treewide: rename Component arg size -> input_shapes

PrincetonUniversity · Oct 3, 2024 · 938e7b6 · 938e7b6
1 parent d60d535
commit 938e7b6
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diff --git a/Scripts/Debug/Gilbert_Shallice_debugging_Interactive_activation b/Scripts/Debug/Gilbert_Shallice_debugging_Interactive_activation
@@ -5,16 +5,16 @@ import psyneulink as pnl
 
 
 ### LAYERS
-WORD_INPUT_LAYER = pnl.TransferMechanism(size = 3,
+WORD_INPUT_LAYER = pnl.TransferMechanism(input_shapes = 3,
                                          function=pnl.Linear,
                                          name='WORD INPUT LAYER')
 
-COLOR_INPUT_LAYER = pnl.TransferMechanism(size = 3,
+COLOR_INPUT_LAYER = pnl.TransferMechanism(input_shapes = 3,
                                          function=pnl.Linear,
                                          name='COLOR INPUT LAYER')
 
 
-WORD_OUTPUT_LAYER = pnl.RecurrentTransferMechanism(size = 3,
+WORD_OUTPUT_LAYER = pnl.RecurrentTransferMechanism(input_shapes = 3,
                                                    auto=0.0,
                                                    hetero=0.0,#-2.0,
                                                    function=pnl.Linear(),
@@ -25,7 +25,7 @@ WORD_OUTPUT_LAYER.set_log_conditions('InputPort-0')
 
 
 
-COLOR_OUTPUT_LAYER = pnl.RecurrentTransferMechanism(size = 3,
+COLOR_OUTPUT_LAYER = pnl.RecurrentTransferMechanism(input_shapes = 3,
                                                    auto=0.0,
                                                    hetero=0.0,#-2.0,
                                                    function=pnl.Linear(),
@@ -35,7 +35,7 @@ COLOR_OUTPUT_LAYER = pnl.RecurrentTransferMechanism(size = 3,
 COLOR_OUTPUT_LAYER.set_log_conditions('value')
 
 
-TASK_DEMAND_LAYER = pnl.RecurrentTransferMechanism(size = 2,
+TASK_DEMAND_LAYER = pnl.RecurrentTransferMechanism(input_shapes = 2,
                                                    auto=0.0,
                                                    hetero=0.0,#-2.0,
                                                    function=pnl.Linear(),

diff --git a/Scripts/Debug/Hebbian_Simon.py b/Scripts/Debug/Hebbian_Simon.py
@@ -14,7 +14,7 @@
 Hebb_comp = pnl.Composition()
 
 Hebb_mech=pnl.RecurrentTransferMechanism(
-    size=sizeF,
+    input_shapes=sizeF,
     function=pnl.Linear,
     #integrator_mode = True,
     #integration_rate = 0.5,

diff --git a/Scripts/Debug/Jason_Reward_rate_with_penalty_with_inputs.py b/Scripts/Debug/Jason_Reward_rate_with_penalty_with_inputs.py
@@ -72,18 +72,18 @@ def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
     punish = pnl.TransferMechanism(name='punish')
 
     inp_clr = pnl.TransferMechanism(
-        size=N_UNITS, function=pnl.Linear, name='COLOR INPUT'
+        input_shapes=N_UNITS, function=pnl.Linear, name='COLOR INPUT'
     )
     inp_wrd = pnl.TransferMechanism(
-        size=N_UNITS, function=pnl.Linear, name='WORD INPUT'
+        input_shapes=N_UNITS, function=pnl.Linear, name='WORD INPUT'
     )
     # task layer, represent the task instruction; color naming / word reading
     inp_task = pnl.TransferMechanism(
-        size=N_UNITS, function=pnl.Linear, name='TASK'
+        input_shapes=N_UNITS, function=pnl.Linear, name='TASK'
     )
     # hidden layer for color and word
     hid_clr = pnl.TransferMechanism(
-        size=N_UNITS,
+        input_shapes=N_UNITS,
         function=hidden_func,
         integrator_mode=True,
         integration_rate=integration_rate,
@@ -92,7 +92,7 @@ def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
         name='COLORS HIDDEN'
     )
     hid_wrd = pnl.TransferMechanism(
-        size=N_UNITS,
+        input_shapes=N_UNITS,
         function=hidden_func,
         integrator_mode=True,
         integration_rate=integration_rate,
@@ -102,7 +102,7 @@ def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
     )
     # output layer
     output = pnl.TransferMechanism(
-        size=N_UNITS,
+        input_shapes=N_UNITS,
         function=pnl.Logistic,
         integrator_mode=True,
         integration_rate=integration_rate,

diff --git a/Scripts/Debug/Markus Stroop.py b/Scripts/Debug/Markus Stroop.py
@@ -7,16 +7,19 @@
 import psyneulink.core.components.functions.stateful.integratorfunctions
 import psyneulink.core.components.functions.nonstateful.transferfunctions
 
