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/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/
Dmultinomial_impl.cu21 __global__ void CheckZeroKernel(const size_t distributions, const size_t categories, const T *input… in CheckZeroKernel() argument
23 …for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (distributions); pos += blockDim.x … in CheckZeroKernel()
32 void CheckZero(const size_t distributions, const size_t categories, const T *input, T *output, in CheckZero() argument
34 …CheckZeroKernel<<<GET_BLOCKS(distributions), GET_THREADS, 0, cuda_stream>>>(distributions, categor… in CheckZero()
54 __global__ void NormInputKernel(T *input, const size_t distributions, const size_t categories) { in NormInputKernel() argument
55 size_t size = distributions * categories; in NormInputKernel()
66 void NormInput(T *input, const size_t distributions, const size_t categories, cudaStream_t cuda_str… in NormInput() argument
67 int count1 = distributions * categories; in NormInput()
68 …NormInputKernel<<<GET_BLOCKS(count1), GET_THREADS, 0, cuda_stream>>>(input, distributions, categor… in NormInput()
92 size_t distributions, size_t categories) { in MultinomialKernel() argument
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Dmultinomial_impl.cuh24 size_t distributions, size_t categories, cudaStream_t cuda_stream);
28 void CheckZero(const size_t distributions, const size_t categories, const T *input, T *output, cuda…
30 void NormInput(T *input, const size_t distributions, const size_t categories, cudaStream_t cuda_str…
/third_party/skia/third_party/externals/abseil-cpp/absl/random/
DBUILD.bazel37 ":distributions",
47 name = "distributions",
56 "distributions.h",
133 ":distributions",
149 ":distributions",
171 ":distributions",
188 ":distributions",
211 ":distributions",
225 ":distributions",
240 ":distributions",
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DBUILD.gn10 ":distributions",
19 absl_source_set("distributions") {
24 "distributions.h",
/third_party/abseil-cpp/absl/random/
DBUILD.bazel37 ":distributions",
47 name = "distributions",
56 "distributions.h",
132 ":distributions",
148 ":distributions",
170 ":distributions",
187 ":distributions",
209 ":distributions",
223 ":distributions",
238 ":distributions",
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/third_party/boost/libs/math/dot_net_example/boost_math/
Dboost_math.cpp159 distribution_info distributions[] = variable
200 return sizeof(distributions) / sizeof(distributions[0]); in size()
208 return gcnew System::String(distributions[i].name);
215 return gcnew System::String(distributions[i].first_param);
222 return gcnew System::String(distributions[i].second_param);
229 return gcnew System::String(distributions[i].third_param);
236 return distributions[i].first_default; in first_param_default()
243 return distributions[i].second_default; in second_param_default()
250 return distributions[i].third_default; in third_param_default()
/third_party/python/Lib/test/test_importlib/
Dtest_main.py19 distributions,
165 dists = list(distributions())
172 list(distributions(context='something', name='else'))
199 importlib.metadata.distributions()
215 list(importlib.metadata.distributions())
273 list(distributions())
Dtest_zip.py8 distributions,
62 dists = list(distributions(path=sys.path[:1]))
/third_party/boost/libs/math/doc/distributions/
Ddist_tutorial.qbk13 This library is centred around statistical distributions, this tutorial
25 the "include all the distributions" header: `<boost/math/distributions.hpp>`.
29 `<boost/math/distributions.hpp>`
53 of discrete distributions behave.
55 Making distributions class types does two things:
58 so, for example, Students-t distributions are always a different C++ type from
59 Chi-Squared distributions.
88 If you need to use the distributions with a type other than `double`,
102 The parameters passed to the distributions can be accessed via getter member
109 /quantiles/ etc for these distributions.
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Dinverse_gaussian.qbk3 ``#include <boost/math/distributions/inverse_gaussian.hpp>``
31 This is the Standard form for all distributions.
35 The inverse Gaussian is one of family of distributions that have been called the
36 [@http://en.wikipedia.org/wiki/Tweedie_distributions Tweedie distributions].
49 The normal-inverse Gaussian distributions form
50 a subclass of the generalised hyperbolic distributions.
112 distributions are supported: __usual_accessors.
162 #[@http://www.brighton-webs.co.uk/distributions/wald.asp Brighton Webs wald].
Dtriangular.qbk4 ``#include <boost/math/distributions/triangular.hpp>``
117 distributions are supported: __usual_accessors.
