| /external/ltp/tools/pounder21/test_scripts/ |
| memxfer5b | 34 # Run this test ten times.
35 TIMES=0
36 while [ $TIMES -lt 2 ]; do
38 TIMES=$((TIMES + 1))
|
| /cts/hostsidetests/sustainedperf/dhrystone/ |
| dhry.h | 30 * In addition, Berkeley UNIX system calls "times ()" or "time ()"
102 * execution times for this version should be the same as
141 * five or more times
143 * six or more times
155 * The "times" function of UNIX (returning process times)
160 * access, use the "times ()" function. If you have
169 * In Berkeley UNIX, the function "times" returns process
191 * For 16-Bit processors (e.g. 80186, 80286), times for all compilation
354 #ifndef TIMES
[all...] |
| dhry_1.c | 45 #ifdef TIMES
47 extern int times ();
48 /* see library function "times" */
115 #ifdef TIMES
116 times (&time_info);
176 #ifdef TIMES
177 times (&time_info);
|
| /external/mockito/src/test/java/org/mockitousage/bugs/ |
| ConcurrentModificationExceptionOnMultiThreadedVerificationTest.java | 28 static final int TIMES = 100;
43 int expectedMaxTestLength = TIMES * INTERVAL_MILLIS + potentialOverhead;
48 verify(target, timeout(expectedMaxTestLength).times(TIMES * nThreads)).targetMethod("arg");
70 for (int i = 0; i < TIMES; i++) {
|
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| head_test.py | 78 feature_keys.TrainEvalFeatures.TIMES:
142 feature_keys.TrainEvalFeatures.TIMES)):
154 features={feature_keys.TrainEvalFeatures.TIMES: [[1]]},
163 feature_keys.TrainEvalFeatures.TIMES)):
166 feature_keys.TrainEvalFeatures.TIMES: [[[1]]],
180 feature_keys.TrainEvalFeatures.TIMES: [[1]],
194 feature_keys.TrainEvalFeatures.TIMES: [[1]],
208 feature_keys.TrainEvalFeatures.TIMES: [[1]],
221 feature_keys.PredictionFeatures.TIMES)):
234 features={feature_keys.PredictionFeatures.TIMES: [[1]]}
[all...] |
| head.py | 118 metrics[feature_keys.FilteringResults.TIMES] = _identity_metric_single(
119 feature_keys.FilteringResults.TIMES, model_outputs.prediction_times)
133 prediction[feature_keys.PredictionResults.TIMES] = features[
134 feature_keys.PredictionFeatures.TIMES]
162 feature_keys.TrainEvalFeatures.TIMES,
163 feature_keys.PredictionFeatures.TIMES
199 "features.".format(feature_keys.TrainEvalFeatures.TIMES,
273 if feature_keys.PredictionFeatures.TIMES not in features:
275 feature_keys.PredictionFeatures.TIMES))
279 times_feature = features[feature_keys.PredictionFeatures.TIMES]
[all...] |
| model_utils.py | 69 def canonicalize_times_or_steps_from_output(times, steps,
71 """Canonicalizes either relative or absolute times, with error checking."""
72 if steps is not None and times is not None:
73 raise ValueError("Only one of `steps` and `times` may be specified.")
74 if steps is None and times is None:
75 raise ValueError("One of `steps` and `times` must be specified.")
76 if times is not None:
77 times = numpy.array(times)
78 if len(times.shape) != 2
[all...] |
| saved_model_utils.py | 62 times=None,
79 evaluation or filtering. If `times` is specified, `steps` must not be; one
81 times: A [batch_size x window_size] array of integers (not a Tensor)
82 indicating times to make predictions for. These times must be after the
83 corresponding evaluation or filtering. If `steps` is specified, `times`
90 is either the `steps` argument or the `window_size` of the `times`
94 and "covariance") and a feature_keys.PredictionResults.TIMES key indicating
95 the times for which the predictions were computed.
97 ValueError: If `times` or `steps` are misspecified
[all...] |
| input_pipeline.py | 26 A series, consisting of times (an increasing vector of integers) and values (one
37 `TrainEvalFeatures.TIMES` (scalar integers, one per timestep) and
39 features may have any shape, but are likewise associated with a timestep. Times
42 features (i.e. times may be missing, but given that a time is specified, every
102 evaluation, steps=None, times=None, exogenous_features=None):
111 FilteringResults.STATE_TUPLE and FilteringResults.TIMES.
113 evaluation. If `times` is specified, `steps` must not be; one is required.
114 times: A [batch_size x window_size] array of integers (not a Tensor)
115 indicating times to make predictions for. These times must be after th
[all...] |
| input_pipeline_test.py | 64 times = example.features.feature[TrainEvalFeatures.TIMES]
65 times.int64_list.value.append(i)
75 times = numpy.arange(num_samples)
76 values = times[:, None] * 2. + numpy.arange(num_features)[None, :]
77 return {TrainEvalFeatures.TIMES: times,
99 features[TrainEvalFeatures.TIMES].shape)
102 # Checks that all times are contiguous
104 features[TrainEvalFeatures.TIMES][batch_position
[all...] |
| feature_keys.py | 34 class Times(object):
35 """Key formats for accepting/returning times."""
37 TIMES = "times"
42 # Floating point, with one or more values corresponding to each time in TIMES.