-colors_input_layer = pnl.TransferMechanism(size=2,
+colors_input_layer = pnl.TransferMechanism(
+    input_shapes=2,
                                            function=psyneulink.core.components.functions.nonstateful.transferfunctions.Linear,
                                            name='COLORS_INPUT')
 
-words_input_layer = pnl.TransferMechanism(size=2,
+words_input_layer = pnl.TransferMechanism(
+    input_shapes=2,
                                           function=psyneulink.core.components.functions.nonstateful.transferfunctions.Linear,
                                           name='WORDS_INPUT')
 
 #   Task layer, tasks: ('name the color', 'read the word')
-task_layer = pnl.TransferMechanism(size=2,
+task_layer = pnl.TransferMechanism(
+    input_shapes=2,
                                    function=psyneulink.core.components.functions.nonstateful.transferfunctions.Linear,
                                    name='TASK')
 
@@ -26,14 +29,16 @@
 #   randomly distributed noise to the net input
 #   time averaging = integration_rate = 0.1
 unit_noise = 0.001
-colors_hidden_layer = pnl.TransferMechanism(size=2,
+colors_hidden_layer = pnl.TransferMechanism(
+    input_shapes=2,
                                             function=psyneulink.core.components.functions.nonstateful.transferfunctions.Logistic(gain=1.0, x_0=4.0),  #should be able to get same result with offset = -4.0
                                             integrator_mode=True,
                                             noise=psyneulink.core.components.functions.nonstateful.distributionfunctions.NormalDist(mean=0, standard_deviation=unit_noise).function,
                                             integration_rate=0.1,
                                             name='COLORS HIDDEN')
 #    words_hidden: ('RED','GREEN')
-words_hidden_layer = pnl.TransferMechanism(size=2,
+words_hidden_layer = pnl.TransferMechanism(
+    input_shapes=2,
                                            function=psyneulink.core.components.functions.nonstateful.transferfunctions.Logistic(gain=1.0, x_0=4.0),
                                            integrator_mode=True,
                                            noise=psyneulink.core.components.functions.nonstateful.distributionfunctions.NormalDist(mean=0, standard_deviation=unit_noise).function,
@@ -43,7 +48,8 @@
 #    OUTPUT UNITS
 
 #   Response layer, provide input to accumulator, responses: ('red', 'green')
-response_layer = pnl.TransferMechanism(size=2,
+response_layer = pnl.TransferMechanism(
+    input_shapes=2,
                                        function=psyneulink.core.components.functions.nonstateful.transferfunctions.Logistic,
                                        integrator_mode=True,
                                        noise=psyneulink.core.components.functions.nonstateful.distributionfunctions.NormalDist(mean=0, standard_deviation=unit_noise).function,

diff --git a/Scripts/Debug/Predator-Prey Sebastian REDUCED.py b/Scripts/Debug/Predator-Prey Sebastian REDUCED.py
@@ -36,9 +36,9 @@ def get_new_episode_flag():
 # **************************************  PROCESSING MECHANISMS ********************************************************
 
 # Perceptual Mechanisms
-player_percept = ProcessingMechanism(size=2, function=GaussianDistort(), name="PLAYER PERCEPT")
-predator_percept = ProcessingMechanism(size=2, function=GaussianDistort(), name="PREDATOR PERCEPT")
-prey_percept = ProcessingMechanism(size=2, function=GaussianDistort(), name="PREY PERCEPT")
+player_percept = ProcessingMechanism(input_shapes=2, function=GaussianDistort(), name="PLAYER PERCEPT")
+predator_percept = ProcessingMechanism(input_shapes=2, function=GaussianDistort(), name="PREDATOR PERCEPT")
+prey_percept = ProcessingMechanism(input_shapes=2, function=GaussianDistort(), name="PREY PERCEPT")
 
 # Mechanism used to encode trialtype from environment
 trial_type_input_mech = ProcessingMechanism(name="TRIAL TYPE INPUT")

diff --git a/Scripts/Debug/Predator-Prey Sebastian.py b/Scripts/Debug/Predator-Prey Sebastian.py
@@ -105,9 +105,9 @@ def get_optimal_action(observation):
 # **************************************  PROCESSING MECHANISMS ********************************************************
 
 # Perceptual Mechanisms
-player_percept = ProcessingMechanism(size=prey_len, function=GaussianDistort(), name="PLAYER PERCEPT")
-predator_percept = ProcessingMechanism(size=predator_len, function=GaussianDistort(), name="PREDATOR PERCEPT")
-prey_percept = ProcessingMechanism(size=prey_len, function=GaussianDistort(), name="PREY PERCEPT")
+player_percept = ProcessingMechanism(input_shapes=prey_len, function=GaussianDistort(), name="PLAYER PERCEPT")
+predator_percept = ProcessingMechanism(input_shapes=predator_len, function=GaussianDistort(), name="PREDATOR PERCEPT")
+prey_percept = ProcessingMechanism(input_shapes=prey_len, function=GaussianDistort(), name="PREY PERCEPT")
 
