, in which case the binomial distribution is used
|
|||
|
|
instead. For example, a sample of 15 people shows 4 who are left
|
|||
|
|
handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
|
|||
|
|
so the binomial distribution should be used in this case.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Dalgaard, Peter, "Introductory Statistics with R",
|
|||
|
|
Springer-Verlag, 2002.
|
|||
|
|
.. [2] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
|
|||
|
|
Fifth Edition, 2002.
|
|||
|
|
.. [3] Lentner, Marvin, "Elementary Applied Statistics", Bogden
|
|||
|
|
and Quigley, 1972.
|
|||
|
|
.. [4] Weisstein, Eric W. "Binomial Distribution." From MathWorld--A
|
|||
|
|
Wolfram Web Resource.
|
|||
|
|
https://mathworld.wolfram.com/BinomialDistribution.html
|
|||
|
|
.. [5] Wikipedia, "Binomial distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Binomial_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> n, p = 10, .5 # number of trials, probability of each trial
|
|||
|
|
>>> s = np.random.binomial(n, p, 1000)
|
|||
|
|
# result of flipping a coin 10 times, tested 1000 times.
|
|||
|
|
|
|||
|
|
A real world example. A company drills 9 wild-cat oil exploration
|
|||
|
|
wells, each with an estimated probability of success of 0.1. All nine
|
|||
|
|
wells fail. What is the probability of that happening?
|
|||
|
|
|
|||
|
|
Let's do 20,000 trials of the model, and count the number that
|
|||
|
|
generate zero positive results.
|
|||
|
|
|
|||
|
|
>>> sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.
|
|||
|
|
# answer = 0.38885, or 38%.
|
|||
|
|
|
|||
|
|
|
|||
|
|
triangular(left, mode, right, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from the triangular distribution over the
|
|||
|
|
interval ``[left, right]``.
|
|||
|
|
|
|||
|
|
The triangular distribution is a continuous probability
|
|||
|
|
distribution with lower limit left, peak at mode, and upper
|
|||
|
|
limit right. Unlike the other distributions, these parameters
|
|||
|
|
directly define the shape of the pdf.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.triangular`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
left : float or array_like of floats
|
|||
|
|
Lower limit.
|
|||
|
|
mode : float or array_like of floats
|
|||
|
|
The value where the peak of the distribution occurs.
|
|||
|
|
The value must fulfill the condition ``left <= mode <= right``.
|
|||
|
|
right : float or array_like of floats
|
|||
|
|
Upper limit, must be larger than `left`.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``left``, ``mode``, and ``right``
|
|||
|
|
are all scalars. Otherwise, ``np.broadcast(left, mode, right).size``
|
|||
|
|
samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized triangular distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.triangular: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density function for the triangular distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x;l, m, r) = \begin{cases}
|
|||
|
|
\frac{2(x-l)}{(r-l)(m-l)}& \text{for $l \leq x \leq m$},\\
|
|||
|
|
\frac{2(r-x)}{(r-l)(r-m)}& \text{for $m \leq x \leq r$},\\
|
|||
|
|
0& \text{otherwise}.
|
|||
|
|
\end{cases}
|
|||
|
|
|
|||
|
|
The triangular distribution is often used in ill-defined
|
|||
|
|
problems where the underlying distribution is not known, but
|
|||
|
|
some knowledge of the limits and mode exists. Often it is used
|
|||
|
|
in simulations.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Wikipedia, "Triangular distribution"
|
|||
|
|
https://en.wikipedia.org/wiki/Triangular_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw values from the distribution and plot the histogram:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=200,
|
|||
|
|
... density=True)
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
wald(mean, scale, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Wald, or inverse Gaussian, distribution.
|
|||
|
|
|
|||
|
|
As the scale approaches infinity, the distribution becomes more like a
|
|||
|
|
Gaussian. Some references claim that the Wald is an inverse Gaussian
|
|||
|
|
with mean equal to 1, but this is by no means universal.
|
|||
|
|
|
|||
|
|
The inverse Gaussian distribution was first studied in relationship to
|
|||
|
|
Brownian motion. In 1956 M.C.K. Tweedie used the name inverse Gaussian
|
|||
|
|
because there is an inverse relationship between the time to cover a
|
|||
|
|
unit distance and distance covered in unit time.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.wald`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
mean : float or array_like of floats
|
|||
|
|
Distribution mean, must be > 0.
|
|||
|
|
scale : float or array_like of floats
|
|||
|
|
Scale parameter, must be > 0.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``mean`` and ``scale`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(mean, scale).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Wald distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.wald: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density function for the Wald distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x;mean,scale) = \sqrt{\frac{scale}{2\pi x^3}}e^
|
|||
|
|
\frac{-scale(x-mean)^2}{2\cdotp mean^2x}
|
|||
|
|
|
|||
|
|
As noted above the inverse Gaussian distribution first arise
|
|||
|
|
from attempts to model Brownian motion. It is also a
|
|||
|
|
competitor to the Weibull for use in reliability modeling and
|
|||
|
|
modeling stock returns and interest rate processes.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Brighton Webs Ltd., Wald Distribution,
|
|||
|
|
https://web.archive.org/web/20090423014010/http://www.brighton-webs.co.uk:80/distributions/wald.asp
|
|||
|
|
.. [2] Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian
|
|||
|
|
Distribution: Theory : Methodology, and Applications", CRC Press,
|
|||
|
|
1988.
