rename AbstractedOnfsmLstar.py method

Examples.py

@@ -321,7 +321,7 @@ def multi_client_mqtt_example():
 
     from aalpy.base import SUL
     from aalpy.oracles import UnseenOutputRandomWalkEqOracle
-    from aalpy.learning_algs import run_abstracted_Lstar_ONFSM
+    from aalpy.learning_algs import run_abstracted_ONFSM_Lstar
     from aalpy.SULs import MealySUL
     from aalpy.utils import load_automaton_from_file
 
@@ -406,7 +406,7 @@ def step(self, letter):
         'UNSUBACK_ALL': 'UNSUBACK'
     }
 
-    learned_onfsm = run_abstracted_Lstar_ONFSM(alphabet, sul, eq_oracle, abstraction_mapping=abstraction_mapping,
+    learned_onfsm = run_abstracted_ONFSM_Lstar(alphabet, sul, eq_oracle, abstraction_mapping=abstraction_mapping,
                                                n_sampling=200, print_level=3)
 
     return learned_onfsm
@@ -421,7 +421,7 @@ def abstracted_onfsm_example():
     """
     from aalpy.SULs import OnfsmSUL
     from aalpy.oracles import UnseenOutputRandomWalkEqOracle
-    from aalpy.learning_algs import run_abstracted_Lstar_ONFSM
+    from aalpy.learning_algs import run_abstracted_ONFSM_Lstar
     from aalpy.utils import get_ONFSM
 
     onfsm = get_ONFSM()
@@ -433,7 +433,7 @@ def abstracted_onfsm_example():
 
     abstraction_mapping = {0: 0, 'O': 0}
 
-    learned_onfsm = run_abstracted_Lstar_ONFSM(alphabet, sul, eq_oracle=eq_oracle,
+    learned_onfsm = run_abstracted_ONFSM_Lstar(alphabet, sul, eq_oracle=eq_oracle,
                                                abstraction_mapping=abstraction_mapping,
                                                n_sampling=50, print_level=3)
 

aalpy/learning_algs/__init__.py

@@ -1,5 +1,5 @@
 # public API for running automata learning algorithms
 from .deterministic.LStar import run_Lstar
 from .non_deterministic.OnfsmLstar import run_non_det_Lstar
-from .non_deterministic.AbstractedOnfsmLstar import run_abstracted_Lstar_ONFSM
+from .non_deterministic.AbstractedOnfsmLstar import run_abstracted_ONFSM_Lstar
 from .stochastic.StochasticLStar import run_stochastic_Lstar

aalpy/learning_algs/non_deterministic/AbstractedOnfsmLstar.py

@@ -8,7 +8,7 @@
 print_options = [0, 1, 2, 3]
 
 
-def run_abstracted_Lstar_ONFSM(alphabet: list, sul: SUL, eq_oracle: Oracle, abstraction_mapping: dict, n_sampling=100,
+def run_abstracted_ONFSM_Lstar(alphabet: list, sul: SUL, eq_oracle: Oracle, abstraction_mapping: dict, n_sampling=100,
                                max_learning_rounds=None, return_data=False, print_level=2):
     """
     Based on ''Learning Abstracted Non-deterministic Finite State Machines'' from Pferscher and Aichernig.

docs/documentation/aalpy/learning_algs.html

@@ -47,10 +47,10 @@ <h1 class="modulename">
 
                         <details>
             <summary>View Source</summary>
-            <div class="codehilite"><pre><span></span><span class="c1"># public API for running learning algorithms</span>
+            <div class="codehilite"><pre><span></span><span class="c1"># public API for running automata learning algorithms</span>
 <span class="kn">from</span> <span class="nn">.deterministic.LStar</span> <span class="kn">import</span> <span class="n">run_Lstar</span>
 <span class="kn">from</span> <span class="nn">.non_deterministic.OnfsmLstar</span> <span class="kn">import</span> <span class="n">run_non_det_Lstar</span>
-<span class="kn">from</span> <span class="nn">.non_deterministic.AbstractedOnfsmLstar</span> <span class="kn">import</span> <span class="n">run_abstracted_Lstar_ONFSM</span>
+<span class="kn">from</span> <span class="nn">.non_deterministic.AbstractedOnfsmLstar</span> <span class="kn">import</span> <span class="n">run_abstracted_ONFSM_Lstar</span>
 <span class="kn">from</span> <span class="nn">.stochastic.StochasticLStar</span> <span class="kn">import</span> <span class="n">run_stochastic_Lstar</span>
 </pre></div>
 

docs/documentation/aalpy/learning_algs/non_deterministic/AbstractedOnfsmLstar.html

@@ -28,7 +28,7 @@
                     <h2>API Documentation</h2>
                         <ul class="memberlist">
             <li>
-                    <a class="function" href="#run_abstracted_Lstar_ONFSM">run_abstracted_Lstar_ONFSM</a>
+                    <a class="function" href="#run_abstracted_ONFSM_Lstar">run_abstracted_ONFSM_Lstar</a>
             </li>
     </ul>
 
@@ -58,7 +58,7 @@ <h1 class="modulename">
 <span class="n">print_options</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 
 