 # Mechanism used to encode trialtype from environment
 trial_type_input_mech = ProcessingMechanism(name="TRIAL TYPE INPUT")

diff --git a/Scripts/Debug/StabilityFlexibility.py b/Scripts/Debug/StabilityFlexibility.py
@@ -77,7 +77,7 @@ def computeAccuracy(variable):
 
 # first element is color task attendance, second element is motion task attendance
 inputLayer = pnl.TransferMechanism(#default_variable=[[0.0, 0.0]],
-                                   size=2,
+                                   input_shapes=2,
                                    function=pnl.Linear(slope=1, intercept=0),
                                    output_ports = [pnl.RESULT],
                                    name='Input')
@@ -100,23 +100,24 @@ def computeAccuracy(variable):
 
 
 stimulusInfo = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
-                                     size = 2,
+                                     input_shapes= 2,
                                      function = pnl.Linear(slope=1, intercept=0),
                                      output_ports = [pnl.RESULT],
                                      name = "Stimulus Info")
 
 stimulusInfo.set_log_conditions([pnl.RESULT])
 
 controlledElement = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
-                                          size = 2,
+                                          input_shapes= 2,
                                           function=pnl.Linear(slope=1, intercept= 0),
                                           input_ports=pnl.InputPort(combine=pnl.PRODUCT),
                                           output_ports = [pnl.RESULT],
                                           name = 'Stimulus Info * Activity')
 
 controlledElement.set_log_conditions([pnl.RESULT])
 
-ddmCombination = pnl.TransferMechanism(size = 1,
+ddmCombination = pnl.TransferMechanism(
+    input_shapes= 1,
                                        function = pnl.Linear(slope=1, intercept=0),
                                        output_ports = [pnl.RESULT],
                                        name = "DDM Integrator")

diff --git a/Scripts/Debug/Yotam LCA Model LLVM.py b/Scripts/Debug/Yotam LCA Model LLVM.py
@@ -132,13 +132,15 @@ def get_trained_network(bipartite_graph, num_features=3, num_hidden=200, epochs=
     lr = learning_rate
 
     # Instantiate layers and projections
-    il = pnl.TransferMechanism(size=D_i, name='input')
-    cl = pnl.TransferMechanism(size=D_c, name='control')
+    il = pnl.TransferMechanism(input_shapes=D_i, name='input')
+    cl = pnl.TransferMechanism(input_shapes=D_c, name='control')
 
-    hl = pnl.TransferMechanism(size=D_h, name='hidden',
+    hl = pnl.TransferMechanism(
+        input_shapes=D_h, name='hidden',
                                                function=pnl.Logistic(bias=-2))
 
-    ol = pnl.TransferMechanism(size=D_o, name='output',
+    ol = pnl.TransferMechanism(
+        input_shapes=D_o, name='output',
                                                function=pnl.Logistic(bias=-2))
 
     pih = pnl.MappingProjection(matrix=wih)
@@ -190,7 +192,8 @@ def get_trained_network(bipartite_graph, num_features=3, num_hidden=200, epochs=
 
     # Apply LCA transform (values from Sebastian's code -- supposedly taken from the original LCA paper from Marius & Jay)
     if attach_LCA:
-        lca = pnl.LCAMechanism(size=D_o,
+        lca = pnl.LCAMechanism(
+            input_shapes=D_o,
                                leak=leak,
                                competition=competition,
                                self_excitation=self_excitation,
@@ -251,14 +254,16 @@ def get_trained_network_multLCA(bipartite_graph, num_features=3, num_hidden=200,
     lr = learning_rate
 
     # Instantiate layers and projections
-    il = pnl.TransferMechanism(size=D_i, name='input')
-    cl = pnl.TransferMechanism(size=D_c, name='control')
+    il = pnl.TransferMechanism(input_shapes=D_i, name='input')
+    cl = pnl.TransferMechanism(input_shapes=D_c, name='control')
 
-    hl = pnl.TransferMechanism(size=D_h,
+    hl = pnl.TransferMechanism(
+        input_shapes=D_h,
                                name='hidden',
                                function=pnl.Logistic(bias=-2))
 
-    ol = pnl.TransferMechanism(size=D_o,
+    ol = pnl.TransferMechanism(
+        input_shapes=D_o,
                                name='output',
                                function=pnl.Logistic(bias=-2))
 
@@ -323,7 +328,8 @@ def get_trained_network_multLCA(bipartite_graph, num_features=3, num_hidden=200,
 
         lca_matrix = get_LCA_matrix(output_dims, num_features, self_excitation, competition)
 
