some optimizations in knowledge.py (aimacode#1034)

knowledge.py

@@ -12,14 +12,13 @@
 # ______________________________________________________________________________
 
 
-def current_best_learning(examples, h, examples_so_far=None):
+def current_best_learning(examples, h, examples_so_far=[]):
     """ [Figure 19.2]
     The hypothesis is a list of dictionaries, with each dictionary representing
     a disjunction."""
     if not examples:
         return h
 
-    examples_so_far = examples_so_far or []
     e = examples[0]
     if is_consistent(e, h):
         return current_best_learning(examples[1:], h, examples_so_far + [e])

knowledge_current_best.ipynb

@@ -654,7 +654,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

tests/test_knowledge.py

@@ -59,27 +59,21 @@ def test_current_best_learning():
     examples = restaurant
     hypothesis = [{'Alt': 'Yes'}]
     h = current_best_learning(examples, hypothesis)
-    values = []
-    for e in examples:
-        values.append(guess_value(e, h))
+    values = [guess_value(e, h) for e in examples]
 
     assert values == [True, False, True, True, False, True, False, True, False, False, False, True]
 
     examples = animals_umbrellas
     initial_h = [{'Species': 'Cat'}]
     h = current_best_learning(examples, initial_h)
-    values = []
-    for e in examples:
-        values.append(guess_value(e, h))
+    values = [guess_value(e, h) for e in examples]
 
     assert values == [True, True, True, False, False, False, True]
 
     examples = party
     initial_h = [{'Pizza': 'Yes'}]
     h = current_best_learning(examples, initial_h)
-    values = []
-    for e in examples:
-        values.append(guess_value(e, h))
+    values = [guess_value(e, h) for e in examples]
 
     assert values == [True, True, False]