Refactor/mechanisms/ execute (#753)

* • IntegratorMechanism (#742)

- added input_states and output_states to
    init and assign_args_to_params_dicts

• LinearMatrix
  - keyword:  fixed bug in which assignment of rows or cols
              could not handle scalars

* Fix/function/stability (#743)

* • IntegratorMechanism
  - added input_states and output_states to
    init and assign_args_to_params_dicts

• LinearMatrix
  - keyword:  fixed bug in which assignment of rows or cols
              could not handle scalars

* • Function
  Stability:
  - function: add PROB_INDICATOR option that assigns a value of 1
    to the probabilistically chosen option
  - _validate_params: moved call to super() to end to allow
      matrix param to be evaluated before continuing validation

* Refactor/context/structured (#744)

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* • Log
  LogCondition - subset of ContextFlags used to specified logPref

• Context
  ContextFlags - implemented as common set of context flags for:
  - initialization
  - execution tracking
  - logging
  ContextStatus - aliases to ContextFlags (for backward compatibility)

* • Log
  LogCondition - subset of ContextFlags used to specified logPref

• Context
  ContextFlags - implemented as common set of context flags for:
  - initialization
  - execution tracking
  - logging
  ContextStatus - aliases to ContextFlags (for backward compatibility)

* • Log
  LogCondition - subset of ContextFlags used to specified logPref

• Context
  ContextFlags - implemented as common set of context flags for:
  - initialization
  - execution tracking
  - logging
  ContextStatus - aliases to ContextFlags (for backward compatibility)

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* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

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* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

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* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

* Refactor/context/deprecate init status (#745)

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* • Log
  LogCondition - subset of ContextFlags used to specified logPref

• Context
  ContextFlags - implemented as common set of context flags for:
  - initialization
  - execution tracking
  - logging
  ContextStatus - aliases to ContextFlags (for backward compatibility)

* • Log
  LogCondition - subset of ContextFlags used to specified logPref

• Context
  ContextFlags - implemented as common set of context flags for:
  - initialization
  - execution tracking
  - logging
  ContextStatus - aliases to ContextFlags (for backward compatibility)

* • Log
  LogCondition - subset of ContextFlags used to specified logPref

• Context
  ContextFlags - implemented as common set of context flags for:
  - initialization
  - execution tracking
  - logging
  ContextStatus - aliases to ContextFlags (for backward compatibility)

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* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

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* • Context
  context.status replaced with specific field assignments
  (.initialization_phase, .execution_phase, and .source)

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* Merge branches 'devel' and 'refactor/context/structured' of https://github.com/PrincetonUniversity/PsyNeuLink into refactor/context/structured

# Conflicts:
#	.idea/runConfigurations/Tests.xml
#	Scripts/McClure.py

* Merge branches 'devel' and 'refactor/context/structured' of https://github.com/PrincetonUniversity/PsyNeuLink into refactor/context/structured

# Conflicts:
#	.idea/runConfigurations/Tests.xml
#	Scripts/McClure.py

* -

* -

* • Context
  - consolidated init_status into context.initialization_status

* • Context
  - consolidated init_status into context.initialization_status

* • Context
  - consolidated init_status into context.initialization_status

* • Context (#746)

- initialization_status.UNSET -> initialization_status.INITALIZING
    where appropriate
  - delete InitStatus class

* test,function/LinearCombination: Rename second test function to prevent overwriting results

Test names should indicate tested feature.
Fixes: daebb62 ("Linear combination fix
(#734)")
Cherry-picked from devel-llvm: fdc686e

* tests,function/LinearCombination: Add tests with absent parameters

Cherry-picked from devel-llvm: 1dcca60

* Scheduling: fix bug where termination conditions persisted across calls to run

* Feat/mechanism/input target label dicts (#751)

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* • Environment
  added _parse_input_labels() and _parse_target_labels() methods
  run(): modified to use _parse_input_labels and _parse_target_labels

* • Environment
  added _parse_input_labels() and _parse_target_labels() methods
  run(): modified to use _parse_input_labels and _parse_target_labels