|
|||
|
|
.. [3] Wikipedia, "Inverse Gaussian distribution"
|
|||
|
|
https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw values from the distribution and plot the histogram:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, density=True)
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
rayleigh(scale=1.0, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Rayleigh distribution.
|
|||
|
|
|
|||
|
|
The :math:`\chi` and Weibull distributions are generalizations of the
|
|||
|
|
Rayleigh.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.rayleigh`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
scale : float or array_like of floats, optional
|
|||
|
|
Scale, also equals the mode. Must be non-negative. Default is 1.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``scale`` is a scalar. Otherwise,
|
|||
|
|
``np.array(scale).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Rayleigh distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.rayleigh: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density function for the Rayleigh distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}
|
|||
|
|
|
|||
|
|
The Rayleigh distribution would arise, for example, if the East
|
|||
|
|
and North components of the wind velocity had identical zero-mean
|
|||
|
|
Gaussian distributions. Then the wind speed would have a Rayleigh
|
|||
|
|
distribution.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Brighton Webs Ltd., "Rayleigh Distribution,"
|
|||
|
|
https://web.archive.org/web/20090514091424/http://brighton-webs.co.uk:80/distributions/rayleigh.asp
|
|||
|
|
.. [2] Wikipedia, "Rayleigh distribution"
|
|||
|
|
https://en.wikipedia.org/wiki/Rayleigh_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw values from the distribution and plot the histogram
|
|||
|
|
|
|||
|
|
>>> from matplotlib.pyplot import hist
|
|||
|
|
>>> values = hist(np.random.rayleigh(3, 100000), bins=200, density=True)
|
|||
|
|
|
|||
|
|
Wave heights tend to follow a Rayleigh distribution. If the mean wave
|
|||
|
|
height is 1 meter, what fraction of waves are likely to be larger than 3
|
|||
|
|
meters?
|
|||
|
|
|
|||
|
|
>>> meanvalue = 1
|
|||
|
|
>>> modevalue = np.sqrt(2 / np.pi) * meanvalue
|
|||
|
|
>>> s = np.random.rayleigh(modevalue, 1000000)
|
|||
|
|
|
|||
|
|
The percentage of waves larger than 3 meters is:
|
|||
|
|
|
|||
|
|
>>> 100.*sum(s>3)/1000000.
|
|||
|
|
0.087300000000000003 # random
|
|||
|
|
|
|||
|
|
|
|||
|
|
lognormal(mean=0.0, sigma=1.0, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a log-normal distribution.
|
|||
|
|
|
|||
|
|
Draw samples from a log-normal distribution with specified mean,
|
|||
|
|
standard deviation, and array shape. Note that the mean and standard
|
|||
|
|
deviation are not the values for the distribution itself, but of the
|
|||
|
|
underlying normal distribution it is derived from.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.lognormal`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
mean : float or array_like of floats, optional
|
|||
|
|
Mean value of the underlying normal distribution. Default is 0.
|
|||
|
|
sigma : float or array_like of floats, optional
|
|||
|
|
Standard deviation of the underlying normal distribution. Must be
|
|||
|
|
non-negative. Default is 1.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``mean`` and ``sigma`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized log-normal distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.lognorm : probability density function, distribution,
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.lognormal: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
A variable `x` has a log-normal distribution if `log(x)` is normally
|
|||
|
|
distributed. The probability density function for the log-normal
|
|||
|
|
distribution is:
|
|||
|
|
|
|||
|
|
.. math:: p(x) = \frac{1}{\sigma x \sqrt{2\pi}}
|
|||
|
|
e^{(-\frac{(ln(x)-\mu)^2}{2\sigma^2})}
|
|||
|
|
|
|||
|
|
where :math:`\mu` is the mean and :math:`\sigma` is the standard
|
|||
|
|
deviation of the normally distributed logarithm of the variable.
|
|||
|
|
A log-normal distribution results if a random variable is the *product*
|
|||
|
|
of a large number of independent, identically-distributed variables in
|
|||
|
|
the same way that a normal distribution results if the variable is the
|
|||
|
|
*sum* of a large number of independent, identically-distributed
|
|||
|
|
variables.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal
|
|||
|
|
Distributions across the Sciences: Keys and Clues,"
|
|||
|
|
BioScience, Vol. 51, No. 5, May, 2001.
|
|||
|
|
https://stat.ethz.ch/~stahel/lognormal/bioscience.pdf
|
|||
|
|
.. [2] Reiss, R.D. and Thomas, M., "Statistical Analysis of Extreme
|
|||
|
|
Values," Basel: Birkhauser Verlag, 2001, pp. 31-32.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> mu, sigma = 3., 1. # mean and standard deviation
|
|||
|
|
>>> s = np.random.lognormal(mu, sigma, 1000)
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with
|
|||
|
|
the probability density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, ignored = plt.hist(s, 100, density=True, align='mid')
|
|||
|
|
|
|||
|
|
>>> x = np.linspace(min(bins), max(bins), 10000)
|
|||
|
|
>>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
|
|||
|
|
... / (x * sigma * np.sqrt(2 * np.pi)))
|
|||
|
|
|
|||
|
|
>>> plt.plot(x, pdf, linewidth=2, color='r')
|
|||
|
|
>>> plt.axis('tight')
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
Demonstrate that taking the products of random samples from a uniform
|
|||
|
|
distribution can be fit well by a log-normal probability density
|
|||
|
|
function.
