| /external/tensorflow/tensorflow/python/distribute/ |
| D | mirrored_strategy_test.py | 67 distribution=[
78 def testMinimizeLoss(self, distribution): argument
80 self._test_minimize_loss_eager(distribution)
82 self._test_minimize_loss_graph(distribution)
84 def testReplicaId(self, distribution): argument
85 self._test_replica_id(distribution)
87 def testNumReplicasInSync(self, distribution): argument
88 self.assertEqual(2, distribution.num_replicas_in_sync)
90 def testCallAndMergeExceptions(self, distribution): argument
91 self._test_call_and_merge_exceptions(distribution)
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| D | custom_training_loop_input_test.py | 79 distribution=strategy_combinations.all_strategies,
82 def testConstantNumpyInput(self, distribution): argument
90 outputs = distribution.experimental_local_results(
91 distribution.run(computation, args=(x,)))
95 constant_op.constant(4., shape=(distribution.num_replicas_in_sync)),
100 distribution=strategy_combinations.all_strategies,
103 def testStatefulExperimentalRunAlwaysExecute(self, distribution): argument
104 with distribution.scope():
114 distribution.run(assign_add)
122 distribution=strategy_combinations.strategies_minus_tpu,
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| D | one_device_strategy_test.py | 31 distribution=[
40 def testMinimizeLoss(self, distribution): argument
42 self._test_minimize_loss_eager(distribution)
44 self._test_minimize_loss_graph(distribution)
46 def testReplicaId(self, distribution): argument
47 self._test_replica_id(distribution)
49 def testCallAndMergeExceptions(self, distribution): argument
50 self._test_call_and_merge_exceptions(distribution)
52 def testReplicateDataset(self, distribution): argument
62 self._test_input_fn_iterable(distribution, input_fn, expected_values)
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| D | mirrored_variable_test.py | 63 distribution=[
103 def testVariableInFuncGraph(self, distribution): argument
110 with func_graph.FuncGraph("fg").as_default(), distribution.scope():
112 v2 = distribution.extended.call_for_each_replica(model_fn)
114 self._test_mv_properties(v1, "foo:0", distribution)
115 self._test_mv_properties(v2, "bar:0", distribution)
117 def testVariableWithTensorInitialValueInFunction(self, distribution): argument
132 return distribution.experimental_local_results(
133 distribution.extended.call_for_each_replica(model_fn))
137 def testSingleVariable(self, distribution): argument
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| D | vars_test.py | 52 # distribution=[
59 distribution=[
70 distribution=[
85 def testAssign(self, distribution, experimental_run_tf_function): argument
94 return test_util.gather(distribution, distribution.run(update_fn))
112 with distribution.scope():
124 def testAssignOnWriteVar(self, distribution, experimental_run_tf_function): argument
126 with distribution.scope():
139 return test_util.gather(distribution, distribution.run(update_fn))
157 with distribution.scope():
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| D | values_test.py | 58 distribution=[
72 distribution=(strategy_combinations.all_strategies_minus_default +
76 def testMakeDistributedValueFromTensor(self, distribution): argument
85 distribution.experimental_distribute_values_from_function(value_fn))
87 ds_test_util.gather(distribution, distributed_values),
88 constant_op.constant(1., shape=(distribution.num_replicas_in_sync)))
92 distribution=(strategy_combinations.all_strategies_minus_default +
96 def testMakeDistributedValueSingleNumpyArrayConstant(self, distribution): argument
105 distribution.experimental_distribute_values_from_function(value_fn))
107 ds_test_util.gather(distribution, distributed_values).numpy(),
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| D | moving_averages_test.py | 44 distribution=all_distributions, mode=["graph"])
47 distribution=all_distributions, mode=["eager"], use_function=[True, False])
53 def testReplicaModeWithoutZeroDebias(self, distribution): argument
65 with distribution.scope():
66 var, assign = distribution.extended.call_for_each_replica(replica_fn)
69 self.evaluate(distribution.experimental_local_results(assign))
80 def testReplicaMode(self, distribution): argument
91 with distribution.scope():
