Home
last modified time | relevance | path

Searched refs:distribution (Results 1 – 25 of 2474) sorted by relevance

12345678910>>...99

/external/tensorflow/tensorflow/contrib/distributions/python/ops/
Dindependent.py30 from tensorflow.python.ops.distributions import distribution as distribution_lib
97 self, distribution, reinterpreted_batch_ndims=None, argument
119 name = name or "Independent" + distribution.name
120 self._distribution = distribution
124 distribution)
142 distribution._graph_parents), # pylint: disable=protected-access
145 distribution, reinterpreted_batch_ndims, validate_args)
148 def distribution(self): member in Independent
157 batch_shape = self.distribution.batch_shape_tensor()
164 batch_shape = self.distribution.batch_shape
[all …]
Dquantized_distribution.py28 from tensorflow.python.ops.distributions import distribution as distributions
182 distribution, argument
218 list(distribution.parameters.values()) +
221 self._dist = distribution
228 tensors=[self.distribution, low, high])
263 return self.distribution.batch_shape_tensor()
266 return self.distribution.batch_shape
269 return self.distribution.event_shape_tensor()
272 return self.distribution.event_shape
279 x_samps = self.distribution.sample(n, seed=seed)
[all …]
Dpoisson_lognormal.py33 from tensorflow.python.ops.distributions import distribution as distribution_lib
116 distribution=normal_lib.Normal(loc=loc, scale=scale),
303 def distribution(self): member in PoissonLogNormalQuadratureCompound
323 self.distribution.batch_shape_tensor(),
328 self.distribution.batch_shape,
369 array_ops.reshape(self.distribution.rate, shape=[-1]), ids)
378 + self.distribution.log_prob(x[..., array_ops.newaxis])),
384 self.mixture_distribution.logits + self.distribution.log_rate,
409 self.distribution.log_rate,
412 math_ops.abs(self.distribution.mean()
Dsinh_arcsinh.py102 distribution=None, argument
156 if distribution is None:
157 distribution = normal.Normal(
163 distribution, dtype, validate_args)
188 distribution=distribution,
/external/tensorflow/tensorflow/tools/api/golden/
Dtensorflow.distributions.pbtxt5 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
9 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
13 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
17 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
21 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
25 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
29 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
33 mtype: "<class \'tensorflow.python.ops.distributions.distribution.ReparameterizationType\'>"
37 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
41 mtype: "<class \'tensorflow.python.ops.distributions.distribution._DistributionMeta\'>"
[all …]
/external/python/cpython3/Lib/distutils/command/
Dbdist_rpm.py206 if not self.distribution.has_ext_modules():
215 "%s <%s>" % (self.distribution.get_contact(),
216 self.distribution.get_contact_email()))
279 "%s.spec" % self.distribution.get_name())
290 saved_dist_files = self.distribution.dist_files[:]
297 self.distribution.dist_files = saved_dist_files
369 if self.distribution.has_ext_modules():
379 self.distribution.dist_files.append(
389 self.distribution.dist_files.append(
401 '%define name ' + self.distribution.get_name(),
[all …]
Dbdist_wininst.py81 bdist = self.distribution.get_command_obj('bdist')
90 if not self.skip_build and self.distribution.has_ext_modules():
104 for script in self.distribution.scripts:
114 (self.distribution.has_ext_modules() or
115 self.distribution.has_c_libraries())):
134 if self.distribution.has_ext_modules():
175 fullname = self.distribution.get_fullname()
180 if self.distribution.has_ext_modules():
184 self.distribution.dist_files.append(('bdist_wininst', pyversion,
196 metadata = self.distribution.metadata
[all …]
Dsdist.py161 check = self.distribution.get_command_obj('check')
219 standards = [('README', 'README.txt'), self.distribution.script_name]
250 if self.distribution.has_pure_modules():
260 if self.distribution.has_data_files():
261 for item in self.distribution.data_files:
273 if self.distribution.has_ext_modules():
277 if self.distribution.has_c_libraries():
281 if self.distribution.has_scripts():
323 base_dir = self.distribution.get_fullname()
420 self.distribution.metadata.write_pkg_info(base_dir)
[all …]
Dinstall_lib.py96 if outfiles is not None and self.distribution.has_pure_modules():
104 if self.distribution.has_pure_modules():
106 if self.distribution.has_ext_modules():
186 self._mutate_outputs(self.distribution.has_pure_modules(),
195 self._mutate_outputs(self.distribution.has_ext_modules(),
209 if self.distribution.has_pure_modules():
213 if self.distribution.has_ext_modules():
/external/python/cpython2/Lib/distutils/command/
Dbdist_rpm.py215 if not self.distribution.has_ext_modules():
226 "%s <%s>" % (self.distribution.get_contact(),
227 self.distribution.get_contact_email()))
293 "%s.spec" % self.distribution.get_name())
304 saved_dist_files = self.distribution.dist_files[:]
311 self.distribution.dist_files = saved_dist_files
383 if self.distribution.has_ext_modules():
393 self.distribution.dist_files.append(
403 self.distribution.dist_files.append(
416 '%define name ' + self.distribution.get_name(),
[all …]
Dbdist_wininst.py89 bdist = self.distribution.get_command_obj('bdist')
98 if not self.skip_build and self.distribution.has_ext_modules():
112 for script in self.distribution.scripts:
124 (self.distribution.has_ext_modules() or
125 self.distribution.has_c_libraries())):
144 if self.distribution.has_ext_modules():
