test prod compile

2 files changed, 122 insertions, 51 deletions

diff --git a/modeling/test_compilation.py b/modeling/test_compilation.py
deleted file mode 100644
index 1312494..0000000
--- a/modeling/test_compilation.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-Testing a simple case of production compilation. The compilation also allows for utility learning, shown in the model below, as well.
-"""
-
-import warnings
-
-import pyactr as actr
-
-class Compilation1:
-    """
-    Model testing compilation -- basic cases.
-    """
-
-    def __init__(self, **kwargs):
-        actr.chunktype("state", "starting ending")
-        self.m = actr.ACTRModel(**kwargs)
-
-        self.m.goal.add(actr.makechunk(nameofchunk="start", typename="state", starting=1))
-
-        self.m.productionstring(name="one", string="""
-            =g>
-            isa     state
-            starting =x
-            ending ~=x
-            ==>
-            =g>
-            isa     state
-            ending =x""", utility=2)
-
-        self.m.productionstring(name="two", string="""
-            =g>
-            isa     state
-            starting =x
-            ending  =x
-            ==>
-            =g>
-            isa     state
-            starting  =x
-            ending 4""")
-
-if __name__ == "__main__":
-    warnings.simplefilter("ignore")
-    mm = Compilation1(production_compilation=True, utility_learning=True)
-
-    model = mm.m
-
-    sim = model.simulation(realtime=True)
-    sim.run(0.5)
-    print(model.productions["one and two"])
diff --git a/modeling/test_compile.py b/modeling/test_compile.py
new file mode 100644
index 0000000..b1e825f
--- /dev/null
+++ b/modeling/test_compile.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+
+import pyactr as actr
+import prod_numbers
+
+Model = actr.ACTRModel()
+DM = Model.decmem
+goal = Model.goal
+imaginal = Model.set_goal(name="imaginal", delay=0.2)
+
+_ = prod_numbers.number(Model)
+
+def get_digit(number, n):
+    return number // 10**n % 10
+
+actr.chunktype("number", ("number", "next", "ones", "tens", "hundreds"))
+actr.chunktype(
+    "math_goal",
+    (
+        "task",
+        "result1",
+        "result2",
+        "op",
+        "nextop",
+        "nextnextop",
+        "arg1",
+        "arg1_idx",
+        "arg2",
+        "arg2_idx",
+        "result",
+        "expand_slot",
+        "hundreds1",
+        "tens1",
+        "ones1",
+        "hundreds2",
+        "tens2",
+        "ones2",
+    ),
+)
+actr.chunktype(
+    "math_op",
+    (
+        "op",
+        "arg1",
+        "arg2",
+        "result",
+    ),
+)
+
+# Add numbers 0-999 to decmem
+for i in range(0, 1000):
+    DM.add(
+        actr.makechunk(
+            f"number{str(i)}",
+            "number",
+            number=i,
+            next=i + 1,
+            ones=get_digit(i, 0),
+            tens=get_digit(i, 1),
+            hundreds=get_digit(i, 2),
+        )
+    )
+
+
+Model.productionstring(
+    name="continue_with_next_op",
+    string="""
+        =g>
+        isa     math_goal
+        op      done
+        nextop  ~None
+        nextop  =nextop
+        nextnextop  =nextnextop
+        ==>
+        =g>
+        isa     math_goal
+        op      =nextop
+        nextop  =nextnextop
+        nextnextop  None
+    """,
+)
+
+Model.productionstring(
+    name="reset",
+    string="""
+    =g>
+    isa         math_goal
+    op          reset
+    ==>
+    =g>
+    isa         math_goal
+    op          expand
+    nextop      reset
+    """
+)
+
+goal.add(
+    actr.makechunk(
+        "",
+        "math_goal",
+        op="expand",
+        nextop="reset",
+        expand_slot=None,
+        ones1=1,
+        tens1=2,
+        hundreds1=3,
+        ones2=4,
+        tens2=5,
+        hundreds2=6,
+    )
+)
+Model.model_parameters["activation_trace"] = True
+Model.model_parameters["instantaneous_noise"] = .2
+Model.model_parameters["utility_alpha"] = 0.5
+Model.model_parameters["utility_noise"] = 0.5
+Model.model_parameters["production_compilation"] = True
+Model.model_parameters["utility_learning"] = True
+
+sim = Model.simulation(
+    gui=False,
+)
+sim.run(max_time=2)