|
|||
|
|
|
|||
|
|
>>> # Generate a thousand samples: each is the product of 100 random
|
|||
|
|
>>> # values, drawn from a normal distribution.
|
|||
|
|
>>> b = []
|
|||
|
|
>>> for i in range(1000):
|
|||
|
|
... a = 10. + np.random.standard_normal(100)
|
|||
|
|
... b.append(np.prod(a))
|
|||
|
|
|
|||
|
|
>>> b = np.array(b) / np.min(b) # scale values to be positive
|
|||
|
|
>>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid')
|
|||
|
|
>>> sigma = np.std(np.log(b))
|
|||
|
|
>>> mu = np.mean(np.log(b))
|
|||
|
|
|
|||
|
|
>>> x = np.linspace(min(bins), max(bins), 10000)
|
|||
|
|
>>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
|
|||
|
|
... / (x * sigma * np.sqrt(2 * np.pi)))
|
|||
|
|
|
|||
|
|
>>> plt.plot(x, pdf, color='r', linewidth=2)
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
logistic(loc=0.0, scale=1.0, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a logistic distribution.
|
|||
|
|
|
|||
|
|
Samples are drawn from a logistic distribution with specified
|
|||
|
|
parameters, loc (location or mean, also median), and scale (>0).
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.logistic`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
loc : float or array_like of floats, optional
|
|||
|
|
Parameter of the distribution. Default is 0.
|
|||
|
|
scale : float or array_like of floats, optional
|
|||
|
|
Parameter of the distribution. Must be non-negative.
|
|||
|
|
Default is 1.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``loc`` and ``scale`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized logistic distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.logistic : probability density function, distribution or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.logistic: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density for the Logistic distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},
|
|||
|
|
|
|||
|
|
where :math:`\mu` = location and :math:`s` = scale.
|
|||
|
|
|
|||
|
|
The Logistic distribution is used in Extreme Value problems where it
|
|||
|
|
can act as a mixture of Gumbel distributions, in Epidemiology, and by
|
|||
|
|
the World Chess Federation (FIDE) where it is used in the Elo ranking
|
|||
|
|
system, assuming the performance of each player is a logistically
|
|||
|
|
distributed random variable.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Reiss, R.-D. and Thomas M. (2001), "Statistical Analysis of
|
|||
|
|
Extreme Values, from Insurance, Finance, Hydrology and Other
|
|||
|
|
Fields," Birkhauser Verlag, Basel, pp 132-133.
|
|||
|
|
.. [2] Weisstein, Eric W. "Logistic Distribution." From
|
|||
|
|
MathWorld--A Wolfram Web Resource.
|
|||
|
|
https://mathworld.wolfram.com/LogisticDistribution.html
|
|||
|
|
.. [3] Wikipedia, "Logistic-distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Logistic_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> loc, scale = 10, 1
|
|||
|
|
>>> s = np.random.logistic(loc, scale, 10000)
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, ignored = plt.hist(s, bins=50)
|
|||
|
|
|
|||
|
|
# plot against distribution
|
|||
|
|
|
|||
|
|
>>> def logist(x, loc, scale):
|
|||
|
|
... return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2)
|
|||
|
|
>>> lgst_val = logist(bins, loc, scale)
|
|||
|
|
>>> plt.plot(bins, lgst_val * count.max() / lgst_val.max())
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
gumbel(loc=0.0, scale=1.0, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Gumbel distribution.
|
|||
|
|
|
|||
|
|
Draw samples from a Gumbel distribution with specified location and
|
|||
|
|
scale. For more information on the Gumbel distribution, see
|
|||
|
|
Notes and References below.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.gumbel`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
loc : float or array_like of floats, optional
|
|||
|
|
The location of the mode of the distribution. Default is 0.
|
|||
|
|
scale : float or array_like of floats, optional
|
|||
|
|
The scale parameter of the distribution. Default is 1. Must be non-
|
|||
|
|
negative.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``loc`` and ``scale`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Gumbel distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.gumbel_l
|
|||
|
|
scipy.stats.gumbel_r
|
|||
|
|
scipy.stats.genextreme
|
|||
|
|
weibull
|
|||
|
|
random.Generator.gumbel: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme
|
|||
|
|
Value Type I) distribution is one of a class of Generalized Extreme
|
|||
|
|
Value (GEV) distributions used in modeling extreme value problems.
|
|||
|
|
The Gumbel is a special case of the Extreme Value Type I distribution
|
|||
|
|
for maximums from distributions with "exponential-like" tails.
|
|||
|
|
|
|||
|
|
The probability density for the Gumbel distribution is
|
|||
|
|
|
|||
|
|
.. math:: p(x) = \frac{e^{-(x - \mu)/ \beta}}{\beta} e^{ -e^{-(x - \mu)/
|
|||
|
|
\beta}},
|
|||
|
|
|
|||
|
|
where :math:`\mu` is the mode, a location parameter, and
|
|||
|
|
:math:`\beta` is the scale parameter.
