Make hW test more comprehensive

Author: emuskardin

tests/test_hW.py

@@ -1,8 +1,8 @@
 import pytest
+
 from aalpy import generate_random_deterministic_automata, bisimilar, run_hW
 from aalpy.SULs import MealySUL
 
-
 SEEDS = list(range(50))
 MODEL_SIZES = [
     (2, 2, 2),
@@ -13,43 +13,40 @@
     (4, 2, 3),
     (4, 3, 2),
     (5, 3, 3),
-    (5, 5, 5),
     (6, 2, 3),
-    (6, 4, 2),
+    (6, 3, 2),
     (10, 2, 3),
     (10, 2, 4),
     (10, 2, 2),
-    (10, 4, 3),
-    (12, 2, 3),
-    (12, 2, 4),
-    (12, 2, 3),
-    (15, 2, 4),
-    (15, 4, 2),
-    (20, 4, 4),
+    (10, 2, 3),
+    (20, 5, 5),
+    (30, 3, 4),
 ]
 
 TEST_CASES = [
     pytest.param(
+        automaton_type,
         seed_val,
         num_states,
         input_size,
         output_size,
-        id=f"states={num_states}-inputs={input_size}-outputs={output_size}-seed={seed_val}",
+        id=f"states={num_states}-inputs={input_size}-outputs={output_size}-seed={seed_val}-automaton_type={automaton_type}",
     )
     for num_states, input_size, output_size in MODEL_SIZES
     for seed_val in SEEDS
+    for automaton_type in ['dfa', 'moore', 'mealy']
 ]
 
 
-@pytest.mark.parametrize("seed_val,num_states,input_size,output_size", TEST_CASES)
-@pytest.mark.timeout(10)
-def test_hw_seed(seed_val, num_states, input_size, output_size):
+@pytest.mark.parametrize("automaton_type,seed_val,num_states,input_size,output_size", TEST_CASES)
+@pytest.mark.timeout(5)
+def test_hw_seed(automaton_type, seed_val, num_states, input_size, output_size):
     from random import seed
 
     seed(seed_val)
 
     model = generate_random_deterministic_automata(
-        'mealy',
+        automaton_type,
         num_states=num_states,
         input_alphabet_size=input_size,
         output_alphabet_size=output_size,
@@ -65,9 +62,11 @@ def test_hw_seed(seed_val, num_states, input_size, output_size):
     sul = MealySUL(model)
     input_alphabet = model.get_input_alphabet()
 
+
     learned_model = run_hW(input_alphabet,
                      sul,
                      num_testing_steps=1000 * num_states,
+                     automaton_type=automaton_type,
                      reset_testing_counter=True,
                      query_for_initial_state=True)
 
@@ -76,3 +75,35 @@ def test_hw_seed(seed_val, num_states, input_size, output_size):
     print(learned_model)
     print(model)
     assert bisimilar(model, learned_model)
+
+
+def test_hw_uses_user_provided_h_and_w():
+    from aalpy import bisimilar, generate_random_deterministic_automata, run_hW, AutomatonSUL
+    from random import seed
+    seed(1)
+
+    model = generate_random_deterministic_automata(
+        'mealy',
+        num_states=100,
+        input_alphabet_size=4,
+        output_alphabet_size=4,
+    )
+
+    sul = AutomatonSUL(model)
+    input_alphabet = model.get_input_alphabet()
+
+    char_set = model.compute_characterization_set()
+
+    assert model.is_minimal() and model.is_minimal()
+
+    learned_model = run_hW(input_alphabet,
+                           sul,
+                           automaton_type='mealy',
+                           provided_characterization_set=char_set,
+                           num_testing_steps=2000,
+                           reset_testing_counter=True,
+                           query_for_initial_state=True)
+
+    assert learned_model.is_minimal()
+    assert bisimilar(model, learned_model)
+