92 var, assign_op = distribution.extended.call_for_each_replica(replica_fn)
95 self.evaluate(distribution.experimental_local_results(assign_op))
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| D | metrics_v1_test.py | 73 distribution=[
84 distribution=[
95 def _test_metric(self, distribution, dataset_fn, metric_fn, expected_fn): argument
96 with ops.Graph().as_default(), distribution.scope():
97 iterator = distribution.make_input_fn_iterator(lambda _: dataset_fn())
98 if strategy_test_lib.is_tpu_strategy(distribution):
100 value, update = distribution.extended.call_for_each_replica(
103 return distribution.group(update)
105 ctx = distribution.extended.experimental_run_steps_on_iterator(
106 step_fn, iterator, iterations=distribution.extended.steps_per_run)
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| D | input_lib_test.py | 287 distribution=[
290 def testMultiDeviceIterInitialize(self, distribution): argument
300 dataset_fn(distribute_lib.InputContext()), input_workers, distribution)
316 distribution=[
321 def testOneDeviceCPU(self, input_type, api_type, iteration_type, distribution, argument
330 distribution.extended.experimental_enable_get_next_as_optional = (
334 expected_values, distribution)
342 distribution=[strategy_combinations.multi_worker_mirrored_2x1_cpu],
345 distribution, enable_get_next_as_optional): argument
353 distribution.extended.experimental_enable_get_next_as_optional = (
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| D | input_lib_type_spec_test.py | 51 distribution=[
56 def testTypeSpec(self, input_type, distribution, argument
63 distribution.extended.experimental_enable_get_next_as_optional = (
66 dist_dataset = distribution.experimental_distribute_dataset(dataset)
67 with distribution.scope():
83 distribution=[
89 distribution, enable_get_next_as_optional): argument
96 distribution.extended.experimental_enable_get_next_as_optional = (
99 dist_dataset = distribution.experimental_distribute_dataset(dataset)
100 with distribution.scope():
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| /external/rust/android-crates-io/crates/criterion/src/stats/ |
| D | tuple.rs | 3 use crate::stats::Distribution;
14 /// A tuple of distributions: `(Distribution<A>, Distribution<B>, ..)`
42 type Distributions = (Distribution<A>,);
46 impl<A> TupledDistributions for (Distribution<A>,) implementation
70 fn complete(self) -> (Distribution<A>,) { in complete()
71 (Distribution(self.0.into_boxed_slice()),) in complete()
80 type Distributions = (Distribution<A>, Distribution<B>);
84 impl<A, B> TupledDistributions for (Distribution<A>, Distribution<B>) implementation
112 fn complete(self) -> (Distribution<A>, Distribution<B>) { in complete()
114 Distribution(self.0.into_boxed_slice()), in complete()
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| /external/python/cpython3/Doc/library/ |
| D | importlib.metadata.rst | 17 the metadata of an installed `Distribution Package <https://packaging.python.org/en/latest/glossary…
27 ``importlib.metadata`` operates on third-party *distribution packages*
39 One *distribution package* can contain multiple *import packages*
42 may map to multiple *distribution packages*
47 By default, distribution metadata can live on the file system
70 `Distribution Package <https://packaging.python.org/en/latest/glossary/#term-Distribution-Package>`…
93 You can get the :ref:`metadata for a distribution <metadata>`::
98 You can also get a :ref:`distribution's version number <version>`, list its
99 :ref:`constituent files <files>`, and get a list of the distribution's
106 module when queried for a distribution package which is not installed in the
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| /external/sdk-platform-java/java-common-protos/proto-google-common-protos/src/main/java/com/google/api/ |
| D | Distribution.java | 17 // source: google/api/distribution.proto
25 * `Distribution` contains summary statistics for a population of values. It
26 * optionally contains a histogram representing the distribution of those values
39 * Protobuf type {@code google.api.Distribution}
41 public final class Distribution extends com.google.protobuf.GeneratedMessageV3 class
43 // @@protoc_insertion_point(message_implements:google.api.Distribution)
46 // Use Distribution.newBuilder() to construct.