46 class TrainEvalFeatures(Times, Values):
51 class PredictionFeatures(Times, State):
56 class FilteringFeatures(Times, Values, State):
61 class PredictionResults(Times):
66 class FilteringResults(Times, State)
[all...] |
| ar_model_test.py | 78 train_data = {TrainEvalFeatures.TIMES: time[0:split],
80 test_data = {TrainEvalFeatures.TIMES: time[split:],
147 train_data_times = train_data[TrainEvalFeatures.TIMES]
149 test_data_times = test_data[TrainEvalFeatures.TIMES]
161 PredictionFeatures.TIMES: training.limit_epochs(
222 return ({TrainEvalFeatures.TIMES: [[1]],
226 return ({TrainEvalFeatures.TIMES: np.arange(16)[None, :],
248 PredictionFeatures.TIMES: [[4, 6, 10]],
264 TrainEvalFeatures.TIMES: [[1, 3, 5, 7, 11]],
314 TrainEvalFeatures.TIMES: [[1, 3, 5, 7, 11]]
[all...] |
| estimators_test.py | 51 times = numpy.arange(20, dtype=numpy.int64)
54 feature_keys.TrainEvalFeatures.TIMES: times,
105 feature_keys.FilteringFeatures.TIMES: times[None, -1] + 2,
117 times[-1] + 3,
119 second_saved_prediction[feature_keys.PredictionResults.TIMES]))
123 feature_keys.FilteringFeatures.TIMES: times[-1] + 3,
|
| model.py | 51 # indicating times for which values in `predictions`
114 A dictionary with keys TrainEvalFeatures.TIMES (mapping to an array with
188 TrainEvalFeatures.TIMES: A [batch size x window size] integer Tensor
189 with times for each observation. If there is no artificial chunking,
201 batch_size=array_ops.shape(features[TrainEvalFeatures.TIMES])[0])
221 features: A dictionary with times, values, and (optionally) exogenous
243 PredictionFeatures.TIMES: A [batch size x window size] Tensor with
244 times to make predictions for. Times must be increasing within each
253 requested times. Keys indicate the type of prediction, and values hav
[all...] |
| state_management_test.py | 71 times = features[feature_keys.TrainEvalFeatures.TIMES]
75 math_utils.batch_start_time(times) - priors_from_time, dtypes.float32)
77 times - math_utils.batch_start_time(times)[:, None], dtypes.float32)
79 array_ops.slice(values, [0, array_ops.shape(times)[1] - 1, 0],
89 posteriors = (times, posterior)
99 times = numpy.concatenate((times_full[:cut_start],
104 times = times_full
107 feature_keys.TrainEvalFeatures.TIMES: times
[all...] |
| /external/capstone/suite/ |
| fuzz.py | 25 TIMES = 64
100 for j in xrange(1, TIMES):
113 for j in xrange(1, TIMES):
|
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| 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]]),
|
| state_space_model_test.py | 81 def get_observation_model(self, times):
92 feature_keys.TrainEvalFeatures.TIMES:
110 feature_keys.TrainEvalFeatures.TIMES:
124 def _gap_test_template(self, times, values):
132 feature_keys.TrainEvalFeatures.TIMES: times,
136 times = features[feature_keys.TrainEvalFeatures.TIMES]
140 feature_keys.TrainEvalFeatures.TIMES: times,
[all...] |
| structural_ensemble_test.py | 47 return {TrainEvalFeatures.TIMES: numpy.reshape(time, [1, -1]),
101 times = [1, 2, 3, 4, 5, 6]
113 features = {TrainEvalFeatures.TIMES: times,
124 evaluation, times=[[7, 8, 9]],
131 times = [1, 2, 3, 4, 5, 6]
138 features = {TrainEvalFeatures.TIMES: times,
148 evaluation, times=[[7, 8, 9]])
|
| /external/tensorflow/tensorflow/contrib/timeseries/examples/ |
| 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],
99 times.append(current_state["times"])
101 all_times = numpy.squeeze(numpy.concatenate(times, axis=1), axis=0)
|
| /external/libxkbcommon/xkbcommon/src/xkbcomp/ |
| parser.h | 86 TIMES = 44,
152 #define TIMES 44
|
| /external/ltp/testcases/kernel/sched/sched_stress/ |
| sched_tc4.c | 47 #include <sys/times.h>
64 * TIMES: number of times to read raw I/O device (~25MB)
70 #define TIMES 5000
113 clock_t start_time; /* start & stop times */
179 | o reads block of size BLOCK_SIZE n times |
209 * Read through predefined number of blocks TIMES number of times.
212 for (i = 0; i < TIMES; i++) {
|
| sched_tc5.c | 49 #include <sys/times.h>
62 * TIMES: number of times preform calculations
67 #define TIMES 20
110 clock_t start_time; /* start & stop times */
148 for (i = 0; i < TIMES; i++)
|
| /device/linaro/bootloader/edk2/UefiCpuPkg/ResetVector/Vtf0/X64/ |
| PageTables.asm | 60 TIMES 0x1000-PGTBLS_OFFSET($) DB 0
70 TIMES 0x2000-PGTBLS_OFFSET($) DB 0
|
| /frameworks/base/services/core/java/com/android/server/notification/ |
| ZenLog.java | 43 private static final long[] TIMES = new long[SIZE];
227 TIMES[sNext] = System.currentTimeMillis();
244 pw.print(FORMAT.format(new Date(TIMES[j])));
|