-<span class="k">def</span> <span class="nf">run_abstracted_Lstar_ONFSM</span><span class="p">(</span><span class="n">alphabet</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">sul</span><span class="p">:</span> <span class="n">SUL</span><span class="p">,</span> <span class="n">eq_oracle</span><span class="p">:</span> <span class="n">Oracle</span><span class="p">,</span> <span class="n">abstraction_mapping</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">n_sampling</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+<span class="k">def</span> <span class="nf">run_abstracted_ONFSM_Lstar</span><span class="p">(</span><span class="n">alphabet</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">sul</span><span class="p">:</span> <span class="n">SUL</span><span class="p">,</span> <span class="n">eq_oracle</span><span class="p">:</span> <span class="n">Oracle</span><span class="p">,</span> <span class="n">abstraction_mapping</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">n_sampling</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                                <span class="n">max_learning_rounds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">print_level</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Based on &#39;&#39;Learning Abstracted Non-deterministic Finite State Machines&#39;&#39; from Pferscher and Aichernig.</span>
@@ -197,12 +197,12 @@ <h1 class="modulename">
         </details>
 
             </section>
-                <section id="run_abstracted_Lstar_ONFSM">
-                            <div class="attr function"><a class="headerlink" href="#run_abstracted_Lstar_ONFSM">#&nbsp;&nbsp</a>
+                <section id="run_abstracted_ONFSM_Lstar">
+                            <div class="attr function"><a class="headerlink" href="#run_abstracted_ONFSM_Lstar">#&nbsp;&nbsp</a>
 
 
             <span class="def">def</span>
-            <span class="name">run_abstracted_Lstar_ONFSM</span><span class="signature">(
+            <span class="name">run_abstracted_ONFSM_Lstar</span><span class="signature">(
     alphabet: list,
     sul: <a href="../../base/SUL.html#SUL">aalpy.base.SUL.SUL</a>,
     eq_oracle: <a href="../../base/Oracle.html#Oracle">aalpy.base.Oracle.Oracle</a>,
@@ -216,7 +216,7 @@ <h1 class="modulename">
 
                 <details>
             <summary>View Source</summary>
-            <div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">run_abstracted_Lstar_ONFSM</span><span class="p">(</span><span class="n">alphabet</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">sul</span><span class="p">:</span> <span class="n">SUL</span><span class="p">,</span> <span class="n">eq_oracle</span><span class="p">:</span> <span class="n">Oracle</span><span class="p">,</span> <span class="n">abstraction_mapping</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">n_sampling</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+            <div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">run_abstracted_ONFSM_Lstar</span><span class="p">(</span><span class="n">alphabet</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">sul</span><span class="p">:</span> <span class="n">SUL</span><span class="p">,</span> <span class="n">eq_oracle</span><span class="p">:</span> <span class="n">Oracle</span><span class="p">,</span> <span class="n">abstraction_mapping</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">n_sampling</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
                                <span class="n">max_learning_rounds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">print_level</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Based on &#39;&#39;Learning Abstracted Non-deterministic Finite State Machines&#39;&#39; from Pferscher and Aichernig.</span>

docs/documentation/aalpy/learning_algs/stochastic/StochasticLStar.html

@@ -207,7 +207,7 @@ <h1 class="modulename">
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># stop based on number of unambiguous rows</span>
             <span class="n">stop_based_on_unambiguity</span> <span class="o">=</span> <span class="n">observation_table</span><span class="o">.</span><span class="n">stop</span><span class="p">(</span><span class="n">learning_rounds</span><span class="p">,</span> <span class="n">target_unambiguity</span><span class="o">=</span><span class="n">target_unambiguity</span><span class="p">,</span>
-                                                               <span class="n">min_rounds</span><span class="o">=</span><span class="n">min_rounds</span><span class="p">,</span><span class="n">max_rounds</span><span class="o">=</span><span class="n">max_rounds</span><span class="p">,</span>
+                                                               <span class="n">min_rounds</span><span class="o">=</span><span class="n">min_rounds</span><span class="p">,</span> <span class="n">max_rounds</span><span class="o">=</span><span class="n">max_rounds</span><span class="p">,</span>
                                                                <span class="n">print_unambiguity</span><span class="o">=</span><span class="n">print_level</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">stop_based_on_unambiguity</span><span class="p">:</span>
                 <span class="k">break</span>
@@ -411,7 +411,7 @@ <h1 class="modulename">
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># stop based on number of unambiguous rows</span>
             <span class="n">stop_based_on_unambiguity</span> <span class="o">=</span> <span class="n">observation_table</span><span class="o">.</span><span class="n">stop</span><span class="p">(</span><span class="n">learning_rounds</span><span class="p">,</span> <span class="n">target_unambiguity</span><span class="o">=</span><span class="n">target_unambiguity</span><span class="p">,</span>
-                                                               <span class="n">min_rounds</span><span class="o">=</span><span class="n">min_rounds</span><span class="p">,</span><span class="n">max_rounds</span><span class="o">=</span><span class="n">max_rounds</span><span class="p">,</span>
+                                                               <span class="n">min_rounds</span><span class="o">=</span><span class="n">min_rounds</span><span class="p">,</span> <span class="n">max_rounds</span><span class="o">=</span><span class="n">max_rounds</span><span class="p">,</span>
                                                                <span class="n">print_unambiguity</span><span class="o">=</span><span class="n">print_level</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">stop_based_on_unambiguity</span><span class="p">:</span>
                 <span class="k">break</span>