| /device/google/contexthub/firmware/os/algos/calibration/online_calibration/gyroscope/gyro_offset_over_temp_cal/ |
| gyro_offset_over_temp_cal.cc | 99 // Sets the pointer to the OTC model dataset and the number of model points.
162 // Loads the new model dataset and uses it to update the linear model
169 // Sets the pointer to the OTC model dataset and the number of model points.
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
| DescriptiveStatistics.java | 40 * Maintains a dataset of values of a single variable and computes descriptive
43 * dataset. The default value, INFINITE_WINDOW, puts no limit on the size of
44 * the dataset. This value should be used with caution, as the backing store
46 * {@link SummaryStatistics}, which does not store the dataset, should be used
48 * more values are added than can be stored in the dataset, new values are
50 * in the dataset.
63 * that can be stored in the dataset.
151 * Adds the value to the dataset. If the dataset is at the maximum size
153 * windowSize), the first (oldest) element in the dataset is discarde
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/gan/ |
| mnist_graph_test.py | 44 dataset = tf.data.Dataset.from_tensors(images_data)
45 images = dataset.repeat().make_one_shot_iterator().get_next()
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/resnet50/ |
| resnet50_graph_test.py | 140 dataset = tf.data.Dataset.from_tensors((np_images, np_labels)).repeat()
141 (images, labels) = dataset.make_one_shot_iterator().get_next()
|
| /external/tensorflow/tensorflow/contrib/slim/python/slim/data/ |
| dataset_data_provider_test.py | 25 from tensorflow.contrib.slim.python.slim.data import dataset
74 return dataset.Dataset(
|
| /external/tensorflow/tensorflow/go/op/ |
| op_test.go | 105 dataset = TensorDataset(s, []tf.Output{c}, shapes)
109 init = MakeIterator(s, dataset, iterator)
131 t.Errorf("Expected sess.Run() to fail since the iterator should have reached the end of the dataset")
|
| /packages/apps/Contacts/src/com/android/contacts/util/ |
| AccountSelectionUtil.java | 124 account.type, account.dataSet);
173 importIntent.putExtra("data_set", account.dataSet);
185 importIntent.putExtra("data_set", account.dataSet);
|
| /packages/apps/Contacts/src/com/android/contacts/list/ |
| CustomContactListFilterActivity.java | 177 if (account.dataSet != null) {
178 groupsUri.appendQueryParameter(Groups.DATA_SET, account.dataSet).build();
200 account.dataSet, hasGroups);
255 String accountType, String dataSet, boolean accountHasGroups) {
259 if (dataSet != null) {
260 settingsUri.appendQueryParameter(Settings.DATA_SET, dataSet);
270 values.put(Settings.DATA_SET, dataSet);
380 String dataSet = this.getAsString(Settings.DATA_SET);
384 if (dataSet == null) {
389 selectionArgs = new String[] {accountName, accountType, dataSet};
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/ |
| evaluator.py | 42 Or, if you are getting your examples from a tf.data.Dataset, you can use
131 def evaluate_on_dataset(self, dataset, *args, **kwargs):
132 """Convenience method for performing an eval on a Dataset.
135 dataset: Dataset object with the input data to evaluate on.
162 call_op = self.__call__(dataset.make_one_shot_iterator().get_next(),
168 for example in datasets.Iterator(dataset):
|
| evaluator_test.py | 114 ds = dataset_ops.Dataset.from_tensor_slices([3.0, 5.0, 7.0, 9.0])
122 ds = dataset_ops.Dataset.from_tensor_slices([3.0, 5.0, 7.0, 9.0])
131 ds = dataset_ops.Dataset.from_tensor_slices([3.0, 5.0, 7.0, 9.0])
|
| /packages/apps/Contacts/src/com/android/contacts/group/ |
| GroupUtil.java | 79 String dataSet = cursor.getString(GroupListLoader.DATA_SET);
98 && TextUtils.equals(dataSet, previousGroupDataSet)) {
103 return new GroupListItem(accountName, accountType, dataSet, groupId, title,
175 intent.putExtra(UiIntentActions.GROUP_ACCOUNT_DATA_SET, groupMetaData.dataSet);
302 public final int dataSet;
315 dataSet = cursor.getColumnIndex(Groups.DATA_SET);
330 dataSet = list.indexOf(Groups.DATA_SET);
|
| /frameworks/base/core/java/android/app/backup/ |
| BackupManager.java | 260 * current backup dataset if the application has stored data there, or from
261 * the dataset used during the last full device setup operation if the current
262 * backup dataset has no matching data. If no backup data exists for this application
266 * dataset from the remote transport, instantiate the application's backup agent, and pass the
267 * dataset to the agent's
293 * current backup dataset if the application has stored data there, or from
294 * the dataset used during the last full device setup operation if the current
295 * backup dataset has no matching data. If no backup data exists for this application
299 * a backed-up dataset from the remote transport, instantiate the application's
300 * backup agent, and pass the dataset to the agent'
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| input_pipeline.py | 165 """When possible, raises an error if the dataset is too small.
169 larger than the dataset size. However, many TimeSeriesReaders will not have
170 access to a dataset size, in which case they do not need to override this
175 contained in the dataset. Readers should attempt to raise an error when
201 """Return the full dataset.