|
|||
|
|
|
|||
|
|
The Gumbel (named for German mathematician Emil Julius Gumbel) was used
|
|||
|
|
very early in the hydrology literature, for modeling the occurrence of
|
|||
|
|
flood events. It is also used for modeling maximum wind speed and
|
|||
|
|
rainfall rates. It is a "fat-tailed" distribution - the probability of
|
|||
|
|
an event in the tail of the distribution is larger than if one used a
|
|||
|
|
Gaussian, hence the surprisingly frequent occurrence of 100-year
|
|||
|
|
floods. Floods were initially modeled as a Gaussian process, which
|
|||
|
|
underestimated the frequency of extreme events.
|
|||
|
|
|
|||
|
|
It is one of a class of extreme value distributions, the Generalized
|
|||
|
|
Extreme Value (GEV) distributions, which also includes the Weibull and
|
|||
|
|
Frechet.
|
|||
|
|
|
|||
|
|
The function has a mean of :math:`\mu + 0.57721\beta` and a variance
|
|||
|
|
of :math:`\frac{\pi^2}{6}\beta^2`.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Gumbel, E. J., "Statistics of Extremes,"
|
|||
|
|
New York: Columbia University Press, 1958.
|
|||
|
|
.. [2] Reiss, R.-D. and Thomas, M., "Statistical Analysis of Extreme
|
|||
|
|
Values from Insurance, Finance, Hydrology and Other Fields,"
|
|||
|
|
Basel: Birkhauser Verlag, 2001.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> mu, beta = 0, 0.1 # location and scale
|
|||
|
|
>>> s = np.random.gumbel(mu, beta, 1000)
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with
|
|||
|
|
the probability density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, ignored = plt.hist(s, 30, density=True)
|
|||
|
|
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
|
|||
|
|
... * np.exp( -np.exp( -(bins - mu) /beta) ),
|
|||
|
|
... linewidth=2, color='r')
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
Show how an extreme value distribution can arise from a Gaussian process
|
|||
|
|
and compare to a Gaussian:
|
|||
|
|
|
|||
|
|
>>> means = []
|
|||
|
|
>>> maxima = []
|
|||
|
|
>>> for i in range(0,1000) :
|
|||
|
|
... a = np.random.normal(mu, beta, 1000)
|
|||
|
|
... means.append(a.mean())
|
|||
|
|
... maxima.append(a.max())
|
|||
|
|
>>> count, bins, ignored = plt.hist(maxima, 30, density=True)
|
|||
|
|
>>> beta = np.std(maxima) * np.sqrt(6) / np.pi
|
|||
|
|
>>> mu = np.mean(maxima) - 0.57721*beta
|
|||
|
|
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
|
|||
|
|
... * np.exp(-np.exp(-(bins - mu)/beta)),
|
|||
|
|
... linewidth=2, color='r')
|
|||
|
|
>>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))
|
|||
|
|
... * np.exp(-(bins - mu)**2 / (2 * beta**2)),
|
|||
|
|
... linewidth=2, color='g')
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
laplace(loc=0.0, scale=1.0, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from the Laplace or double exponential distribution with
|
|||
|
|
specified location (or mean) and scale (decay).
|
|||
|
|
|
|||
|
|
The Laplace distribution is similar to the Gaussian/normal distribution,
|
|||
|
|
but is sharper at the peak and has fatter tails. It represents the
|
|||
|
|
difference between two independent, identically distributed exponential
|
|||
|
|
random variables.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.laplace`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
loc : float or array_like of floats, optional
|
|||
|
|
The position, :math:`\mu`, of the distribution peak. Default is 0.
|
|||
|
|
scale : float or array_like of floats, optional
|
|||
|
|
:math:`\lambda`, the exponential decay. Default is 1. Must be non-
|
|||
|
|
negative.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``loc`` and ``scale`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Laplace distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.laplace: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
It has the probability density function
|
|||
|
|
|
|||
|
|
.. math:: f(x; \mu, \lambda) = \frac{1}{2\lambda}
|
|||
|
|
\exp\left(-\frac{|x - \mu|}{\lambda}\right).
|
|||
|
|
|
|||
|
|
The first law of Laplace, from 1774, states that the frequency
|
|||
|
|
of an error can be expressed as an exponential function of the
|
|||
|
|
absolute magnitude of the error, which leads to the Laplace
|
|||
|
|
distribution. For many problems in economics and health
|
|||
|
|
sciences, this distribution seems to model the data better
|
|||
|
|
than the standard Gaussian distribution.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Abramowitz, M. and Stegun, I. A. (Eds.). "Handbook of
|
|||
|
|
Mathematical Functions with Formulas, Graphs, and Mathematical
|
|||
|
|
Tables, 9th printing," New York: Dover, 1972.
|
|||
|
|
.. [2] Kotz, Samuel, et. al. "The Laplace Distribution and
|
|||
|
|
Generalizations, " Birkhauser, 2001.
|
|||
|
|
.. [3] Weisstein, Eric W. "Laplace Distribution."
|
|||
|
|
From MathWorld--A Wolfram Web Resource.
|
|||
|
|
https://mathworld.wolfram.com/LaplaceDistribution.html
|
|||
|
|
.. [4] Wikipedia, "Laplace distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Laplace_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution
|
|||
|
|
|
|||
|
|
>>> loc, scale = 0., 1.
