Feat/ddm/selected input array

Scripts/Examples/RL-DDM.py

@@ -16,14 +16,6 @@
     name='Input Layer'
 )
 
-# def decision_variable_to_one_hot(x):
-#     """Generate "one-hot" 1d array designating selected action from DDM's scalar decision variable
-#     (used to generate value of OutputState for action_selection Mechanism"""
-#     if x > 0:
-#         return [1,0]
-#     else:
-#         return [0,-1]
-
 # Takes sum of input layer elements as external component of drift rate
 # Notes:
 #    - drift_rate parameter in constructor for DDM is the "internally modulated" component of the drift_rate;
@@ -111,8 +103,8 @@ def show_weights():
 
 
 # Specify reward values associated with each action (corresponding to elements of esaction_selection.output_state.value)
-reward_values = [10, 0]
-# reward_values = [0, 10]
+# reward_values = [10, 0]
+reward_values = [0, 10]
 
 # Used by System to generate a reward on each trial based on the outcome of the action_selection (DDM) Mechanism
 def reward():

psyneulink/components/mechanisms/mechanism.py

@@ -2243,7 +2243,7 @@ def _update_parameter_states(self, runtime_params=None, context=None):
     def _update_params_dicts(self, context=None):
         from psyneulink.globals.keywords import NOISE
         for state in self._parameter_states:
-            if NOISE in state.name:
+            if NOISE in state.name and INITIALIZING in context:
                 continue
             if state.name in self.user_params:
                 self.user_params.__additem__(state.name, state.value)

tests/states/test_parameter_states.py

@@ -167,10 +167,10 @@ def test_configurable_params(self):
         assert np.allclose(T._smoothing_factor, new_value)
         assert np.allclose(T.mod_smoothing_factor, new_value)
 
-        # assert np.allclose(T.user_params["noise"], new_value)
-        # assert np.allclose(T.noise, new_value)
-        # assert np.allclose(T._noise, new_value)
-        # assert np.allclose(T.mod_noise, new_value)
+        assert np.allclose(T.user_params["noise"], new_value)
+        assert np.allclose(T.noise, new_value)
+        assert np.allclose(T._noise, new_value)
+        assert np.allclose(T.mod_noise, new_value)
 
 class TestModParams:
     def test_mod_param_error(self):