| /third_party/boost/boost/math/distributions/detail/ |
| D | derived_accessors.hpp | 2 // Use, modification and distribution are subject to the
15 // here, each distribution MUST SPECIALIZE AT LEAST ONE OF THESE
20 // that are specific to a particular distribution, but these generic
24 // of the distribution header, AFTER the distribution and its core
41 template <class Distribution>
42 typename Distribution::value_type variance(const Distribution& dist);
44 template <class Distribution>
45 inline typename Distribution::value_type standard_deviation(const Distribution& dist) in standard_deviation()
51 template <class Distribution>
52 inline typename Distribution::value_type variance(const Distribution& dist) in variance()
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| /third_party/boost/libs/math/doc/distributions/ |
| D | non_members.qbk | 38 * [link math_toolkit.dist_ref.nmp.cdf_inv Inverse Cumulative Distribution Function].
63 [h4:cdf Cumulative Distribution Function]
66 RealType cdf(const ``['Distribution-Type]``<RealType, ``__Policy``>& dist, const RealType& x);
73 the defined range for the distribution.
76 normal distribution:
80 [h4:ccdf Complement of the Cumulative Distribution Function]
82 template <class Distribution, class RealType>
83 RealType cdf(const ``['Unspecified-Complement-Type]``<Distribution, RealType>& comp);
93 the defined range for the distribution.
98 // standard normal distribution object:
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| D | gamma.qbk | 1 [section:gamma_dist Gamma (and Erlang) Distribution]
23 The gamma distribution is a continuous probability distribution.
25 Erlang Distribution. It is also closely related to the Poisson
28 When the shape parameter has an integer value, the distribution is the
29 [@http://en.wikipedia.org/wiki/Erlang_distribution Erlang distribution].
31 integer value > 0, the Erlang distribution is not separately implemented.
35 distribution does not provide the typedef:
39 Instead if you want a double precision gamma distribution you can write
51 distribution can be defined by the PDF:
58 throughout. Therefore to construct a Gamma Distribution from a ['rate
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| D | triangular.qbk | 1 [section:triangular_dist Triangular Distribution]
21 …wer(lower), m_mode(mode), m_upper(upper) // Default is -1, 0, +1 symmetric triangular distribution.
30 The [@http://en.wikipedia.org/wiki/Triangular_distribution triangular distribution]
32 [@http://en.wikipedia.org/wiki/Probability_distribution probability distribution]
37 The triangular distribution is often used where the distribution is only vaguely known,
38 …ike the [@http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29 uniform distribution],
41 …//www.worldscibooks.com/mathematics/etextbook/5720/5720_chap1.pdf proxy for the beta distribution.]
42 The distribution is used in business decision making and project planning.
44 The [@http://en.wikipedia.org/wiki/Triangular_distribution triangular distribution]
45 is a distribution with the
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| D | inverse_chi_squared.qbk | 1 [section:inverse_chi_squared_dist Inverse Chi Squared Distribution]
24 The inverse chi squared distribution is a continuous probability distribution
25 of the *reciprocal* of a variable distributed according to the chi squared distribution.
28 using different symbols for the distribution pdf,
34 …wikipedia.org/wiki/Scaled-inverse-chi-square_distribution scaled inverse chi_squared distribution].
37 …/en.wikipedia.org/wiki/Inverse-chi-square_distribution Wikipedia inverse chi_squared distribution].
38 The 2nd Wikipedia inverse chi_squared distribution definition can be implemented
45 * Inverse chi_squared distribution [@http://en.wikipedia.org/wiki/Inverse-chi-square_distribution]
46 * Scaled inverse chi_squared distribution[@http://en.wikipedia.org/wiki/Scaled-inverse-chi-square_d…
47 * R inverse chi_squared distribution functions [@http://hosho.ees.hokudai.ac.jp/~kubo/Rdoc/library/…
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| D | skew_normal.qbk | 1 [section:skew_normal_dist Skew Normal Distribution]
24 …RealType shape()const; // The distribution is right skewed if shape > 0 and is left skewed if shap…
25 // The distribution is normal if shape is zero.
30 The skew normal distribution is a variant of the most well known
31 Gaussian statistical distribution.
33 The skew normal distribution with shape zero resembles the
34 [@http://en.wikipedia.org/wiki/Normal_distribution Normal Distribution],
35 hence the latter can be regarded as a special case of the more generic skew normal distribution.
