/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
D | head.py | 122 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, [all …]
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D | input_pipeline.py | 137 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: [all …]
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D | input_pipeline_test.py | 64 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( [all …]
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D | head_test.py | 100 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)): [all …]
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D | saved_model_utils.py | 106 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]
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D | ar_model_test.py | 79 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]], [all …]
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D | model_utils.py | 80 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 +
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D | estimators_test.py | 61 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,
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D | estimators.py | 168 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
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D | model.py | 202 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]
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D | state_management_test.py | 71 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),
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D | ar_model.py | 511 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))
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/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
D | state_space_model_test.py | 92 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: [all …]
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D | varma_test.py | 44 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]), 62 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]), 84 TrainEvalFeatures.TIMES: constant_op.constant([[1, 2]]),
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D | structural_ensemble_test.py | 47 return {TrainEvalFeatures.TIMES: numpy.reshape(time, [1, -1]), 113 features = {TrainEvalFeatures.TIMES: times, 138 features = {TrainEvalFeatures.TIMES: times,
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/external/mockito/src/test/java/org/mockitousage/bugs/ |
D | ConcurrentModificationExceptionOnMultiThreadedVerificationTest.java | 28 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()
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/external/tensorflow/tensorflow/contrib/timeseries/examples/ |
D | multivariate.py | 54 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],
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/external/capstone/suite/ |
D | fuzz.py | 25 TIMES = 64 variable 100 for j in xrange(1, TIMES): 113 for j in xrange(1, TIMES):
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/external/cldr/tools/java/org/unicode/cldr/util/data/ |
D | ApproximateWidth.txt | 283 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 [all …]
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/external/swiftshader/third_party/llvm-7.0/llvm/test/tools/llvm-objdump/ |
D | macho-bad-bind.test | 40 …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…
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/external/ltp/testcases/kernel/sched/sched_stress/ |
D | sched_tc4.c | 70 #define TIMES 5000 macro 212 for (i = 0; i < TIMES; i++) { in read_raw_device()
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D | sched_tc5.c | 67 #define TIMES 20 macro 148 for (i = 0; i < TIMES; i++) in main()
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/external/libxkbcommon/xkbcommon/src/xkbcomp/ |
D | parser.h | 86 TIMES = 44, enumerator 152 #define TIMES 44 macro
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/external/u-boot/lib/dhry/ |
D | dhry_1.c | 82 #ifdef TIMES 174 #ifdef TIMES in dhry() 233 #ifdef TIMES in dhry()
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/external/cldr/common/uca/ |
D | allkeys_CLDR.txt | 253 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 [all …]
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