Merge pull request #6 from kaklise/parmest-redesign

changed yhat to y_hat

pyomo/contrib/parmest/examples/reactor_design/datarec_example.py

@@ -132,8 +132,8 @@ def main():
     # Define sum of squared error objective function for data rec
     def SSE_with_std(model):
         expr = sum(
-            ((y - yhat) / model.experiment_outputs_std[y]) ** 2
-            for y, yhat in model.experiment_outputs.items()
+            ((y - y_hat) / model.experiment_outputs_std[y]) ** 2
+            for y, y_hat in model.experiment_outputs.items()
         )
         return expr
 

pyomo/contrib/parmest/examples/reactor_design/multisensor_data_example.py

@@ -74,10 +74,10 @@ def main():
     # Define sum of squared error
     def SSE_multisensor(model):
         expr = 0
-        for y, yhat in model.experiment_outputs.items():
-            num_outputs = len(yhat)
+        for y, y_hat in model.experiment_outputs.items():
+            num_outputs = len(y_hat)
             for i in range(num_outputs):
-                expr += ((y - yhat[i]) ** 2) * (1 / num_outputs)
+                expr += ((y - y_hat[i]) ** 2) * (1 / num_outputs)
         return expr
 
     # View one model

pyomo/contrib/parmest/examples/reactor_design/timeseries_data_example.py

@@ -59,10 +59,10 @@ def main():
     def SSE_timeseries(model):
 
         expr = 0
-        for y, yhat in model.experiment_outputs.items():
-            num_time_points = len(yhat)
+        for y, y_hat in model.experiment_outputs.items():
+            num_time_points = len(y_hat)
             for i in range(num_time_points):
-                expr += ((y - yhat[i]) ** 2) * (1 / num_time_points)
+                expr += ((y - y_hat[i]) ** 2) * (1 / num_time_points)
 
         return expr
 

pyomo/contrib/parmest/parmest.py

@@ -242,7 +242,7 @@ def SSE(model):
     """
     Sum of squared error between `experiment_output` model and data values
     """
-    expr = sum((y - yhat) ** 2 for y, yhat in model.experiment_outputs.items())
+    expr = sum((y - y_hat) ** 2 for y, y_hat in model.experiment_outputs.items())
     return expr