37 If the standard (mean = 0, scale = 1) normal distribution probability density function is
41 and the cumulative distribution function
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| D | dist_tutorial.qbk | 3 [def __binomial_distrib [link math_toolkit.dist_ref.dists.binomial_dist Binomial Distribution]]
4 …__chi_squared_distrib [link math_toolkit.dist_ref.dists.chi_squared_dist Chi Squared Distribution]]
5 [def __normal_distrib [link math_toolkit.dist_ref.dists.normal_dist Normal Distribution]]
6 [def __F_distrib [link math_toolkit.dist_ref.dists.f_dist Fisher F Distribution]]
7 [def __students_t_distrib [link math_toolkit.dist_ref.dists.students_t_dist Students t Distribution…
23 In order to use a distribution /my_distribution/ you will need to include
27 For example, to use the Students-t distribution include either
31 You also need to bring distribution names into scope,
42 Each kind of distribution in this library is a class type - an object, with member functions.
57 * It encapsulates the kind of distribution in the C++ type system;
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| D | inverse_gaussian.qbk | 1 [section:inverse_gaussian_dist Inverse Gaussian (or Inverse Normal) Distribution]
25 The Inverse Gaussian distribution distribution is a continuous probability distribution.
27 The distribution is also called 'normal-inverse Gaussian distribution',
28 and 'normal Inverse' distribution.
32 The Inverse Gaussian distribution was first studied in relation to Brownian motion.
38 (So ['inverse] in the name may mislead: it does [*not] relate to the inverse of a distribution).
40 The tails of the distribution decrease more slowly than the normal distribution.
42 where numerically large values are more probable than is the case for the normal distribution.
53 [@http://en.wikipedia.org/wiki/Normal-inverse_Gaussian_distribution distribution].
55 Weisstein, Eric W. "Inverse Gaussian Distribution." From MathWorld--A Wolfram Web Resource.]
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| /third_party/boost/libs/random/doc/ |
| D | distributions.qbk | 10 this library provides distribution functions which map one distribution
11 (often a uniform distribution provided by some generator) to another.
18 values of the specified distribution or otherwise do not converge
22 [[distribution] [explanation] [example]]
23 [[__uniform_smallint] [discrete uniform distribution on a small set of integers
27 [[__uniform_int_distribution] [discrete uniform distribution on a set of integers; the
31 [[__uniform_01] [continuous uniform distribution on the range [0,1);
34 [[__uniform_real_distribution] [continuous uniform distribution on some range [min, max) of
42 [[distribution] [explanation] [example]]
44 distribution with configurable probability]
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| /third_party/mindspore/mindspore/nn/probability/distribution/ |
| D | transformed_distribution.py | 15 """Transformed Distribution"""
21 from .distribution import Distribution
26 class TransformedDistribution(Distribution):
28 Transformed Distribution.
29 This class contains a bijector and a distribution and transforms the original distribution
30 to a new distribution through the operation defined by the bijector.
34 distribution (Distribution): The original distribution. Must has a float dtype.
37 will use this seed; elsewise, the underlying distribution's seed will be used.
38 name (str): The name of the transformed distribution. Default: 'transformed_distribution'.
44 The arguments used to initialize the original distribution cannot be None.
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| D | log_normal.py | 15 """LogNormal Distribution"""
20 import mindspore.nn.probability.distribution as msd
27 LogNormal distribution.
28 …A log-normal (or lognormal) distribution is a continuous probability distribution of a random vari…
29 …normally distributed. It is constructed as the exponential transformation of a Normal distribution.
32 …float, list, numpy.ndarray, Tensor): The mean of the underlying Normal distribution. Default: None.
34 Normal distribution. Default: None.
36 dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
37 name (str): the name of the distribution. Default: 'LogNormal'.
51 >>> import mindspore.nn.probability.distribution as msd
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| D | uniform.py | 15 """Uniform Distribution"""
21 from .distribution import Distribution
26 class Uniform(Distribution):
28 Example class: Uniform Distribution.
31 … low (int, float, list, numpy.ndarray, Tensor): The lower bound of the distribution. Default: None.
32 …high (int, float, list, numpy.ndarray, Tensor): The upper bound of the distribution. Default: None.
35 name (str): The name of the distribution. Default: 'Uniform'.
48 >>> import mindspore.nn.probability.distribution as msd
50 >>> # To initialize a Uniform distribution of the lower bound 0.0 and the higher bound 1.0.
52 >>> # A Uniform distribution can be initialized without arguments.
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| D | cauchy.py | 15 """Cauchy Distribution"""
21 from .distribution import Distribution
26 class Cauchy(Distribution):
28 Cauchy distribution.
31 loc (int, float, list, numpy.ndarray, Tensor): The location of the Cauchy distribution.
32 scale (int, float, list, numpy.ndarray, Tensor): The scale of the Cauchy distribution.
35 name (str): The name of the distribution. Default: 'Cauchy'.
44 Cauchy distribution is not supported on GPU backend.
49 >>> import mindspore.nn.probability.distribution as msd
51 >>> # To initialize a Cauchy distribution of loc 3.0 and scale 4.0.