* • Environment
  added _parse_input_labels() and _parse_target_labels() methods
  run(): modified to use _parse_input_labels and _parse_target_labels

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* • Mechanism
  added input_labels and output_labels propoerties
  added _get_state_value_labels() method

* • Merged with devel

* testing: correct pytest setup ovewrite, losing some settings

* testing: Resolve leftover merge conflicts, fixes #747

* Feat/mechanism/input target lable dicts (#752)

* -

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* -

* • Environment
  added _parse_input_labels() and _parse_target_labels() methods
  run(): modified to use _parse_input_labels and _parse_target_labels

* • Environment
  added _parse_input_labels() and _parse_target_labels() methods
  run(): modified to use _parse_input_labels and _parse_target_labels

* • Environment
  added _parse_input_labels() and _parse_target_labels() methods
  run(): modified to use _parse_input_labels and _parse_target_labels

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* • Mechanism
  added input_labels and output_labels propoerties
  added _get_state_value_labels() method

* • Merged with devel

* • Environment
  _parse_input_labels(): added error messages

* • Environment
  _parse_input_labels(): added error messages

* • Environment
  Run: removed _parse_target_labels (they are just the input_labels
       for a TARGET Mechanism

• Mechanism
  docstring revs for INPUT_LABELS_DICDT and OUTPUT_LABELS_DICT

* • Environment
  Run: removed _parse_target_labels (they are just the input_labels
       for a TARGET Mechanism

• Mechanism
  docstring revs for INPUT_LABELS_DICDT and OUTPUT_LABELS_DICT

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* • Component
  - _execute:  added **kwargs argument, passed to call to function
               to accomodate LearningMechanism that uses this

• AutoassociativeLearningMechanism
  - _execute calls super(LearningMechanism, self) to skip Learning

• LearningMechanism:
  _ execute calls super()._execute with **kwargs

* • Component
  - _execute:  added **kwargs argument, passed to call to function
               to accomodate LearningMechanism that uses this

• AutoassociativeLearningMechanism
  - _execute calls super(LearningMechanism, self) to skip Learning

• LearningMechanism:
  _ execute calls super()._execute with **kwargs

• PrecictionMechanism:
  _execute calls super()._execute

* • Component
  - _execute:  added **kwargs argument, passed to call to function
               to accomodate LearningMechanism and EVCControlMechanism,
               the functions for which expect additional arguments

• AutoassociativeLearningMechanism
  - _execute calls super(LearningMechanism, self) to skip Learning

• LearningMechanism:
  _ execute calls super()._execute with **kwargs

• PrecictionMechanism:
  _execute calls super()._execute

* • Component
  - _execute:  added **kwargs argument, passed to call to function
               to accomodate LearningMechanism and EVCControlMechanism,
               the functions for which expect additional arguments

• AutoassociativeLearningMechanism
  - _execute calls super(LearningMechanism, self) to skip Learning

• LearningMechanism:
  _ execute calls super()._execute with **kwargs

• PrecictionMechanism:
  _execute calls super()._execute

Scripts/Examples/EVC-Gratton.py

@@ -145,8 +145,8 @@
 
 # Show graph of system (with control components)
 # mySystem.show_graph(show_dimensions=pnl.ALL, show_projection_labels=True)
-# mySystem.show_graph(show_control=True, show_projection_labels=True)
-mySystem.show_graph(show_control=True, show_mechanism_structure=True, show_headers=False)
+mySystem.show_graph(show_control=True, show_projection_labels=False)
+# mySystem.show_graph(show_control=True, show_mechanism_structure=True, show_headers=False)
 
