prepare proper overall error measures

2 files changed, 15 insertions, 0 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index fac8b00..99b3012 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -403,6 +403,10 @@ if __name__ == '__main__':
 
     if 'table' in show_quality or 'all' in show_quality:
         model_quality_table([static_quality, analytic_quality, lut_quality], [None, param_info, None])
+    if 'overall' in show_quality or 'all' in show_quality:
+        print('overall MAE of static model: {} µW'.format(model.assess_states(static_model)))
+        print('overall MAE of param model: {} µW'.format(model.assess_states(param_model)))
+        print('overall MAE of LUT model: {} µW'.format(model.assess_states(lut_model)))
     if 'summary' in show_quality or 'all' in show_quality:
         model_summary_table([model.assess_on_traces(static_model), model.assess_on_traces(param_model), model.assess_on_traces(lut_model)])
 
diff --git a/lib/dfatool.py b/lib/dfatool.py
index 4ba4911..dd9ed61 100644
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -1719,12 +1719,15 @@ class PTAModel:
         return self.pta.to_json()
 
     def states(self):
+        """Return sorted list of state names."""
         return sorted(list(filter(lambda k: self.by_name[k]['isa'] == 'state', self.by_name.keys())))
 
     def transitions(self):
+        """Return sorted list of transition names."""
         return sorted(list(filter(lambda k: self.by_name[k]['isa'] == 'transition', self.by_name.keys())))
 
     def states_and_transitions(self):
+        """Return list of states and transition names."""
         ret = self.states()
         ret.extend(self.transitions())
         return ret
@@ -1760,6 +1763,14 @@ class PTAModel:
             'by_name' : detailed_results
         }
 
+    def assess_states(self, model_function, model_attribute = 'power'):
+        """
+        Calculate overall model error assuming equal distribution of states
+        """
+        model_quality = self.assess(model_function)
+        total_error = np.sqrt(sum(map(lambda x: np.square(model_quality['by_name'][x][model_attribute]['mae']), self.states())))
+        return total_error
+
 
     def assess_on_traces(self, model_function):
         """