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| D | gamma.py | 15 """Gamma Distribution"""
22 from .distribution import Distribution
27 class Gamma(Distribution):
29 Gamma distribution.
33 also know as alpha of the Gamma distribution. Default: None.
35 beta of the Gamma distribution. Default: None.
38 name (str): The name of the distribution. Default: 'Gamma'.
51 >>> import mindspore.nn.probability.distribution as msd
53 >>> # To initialize a Gamma distribution of the concentration 3.0 and the rate 4.0.
55 >>> # A Gamma distribution can be initialized without arguments.
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| D | normal.py | 15 """Normal Distribution"""
21 from .distribution import Distribution
26 class Normal(Distribution):
28 Normal distribution.
31 …mean (int, float, list, numpy.ndarray, Tensor): The mean of the Normal distribution. Default: None.
32 …at, list, numpy.ndarray, Tensor): The standard deviation of the Normal distribution. Default: None.
35 name (str): The name of the distribution. Default: 'Normal'.
48 >>> import mindspore.nn.probability.distribution as msd
50 >>> # To initialize a Normal distribution of the mean 3.0 and the standard deviation 4.0.
52 >>> # A Normal distribution can be initialized without arguments.
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| D | logistic.py | 15 """Logistic Distribution"""
21 from .distribution import Distribution
26 class Logistic(Distribution):
28 Logistic distribution.
31 …loc (int, float, list, numpy.ndarray, Tensor): The location of the Logistic distribution. Default:…
32 …scale (int, float, list, numpy.ndarray, Tensor): The scale of the Logistic distribution. Default: …
35 name (str): The name of the distribution. Default: 'Logistic'.
48 >>> import mindspore.nn.probability.distribution as msd
50 >>> # To initialize a Logistic distribution of loc 3.0 and scale 4.0.
52 >>> # A Logistic distribution can be initialized without arguments.
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| /third_party/boost/libs/math/example/ |
| D | inverse_chi_squared_bayes_eg.cpp | 6 // Use, modification and distribution are subject to the
37 // Examples of using the inverse_chi_squared distribution. in main()
41 The scaled-inversed-chi-squared distribution is the conjugate prior distribution in main()
42 for the variance ([sigma][super 2]) parameter of a normal distribution in main()
48 itself described through a distribution. Parameters in main()
52 the scaled-inverse-chi-squared distribution as prior and posterior distribution in main()
53 for the variance parameter of a normal distribution. in main()
63 (As the scaled-inversed-chi-squared is another parameterization of the inverse-gamma distribution, in main()
64 this example could also have used the inverse-gamma distribution). in main()
70 following (approximately) a normal distribution. Depending on various production conditions in main()
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| /third_party/boost/boost/graph/distributed/adjlist/ |
| D | initialize.hpp | 3 // Use, modification and distribution is subject to the Boost Software
24 vertices_size_type, const base_distribution_type& distribution, in initialize() argument
29 if ((process_id_type)distribution(first->first) == id) { in initialize()
30 vertex_descriptor source(id, distribution.local(first->first)); in initialize()
31 vertex_descriptor target(distribution(first->second), in initialize()
32 distribution.local(first->second)); in initialize()
47 vertices_size_type, const base_distribution_type& distribution, in initialize() argument
52 if (static_cast<process_id_type>(distribution(first->first)) == id) { in initialize()
53 vertex_descriptor source(id, distribution.local(first->first)); in initialize()
54 vertex_descriptor target(distribution(first->second), in initialize()
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| /third_party/mindspore/mindspore/nn/probability/bnn_layers/ |
| D | dense_variational.py | 20 from ..distribution.normal import Normal
128 matrix sampling from posterior distribution of weight, and :math:`\text{bias}` is a
130 has_bias is True). The bias vector is sampling from posterior distribution of
141 weight_prior_fn (Cell): The prior distribution for weight.
142 It must return a mindspore distribution instance.
144 normal distribution). The current version only supports normal distribution.
145 weight_posterior_fn (function): The posterior distribution for sampling weight.
147 distribution instance. Default: normal_post_fn.
