[dyn] update reset_state logic

brainpy/_src/dynsys.py

@@ -13,7 +13,7 @@
 from brainpy._src.deprecations import _update_deprecate_msg
 from brainpy._src.initialize import parameter, variable_
 from brainpy._src.mixin import SupportAutoDelay, Container, SupportInputProj, DelayRegister, global_delay_data
-from brainpy.errors import NoImplementationError, UnsupportedError
+from brainpy.errors import NoImplementationError, UnsupportedError, APIChangedError
 from brainpy.types import ArrayType, Shape
 
 __all__ = [
@@ -31,7 +31,6 @@
 
 
 def not_implemented(fun):
-
   def new_fun(*args, **kwargs):
     return fun(*args, **kwargs)
 
@@ -153,16 +152,20 @@ def update(self, *args, **kwargs):
     """
     raise NotImplementedError('Must implement "update" function by subclass self.')
 
-  def reset(self, *args, **kwargs):
+  def reset(self, *args, include_self: bool = False, **kwargs):
     """Reset function which reset the whole variables in the model (including its children models).
 
     ``reset()`` function is a collective behavior which resets all states in this model.
 
     See https://brainpy.readthedocs.io/en/latest/tutorial_toolbox/state_resetting.html for details.
+
+    Args::
+      include_self: bool. Reset states including the node self. Please turn on this if the node has
+        implemented its ".reset_state()" function.
     """
-    child_nodes = self.nodes().subset(DynamicalSystem).unique()
+    child_nodes = self.nodes(include_self=include_self).subset(DynamicalSystem).unique()
     for node in child_nodes.values():
-        node.reset_state(*args, **kwargs)
+      node.reset_state(*args, **kwargs)
 
   def reset_state(self, *args, **kwargs):
     """Reset function which resets local states in this model.
@@ -172,7 +175,17 @@ def reset_state(self, *args, **kwargs):
 
     See https://brainpy.readthedocs.io/en/latest/tutorial_toolbox/state_resetting.html for details.
     """
-    pass
+    raise APIChangedError(
+      '''
+    From version >= 2.4.6, the policy of ``.reset_state()`` has been changed.
+    
+    1. If you are resetting all states in a network by calling ".reset_state()", please use ".reset()" function. 
+       ".reset_state()" only defines the resetting of local states in a local node (excluded its children nodes). 
+    
+    2. If you does not customize "reset_state()" function for a local node, please implement it in your subclass.
+    
+      '''
+    )
 
   def clear_input(self, *args, **kwargs):
     """Clear the input at the current time step."""

brainpy/_src/integrators/ode/explicit_rk.py

@@ -140,13 +140,13 @@ def __init__(self,
                show_code=False,
                state_delays=None,
                neutral_delays=None):
-    super(ExplicitRKIntegrator, self).__init__(f=f,
-                                               var_type=var_type,
-                                               dt=dt,
-                                               name=name,
-                                               show_code=show_code,
-                                               state_delays=state_delays,
-                                               neutral_delays=neutral_delays)
+    super().__init__(f=f,
+                     var_type=var_type,
+                     dt=dt,
+                     name=name,
+                     show_code=show_code,
+                     state_delays=state_delays,
+                     neutral_delays=neutral_delays)
 
     # integrator keywords
     keywords = {

brainpy/_src/runners.py

@@ -455,7 +455,7 @@ def predict(
 
     # reset the states of the model and the runner
     if reset_state:
-      self.target.reset_state(self._get_input_batch_size(inputs))
+      self.target.reset(self._get_input_batch_size(inputs))
       self.reset_state()
 
     # shared arguments and inputs

brainpy/errors.py

@@ -6,6 +6,10 @@ class BrainPyError(Exception):
   pass
 
 
+class APIChangedError(BrainPyError):
+  pass
+
+
 class RunningError(BrainPyError):
   """The error occurred in the running function."""
   pass

tests/training/test_ESN.py

@@ -120,7 +120,7 @@ def test_ngrc_bacth(self, num_in=10, num_out=30):
     with bm.batching_environment():
       model = NGRC(num_in, num_out)
     batch_size = 10
-    model.reset_state(batch_size)
+    model.reset(batch_size)
     X = bm.random.random((batch_size, 200, num_in))
     Y = bm.random.random((batch_size, 200, num_out))
     trainer = bp.RidgeTrainer(model, alpha=1e-6)