-        lca = pnl.RecurrentTransferMechanism(size=D_o,
+        lca = pnl.RecurrentTransferMechanism(
+            input_shapes=D_o,
                                              matrix=lca_matrix,
                                              integrator_mode=True,
                                              integrator_function=lci,
@@ -339,7 +345,8 @@ def get_trained_network_multLCA(bipartite_graph, num_features=3, num_hidden=200,
 
         # Dummy to save mnet results
         if str(LCA_BIN_EXECUTE).startswith("LLVM"):
-            dummy = pnl.TransferMechanism(size=D_o,
+            dummy = pnl.TransferMechanism(
+                input_shapes=D_o,
                                           name="MNET_OUT")
             wrapper_composition.add_linear_processing_pathway([mnet, dummy])
 

diff --git a/Scripts/Debug/Yotam LCA Model.py b/Scripts/Debug/Yotam LCA Model.py
@@ -117,13 +117,15 @@ def get_trained_network(bipartite_graph, num_features=3, num_hidden=200, epochs=
     lr = learning_rate
 
     # Instantiate layers and projections
-    il = pnl.TransferMechanism(size=D_i, name='input')
-    cl = pnl.TransferMechanism(size=D_c, name='control')
+    il = pnl.TransferMechanism(input_shapes=D_i, name='input')
+    cl = pnl.TransferMechanism(input_shapes=D_c, name='control')
 
-    hl = pnl.TransferMechanism(size=D_h, name='hidden',
+    hl = pnl.TransferMechanism(
+        input_shapes=D_h, name='hidden',
                                                function=pnl.Logistic(bias=-2))
 
-    ol = pnl.TransferMechanism(size=D_o, name='output',
+    ol = pnl.TransferMechanism(
+        input_shapes=D_o, name='output',
                                                function=pnl.Logistic(bias=-2))
 
     pih = pnl.MappingProjection(matrix=wih)
@@ -174,7 +176,8 @@ def get_trained_network(bipartite_graph, num_features=3, num_hidden=200, epochs=
 
     # Apply LCA transform (values from Sebastian's code -- supposedly taken from the original LCA paper from Marius & Jay)
     if attach_LCA:
-        lca = pnl.LCAMechanism(size=D_o,
+        lca = pnl.LCAMechanism(
+            input_shapes=D_o,
                                leak=leak,
                                competition=competition,
                                self_excitation=self_excitation,
@@ -237,14 +240,16 @@ def get_trained_network_multLCA(bipartite_graph, num_features=3, num_hidden=200,
     lr = learning_rate
 
     # Instantiate layers and projections
-    il = pnl.TransferMechanism(size=D_i, name='input')
-    cl = pnl.TransferMechanism(size=D_c, name='control')
+    il = pnl.TransferMechanism(input_shapes=D_i, name='input')
+    cl = pnl.TransferMechanism(input_shapes=D_c, name='control')
 
-    hl = pnl.TransferMechanism(size=D_h,
+    hl = pnl.TransferMechanism(
+        input_shapes=D_h,
                                name='hidden',
                                function=pnl.Logistic(bias=-2))
 
-    ol = pnl.TransferMechanism(size=D_o,
+    ol = pnl.TransferMechanism(
+        input_shapes=D_o,
                                name='output',
                                function=pnl.Logistic(bias=-2))
 
@@ -304,7 +309,8 @@ def get_trained_network_multLCA(bipartite_graph, num_features=3, num_hidden=200,
 
         lca_matrix = get_LCA_matrix(output_dims, num_features, self_excitation, competition)
 
-        lca = pnl.RecurrentTransferMechanism(size=D_o,
+        lca = pnl.RecurrentTransferMechanism(
+            input_shapes=D_o,
                                              matrix=lca_matrix,
                                              integrator_mode=True,
                                              integrator_function=lci,

diff --git a/Scripts/Debug/bryant_lca_with_termination.py b/Scripts/Debug/bryant_lca_with_termination.py
@@ -1,19 +1,19 @@
 import psyneulink as pnl
 
 cueInterval = pnl.TransferMechanism(default_variable=[[0.0]],
-                                    size=1,
+                                    input_shapes=1,
                                     function=pnl.Linear(slope=1, intercept=0),
                                     output_ports=[pnl.RESULT],
                                     name='Cue-Stimulus Interval')
 
 taskLayer = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
-                                  size=2,
+                                  input_shapes=2,
                                   function=pnl.Linear(slope=1, intercept=0),
                                   output_ports=[pnl.RESULT],
                                   name='Task Input [I1, I2]')
 
 activation = pnl.LCAMechanism(default_variable=[[0.0, 0.0]],
-                              size=2,
+                              input_shapes=2,
                               function=pnl.Logistic(gain=1),
                               leak=.5,
                               competition=2,