148 The current version only supports normal distribution.
149 bias_prior_fn (Cell): The prior distribution for bias vector. It must return
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| /third_party/boost/libs/math/doc/html/indexes/ |
| D | s03.html | 36 …ef/dists/arcine_dist.html" title="Arcsine Distribution"><span class="index-entry-level-1">Arcsine …
49 …ts/bernoulli_dist.html" title="Bernoulli Distribution"><span class="index-entry-level-1">Bernoulli…
53 …/dist_ref/dists/beta_dist.html" title="Beta Distribution"><span class="index-entry-level-1">Beta D…
57 …dists/binomial_dist.html" title="Binomial Distribution"><span class="index-entry-level-1">Binomial…
67 …auchy_dist.html" title="Cauchy-Lorentz Distribution"><span class="index-entry-level-1">Cauchy-Lore…
76 …i_squared_dist.html" title="Chi Squared Distribution"><span class="index-entry-level-1">Chi Square…
114 …dists/exp_dist.html" title="Exponential Distribution"><span class="index-entry-level-1">Exponentia…
118 …xtreme_dist.html" title="Extreme Value Distribution"><span class="index-entry-level-1">Extreme Val…
128 …toolkit/dist_ref/dists/f_dist.html" title="F Distribution"><span class="index-entry-level-1">F Dis…
159 …dist.html" title="Gamma (and Erlang) Distribution"><span class="index-entry-level-1">Gamma (and Er…
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| /third_party/boost/libs/compute/test/ |
| D | test_discrete_distribution.cpp | 40 // setup the discrete distribution to produce integers 0 and 1 in BOOST_AUTO_TEST_CASE()
42 boost::compute::discrete_distribution<uint_> distribution(weights, weights+2); in BOOST_AUTO_TEST_CASE() local
45 distribution.generate(vec.begin(), vec.end(), engine, queue); in BOOST_AUTO_TEST_CASE()
70 // setup the discrete distribution in BOOST_AUTO_TEST_CASE()
71 boost::compute::discrete_distribution<uint_> distribution( in BOOST_AUTO_TEST_CASE() local
75 std::vector<double> p = distribution.probabilities(); in BOOST_AUTO_TEST_CASE()
81 BOOST_CHECK_EQUAL((distribution.min)(), uint_(0)); in BOOST_AUTO_TEST_CASE()
82 BOOST_CHECK_EQUAL((distribution.max)(), uint_(3)); in BOOST_AUTO_TEST_CASE()
85 distribution.generate(vec.begin(), vec.end(), engine, queue); in BOOST_AUTO_TEST_CASE()
107 boost::compute::discrete_distribution<uint_> distribution; in BOOST_AUTO_TEST_CASE() local
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| /third_party/boost/libs/math/doc/html/math_toolkit/dist_ref/ |
| D | dists.html | 10 <link rel="next" href="dists/arcine_dist.html" title="Arcsine Distribution">
30 <dt><span class="section"><a href="dists/arcine_dist.html">Arcsine Distribution</a></span></dt>
32 Distribution</a></span></dt>
33 <dt><span class="section"><a href="dists/beta_dist.html">Beta Distribution</a></span></dt>
35 Distribution</a></span></dt>
37 Distribution</a></span></dt>
39 Distribution</a></span></dt>
41 Cumulative Distribution Function</a></span></dt>
42 <dt><span class="section"><a href="dists/exp_dist.html">Exponential Distribution</a></span></dt>
44 Distribution</a></span></dt>
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| /third_party/boost/libs/math/doc/html/math_toolkit/ |
| D | dist_ref.html | 33 <dt><span class="section"><a href="dist_ref/dists/arcine_dist.html">Arcsine Distribution</a></span>…
35 Distribution</a></span></dt>
36 <dt><span class="section"><a href="dist_ref/dists/beta_dist.html">Beta Distribution</a></span></dt>
38 Distribution</a></span></dt>
40 Distribution</a></span></dt>
42 Distribution</a></span></dt>
44 Cumulative Distribution Function</a></span></dt>
45 <dt><span class="section"><a href="dist_ref/dists/exp_dist.html">Exponential Distribution</a></span…
47 Distribution</a></span></dt>
48 <dt><span class="section"><a href="dist_ref/dists/f_dist.html">F Distribution</a></span></dt>
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| /third_party/boost/boost/random/ |
| D | variate_generator.hpp | 32 * generator together with a random number distribution.
50 template<class Engine, class Distribution>
58 typedef Distribution distribution_type;
59 typedef typename Distribution::result_type result_type;
67 * Distribution throws.
69 variate_generator(Engine e, Distribution d) in variate_generator()
72 /** Returns: distribution()(engine()) */
75 * Returns: distribution()(engine(), value).
90 distribution_type& distribution() { return _dist; } in distribution() function in boost::random::variate_generator
92 * Returns: A reference to the associated random distribution.
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| D | non_central_chi_squared_distribution.hpp | 32 * The noncentral chi-squared distribution is a real valued distribution with
33 * two parameter, @c k and @c lambda. The distribution produces values > 0.
35 * This is the distribution of the sum of squares of k Normal distributed
39 * The distribution function is
64 * @c k and @c lambda are the parameter of the distribution.
76 /** Returns the @c k parameter of the distribution */
79 /** Returns the @c lambda parameter of the distribution */
82 /** Writes the parameters of the distribution to a @c std::ostream. */
89 /** Reads the parameters of the distribution from a @c std::istream. */
110 * @c lambda are the parameter of the distribution.
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