148 (See [@../../../../boost/math/distributions/triangular.hpp /boost/math/distributions/triangular.hpp…
149 …ement][As quantile (See [@../../../../boost/math/distributions/triangular.hpp /boost/math/distribu…
153 [[skewness][(See [@../../../../boost/math/distributions/triangular.hpp /boost/math/distributions/tr…
Drayleigh.qbk4 ``#include <boost/math/distributions/rayleigh.hpp>``
49 [h4 Related distributions]
51 The absolute value of two independent normal distributions X and Y, [radic] (X[super 2] + Y[super 2…
56 and [@http://en.wikipedia.org/wiki/Weibull_distribution Weibull] distributions are generalizations …
76 distributions are supported: __usual_accessors.
Dweibull.qbk3 ``#include <boost/math/distributions/weibull.hpp>``
38 in particular it can mimic distributions where the failure rate varies over time.
53 [h4 Related distributions]
60 The relationship of the types of extreme value distributions, of which the Weibull is but one, is
87 distributions are supported: __usual_accessors.
Dc_sharp.qbk3 The distributions in this library can be used from the C# programming language
10 actual distributions, and a C# GUI that allows you to explore their properties.
Ddist_algorithms.qbk3 [h4 Finding the Location and Scale for Normal and similar distributions]
7 Only applies to distributions like normal, lognormal, extreme value, Cauchy, (and symmetrical trian…
19 ``#include <boost/math/distributions/find_location.hpp>``
42 ``#include <boost/math/distributions/find_scale.hpp>``
Dextreme_value.qbk3 ``#include <boost/math/distributions/extreme.hpp>``
24 [@http://mathworld.wolfram.com/ExtremeValueDistribution.html extreme value distributions]
39 The relationship of the types of extreme value distributions, of which this is but one, is
80 that are generic to all distributions are supported: __usual_accessors.
Dexponential.qbk3 ``#include <boost/math/distributions/exponential.hpp>``
56 that are generic to all distributions are supported: __usual_accessors.
98 discuss the relationship of the types of extreme value distributions.
Dnormal.qbk3 ``#include <boost/math/distributions/normal.hpp>``
72 (Redundant location and scale function are provided to match other similar distributions,
78 distributions are supported: __usual_accessors.
/third_party/python/Doc/distutils/
Dbuiltdist.rst20 able to create built distributions for every platform under the sun, so the
22 specialty---writing code and creating source distributions---while an
23 intermediary species called *packagers* springs up to turn source distributions
24 into built distributions for as many platforms as there are packagers.
29 source distributions and turning them into built distributions for as many
32 distributions.
42 format for built distributions is a "dumb" tar file on Unix, and a simple
52 distributions relative to :file:`{prefix}`.)
54 Obviously, for pure Python distributions, this isn't any simpler than just
55 running ``python setup.py install``\ ---but for non-pure distributions, which
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Dintroduction.rst31 * (optional) create one or more built (binary) distributions
36 always feasible to expect them to create a multitude of built distributions. It
38 address this need. Packagers will take source distributions released by module
40 distributions. Thus, users on the most popular platforms will be able to
41 install most popular Python module distributions in the most natural way for
187 module distributions are NumPy, SciPy, Pillow,
/third_party/ltp/doc/
Dsupported-kernel-libc-versions.txt8 to ensure LTP builds on various distributions including old, current and bleeding edge.
15 the test compiles fine on variety of different distributions and releases.
18 1.1 Oldest tested distributions
29 Older distributions are not officially supported, which means that it
31 to compile latest LTP even on slightly older distributions than we
/third_party/boost/libs/math/doc/overview/
Dcredits.qbk13 of the statistical distributions.
21 implemented a few of the statistical distributions. PAB also tirelessly
51 (Thijs has also threatened to implement some multivariate distributions).
53 Thomas Mang requested the inverse gamma in chi squared distributions
69 Plots of the functions and distributions were prepared in
93 We thank Thomas Mang for persuading us to allow t distributions
100 which now can be used to allow most functions and distributions
125 These also promise to help improve algorithms for computation of quantile of several distributions,
Dissues.qbk61 [h4 Statistical distributions]
68 The following table lists distributions that are found in other packages
117 * Add support for interpolated distributions, possibly combine with numeric
119 * Add support for bivariate and multivariate distributions: most especially the normal.
122 some special function calls for some distributions
/third_party/boost/libs/math/test/compile_test/
Dgenerate.sh16 for file in ../../../../boost/math/distributions/*.hpp; do
23 // Basic sanity check that header <boost/math/distributions/$(basename $file)>
/third_party/boost/libs/random/doc/
Drandom.qbk19 [template distributions[text] [link boost_random.reference.distributions [text]]]
108 [generators generators] and [distributions distributions] to produce
121 // see random number distributions
141 [include distributions.qbk]

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