47 private Distribution(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) { in Distribution() method in Distribution
51 private Distribution() { in Distribution() method in Distribution
59 return new Distribution(); in newInstance()
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| D | DistributionOrBuilder.java | 17 // source: google/api/distribution.proto
23 // @@protoc_insertion_point(interface_extends:google.api.Distribution)
81 * <code>.google.api.Distribution.Range range = 4;</code>
94 * <code>.google.api.Distribution.Range range = 4;</code>
98 com.google.api.Distribution.Range getRange(); in getRange()
107 * <code>.google.api.Distribution.Range range = 4;</code>
109 com.google.api.Distribution.RangeOrBuilder getRangeOrBuilder(); in getRangeOrBuilder()
115 * Defines the histogram bucket boundaries. If the distribution does not
119 * <code>.google.api.Distribution.BucketOptions bucket_options = 6;</code>
128 * Defines the histogram bucket boundaries. If the distribution does not
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| /external/apache-commons-math/src/main/java/org/apache/commons/math3/random/ |
| D | RandomDataImpl.java | 20 import org.apache.commons.math3.distribution.IntegerDistribution;
21 import org.apache.commons.math3.distribution.RealDistribution;
243 * org.apache.commons.math3.distribution.BetaDistribution Beta Distribution}. This
247 * @param alpha first distribution shape parameter
248 * @param beta second distribution shape parameter
249 * @return random value sampled from the beta(alpha, beta) distribution
258 * org.apache.commons.math3.distribution.BinomialDistribution Binomial Distribution}. This
262 * @param numberOfTrials number of trials of the Binomial distribution
263 * @param probabilityOfSuccess probability of success of the Binomial distribution
265 * distribution
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| /external/tensorflow/tensorflow/python/ops/ |
| D | nn_loss_scaling_utilities_test.py | 69 distribution=[
73 def testComputeAverageLossDefaultGlobalBatchSize(self, distribution): argument
80 with distribution.scope():
81 per_replica_losses = distribution.run(
83 loss = distribution.reduce("SUM", per_replica_losses, axis=None)
88 distribution=[
92 def testComputeAverageLossSampleWeights(self, distribution): argument
93 with distribution.scope():
95 per_replica_losses = distribution.run(
99 loss = distribution.reduce("SUM", per_replica_losses, axis=None)
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| /external/google-cloud-java/java-service-control/proto-google-cloud-service-control-v1/src/main/java/com/google/api/servicecontrol/v1/ |
| D | Distribution.java | 17 // source: google/api/servicecontrol/v1/distribution.proto
25 * Distribution represents a frequency distribution of double-valued sample
34 * Protobuf type {@code google.api.servicecontrol.v1.Distribution}
36 public final class Distribution extends com.google.protobuf.GeneratedMessageV3 class
38 // @@protoc_insertion_point(message_implements:google.api.servicecontrol.v1.Distribution)
41 // Use Distribution.newBuilder() to construct.
42 private Distribution(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) { in Distribution() method in Distribution
46 private Distribution() { in Distribution() method in Distribution
54 return new Distribution(); in newInstance()
73 com.google.api.servicecontrol.v1.Distribution.class, in internalGetFieldAccessorTable()
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| D | DistributionOrBuilder.java | 17 // source: google/api/servicecontrol/v1/distribution.proto
23 // @@protoc_insertion_point(interface_extends:google.api.servicecontrol.v1.Distribution)
30 * The total number of samples in the distribution. Must be >= 0.
43 * The arithmetic mean of the samples in the distribution. If `count` is
164 * <code>.google.api.servicecontrol.v1.Distribution.LinearBuckets linear_buckets = 7;</code>
176 * <code>.google.api.servicecontrol.v1.Distribution.LinearBuckets linear_buckets = 7;</code>
180 com.google.api.servicecontrol.v1.Distribution.LinearBuckets getLinearBuckets(); in getLinearBuckets()
188 * <code>.google.api.servicecontrol.v1.Distribution.LinearBuckets linear_buckets = 7;</code>
190 com.google.api.servicecontrol.v1.Distribution.LinearBucketsOrBuilder getLinearBucketsOrBuilder(); in getLinearBucketsOrBuilder()
199 * <code>.google.api.servicecontrol.v1.Distribution.ExponentialBuckets exponential_buckets = 8;
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.distributions.pbtxt | 5 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\'>"
24 name: "Distribution"
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\'>"
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | random_op_gpu.h | 31 template <class Distribution, bool VariableSamplesPerOutput>
34 template <class Distribution>
35 struct FillPhiloxRandomKernel<Distribution, false> {
36 typedef typename Distribution::ResultElementType T;
39 Distribution dist);
42 template <class Distribution>
43 struct FillPhiloxRandomKernel<Distribution, true> {
44 typedef typename Distribution::ResultElementType T;
47 int64 size, Distribution dist);
139 // distribution. Each output takes a fixed number of samples.
