test prod compile

1 files changed, 122 insertions, 0 deletions

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)