185 fullname = self.distribution.get_fullname()
190 if self.distribution.has_ext_modules():
194 self.distribution.dist_files.append(('bdist_wininst', pyversion,
209 metadata = self.distribution.metadata
[all …]
Dsdist.py174 check = self.distribution.get_command_obj('check')
233 standards = [('README', 'README.txt'), self.distribution.script_name]
265 if self.distribution.has_pure_modules():
275 if self.distribution.has_data_files():
276 for item in self.distribution.data_files:
288 if self.distribution.has_ext_modules():
292 if self.distribution.has_c_libraries():
296 if self.distribution.has_scripts():
342 base_dir = self.distribution.get_fullname()
441 self.distribution.metadata.write_pkg_info(base_dir)
[all …]
Dinstall_lib.py100 if outfiles is not None and self.distribution.has_pure_modules():
108 if self.distribution.has_pure_modules():
110 if self.distribution.has_ext_modules():
188 self._mutate_outputs(self.distribution.has_pure_modules(),
197 self._mutate_outputs(self.distribution.has_ext_modules(),
211 if self.distribution.has_pure_modules():
215 if self.distribution.has_ext_modules():
/external/tensorflow/tensorflow/python/ops/distributions/
Dtransformed_distribution.py32 from tensorflow.python.ops.distributions import distribution as distribution_lib
234 distribution, argument
260 distribution.name)
274 batch_shape, distribution.is_scalar_batch(), validate_args,
283 event_shape, distribution.is_scalar_event(), validate_args,
300 _logical_not(distribution.is_scalar_batch()))
309 self._distribution = distribution
319 graph_parents=(distribution._graph_parents + # pylint: disable=protected-access
324 def distribution(self): member in TransformedDistribution
338 self.distribution.event_shape_tensor()))
[all …]
/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.poisson/
Deval.pass.cpp39 std::poisson_distribution<std::int16_t> distribution(32710.9); in test_bad_ranges() local
41 volatile std::int16_t res = distribution(eng); in test_bad_ranges()
46 std::poisson_distribution<std::int16_t> distribution(std::numeric_limits<std::int16_t>::max()); in test_bad_ranges() local
48 volatile std::int16_t res = distribution(eng); in test_bad_ranges()
53 std::poisson_distribution<std::int16_t> distribution( in test_bad_ranges()
56 volatile std::int16_t res = distribution(eng); in test_bad_ranges()
61 std::poisson_distribution<std::int16_t> distribution( in test_bad_ranges()
64 volatile std::int16_t res = distribution(eng); in test_bad_ranges()
70 std::poisson_distribution<std::int16_t> distribution(std::numeric_limits<double>::infinity()); in test_bad_ranges()
72 volatile std::int16_t res = distribution(eng); in test_bad_ranges()
[all …]
/external/iproute2/netem/
DREADME.distribution1 Notes about distribution tables from Nistnet
3 I. About the distribution tables
5 The table used for "synthesizing" the distribution is essentially a scaled,
6 translated, inverse to the cumulative distribution function.
8 Here's how to think about it: Let F() be the cumulative distribution
9 function for a probability distribution X. We'll assume we've scaled
26 distribution has the same approximate "shape" as X, simply by letting
28 To see this, it's enough to show that Y's cumulative distribution function,
41 II. How to create distribution tables (in theory)
45 pareto distribution is one example of this. In other cases, and
[all …]
/external/apache-commons-math/src/main/java/org/apache/commons/math/random/
DRandomDataImpl.java28 import org.apache.commons.math.distribution.BetaDistributionImpl;
29 import org.apache.commons.math.distribution.BinomialDistributionImpl;
30 import org.apache.commons.math.distribution.CauchyDistributionImpl;
31 import org.apache.commons.math.distribution.ChiSquaredDistributionImpl;
32 import org.apache.commons.math.distribution.ContinuousDistribution;
33 import org.apache.commons.math.distribution.FDistributionImpl;
34 import org.apache.commons.math.distribution.GammaDistributionImpl;
35 import org.apache.commons.math.distribution.HypergeometricDistributionImpl;
36 import org.apache.commons.math.distribution.IntegerDistribution;
37 import org.apache.commons.math.distribution.PascalDistributionImpl;
[all …]
/external/python/cpython2/Lib/distutils/tests/
Dtest_install_lib.py62 cmd.distribution.py_modules = [pkg_dir]
63 cmd.distribution.ext_modules = [Extension('foo', ['xxx'])]
64 cmd.distribution.packages = [pkg_dir]
65 cmd.distribution.script_name = 'setup.py'
79 cmd.distribution.py_modules = [pkg_dir]
80 cmd.distribution.ext_modules = [Extension('foo', ['xxx'])]
81 cmd.distribution.packages = [pkg_dir]
82 cmd.distribution.script_name = 'setup.py'
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/regression/
DSimpleRegression.java23 import org.apache.commons.math.distribution.TDistribution;
24 import org.apache.commons.math.distribution.TDistributionImpl;
64 private TDistribution distribution; field in SimpleRegression
155 distribution.setDegreesOfFreedom(n - 2); in addData()
187 distribution.setDegreesOfFreedom(n - 2); in removeData()
571 distribution.inverseCumulativeProbability(1d - alpha / 2d); in getSlopeConfidenceInterval()
596 return 2d * (1.0 - distribution.cumulativeProbability( in getSignificance()
632 distribution = value; in setDistribution()
636 distribution.setDegreesOfFreedom(n - 2); in setDistribution()
/external/iproute2/
DREADME.distribution1 I. About the distribution tables
3 The table used for "synthesizing" the distribution is essentially a scaled,
4 translated, inverse to the cumulative distribution function.
