diff options
author | Daniel Friesel <daniel.friesel@uos.de> | 2020-10-08 16:16:12 +0200 |
---|---|---|
committer | Daniel Friesel <daniel.friesel@uos.de> | 2020-10-08 16:16:12 +0200 |
commit | 6fce6af60e7c99e77fcc24084db001367a7813be (patch) | |
tree | e70495cc5dd9f60e9976ac52c4429e0b15c280a9 /lib/model.py | |
parent | d5ace16a8efb627aa9882e595a8bfd1778b6ae36 (diff) |
BA Janis import: working detection of optimal number of changepoints
Diffstat (limited to 'lib/model.py')
-rw-r--r-- | lib/model.py | 25 |
1 files changed, 23 insertions, 2 deletions
diff --git a/lib/model.py b/lib/model.py index bb4a45b..d7a9bc9 100644 --- a/lib/model.py +++ b/lib/model.py @@ -375,7 +375,7 @@ def _num_args_from_by_name(by_name): class AnalyticModel: - u""" + """ Parameter-aware analytic energy/data size/... model. Supports both static and parameter-based model attributes, and automatic detection of parameter-dependence. @@ -663,7 +663,7 @@ class AnalyticModel: class PTAModel: - u""" + """ Parameter-aware PTA-based energy model. Supports both static and parameter-based model attributes, and automatic detection of parameter-dependence. @@ -704,6 +704,7 @@ class PTAModel: function_override={}, use_corrcoef=False, pta=None, + pelt=None, ): """ Prepare a new PTA energy model. @@ -726,6 +727,7 @@ class PTAModel: use_corrcoef -- use correlation coefficient instead of stddev comparison to detect whether a model attribute depends on a parameter pta -- hardware model as `PTA` object + pelt -- perform sub-state detection via PELT and model sub-states as well. Requires traces to be set. """ self.by_name = by_name self.by_param = by_name_to_by_param(by_name) @@ -733,6 +735,12 @@ class PTAModel: self._num_args = arg_count self._use_corrcoef = use_corrcoef self.traces = traces + if traces is not None and pelt is not None: + from .pelt import PELT + + self.pelt = PELT(**pelt) + else: + self.pelt = None self.stats = ParamStats( self.by_name, self.by_param, @@ -927,6 +935,19 @@ class PTAModel: return model_getter, info_getter + def get_substates(self): + states = self.states() + for k in self.by_param.keys(): + if k[0] == "TX": + if self.pelt.needs_refinement(self.by_param[k]["power_traces"]): + print(f"PELT: {k} needs refinement") + penalty = self.pelt.get_penalty_value( + self.by_param[k]["power_traces"] + ) + print(f" penalty: {penalty}") + + return None, None + def to_json(self): static_model = self.get_static() static_quality = self.assess(static_model) |