207 Same return type as `read`, but with the full dataset rather than an
210 number of samples in the entire dataset. These `Tensor`s should be
245 """Raise an error if the dataset is too small."""
250 "but only {} records were available in the dataset. Either decrease "
275 queue_capacity=2, # Each queue element is a full copy of the dataset
[all...] |
| /developers/samples/android/input/autofill/AutofillFramework/kotlinApp/ |
| README.md | 106 (wrapped in a `Response` object), the user can pick which `Dataset` they want to autofill their
107 views with. When a `Dataset` is selected, this method is invoked for all of the views that were
108 associated with that `Dataset` by the service. For example, the `Dataset` might contain Autofill
115 // User has just selected a Dataset from the list of autofill suggestions.
116 // The Dataset is comprised of a list of AutofillValues, with each AutofillValue meant
|
| /developers/samples/android/input/autofill/AutofillFramework/ |
| template-params.xml | 199 (wrapped in a `Response` object), the user can pick which `Dataset` they want to autofill their
200 views with. When a `Dataset` is selected, this method is invoked for all of the views that were
201 associated with that `Dataset` by the service. For example, the `Dataset` might contain Autofill
209 // User has just selected a Dataset from the list of autofill suggestions.
210 // The Dataset is comprised of a list of AutofillValues, with each AutofillValue meant
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/rnn_colorbot/ |
| rnn_colorbot.py | 77 """Parse a line from the colors dataset."""
79 # Each line of the dataset is comma-separated and formatted as
95 # Downloads data at url into data_dir/basename(url). The dataset has a header
105 dataset = tf.data.TextLineDataset(path).skip(1).map(parse).shuffle(
108 return dataset
191 """Computes the average loss on eval_data, which should be a Dataset."""
|
| /external/tensorflow/tensorflow/contrib/kernel_methods/g3doc/ |
| tutorial.md | 7 training dataset both in terms of training/inference times and in terms of
17 will use MNIST, a widely-used dataset containing images of handwritten digits
27 utility command from tf.contrib.learn loads the MNIST dataset:
32 This loads the entire MNIST dataset (containing 70K samples) and splits it into
78 We can now train a linear model over the MNIST dataset. We will use the
131 achieve on this dataset caps at around **93%**.
|
| /external/tensorflow/tensorflow/contrib/kfac/examples/ |
| mlp.py | 163 use_fake_data: bool. If True, generate a synthetic dataset.
168 # Load a dataset.
196 use_fake_data: bool. If True, generate a synthetic dataset.
201 # Load a dataset.
250 use_fake_data: bool. If True, generate a synthetic dataset.
256 # Load a dataset.
|
| /external/tensorflow/tensorflow/docs_src/api_guides/python/ |
| threading_and_queues.md | 62 We recommend using the @{tf.data.Dataset.shuffle$`shuffle`}
63 and @{tf.data.Dataset.batch$`batch`} methods of a
64 @{tf.data.Dataset$`Dataset`} to accomplish this. However, if you'd prefer
105 # create a dataset that counts from 0 to 99
107 input = tf.data.Dataset.from_tensor_slices(input)
|
| /external/tensorflow/tensorflow/docs_src/tutorials/ |
| kernel_methods.md | 12 training dataset both in terms of training/inference times and in terms of
31 is a good place to start. We will use the MNIST dataset. The tutorial consists
40 Run the following utility command to load the MNIST dataset:
45 The preceding method loads the entire MNIST dataset (containing 70K samples) and
92 We can now train a linear model over the MNIST dataset. We will use the
148 can achieve on this dataset caps at around **93%**.
|
| recurrent_quickdraw.md | 28 available](https://quickdraw.withgoogle.com/data). This dataset contains of 50M
71 [dataset](https://quickdraw.withgoogle.com/data) is available on Google Cloud
78 download the entire dataset. Note that the original .ndjson files require
98 Then create a folder and download the dataset there.
401 Note that this tutorial is just a quick example on a relatively small dataset to
403 models can be even more powerful if you try them on a large dataset.
|
| /external/snakeyaml/src/test/java/org/yaml/snakeyaml/issues/issue100/ |
| MergeJavaBeanTest.java | 83 Set<Data> dataSet = new HashSet<Data>();
86 dataSet.add((Data) data);
89 assertEquals("Must be all but one Data instances.", list.size() - 1, dataSet.size());
|
| /frameworks/base/services/autofill/java/com/android/server/autofill/ |
| ViewState.java | 54 /** View id is present in a dataset returned by the service. */
56 /** View was autofilled after user selected a dataset. */
64 /** User select a dataset in this view, but service must authenticate first. */
|
| /packages/apps/Contacts/src/com/android/contacts/ |
| SplitAggregateView.java | 118 String dataSet;
171 info.dataSet = cursor.getString(SplitQuery.DATA_SET);
254 AccountType accountType = mAccountTypes.getAccountType(info.accountType, info.dataSet);
|
| /packages/apps/Contacts/tests/src/com/android/contacts/editor/ |
| ContactEditorUtilsTest.java | 235 public MockAccountType(String accountType, String dataSet, boolean areContactsWritable) {
237 this.dataSet = dataSet;
|
