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/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/
Dhead.py122 metrics[feature_keys.FilteringResults.TIMES] = _identity_metric_single(
123 feature_keys.FilteringResults.TIMES, model_outputs.prediction_times)
140 prediction[feature_keys.PredictionResults.TIMES] = features[
141 feature_keys.PredictionFeatures.TIMES]
179 feature_keys.TrainEvalFeatures.TIMES,
180 feature_keys.PredictionFeatures.TIMES
211 if feature_keys.PredictionFeatures.TIMES not in features:
213 feature_keys.PredictionFeatures.TIMES))
217 times_feature = features[feature_keys.PredictionFeatures.TIMES]
221 "(got shape {})").format(feature_keys.PredictionFeatures.TIMES,
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Dinput_pipeline.py137 feature_keys.PredictionFeatures.TIMES:
246 dataset_size = self._features[feature_keys.TrainEvalFeatures.TIMES].shape[1]
428 column_names=(feature_keys.TrainEvalFeatures.TIMES,
455 if feature_keys.TrainEvalFeatures.TIMES not in column_names:
457 feature_keys.TrainEvalFeatures.TIMES))
467 if column_name == feature_keys.TrainEvalFeatures.TIMES) != 1:
470 "one is required.".format(feature_keys.TrainEvalFeatures.TIMES))
484 if column_name == feature_keys.TrainEvalFeatures.TIMES
495 if column_name == feature_keys.TrainEvalFeatures.TIMES:
519 if feature_keys.TrainEvalFeatures.TIMES not in features:
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Dinput_pipeline_test.py64 times = example.features.feature[TrainEvalFeatures.TIMES]
77 return {TrainEvalFeatures.TIMES: times,
99 features[TrainEvalFeatures.TIMES].shape)
104 features[TrainEvalFeatures.TIMES][batch_position,
106 features[TrainEvalFeatures.TIMES][batch_position,
110 self.assertEqual("int64", features[TrainEvalFeatures.TIMES].dtype)
113 features[TrainEvalFeatures.TIMES] * 2. + feature_number,
136 TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature(
213 column_names=(TrainEvalFeatures.TIMES, TrainEvalFeatures.VALUES,
225 TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature(
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Dhead_test.py100 feature_keys.TrainEvalFeatures.TIMES:
153 feature_keys.TrainEvalFeatures.TIMES: [[1, 2, 3], [7, 8, 9]],
203 feature_keys.TrainEvalFeatures.TIMES)):
215 features={feature_keys.TrainEvalFeatures.TIMES: [[1]]},
224 feature_keys.TrainEvalFeatures.TIMES)):
227 feature_keys.TrainEvalFeatures.TIMES: [[[1]]],
241 feature_keys.TrainEvalFeatures.TIMES: [[1]],
255 feature_keys.TrainEvalFeatures.TIMES: [[1]],
269 feature_keys.TrainEvalFeatures.TIMES: [[1]],
282 feature_keys.PredictionFeatures.TIMES)):
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Dsaved_model_utils.py106 features = {_feature_keys.PredictionFeatures.TIMES: predict_times}
116 output[_feature_keys.PredictionResults.TIMES] = features[
117 _feature_keys.PredictionFeatures.TIMES]
165 output[_feature_keys.FilteringResults.TIMES] = features[
166 _feature_keys.FilteringFeatures.TIMES]
216 output[_feature_keys.FilteringResults.TIMES] = features[
217 _feature_keys.FilteringFeatures.TIMES]
Dar_model_test.py79 train_data = {TrainEvalFeatures.TIMES: time[0:split],
81 test_data = {TrainEvalFeatures.TIMES: time[split:],
148 train_data_times = train_data[TrainEvalFeatures.TIMES]
150 test_data_times = test_data[TrainEvalFeatures.TIMES]
163 PredictionFeatures.TIMES: training.limit_epochs(
226 return ({TrainEvalFeatures.TIMES: [[1]],
230 return ({TrainEvalFeatures.TIMES: np.arange(16)[None, :],
254 PredictionFeatures.TIMES: [[4, 6, 10]],
275 PredictionFeatures.TIMES: [[4, 6, 10]],
292 TrainEvalFeatures.TIMES: [[1, 3, 5, 7, 11]],