|
|||
|
|
>>> s = np.random.laplace(loc, scale, 1000)
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with
|
|||
|
|
the probability density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, ignored = plt.hist(s, 30, density=True)
|
|||
|
|
>>> x = np.arange(-8., 8., .01)
|
|||
|
|
>>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)
|
|||
|
|
>>> plt.plot(x, pdf)
|
|||
|
|
|
|||
|
|
Plot Gaussian for comparison:
|
|||
|
|
|
|||
|
|
>>> g = (1/(scale * np.sqrt(2 * np.pi)) *
|
|||
|
|
... np.exp(-(x - loc)**2 / (2 * scale**2)))
|
|||
|
|
>>> plt.plot(x,g)
|
|||
|
|
|
|||
|
|
|
|||
|
|
power(a, size=None)
|
|||
|
|
|
|||
|
|
Draws samples in [0, 1] from a power distribution with positive
|
|||
|
|
exponent a - 1.
|
|||
|
|
|
|||
|
|
Also known as the power function distribution.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.power`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : float or array_like of floats
|
|||
|
|
Parameter of the distribution. Must be non-negative.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``a`` is a scalar. Otherwise,
|
|||
|
|
``np.array(a).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized power distribution.
|
|||
|
|
|
|||
|
|
Raises
|
|||
|
|
------
|
|||
|
|
ValueError
|
|||
|
|
If a <= 0.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.power: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density function is
|
|||
|
|
|
|||
|
|
.. math:: P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0.
|
|||
|
|
|
|||
|
|
The power function distribution is just the inverse of the Pareto
|
|||
|
|
distribution. It may also be seen as a special case of the Beta
|
|||
|
|
distribution.
|
|||
|
|
|
|||
|
|
It is used, for example, in modeling the over-reporting of insurance
|
|||
|
|
claims.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Christian Kleiber, Samuel Kotz, "Statistical size distributions
|
|||
|
|
in economics and actuarial sciences", Wiley, 2003.
|
|||
|
|
.. [2] Heckert, N. A. and Filliben, James J. "NIST Handbook 148:
|
|||
|
|
Dataplot Reference Manual, Volume 2: Let Subcommands and Library
|
|||
|
|
Functions", National Institute of Standards and Technology
|
|||
|
|
Handbook Series, June 2003.
|
|||
|
|
https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> a = 5. # shape
|
|||
|
|
>>> samples = 1000
|
|||
|
|
>>> s = np.random.power(a, samples)
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with
|
|||
|
|
the probability density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, ignored = plt.hist(s, bins=30)
|
|||
|
|
>>> x = np.linspace(0, 1, 100)
|
|||
|
|
>>> y = a*x**(a-1.)
|
|||
|
|
>>> normed_y = samples*np.diff(bins)[0]*y
|
|||
|
|
>>> plt.plot(x, normed_y)
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
Compare the power function distribution to the inverse of the Pareto.
|
|||
|
|
|
|||
|
|
>>> from scipy import stats # doctest: +SKIP
|
|||
|
|
>>> rvs = np.random.power(5, 1000000)
|
|||
|
|
>>> rvsp = np.random.pareto(5, 1000000)
|
|||
|
|
>>> xx = np.linspace(0,1,100)
|
|||
|
|
>>> powpdf = stats.powerlaw.pdf(xx,5) # doctest: +SKIP
|
|||
|
|
|
|||
|
|
>>> plt.figure()
|
|||
|
|
>>> plt.hist(rvs, bins=50, density=True)
|
|||
|
|
>>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP
|
|||
|
|
>>> plt.title('np.random.power(5)')
|
|||
|
|
|
|||
|
|
>>> plt.figure()
|
|||
|
|
>>> plt.hist(1./(1.+rvsp), bins=50, density=True)
|
|||
|
|
>>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP
|
|||
|
|
>>> plt.title('inverse of 1 + np.random.pareto(5)')
|
|||
|
|
|
|||
|
|
>>> plt.figure()
|
|||
|
|
>>> plt.hist(1./(1.+rvsp), bins=50, density=True)
|
|||
|
|
>>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP
|
|||
|
|
>>> plt.title('inverse of stats.pareto(5)')
|
|||
|
|
|
|||
|
|
|
|||
|
|
weibull(a, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Weibull distribution.
|
|||
|
|
|
|||
|
|
Draw samples from a 1-parameter Weibull distribution with the given
|
|||
|
|
shape parameter `a`.
|
|||
|
|
|
|||
|
|
.. math:: X = (-ln(U))^{1/a}
|
|||
|
|
|
|||
|
|
Here, U is drawn from the uniform distribution over (0,1].
|
|||
|
|
|
|||
|
|
The more common 2-parameter Weibull, including a scale parameter
|
|||
|
|
:math:`\lambda` is just :math:`X = \lambda(-ln(U))^{1/a}`.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.weibull`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : float or array_like of floats
|
|||
|
|
Shape parameter of the distribution. Must be nonnegative.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``a`` is a scalar. Otherwise,
|
|||
|
|
``np.array(a).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Weibull distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.weibull_max
|
|||
|
|
scipy.stats.weibull_min
|
|||
|
|
scipy.stats.genextreme
|
|||
|
|
gumbel
|
|||
|
|
random.Generator.weibull: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The Weibull (or Type III asymptotic extreme value distribution
|
|||
|
|
for smallest values, SEV Type III, or Rosin-Rammler
|
|||
|
|
distribution) is one of a class of Generalized Extreme Value
|
|||
|
|
(GEV) distributions used in modeling extreme value problems.
|
|||
|
|
This class includes the Gumbel and Frechet distributions.
