From 162a0c287f5dab664e9168a60d76c9f8da07e46a Mon Sep 17 00:00:00 2001 From: Daniel Friesel Date: Tue, 16 Mar 2021 08:38:24 +0100 Subject: move codependent parameter detection to Model / ModelAttribute Still TODO: Ignore codependent parameters when partitioning data for analytic modeling / regression --- lib/model.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'lib/model.py') diff --git a/lib/model.py b/lib/model.py index 829ca37..75f7195 100644 --- a/lib/model.py +++ b/lib/model.py @@ -5,7 +5,7 @@ import numpy as np import os from .automata import PTA, ModelAttribute from .functions import StaticFunction, SubstateFunction -from .parameters import ParallelParamStats +from .parameters import ParallelParamStats, codependent_param_dict from .paramfit import ParallelParamFit from .utils import soft_cast_int, by_name_to_by_param, regression_measures @@ -126,10 +126,12 @@ class AnalyticModel: return f"AnalyticModel" def _compute_stats(self, by_name): + paramstats = ParallelParamStats() for name, data in by_name.items(): self.attr_by_name[name] = dict() + codependent_param = codependent_param_dict(data["param"]) for attr in data["attributes"]: model_attr = ModelAttribute( name, @@ -138,6 +140,7 @@ class AnalyticModel: data["param"], self.parameters, self._num_args.get(name, 0), + codependent_param=codependent_param, ) self.attr_by_name[name][attr] = model_attr paramstats.enqueue((name, attr), model_attr) -- cgit v1.2.3