 # configure EVC components
 mySystem.controller.control_signals[0].intensity_cost_function = pnl.Exponential(rate=0.8046).function
@@ -193,7 +193,7 @@
 
 mySystem.controller.reportOutputPref = True
 
-Flanker_Rep.set_log_conditions((pnl.SLOPE, pnl.ContextStatus.CONTROL))
+Flanker_Rep.set_log_conditions((pnl.SLOPE, pnl.ContextFlags.CONTROL))
 
 mySystem.run(
     num_trials=nTrials,

Scripts/Examples/Multilayer-Learning FOR FIG.py

@@ -0,0 +1,134 @@
+import functools
+import numpy as np
+import psyneulink as pnl
+
+Input_Layer = pnl.TransferMechanism(
+    name='Input',
+    function=pnl.Logistic,
+    params={pnl.INPUT_LABELS_DICT:{'red': [-1, 30]}},
+    default_variable=np.zeros((2,)))
+
+Hidden_Layer_1 = pnl.TransferMechanism(
+    name='Hidden1',
+    function=pnl.Logistic(),
+    default_variable=np.zeros((5,)))
+
+Hidden_Layer_2 = pnl.TransferMechanism(
+    name='Hidden2',
+    function=pnl.Logistic(),
+    default_variable=[0, 0, 0, 0])
+
+Output_Layer = pnl.TransferMechanism(
+    name='Output',
+    function=pnl.Logistic,
+    default_variable=[0, 0, 0])
+
+Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+
+# This Projection will be used by the Process below by referencing it in the Process' pathway;
+#    note: sender and receiver args don't need to be specified
+Input_Weights = pnl.MappingProjection(
+    name='Input Weights',
+    matrix=Input_Weights_matrix
+)
+
+# This Projection will be used by the Process below by assigning its sender and receiver args
+#    to mechanisms in the pathway
+Middle_Weights = pnl.MappingProjection(
+    name='Middle Weights',
+    sender=Hidden_Layer_1,
+    receiver=Hidden_Layer_2,
+    matrix=Middle_Weights_matrix
+)
+
+# Treated same as Middle_Weights Projection
+Output_Weights = pnl.MappingProjection(
+    name='Output Weights',
+    sender=Hidden_Layer_2,
+    receiver=Output_Layer,
+    matrix=Output_Weights_matrix
+)
+
+z = pnl.Process(
+    default_variable=[0, 0],
+    pathway=[
+        Input_Layer,
+        # The following reference to Input_Weights is needed to use it in the pathway
+        #    since it's sender and receiver args are not specified in its declaration above
+        Input_Weights,
+        Hidden_Layer_1,
+        # Middle_Weights,
+        # No Projection specification is needed here since the sender arg for Middle_Weights
+        #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+        Hidden_Layer_2,
+        # Output_Weights,
+        # Output_Weights does not need to be listed for the same reason as Middle_Weights
+        # If Middle_Weights and/or Output_Weights is not declared above, then the Process
+        #    will assign a default for rhe missing Projection
+        Output_Layer
+    ],
+    clamp_input=pnl.SOFT_CLAMP,
+    learning=pnl.LEARNING,
+    target=[0, 0, 1],
+    prefs={
+        pnl.VERBOSE_PREF: False,
+        pnl.REPORT_OUTPUT_PREF: True
+    }
+)
+
+
+def print_header(system):
+    print("\n\n**** Time: ", system.scheduler_processing.clock.simple_time)
+
+
+def show_target(system):
+    i = system.input
+    t = system.target_input_states[0].value
+    print('\nOLD WEIGHTS: \n')
+    print('- Input Weights: \n', Input_Weights.matrix)
+    print('- Middle Weights: \n', Middle_Weights.matrix)
+    print('- Output Weights: \n', Output_Weights.matrix)
+
+    print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+    print('ACTIVITY FROM OLD WEIGHTS: \n')
+    print('- Middle 1: \n', Hidden_Layer_1.value)
+    print('- Middle 2: \n', Hidden_Layer_2.value)
+    print('- Output:\n', Output_Layer.value)
+
+
+mySystem = pnl.System(
+    processes=[z],
+    targets=[0, 0, 1],
+    learning_rate=2.0
+)
+
+# Log Middle_Weights of MappingProjection to Hidden_Layer_2
+# Hidden_Layer_2.set_log_conditions('Middle Weights')
+Middle_Weights.set_log_conditions('matrix')
+
+mySystem.reportOutputPref = True
+# Shows graph will full information:
+# mySystem.show_graph(show_dimensions=pnl.ALL)
+mySystem.show_graph()
+# mySystem.show_graph(show_learning=pnl.ALL, show_dimensions=pnl.ALL, show_mechanism_structure=True)
+# Shows minimal graph:
+# mySystem.show_graph()
+
+
+stim_list = {Input_Layer: ['red']}
+target_list = {Output_Layer: [[0, 0, 1]]}
+
+mySystem.run(
+    num_trials=10,
+    inputs=stim_list,
+    targets=target_list,
+    call_before_trial=functools.partial(print_header, mySystem),
+    call_after_trial=functools.partial(show_target, mySystem),
+    termination_processing={pnl.TimeScale.TRIAL: pnl.AfterNCalls(Output_Layer, 1)}
+)
+
+# Print out logged weights for Middle_Weights
+# print('\nMiddle Weights (to Hidden_Layer_2): \n', Hidden_Layer_2.log.nparray(entries='Middle Weights', header=False))
+print('\nMiddle Weights (to Hidden_Layer_2): \n', Middle_Weights.log.nparray(entries='matrix', header=False))