|
|||
|
|
|
|||
|
|
The probability density for the Weibull distribution is
|
|||
|
|
|
|||
|
|
.. math:: p(x) = \frac{a}
|
|||
|
|
{\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},
|
|||
|
|
|
|||
|
|
where :math:`a` is the shape and :math:`\lambda` the scale.
|
|||
|
|
|
|||
|
|
The function has its peak (the mode) at
|
|||
|
|
:math:`\lambda(\frac{a-1}{a})^{1/a}`.
|
|||
|
|
|
|||
|
|
When ``a = 1``, the Weibull distribution reduces to the exponential
|
|||
|
|
distribution.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Waloddi Weibull, Royal Technical University, Stockholm,
|
|||
|
|
1939 "A Statistical Theory Of The Strength Of Materials",
|
|||
|
|
Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,
|
|||
|
|
Generalstabens Litografiska Anstalts Forlag, Stockholm.
|
|||
|
|
.. [2] Waloddi Weibull, "A Statistical Distribution Function of
|
|||
|
|
Wide Applicability", Journal Of Applied Mechanics ASME Paper
|
|||
|
|
1951.
|
|||
|
|
.. [3] Wikipedia, "Weibull distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Weibull_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> a = 5. # shape
|
|||
|
|
>>> s = np.random.weibull(a, 1000)
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with
|
|||
|
|
the probability density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> x = np.arange(1,100.)/50.
|
|||
|
|
>>> def weib(x,n,a):
|
|||
|
|
... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
|
|||
|
|
|
|||
|
|
>>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))
|
|||
|
|
>>> x = np.arange(1,100.)/50.
|
|||
|
|
>>> scale = count.max()/weib(x, 1., 5.).max()
|
|||
|
|
>>> plt.plot(x, weib(x, 1., 5.)*scale)
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
pareto(a, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a Pareto II or Lomax distribution with
|
|||
|
|
specified shape.
|
|||
|
|
|
|||
|
|
The Lomax or Pareto II distribution is a shifted Pareto
|
|||
|
|
distribution. The classical Pareto distribution can be
|
|||
|
|
obtained from the Lomax distribution by adding 1 and
|
|||
|
|
multiplying by the scale parameter ``m`` (see Notes). The
|
|||
|
|
smallest value of the Lomax distribution is zero while for the
|
|||
|
|
classical Pareto distribution it is ``mu``, where the standard
|
|||
|
|
Pareto distribution has location ``mu = 1``. Lomax can also
|
|||
|
|
be considered as a simplified version of the Generalized
|
|||
|
|
Pareto distribution (available in SciPy), with the scale set
|
|||
|
|
to one and the location set to zero.
|
|||
|
|
|
|||
|
|
The Pareto distribution must be greater than zero, and is
|
|||
|
|
unbounded above. It is also known as the "80-20 rule". In
|
|||
|
|
this distribution, 80 percent of the weights are in the lowest
|
|||
|
|
20 percent of the range, while the other 20 percent fill the
|
|||
|
|
remaining 80 percent of the range.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.pareto`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : float or array_like of floats
|
|||
|
|
Shape of the distribution. Must be positive.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``a`` is a scalar. Otherwise,
|
|||
|
|
``np.array(a).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized Pareto distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.lomax : probability density function, distribution or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
scipy.stats.genpareto : probability density function, distribution or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.pareto: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density for the Pareto distribution is
|
|||
|
|
|
|||
|
|
.. math:: p(x) = \frac{am^a}{x^{a+1}}
|
|||
|
|
|
|||
|
|
where :math:`a` is the shape and :math:`m` the scale.
|
|||
|
|
|
|||
|
|
The Pareto distribution, named after the Italian economist
|
|||
|
|
Vilfredo Pareto, is a power law probability distribution
|
|||
|
|
useful in many real world problems. Outside the field of
|
|||
|
|
economics it is generally referred to as the Bradford
|
|||
|
|
distribution. Pareto developed the distribution to describe
|
|||
|
|
the distribution of wealth in an economy. It has also found
|
|||
|
|
use in insurance, web page access statistics, oil field sizes,
|
|||
|
|
and many other problems, including the download frequency for
|
|||
|
|
projects in Sourceforge [1]_. It is one of the so-called
|
|||
|
|
"fat-tailed" distributions.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of
|
|||
|
|
Sourceforge projects.
|
|||
|
|
.. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.
|
|||
|
|
.. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme
|
|||
|
|
Values, Birkhauser Verlag, Basel, pp 23-30.
|
|||
|
|
.. [4] Wikipedia, "Pareto distribution",
|
|||
|
|
https://en.wikipedia.org/wiki/Pareto_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> a, m = 3., 2. # shape and mode
|
|||
|
|
>>> s = (np.random.pareto(a, 1000) + 1) * m
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with the probability
|
|||
|
|
density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> count, bins, _ = plt.hist(s, 100, density=True)
|
|||
|
|
>>> fit = a*m**a / bins**(a+1)
|
|||
|
|
>>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
vonmises(mu, kappa, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a von Mises distribution.
|
|||
|
|
|
|||
|
|
Samples are drawn from a von Mises distribution with specified mode
|
|||
|
|
(mu) and concentration (kappa), on the interval [-pi, pi].