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Dmodel_utils.py80 if (previous_model_output[feature_keys.FilteringResults.TIMES].shape[0] !=
87 feature_keys.FilteringResults.TIMES].shape[0]))
88 if not (previous_model_output[feature_keys.FilteringResults.TIMES][:, -1] <
94 previous_model_output[feature_keys.FilteringResults.TIMES][:, -1:] + 1 +
Destimators_test.py61 feature_keys.TrainEvalFeatures.TIMES: times,
122 feature_keys.FilteringFeatures.TIMES: times[None, -1] + 2,
141 second_saved_prediction[feature_keys.PredictionResults.TIMES]))
145 feature_keys.FilteringFeatures.TIMES: times[-1] + 3,
155 [feature_keys.FilteringFeatures.TIMES,
167 feature_keys.FilteringFeatures.TIMES: batch_numpy_times,
248 feature_keys.TrainEvalFeatures.TIMES: times,
Destimators.py168 key=feature_keys.TrainEvalFeatures.TIMES, dtype=dtypes.int64)
199 features[feature_keys.TrainEvalFeatures.TIMES] = array_ops.squeeze(
200 features[feature_keys.TrainEvalFeatures.TIMES], axis=-1)
207 features[feature_keys.TrainEvalFeatures.TIMES])[0],
238 name=feature_keys.TrainEvalFeatures.TIMES,
241 placeholders[feature_keys.TrainEvalFeatures.TIMES] = time_placeholder
Dmodel.py202 batch_size=array_ops.shape(features[TrainEvalFeatures.TIMES])[0])
548 prediction_times=features[TrainEvalFeatures.TIMES])
567 TrainEvalFeatures.TIMES,
609 times = math_ops.cast(features[TrainEvalFeatures.TIMES], dtype=dtypes.int64)
616 if key not in [TrainEvalFeatures.TIMES,
651 predict_times = ops.convert_to_tensor(features[PredictionFeatures.TIMES],
660 [PredictionFeatures.TIMES, PredictionFeatures.STATE_TUPLE]
Dstate_management_test.py71 times = features[feature_keys.TrainEvalFeatures.TIMES]
107 feature_keys.TrainEvalFeatures.TIMES: times,
271 prediction_times=features[feature_keys.TrainEvalFeatures.TIMES])
292 feature_keys.FilteringFeatures.TIMES: numpy.arange(5),
Dar_model.py511 ops.convert_to_tensor(features[PredictionFeatures.TIMES]), dtypes.int32)
515 if key not in [TrainEvalFeatures.TIMES,
668 times = math_ops.cast(features[TrainEvalFeatures.TIMES], dtypes.int64)
687 times_feature=TrainEvalFeatures.TIMES,
756 times = features[TrainEvalFeatures.TIMES]
760 if key not in [TrainEvalFeatures.TIMES,
784 times.get_shape(), TrainEvalFeatures.TIMES))
/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/
Dstate_space_model_test.py92 feature_keys.TrainEvalFeatures.TIMES:
110 feature_keys.TrainEvalFeatures.TIMES:
132 feature_keys.TrainEvalFeatures.TIMES: times,
136 times = features[feature_keys.TrainEvalFeatures.TIMES]
140 feature_keys.TrainEvalFeatures.TIMES: times,
183 feature_keys.FilteringFeatures.TIMES: [1, 2, 3, 4],
207 feature_keys.FilteringFeatures.TIMES: [1, 2],
215 feature_keys.FilteringFeatures.TIMES: [3, 4],
225 feature_keys.FilteringFeatures.TIMES: [1, 2, 3, 4],
244 if state_key == feature_keys.FilteringResults.TIMES:
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Dvarma_test.py44 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]),
62 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]),
84 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]),
Dstructural_ensemble_test.py47 return {TrainEvalFeatures.TIMES: numpy.reshape(time, [1, -1]),
113 features = {TrainEvalFeatures.TIMES: times,
138 features = {TrainEvalFeatures.TIMES: times,
/external/mockito/src/test/java/org/mockitousage/bugs/
DConcurrentModificationExceptionOnMultiThreadedVerificationTest.java28 static final int TIMES = 100; field in ConcurrentModificationExceptionOnMultiThreadedVerificationTest
43 int expectedMaxTestLength = TIMES * INTERVAL_MILLIS + potentialOverhead; in shouldSuccessfullyVerifyConcurrentInvocationsWithTimeout()
48 verify(target, timeout(expectedMaxTestLength).times(TIMES * nThreads)).targetMethod("arg"); in shouldSuccessfullyVerifyConcurrentInvocationsWithTimeout()
