Searched refs:periodicities (Results 1 – 10 of 10) sorted by relevance
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| D | structural_ensemble.py | 99 periodicities, argument
134 periodicity_list = nest.flatten(periodicities)
186 periodicities, argument
236 if periodicities is None:
237 periodicities = []
238 periodicity_list = nest.flatten(periodicities)
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| D | structural_ensemble_test.py | 108 periodicities=[],
134 periodicities=None,
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| D | estimators.py | 300 self, periodicities, input_window_size, output_window_size, argument
356 periodicities=periodicities, num_features=num_features,
370 periodicities=periodicities,
475 periodicities, argument
527 periodicities=periodicities,
596 periodicities, argument
684 periodicities=periodicities,
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| D | ar_model.py | 235 periodicities, argument
296 if periodicities is None or not periodicities:
297 periodicities = []
298 elif (not isinstance(periodicities, list) and
299 not isinstance(periodicities, tuple)):
300 periodicities = [periodicities]
301 self._periodicities = [int(p) for p in periodicities]
920 periodicities, argument
937 periodicities=periodicities,
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| D | estimators_test.py | 188 periodicities=10, input_window_size=10, output_window_size=6,
200 periodicities=10, input_window_size=10, output_window_size=6,
214 num_features=1, periodicities=10, model_dir=model_dir, dtype=dtype,
223 num_features=1, periodicities=[], model_dir=self.get_temp_dir(),
236 periodicities=10,
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| D | ar_model_test.py | 113 periodicities=self.period,
223 periodicities=10, num_features=1,
244 model = ar_model.ARModel(periodicities=2,
265 model = ar_model.ARModel(periodicities=2,
286 model = ar_model.ARModel(periodicities=2,
336 model = ar_model.ARModel(periodicities=2,
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| D | head_test.py | 343 periodicities=None,
354 periodicities=10, input_window_size=10, output_window_size=6,
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| /external/tensorflow/tensorflow/contrib/timeseries/examples/ |
| D | predict.py | 54 periodicities=100, num_features=1, cycle_num_latent_values=5)
63 periodicities=100, input_window_size=10, output_window_size=6,
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| D | known_anomaly.py | 58 periodicities=12,
76 periodicities=12,
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| D | multivariate.py | 51 periodicities=[], num_features=5)
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