add PAPNI: passive learning of VPAs

Examples.py

@@ -799,24 +799,21 @@ def rpni_example():
 
 
 def rpni_check_model_example():
-    import random
     from aalpy.SULs import AutomatonSUL
     from aalpy.learning_algs import run_RPNI
     from aalpy.oracles import StatePrefixEqOracle
-    from aalpy.utils import generate_random_moore_machine, generate_random_dfa
+    from aalpy.utils import generate_random_moore_machine
+    from aalpy.utils import generate_input_output_data_from_automata, convert_i_o_traces_for_RPNI
 
-    model = generate_random_dfa(num_states=5, alphabet=[1, 2, 3], num_accepting_states=2)
+    # model = generate_random_dfa(num_states=5, alphabet=[1, 2, 3], num_accepting_states=2)
     model = generate_random_moore_machine(num_states=5, input_alphabet=[1, 2, 3], output_alphabet=['a', 'b'])
 
     input_al = model.get_input_alphabet()
 
-    num_sequences = 1000
-    data = []
-    for _ in range(num_sequences):
-        seq_len = random.randint(1, 20)
-        random_seq = random.choices(input_al, k=seq_len)
-        output = model.compute_output_seq(model.initial_state, random_seq)[-1]
-        data.append((random_seq, output))
+    data = generate_input_output_data_from_automata(model, num_sequances=2000,
+                                                    min_seq_len=1, max_seq_len=12)
+
+    data = convert_i_o_traces_for_RPNI(data)
 
     rpni_model = run_RPNI(data, automaton_type='moore', print_info=True)
 
@@ -830,7 +827,7 @@ def rpni_check_model_example():
     if cex is None:
         print("Could not find a counterexample between the RPNI-model and the original model.")
     else:
-        print('Counterexample found. Either RPNI data was incomplete, or there is a bug in RPNI algorithm :o ')
+        print('Counterexample found. Either RPNI data was incomplete (or there is a bug in RPNI algorithm :o )')
 
 
 def rpni_mealy_example():
@@ -1062,3 +1059,68 @@ def random_sevpa_learning():
     model = run_KV(alphabet=alphabet, sul=sul, eq_oracle=eq_oracle, automaton_type='vpa',
                    print_level=2, cex_processing='rs')
 
+
+def passive_vpa_learning_on_lists():
+    from aalpy.learning_algs import run_PAPNI
+    from aalpy.automata import VpaAlphabet
+
+    vpa_alphabet = VpaAlphabet(internal_alphabet=['1'], call_alphabet=['(', '['], return_alphabet=[')', ']'])
+
+    list_data = [
+        (tuple(), False),
+        (('[', '[', ']', ']'), True),
+        (('(', '(', ')', ')'), True),
+
+        (('[', '[', ']', ')'), False),
+        (('[', '[', ')', ']'), False),
+        (('[', ')'), False),
+        (('(', ']'), False),
+        (('[', '[', ']', '1', ']'), True),
+        (('[', '1', '[', ']', '1', ']'), True),
+        (('(', '1', '[', ']', '1', ')'), True),
+        (('(', ')'), True),
+        (('[', ']'), True),
+        (('[', '1', '(', ']', '1', ')'), False),
+    ]
+
+    papni = run_PAPNI(list_data, vpa_alphabet, algorithm='gsm', print_info=True)
+    papni.visualize()
+
+
+def passive_vpa_learning_arithmetics():
+    from aalpy.learning_algs import run_PAPNI
+    from aalpy.utils.BenchmarkVpaModels import gen_arithmetic_data
+    arithmetic_data, vpa_alphabet = gen_arithmetic_data(num_sequances=4000, min_seq_len=2, max_seq_len=10)
+
+    print(f"Alphabet: {vpa_alphabet}")
+
+    print('Data:')
+    for i in range(10):
+        print(arithmetic_data[i])
+
+    learned_model = run_PAPNI(arithmetic_data, vpa_alphabet)
+    learned_model.visualize()
+
+
+def passive_vpa_learning_on_all_benchmark_models():
+    from aalpy.learning_algs import run_PAPNI
+    from aalpy.utils.BenchmarkVpaModels import get_all_VPAs
+    from aalpy.utils import generate_input_output_data_from_vpa, convert_i_o_traces_for_RPNI
+
+    for gt in get_all_VPAs():
+
+        vpa_alphabet = gt.input_alphabet
+        data = generate_input_output_data_from_vpa(gt, num_sequances=2000, min_seq_len=1, max_seq_len=16)
+
+        data = convert_i_o_traces_for_RPNI(data)
+
+        papni = run_PAPNI(data, vpa_alphabet, algorithm='gsm', print_info=True)
+
+        for seq, o in data:
+            papni.reset_to_initial()
+            learned_output = papni.execute_sequence(papni.initial_state, seq)[-1]
+            if o != learned_output:
+                print(seq, o, learned_output)
+                assert False, 'Papni Learned Model not consistent with data.'
+
+        print('PAPNI model conforms to data.')

README.md

@@ -35,7 +35,7 @@ AALpy supports both **active** and **passive** automata learning algorithms that
 | Deterministic     |                 DFAs <br /> Mealy Machines <br /> Moore Machines                 |      L* <br /> KV <br /> RPNI      | Seamless Caching <br /> Counterexample Processing <br /> 13 Equivalence Oracles  |
 | Non-Deterministic |                      ONFSM <br /> Abstracted ONFSM                      |        L*<sub>ONFSM</sub>       |                                 Size Reduction  Trough Abstraction |
 | Stochastic        | Markov Decision Processes <br /> Stochastic Mealy Machines <br /> Markov Chains | L*<sub>MDP</sub> <br /> L*<sub>SMM</sub> <br /> ALERGIA |               Counterexample Processing <br /> Exportable to PRISM format  <br /> Bindings to jALERGIA|
-| Pushdown          |          VPDA/SEVPA                                                            | KV<sub>VPA</sub> | Specification of exclusive <br/> call-return pairs
+| Pushdown          |          VPA/SEVPA                                                            | KV<sub>VPA</sub> <br /> PAPNI | Passive learning of VPAs <br /> Specification of exclusive <br/> call-return pairs
 </div>
 
 ## Installation