| /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/boost/boost/graph/distributed/adjlist/ |
| D | initialize.hpp | 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()
55 distribution.local(first->second)); in initialize()
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| /third_party/boost/libs/compute/test/ |
| D | test_discrete_distribution.cpp | 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()
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
109 std::vector<double> p = distribution.probabilities(); in BOOST_AUTO_TEST_CASE()
113 distribution.generate(vec.begin(), vec.end(), engine, queue); in BOOST_AUTO_TEST_CASE()
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| /third_party/grpc/src/python/grpcio_tests/ |
| D | commands.py | 105 self.distribution.fetch_build_eggs(self.distribution.install_requires)
106 self.distribution.fetch_build_eggs(self.distribution.tests_require)
136 self.distribution.fetch_build_eggs(self.distribution.install_requires)
137 self.distribution.fetch_build_eggs(self.distribution.tests_require)
168 self.distribution.fetch_build_eggs(self.distribution.install_requires)
169 self.distribution.fetch_build_eggs(self.distribution.tests_require)
291 if self.distribution.install_requires:
292 self.distribution.fetch_build_eggs(
293 self.distribution.install_requires)
294 if self.distribution.tests_require:
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| /third_party/mindspore/mindspore/nn/probability/distribution/ |
| D | transformed_distribution.py | 21 from .distribution import Distribution
85 distribution, argument
94 validator.check_value_type('distribution', distribution,
96 … validator.check_type_name("dtype", distribution.dtype, mstype.float_type, type(self).__name__)
97 super(TransformedDistribution, self).__init__(seed, distribution.dtype, name, param)
100 self._distribution = distribution
104 self._dtype = self.distribution.dtype
105 self._is_scalar_batch = self.distribution.is_scalar_batch and self.bijector.is_scalar_batch
106 self._batch_shape = self.distribution.batch_shape
108 self.default_parameters = self.distribution.default_parameters
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| D | log_normal.py | 20 import mindspore.nn.probability.distribution as msd
74 super(LogNormal, self).__init__(distribution=msd.Normal(loc, scale, dtype=dtype),
144 var = self.distribution("var", mean=mean, sd=sd)
152 var = self.distribution("var", mean=mean, sd=sd)
160 var = self.distribution("var", mean=mean, sd=sd)
184 cdf = self.distribution("cdf", inverse_value, mean, sd)
203 unadjust_prob = self.distribution("log_prob", inverse_value, mean, sd)
237 return self.distribution("kl_loss", 'Normal', loc_b, scale_b, loc_a, scale_a)
249 org_sample = self.distribution("sample", sample_shape, mean, sd)
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| /third_party/boost/libs/math/doc/distributions/ |
| D | non_members.qbk | 73 the defined range for the distribution.
76 normal distribution:
93 the defined range for the distribution.
98 // standard normal distribution object:
104 normal distribution:
115 Returns the __hazard of /x/ and distribution /dist/.
118 the defined range for the distribution.
131 Returns the __chf of /x/ and distribution /dist/.
134 the defined range for the distribution.
146 Returns the mean of the distribution /dist/.
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| D | triangular.qbk | 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
57 The triangular distribution may be appropriate when an assumption of a normal distribution
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| D | inverse_chi_squared.qbk | 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/…
48 * Inverse chi_squared distribution functions [@http://mathworld.wolfram.com/InverseChi-SquaredDistr…
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| D | inverse_gaussian.qbk | 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].
57 If you want a `double` precision inverse_gaussian distribution you can use
68 the inverse_gaussian distribution is defined by the probability density function (PDF):
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| D | inverse_gamma.qbk | 23 The inverse_gamma distribution is a continuous probability distribution
24 of the reciprocal of a variable distributed according to the gamma distribution.
26 The inverse_gamma distribution is used in Bayesian statistics.
28 See [@http://en.wikipedia.org/wiki/Inverse-gamma_distribution inverse gamma distribution].
30 [@http://rss.acs.unt.edu/Rdoc/library/pscl/html/igamma.html R inverse gamma distribution functions].
32 …ence.wolfram.com/mathematica/ref/InverseGammaDistribution.html Wolfram inverse gamma distribution].