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| D | random_op_cpu.h | 60 template <typename Device, class Distribution>
62 typedef typename Distribution::ResultElementType T;
65 int64_t size, Distribution dist) { in operator()
71 "not support this device or random distribution yet.")); in operator()
76 template <class Distribution, bool VariableSamplesPerOutput>
79 // Specialization for distribution that takes a fixed number of samples for
81 template <class Distribution>
82 struct FillPhiloxRandomTask<Distribution, false> {
83 typedef typename Distribution::ResultElementType T;
85 int64_t start_group, int64_t limit_group, Distribution dist) {
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | transformed_distribution.py | 15 """A Transformed Distribution class."""
26 from tensorflow.python.ops.distributions import distribution as distribution_lib
118 class TransformedDistribution(distribution_lib.Distribution):
119 """A Transformed Distribution.
121 A `TransformedDistribution` models `p(y)` given a base distribution `p(x)`,
124 distribution is typically an instance of the `Distribution` class.
133 `Distribution` associated with a random variable (rv) `X`.
135 Write `cdf(Y=y)` for an absolutely continuous cumulative distribution function
147 Programmatically: `bijector.forward(distribution.sample(...))`
152 Programmatically: `(distribution.log_prob(bijector.inverse(y))
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| /external/pytorch/torch/distributions/ |
| D | distribution.py | 12 __all__ = ["Distribution"]
15 class Distribution: class
17 Distribution is the abstract base class for probability distributions.
39 Distribution._validate_args = value
58 + "Please set `arg_constraints = {}` or initialize the distribution "
74 f"of distribution {repr(self)} "
82 Returns a new distribution instance (or populates an existing instance
85 the distribution's parameters. As such, this does not allocate new
86 memory for the expanded distribution instance. Additionally,
96 New distribution instance with batch dimensions expanded to
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
| D | PoissonDistributionImpl.java | 17 package org.apache.commons.math.distribution;
51 /** Distribution used to compute normal approximation. */
55 * Holds the Poisson mean for the distribution.
73 * Create a new Poisson distribution with the given the mean. The mean value
84 * Create a new Poisson distribution with the given mean, convergence criterion
99 * Create a new Poisson distribution with the given mean and convergence criterion.
111 * Create a new Poisson distribution with the given mean and maximum number of iterations.
124 * Create a new Poisson distribution with the given the mean. The mean value
128 * @param z a normal distribution used to compute normal approximations.
141 * Get the Poisson mean for the distribution.
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| /external/google-cloud-java/java-containeranalysis/proto-google-cloud-containeranalysis-v1beta1/src/main/java/io/grafeas/v1beta1/pkg/ |
| D | Package.java | 87 distribution_ = new java.util.ArrayList<io.grafeas.v1beta1.pkg.Distribution>(); in Package()
92 io.grafeas.v1beta1.pkg.Distribution.parser(), extensionRegistry)); in Package()
181 private java.util.List<io.grafeas.v1beta1.pkg.Distribution> distribution_;
189 * <code>repeated .grafeas.v1beta1.package.Distribution distribution = 10;</code>
192 public java.util.List<io.grafeas.v1beta1.pkg.Distribution> getDistributionList() { in getDistributionList()
202 * <code>repeated .grafeas.v1beta1.package.Distribution distribution = 10;</code>
216 * <code>repeated .grafeas.v1beta1.package.Distribution distribution = 10;</code>
229 * <code>repeated .grafeas.v1beta1.package.Distribution distribution = 10;</code>
232 public io.grafeas.v1beta1.pkg.Distribution getDistribution(int index) { in getDistribution()
242 * <code>repeated .grafeas.v1beta1.package.Distribution distribution = 10;</code>
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