Scripts/Examples/Multilayer-Learning.py

@@ -5,6 +5,7 @@
 Input_Layer = pnl.TransferMechanism(
     name='Input Layer',
     function=pnl.Logistic,
+    params={pnl.INPUT_LABELS_DICT:{'red': [-1, 30]}},
     default_variable=np.zeros((2,)))
 
 Hidden_Layer_1 = pnl.TransferMechanism(
@@ -110,12 +111,13 @@ def show_target(system):
 mySystem.reportOutputPref = True
 # Shows graph will full information:
 # mySystem.show_graph(show_dimensions=pnl.ALL)
-mySystem.show_graph(show_learning=pnl.ALL, show_dimensions=pnl.ALL)
+mySystem.show_graph(show_learning=True)
 # mySystem.show_graph(show_learning=pnl.ALL, show_dimensions=pnl.ALL, show_mechanism_structure=True)
 # Shows minimal graph:
 # mySystem.show_graph()
 
-stim_list = {Input_Layer: [[-1, 30]]}
+
+stim_list = {Input_Layer: ['red']}
 target_list = {Output_Layer: [[0, 0, 1]]}
 
 mySystem.run(

Scripts/Laura Stroop w EVC.py

@@ -0,0 +1,18 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import psyneulink as pnl
+
+ci = pnl.TransferMechanism(size=2, name='COLORS INPUT')
+wi = pnl.TransferMechanism(size=2, name='WORDS INPUT')
+ch = pnl.TransferMechanism(size=2, function=pnl.Logistic, name='COLORS HIDDEN')
+wh = pnl.TransferMechanism(size=2, function=pnl.Logistic, name='WORDS HIDDEN')
+tl = pnl.TransferMechanism(size=2, function=pnl.Logistic(gain=pnl.CONTROL), name='TASK CONTROL')
+rl = pnl.LCA(size=2, function=pnl.Logistic, name='RESPONSE')
+cp = pnl.Process(pathway=[ci, ch, rl])
+wp = pnl.Process(pathway=[wi, wh, rl])
+tc = pnl.Process(pathway=[tl, ch])
+tw = pnl.Process(pathway=[tl,wh])
+s = pnl.System(processes=[tc, tw, cp, wp],
+               controller=pnl.EVCControlMechanism(name='EVC Mechanimsm'),
+               monitor_for_control=[rl])
+s.show_graph()

Scripts/Laura Stroop.py

@@ -312,7 +312,8 @@ def pass_threshold(mech1, mech2, thresh):
 respond_green_accumulator.reinitialize(0)
 respond_red_accumulator.reinitialize(0)
 # now run test trial
-my_Stroop.show_graph(show_mechanism_structure=pnl.VALUES)
+my_Stroop.show_graph()
+# my_Stroop.show_graph(show_mechanism_structure=pnl.VALUES)
 my_Stroop.run(inputs=CN_incongruent_trial_input, termination_processing=terminate_trial)