diff options
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/model.py | 9 |
1 files changed, 0 insertions, 9 deletions
diff --git a/lib/model.py b/lib/model.py index e908af4..082fe8a 100644 --- a/lib/model.py +++ b/lib/model.py @@ -700,7 +700,6 @@ class PTAModel: arg_count, traces=[], ignore_trace_indexes=[], - discard_outliers=None, function_override={}, use_corrcoef=False, pta=None, @@ -716,13 +715,6 @@ class PTAModel: arg_count -- function arguments, as returned by pta_trace_to_aggregate traces -- list of preprocessed DFA traces, as returned by RawData.get_preprocessed_data() ignore_trace_indexes -- list of trace indexes. The corresponding traces will be ignored. - discard_outliers -- currently not supported: threshold for outlier detection and removel (float). - Outlier detection is performed individually for each state/transition in each trace, - so it only works if the benchmark ran several times. - Given "data" (a set of measurements of the same thing, e.g. TX duration in the third benchmark trace), - "m" (the median of all attribute measurements with the same parameters, which may include data from other traces), - a data point X is considered an outlier if - | 0.6745 * (X - m) / median(|data - m|) | > discard_outliers . function_override -- dict of overrides for automatic parameter function generation. If (state or transition name, model attribute) is present in function_override, the corresponding text string is the function used for analytic (parameter-aware/fitted) @@ -749,7 +741,6 @@ class PTAModel: ) self.cache = {} np.seterr("raise") - self._outlier_threshold = discard_outliers self.function_override = function_override.copy() self.pta = pta self.ignore_trace_indexes = ignore_trace_indexes |