|
|||
|
|
|
|||
|
|
The von Mises distribution (also known as the circular normal
|
|||
|
|
distribution) is a continuous probability distribution on the unit
|
|||
|
|
circle. It may be thought of as the circular analogue of the normal
|
|||
|
|
distribution.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.vonmises`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
mu : float or array_like of floats
|
|||
|
|
Mode ("center") of the distribution.
|
|||
|
|
kappa : float or array_like of floats
|
|||
|
|
Concentration of the distribution, has to be >=0.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``mu`` and ``kappa`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(mu, kappa).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized von Mises distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.stats.vonmises : probability density function, distribution, or
|
|||
|
|
cumulative density function, etc.
|
|||
|
|
random.Generator.vonmises: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density for the von Mises distribution is
|
|||
|
|
|
|||
|
|
.. math:: p(x) = \frac{e^{\kappa cos(x-\mu)}}{2\pi I_0(\kappa)},
|
|||
|
|
|
|||
|
|
where :math:`\mu` is the mode and :math:`\kappa` the concentration,
|
|||
|
|
and :math:`I_0(\kappa)` is the modified Bessel function of order 0.
|
|||
|
|
|
|||
|
|
The von Mises is named for Richard Edler von Mises, who was born in
|
|||
|
|
Austria-Hungary, in what is now the Ukraine. He fled to the United
|
|||
|
|
States in 1939 and became a professor at Harvard. He worked in
|
|||
|
|
probability theory, aerodynamics, fluid mechanics, and philosophy of
|
|||
|
|
science.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Abramowitz, M. and Stegun, I. A. (Eds.). "Handbook of
|
|||
|
|
Mathematical Functions with Formulas, Graphs, and Mathematical
|
|||
|
|
Tables, 9th printing," New York: Dover, 1972.
|
|||
|
|
.. [2] von Mises, R., "Mathematical Theory of Probability
|
|||
|
|
and Statistics", New York: Academic Press, 1964.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples from the distribution:
|
|||
|
|
|
|||
|
|
>>> mu, kappa = 0.0, 4.0 # mean and concentration
|
|||
|
|
>>> s = np.random.vonmises(mu, kappa, 1000)
|
|||
|
|
|
|||
|
|
Display the histogram of the samples, along with
|
|||
|
|
the probability density function:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> from scipy.special import i0 # doctest: +SKIP
|
|||
|
|
>>> plt.hist(s, 50, density=True)
|
|||
|
|
>>> x = np.linspace(-np.pi, np.pi, num=51)
|
|||
|
|
>>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa)) # doctest: +SKIP
|
|||
|
|
>>> plt.plot(x, y, linewidth=2, color='r') # doctest: +SKIP
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
standard_t(df, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a standard Student's t distribution with `df` degrees
|
|||
|
|
of freedom.
|
|||
|
|
|
|||
|
|
A special case of the hyperbolic distribution. As `df` gets
|
|||
|
|
large, the result resembles that of the standard normal
|
|||
|
|
distribution (`standard_normal`).
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the `~numpy.random.Generator.standard_t`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
df : float or array_like of floats
|
|||
|
|
Degrees of freedom, must be > 0.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``df`` is a scalar. Otherwise,
|
|||
|
|
``np.array(df).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized standard Student's t distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.standard_t: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density function for the t distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x, df) = \frac{\Gamma(\frac{df+1}{2})}{\sqrt{\pi df}
|
|||
|
|
\Gamma(\frac{df}{2})}\Bigl( 1+\frac{x^2}{df} \Bigr)^{-(df+1)/2}
|
|||
|
|
|
|||
|
|
The t test is based on an assumption that the data come from a
|
|||
|
|
Normal distribution. The t test provides a way to test whether
|
|||
|
|
the sample mean (that is the mean calculated from the data) is
|
|||
|
|
a good estimate of the true mean.
|
|||
|
|
|
|||
|
|
The derivation of the t-distribution was first published in
|
|||
|
|
1908 by William Gosset while working for the Guinness Brewery
|
|||
|
|
in Dublin. Due to proprietary issues, he had to publish under
|
|||
|
|
a pseudonym, and so he used the name Student.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Dalgaard, Peter, "Introductory Statistics With R",
|
|||
|
|
Springer, 2002.
|
|||
|
|
.. [2] Wikipedia, "Student's t-distribution"
|
|||
|
|
https://en.wikipedia.org/wiki/Student's_t-distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
From Dalgaard page 83 [1]_, suppose the daily energy intake for 11
|
|||
|
|
women in kilojoules (kJ) is:
|
|||
|
|
|
|||
|
|
>>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \
|
|||
|
|
... 7515, 8230, 8770])
|
|||
|
|
|
|||
|
|
Does their energy intake deviate systematically from the recommended
|
|||
|
|
value of 7725 kJ? Our null hypothesis will be the absence of deviation,
|
|||
|
|
and the alternate hypothesis will be the presence of an effect that could be
|
|||
|
|
either positive or negative, hence making our test 2-tailed.
|
|||
|
|
|
|||
|
|
Because we are estimating the mean and we have N=11 values in our sample,
|
|||
|
|
we have N-1=10 degrees of freedom. We set our significance level to 95% and
|
|||
|
|
compute the t statistic using the empirical mean and empirical standard
|
|||
|
|
deviation of our intake. We use a ddof of 1 to base the computation of our
|
|||
|
|
empirical standard deviation on an unbiased estimate of the variance (note:
|
|||
|
|
the final estimate is not unbiased due to the concave nature of the square
|
|||
|
|
root).
|
|||
|
|
|
|||
|
|
>>> np.mean(intake)
|
|||
|
|
6753.636363636364
|
|||
|
|
>>> intake.std(ddof=1)
|
|||
|
|
1142.1232221373727
|
|||
|
|
>>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))
|
|||
|
|
>>> t
|
|||
|
|
-2.8207540608310198
|
|||
|
|
|
|||
|
|
We draw 1000000 samples from Student's t distribution with the adequate
|
|||
|
|
degrees of freedom.