6 Here's how to think about it: Let F() be the cumulative distribution
7 function for a probability distribution X. We'll assume we've scaled
24 distribution has the same approximate "shape" as X, simply by letting
26 To see this, it's enough to show that Y's cumulative distribution function,
39 II. How to create distribution tables (in theory)
43 Pareto distribution is one example of this. In other cases, and
44 especially for matching an experimentally observed distribution, it's
[all …]
/external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/
Dquantized_distribution_test.py60 distribution=distributions.Uniform(low=0.0, high=3.0),
105 distribution=distributions.Uniform(low=-3., high=3.),
144 distribution=uniform, low=None, high=None)
176 distribution=normal, low=0., high=None)
204 distribution=distributions.Exponential(rate=0.01))
227 distribution=distributions.Exponential(rate=0.2))
248 distribution=distributions.Normal(
265 distribution=distributions.Normal(
280 distribution=distributions.Normal(loc=0., scale=1.),
302 distribution=distributions.Normal(loc=0., scale=1.),
[all …]
/external/tensorflow/tensorflow/docs_src/programmers_guide/
Dtensorboard_histograms.md3 The TensorBoard Histogram Dashboard displays how the distribution of some
20 normally distributed data, where the mean of the distribution increases over
28 # Make a normal distribution, with a shifting mean
30 # Record that distribution into a histogram summary
126 distributions. Let's construct a simple bimodal distribution by concatenating
135 # Make a normal distribution, with a shifting mean
137 # Record that distribution into a histogram summary
140 # Make a normal distribution with shrinking variance
142 # Record that distribution too
147 # We add another histogram summary to record the combined distribution
[all …]
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/inference/
DChiSquareTestImpl.java21 import org.apache.commons.math.distribution.ChiSquaredDistribution;
22 import org.apache.commons.math.distribution.ChiSquaredDistributionImpl;
35 private ChiSquaredDistribution distribution; field in ChiSquareTestImpl
117 distribution.setDegreesOfFreedom(expected.length - 1.0); in chiSquareTest()
118 return 1.0 - distribution.cumulativeProbability( in chiSquareTest()
192 distribution.setDegreesOfFreedom(df); in chiSquareTest()
193 return 1 - distribution.cumulativeProbability(chiSquare(counts)); in chiSquareTest()
295 distribution.setDegreesOfFreedom((double) observed1.length - 1); in chiSquareTestDataSetsComparison()
296 return 1 - distribution.cumulativeProbability( in chiSquareTestDataSetsComparison()
422 distribution = value; in setDistribution()
/external/tensorflow/tensorflow/python/keras/_impl/keras/
Dinitializers.py58 scale=1., mode='fan_in', distribution='normal', seed=seed)
80 scale=1., mode='fan_in', distribution='uniform', seed=seed)
103 scale=1., mode='fan_avg', distribution='normal', seed=seed)
126 scale=1., mode='fan_avg', distribution='uniform', seed=seed)
147 scale=2., mode='fan_in', distribution='normal', seed=seed)
168 scale=2., mode='fan_in', distribution='uniform', seed=seed)
/external/tensorflow/tensorflow/compiler/xla/tests/
Dreshape_test.cc721 std::uniform_real_distribution<float> distribution; in XLA_TEST_P() local
724 [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */, in XLA_TEST_P()
725 float* cell) { *cell = distribution(rng); }); in XLA_TEST_P()
743 std::uniform_real_distribution<float> distribution; in XLA_TEST_P() local
746 [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */, in XLA_TEST_P()
747 float* cell) { *cell = distribution(rng); }); in XLA_TEST_P()
766 std::uniform_real_distribution<float> distribution; in XLA_TEST_P() local
769 [&rng, &distribution](tensorflow::gtl::ArraySlice<int64> /* indices */, in XLA_TEST_P()
770 float* cell) { *cell = distribution(rng); }); in XLA_TEST_P()
793 std::uniform_real_distribution<float> distribution; in XLA_TEST_P() local
[all …]

12345678910>>...99