70 for (int i = 0; i < TIMES; i++) { in call()
/external/tensorflow/tensorflow/contrib/timeseries/examples/
Dmultivariate.py54 column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
63 times = [current_state[tf.contrib.timeseries.FilteringResults.TIMES]]
88 tf.contrib.timeseries.TrainEvalFeatures.TIMES: current_prediction[
89 tf.contrib.timeseries.FilteringResults.TIMES],
/external/capstone/suite/
Dfuzz.py25 TIMES = 64 variable
100 for j in xrange(1, TIMES):
113 for j in xrange(1, TIMES):
/external/cldr/tools/java/org/unicode/cldr/util/data/
DApproximateWidth.txt283 1200B..1200F; 0; # CUNEIFORM SIGN AB TIMES ASH2..CUNEIFORM SIGN AB TIMES HA
284 12028..1202F; 0; # CUNEIFORM SIGN AL TIMES USH..CUNEIFORM SIGN AN THREE TIMES
286 12060; 0; # CUNEIFORM SIGN DAG KISIM5 TIMES HA
287 1206A..1206F; 0; # CUNEIFORM SIGN DAG KISIM5 TIMES SI..CUNEIFORM SIGN DAR
288 120DD; 0; # CUNEIFORM SIGN GA2 TIMES LA
289 120E3; 0; # CUNEIFORM SIGN GA2 TIMES SAL
290 120E5..120E6; 0; # CUNEIFORM SIGN GA2 TIMES SHE..CUNEIFORM SIGN GA2 TIMES SHE PLUS TUR
291 120E8..120FF; 0; # CUNEIFORM SIGN GA2 TIMES SUM..CUNEIFORM SIGN GESHTIN TIMES KUR
374 12005..12006; 1; # CUNEIFORM SIGN A TIMES IGI..CUNEIFORM SIGN A TIMES LAGAR GUNU
377 1205E..1205F; 1; # CUNEIFORM SIGN DAG KISIM5 TIMES GIR2..CUNEIFORM SIGN DAG KISIM5 TIMES GUD
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/external/swiftshader/third_party/llvm-7.0/llvm/test/tools/llvm-objdump/
Dmacho-bad-bind.test40 …uts/macho-bind-uleb-times-skipping-uleb 2>&1 | FileCheck -check-prefix ULEB-TIMES-SKIPPING-ULEB %s
41 ULEB-TIMES-SKIPPING-ULEB: macho-bind-uleb-times-skipping-uleb': truncated or malformed object (for …
58 …d-uleb-times-skipping-uleb 2>&1 | FileCheck -check-prefix LAZY-DO-BIND-ULEB-TIMES-SKIPPING-ULEB %s
59 LAZY-DO-BIND-ULEB-TIMES-SKIPPING-ULEB: macho-lazy-do-bind-uleb-times-skipping-uleb': truncated or m…
88 …-macho -rebase %p/Inputs/macho-rebase-imm-times 2>&1 | FileCheck -check-prefix REBASE-IMM-TIMES %s
89 REBASE-IMM-TIMES: macho-rebase-imm-times': truncated or malformed object (for REBASE_OPCODE_DO_REBA…
91 …acho -rebase %p/Inputs/macho-rebase-uleb-times 2>&1 | FileCheck -check-prefix REBASE-ULEB-TIMES %s
92 REBASE-ULEB-TIMES: macho-rebase-uleb-times': truncated or malformed object (for REBASE_OPCODE_DO_RE…
97 …-rebase-uleb-times-skipping-uleb 2>&1 | FileCheck -check-prefix REBASE-ULEB-TIMES-SKIPPING-ULEB %s
98 REBASE-ULEB-TIMES-SKIPPING-ULEB: macho-rebase-uleb-times-skipping-uleb': truncated or malformed obj…
/external/ltp/testcases/kernel/sched/sched_stress/
Dsched_tc4.c70 #define TIMES 5000 macro
212 for (i = 0; i < TIMES; i++) { in read_raw_device()
Dsched_tc5.c67 #define TIMES 20 macro
148 for (i = 0; i < TIMES; i++) in main()
/external/libxkbcommon/xkbcommon/src/xkbcomp/
Dparser.h86 TIMES = 44, enumerator
152 #define TIMES 44 macro
/external/u-boot/lib/dhry/
Ddhry_1.c82 #ifdef TIMES
174 #ifdef TIMES in dhry()
233 #ifdef TIMES in dhry()
/external/cldr/common/uca/
Dallkeys_CLDR.txt253 2062 ; [.0000.0000.0000] # INVISIBLE TIMES
3018 2297 ; [.05B8.0020.0002] # CIRCLED TIMES
3027 22A0 ; [.05C1.0020.0002] # SQUARED TIMES
3071 22C7 ; [.05E5.0020.0002] # DIVISION TIMES
4334 29D4 ; [.0AD1.0020.0002] # TIMES WITH LEFT HALF BLACK
4335 29D5 ; [.0AD2.0020.0002] # TIMES WITH RIGHT HALF BLACK
4374 2A02 ; [.0AF9.0020.0002] # N-ARY CIRCLED TIMES OPERATOR
4381 2A09 ; [.0B00.0020.0002] # N-ARY TIMES OPERATOR
4395 2A18 ; [.0B0E.0020.0002] # INTEGRAL WITH TIMES SIGN
8866 12432 ; [.1C6B.0020.0002] # CUNEIFORM NUMERIC SIGN SHAR2 TIMES GAL PLUS DISH
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