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> s = np.random.standard_t(10, size=1000000)
|
|||
|
|
>>> h = plt.hist(s, bins=100, density=True)
|
|||
|
|
|
|||
|
|
Does our t statistic land in one of the two critical regions found at
|
|||
|
|
both tails of the distribution?
|
|||
|
|
|
|||
|
|
>>> np.sum(np.abs(t) < np.abs(s)) / float(len(s))
|
|||
|
|
0.018318 #random < 0.05, statistic is in critical region
|
|||
|
|
|
|||
|
|
The probability value for this 2-tailed test is about 1.83%, which is
|
|||
|
|
lower than the 5% pre-determined significance threshold.
|
|||
|
|
|
|||
|
|
Therefore, the probability of observing values as extreme as our intake
|
|||
|
|
conditionally on the null hypothesis being true is too low, and we reject
|
|||
|
|
the null hypothesis of no deviation.
|
|||
|
|
|
|||
|
|
|
|||
|
|
standard_cauchy(size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a standard Cauchy distribution with mode = 0.
|
|||
|
|
|
|||
|
|
Also known as the Lorentz distribution.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the
|
|||
|
|
`~numpy.random.Generator.standard_cauchy`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. Default is None, in which case a
|
|||
|
|
single value is returned.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
samples : ndarray or scalar
|
|||
|
|
The drawn samples.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.standard_cauchy: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density function for the full Cauchy distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+
|
|||
|
|
(\frac{x-x_0}{\gamma})^2 \bigr] }
|
|||
|
|
|
|||
|
|
and the Standard Cauchy distribution just sets :math:`x_0=0` and
|
|||
|
|
:math:`\gamma=1`
|
|||
|
|
|
|||
|
|
The Cauchy distribution arises in the solution to the driven harmonic
|
|||
|
|
oscillator problem, and also describes spectral line broadening. It
|
|||
|
|
also describes the distribution of values at which a line tilted at
|
|||
|
|
a random angle will cut the x axis.
|
|||
|
|
|
|||
|
|
When studying hypothesis tests that assume normality, seeing how the
|
|||
|
|
tests perform on data from a Cauchy distribution is a good indicator of
|
|||
|
|
their sensitivity to a heavy-tailed distribution, since the Cauchy looks
|
|||
|
|
very much like a Gaussian distribution, but with heavier tails.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy
|
|||
|
|
Distribution",
|
|||
|
|
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
|
|||
|
|
.. [2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A
|
|||
|
|
Wolfram Web Resource.
|
|||
|
|
https://mathworld.wolfram.com/CauchyDistribution.html
|
|||
|
|
.. [3] Wikipedia, "Cauchy distribution"
|
|||
|
|
https://en.wikipedia.org/wiki/Cauchy_distribution
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Draw samples and plot the distribution:
|
|||
|
|
|
|||
|
|
>>> import matplotlib.pyplot as plt
|
|||
|
|
>>> s = np.random.standard_cauchy(1000000)
|
|||
|
|
>>> s = s[(s>-25) & (s<25)] # truncate distribution so it plots well
|
|||
|
|
>>> plt.hist(s, bins=100)
|
|||
|
|
>>> plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
noncentral_chisquare(df, nonc, size=None)
|
|||
|
|
|
|||
|
|
Draw samples from a noncentral chi-square distribution.
|
|||
|
|
|
|||
|
|
The noncentral :math:`\chi^2` distribution is a generalization of
|
|||
|
|
the :math:`\chi^2` distribution.
|
|||
|
|
|
|||
|
|
.. note::
|
|||
|
|
New code should use the
|
|||
|
|
`~numpy.random.Generator.noncentral_chisquare`
|
|||
|
|
method of a `~numpy.random.Generator` instance instead;
|
|||
|
|
please see the :ref:`random-quick-start`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
df : float or array_like of floats
|
|||
|
|
Degrees of freedom, must be > 0.
|
|||
|
|
nonc : float or array_like of floats
|
|||
|
|
Non-centrality, must be non-negative.
|
|||
|
|
size : int or tuple of ints, optional
|
|||
|
|
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
|
|||
|
|
``m * n * k`` samples are drawn. If size is ``None`` (default),
|
|||
|
|
a single value is returned if ``df`` and ``nonc`` are both scalars.
|
|||
|
|
Otherwise, ``np.broadcast(df, nonc).size`` samples are drawn.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
out : ndarray or scalar
|
|||
|
|
Drawn samples from the parameterized noncentral chi-square distribution.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
random.Generator.noncentral_chisquare: which should be used for new code.
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
The probability density function for the noncentral Chi-square
|
|||
|
|
distribution is
|
|||
|
|
|
|||
|
|
.. math:: P(x;df,nonc) = \sum^{\infty}_{i=0}
|
|||
|
|
\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}
|
|||
|
|
P_{Y_{df+2i}}(x),
|
|||
|
|
|