proper paramfuncton support (no crossvalidation yet for assessment)

1 files changed, 15 insertions, 1 deletions

diff --git a/lib/dfatool.py b/lib/dfatool.py
index e39f58f..3387de0 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -426,6 +426,15 @@ class AnalyticFunction:
                 return
             self._regression_args = res.x
 
+    def is_predictable(self, param_list):
+        for i, param in enumerate(param_list):
+            if self._dependson[i] and not is_numeric(param):
+                return False
+        return True
+
+    def eval(self, param_list):
+        return self._function(self._regression_args, param_list)
+
 class analytic:
     _num0_8 = np.vectorize(lambda x: 8 - bin(int(x)).count("1"))
     _num0_16 = np.vectorize(lambda x: 16 - bin(int(x)).count("1"))
@@ -742,7 +751,7 @@ class EnergyModel:
         static_model = self._get_model_from_dict(self.by_name, np.median)
 
     def get_fitted(self):
-        static_model = self._get_model_from_dict(self.by_name, np.mean)
+        static_model = self._get_model_from_dict(self.by_name, np.median)
         param_model = dict([[state_or_tran, {}] for state_or_tran in self.by_name.keys()])
         for state_or_tran in self.by_name.keys():
             if self.by_name[state_or_tran]['isa'] == 'state':
@@ -764,6 +773,11 @@ class EnergyModel:
                     }
 
         def model_getter(name, key, **kwargs):
+            if key in param_model[name]:
+                param_list = kwargs['param']
+                param_function = param_model[name][key]['function']
+                if param_function.is_predictable(param_list):
+                    return param_function.eval(param_list)
             return static_model[name][key]
 
         def info_getter(name, key):