38 distribution *does* provide the typedef:
42 If you want a `double` precision gamma distribution you can use
57 The following graphs illustrate how the PDF and CDF of the inverse gamma distribution
68 Constructs an inverse gamma distribution with shape [alpha] and scale [beta].
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| D | skew_normal.qbk | 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
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
46 of the [@http://en.wikipedia.org/wiki/Skew_normal_distribution skew normal distribution]
64 where ['T(h,a)] is Owen's T function, and ['[Phi](x)] is the normal distribution.
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| D | bernoulli.qbk | 26 The Bernoulli distribution is a discrete distribution of the outcome
30 The Bernoulli distribution is the simplest building block
34 The Bernoulli is the binomial distribution (k = 1, p) with only one trial.
39 [@http://en.wikipedia.org/wiki/Cumulative_Distribution_Function Cumulative distribution function]
48 and the [@http://en.wikipedia.org/wiki/Cumulative_Distribution_Function Cumulative distribution fun…
57 bernoulli distribution] with success_fraction /p/.
61 Returns the /success_fraction/ parameter of this distribution.
76 The Bernoulli distribution is implemented with simple arithmetic operators
84 [note The Bernoulli distribution is implemented here as a /strict discrete/ distribution.
86 the binomial distribution with a single trial should be used, for example:
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| D | uniform.qbk | 20 : m_lower(lower), m_upper(upper) // Default is standard uniform distribution.
28 The uniform distribution, also known as a rectangular distribution,
29 is a probability distribution that has constant probability.
31 …http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29 continuous uniform distribution]
32 is a distribution with the
42 The choice of /x = lower/ or /x = upper/ is made because statistical use of this distribution judge…
45 …so a [@http://en.wikipedia.org/wiki/Discrete_uniform_distribution *discrete* uniform distribution].
72 uniform distribution] with lower /lower/ (a) and upper /upper/ (b).
79 Returns the /lower/ parameter of this distribution.
83 Returns the /upper/ parameter of this distribution.
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| D | gamma.qbk | 23 The gamma distribution is a continuous probability distribution.
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:
61 The following two graphs illustrate how the PDF of the gamma distribution
81 Constructs a gamma distribution with shape /shape/ and
89 Returns the /shape/ parameter of this distribution.
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| D | cauchy.qbk | 24 The [@http://en.wikipedia.org/wiki/Cauchy_distribution Cauchy-Lorentz distribution]
26 It is a [@http://en.wikipedia.org/wiki/Probability_distribution continuous probability distribution]
27 with [@http://en.wikipedia.org/wiki/Probability_distribution probability distribution function PDF]
33 peak of the distribution (the mode of the distribution),
37 distribution.
39 The distribution is important in physics as it is the solution
50 the distribution:
58 Constructs a Cauchy distribution, with location parameter /location/
67 Returns the location parameter of the distribution.
71 Returns the scale parameter of the distribution.
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| D | hyperexponential.qbk | 60 …ents a [@http://en.wikipedia.org/wiki/Hyperexponential_distribution hyperexponential distribution].
62 …yperexponential distribution is a [@http://en.wikipedia.org/wiki/Continuous_probability_distributi…
63 It is also referred to as /mixed exponential distribution/ or parallel /k-phase exponential distrib…
65 A /k/-phase hyperexponential distribution is characterized by two parameters, namely a /phase proba…
67 …robability density function] for random variate /x/ in a hyperexponential distribution is given by:
71 The following graph illustrates the PDF of the hyperexponential distribution with five different pa…
73 …*[alpha]]=(1.0)] and ['[*[lambda]]=(1.0)] (which degenerates to a simple exponential distribution),
81 Also, the following graph illustrates the PDF of the hyperexponential distribution (solid lines) wh…
86 # Exponential distribution with parameter ['[lambda]=0.5],
87 # Exponential distribution with parameter ['[lambda]=1.5].
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| D | students_t.qbk | 36 Student's t-distribution is a statistical distribution published by William Gosset in 1908.
47 [@https://en.wikipedia.org/wiki/Student%27s_t-distribution Student's t-distribution]
48 is defined as the distribution of the random
54 The Student's t-distribution takes a single parameter: the number of
56 /one/ then this distribution is the same as the Cauchy-distribution.
58 distribution approaches the normal-distribution. The following graph
67 Constructs a Student's t-distribution with /v/ degrees of freedom.
76 returns the number of degrees of freedom of this distribution.
119 distribution.
