add parameter normalization support to YAML DFA models

2 files changed, 16 insertions, 1 deletions

diff --git a/lib/automata.py b/lib/automata.py
index 6437839..3220d81 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -1,6 +1,6 @@
 """Classes and helper functions for PTA and other automata."""
 
-from functions import AnalyticFunction
+from functions import AnalyticFunction, NormalizationFunction
 import itertools
 
 def _dict_to_list(input_dict: dict) -> list:
@@ -310,6 +310,9 @@ class PTA:
             if parameter in self.parameter_normalization:
                 if 'enum' in self.parameter_normalization[parameter] and value in self.parameter_normalization[parameter]['enum']:
                     normalized_param[parameter] = self.parameter_normalization[parameter]['enum'][value]
+                if 'formula' in self.parameter_normalization[parameter]:
+                    normalization_formula = NormalizationFunction(self.parameter_normalization[parameter]['formula'])
+                    normalized_param[parameter] = normalization_formula.eval(value)
         return normalized_param
 
     @classmethod
diff --git a/lib/functions.py b/lib/functions.py
index ebeb2a2..c30a5f7 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -87,6 +87,18 @@ class ParamFunction:
         """
         return self._param_function(P, X) - y
 
+class NormalizationFunction:
+    """
+    Hi
+    """
+
+    def __init__(self, function_str):
+        self._function_str = function_str
+        self._function = eval('lambda param: ' + function_str)
+
+    def eval(self, param_value):
+        return self._function(param_value)
+
 class AnalyticFunction:
     """
     A multi-dimensional model function, generated from a string, which can be optimized using regression.