123 The normal distribution is implemented in terms of the
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| D | weibull.qbk | 28 The [@http://en.wikipedia.org/wiki/Weibull_distribution Weibull distribution]
29 is a continuous distribution
37 The Weibull distribution is often used in the field of failure analysis;
56 [@http://en.wikipedia.org/wiki/Weibull_distribution Weibull distribution] appears similar to the
57 [@http://en.wikipedia.org/wiki/Normal_distribution normal distribution].
58 When ['[alpha]] = 1, the Weibull distribution reduces to the
59 [@http://en.wikipedia.org/wiki/Exponential_distribution exponential distribution].
71 Weibull distribution] with shape /shape/ and scale /scale/.
78 Returns the /shape/ parameter of this distribution.
82 Returns the /scale/ parameter of this distribution.
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| D | beta.qbk | 55 [@http://en.wikipedia.org/wiki/Probability_distribution probability distribution function].
57 The [@http://mathworld.wolfram.com/BetaDistribution.htm beta distribution ]
58 is used as a [@http://en.wikipedia.org/wiki/Prior_distribution prior distribution]
63 …om/calculationcenter/v2/Functions/ListsMatrices/Statistics/BetaDistribution.html beta distribution]
66 How the beta distribution is used for
72 for the [@http://en.wikipedia.org/wiki/Beta_distribution beta distribution]
85 distribution.
90 [@http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29 uniform distribution],
103 Constructs a beta distribution with shape parameters /alpha/ and /beta/.
106 technically the beta distribution is defined for alpha,beta >= 0, but
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| D | dist_tutorial.qbk | 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;
60 * The distribution objects store any parameters associated with the
61 distribution: for example, the Students-t distribution has a
62 ['degrees of freedom] parameter that controls the shape of the distribution.
66 Although the distribution classes in this library are templates, there
68 distribution
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| /third_party/gettext/gettext-tools/src/ |
| D | format.c | 76 const struct plural_distribution *distribution, in check_msgid_msgstr_format_i() argument
136 || (distribution != NULL in check_msgid_msgstr_format_i()
137 && distribution->often != NULL in check_msgid_msgstr_format_i()
138 && j < distribution->often_length in check_msgid_msgstr_format_i()
139 && distribution->often[j] in check_msgid_msgstr_format_i()
141 && distribution->histogram (distribution, in check_msgid_msgstr_format_i()
178 const struct plural_distribution *distribution, in check_msgid_msgstr_format() argument
195 distribution, in check_msgid_msgstr_format()
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| D | msgl-check.c | 103 struct plural_distribution *distribution) in check_plural_eval() argument
169 distribution->expr = plural_expr; in check_plural_eval()
170 distribution->often = array; in check_plural_eval()
171 distribution->often_length = (array != NULL ? nplurals_value : 0); in check_plural_eval()
172 distribution->histogram = plural_expression_histogram; in check_plural_eval()
298 struct plural_distribution distribution; in check_plural() local
308 distribution.expr = NULL; in check_plural()
309 distribution.often = NULL; in check_plural()
310 distribution.often_length = 0; in check_plural()
311 distribution.histogram = NULL; in check_plural()
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| /third_party/python/Lib/distutils/command/ |
| D | bdist_rpm.py | 205 if not self.distribution.has_ext_modules():
214 "%s <%s>" % (self.distribution.get_contact(),
215 self.distribution.get_contact_email()))
278 "%s.spec" % self.distribution.get_name())
289 saved_dist_files = self.distribution.dist_files[:]
296 self.distribution.dist_files = saved_dist_files
365 if self.distribution.has_ext_modules():
375 self.distribution.dist_files.append(
385 self.distribution.dist_files.append(
397 '%define name ' + self.distribution.get_name(),
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| /third_party/protobuf/java/core/src/test/java/com/google/protobuf/ |
| D | Utf8Utils.java | 159 final int[] distribution = utf8Distribution.getDistribution(); in randomStringsWithDistribution() local
161 distribution[i + 1] += distribution[i]; in randomStringsWithDistribution()
171 codePoint = rnd.nextInt(distribution[3]); in randomStringsWithDistribution()
172 if (codePoint < distribution[0]) { in randomStringsWithDistribution()
175 } else if (codePoint < distribution[1]) { in randomStringsWithDistribution()
178 } else if (codePoint < distribution[2]) { in randomStringsWithDistribution()
|