fixed error in get equivalence classes

learning/learning.py

@@ -313,7 +313,7 @@ class LearningAlgorithm:
     def getHypothesis(self, chaos=False):
         # If table is not closed, ERROR
         if self._table.isNotGloballyClosed():
-            self._logger.error("Tried to get hipotheses with table not \
+            self._logger.error("Tried to get hypotheses with table not \
                                 closed or not consistent")
             return None, None
         # Get equivalence classes

learning/observationtable.py

@@ -93,13 +93,23 @@ class Table:
         # () is always in the equivalence classes
         rows = set()
         rows.add(())
+        # for each row in S, check if there is a singleRow in equivalence classes that is
+        # more specific, if not, add row to equivalence classes.
+        #
+        # for each singleRow in equivalence classes check if row is more specific than singleRow
+        # if so, remove singleRow and add row.
         for row in self._rowsInS - set(()):
             found = False
             for singleRow in rows:
-                if  self._moreSpecificRow(row, singleRow, plus):
+                if self._moreSpecificRow(singleRow, row, plus):
                     found = True
                     break
             if not found:
+                toBeRemoved = set()
+                for singleRow in rows:
+                    if self._moreSpecificRow(row, singleRow, plus):
+                        toBeRemoved.add(singleRow)
+                rows = rows - toBeRemoved
                 rows.add(row)
         return rows
 
@@ -623,6 +633,7 @@ class Table:
             return self._inputPurpose.getEnabled(trace)
 
     # Given two entries, check if the former is more specific than the latter
+    # entry1 ⊑ entry2 => entry2.first is subset of entry1.first and entry1.second is subset of entry2.second
     def _moreSpecificEntry(self, entry1, entry2, plus):
         if not plus:
             return entry1[0] == entry2[0]
@@ -632,6 +643,7 @@ class Table:
             return (entry2[0].issubset(entry1[0]) and entry1[1].issubset(entry2[1]))
 
     # Given two rows, check if the former is more specific than the latter
+    # row1 ⊑ row2 => for each column, row1[column] ⊑ row2[column]
     def _moreSpecificRow(self, row1, row2, plus=False):
         # First: if they do not enable the same inputs, they are not in such
         # a relation
@@ -645,7 +657,7 @@ class Table:
             entry1 = th.flatten(row1 + column, self._quiescence)
             entry2 = th.flatten(row2 + column, self._quiescence)
             if entry1 not in self._entries and entry2 not in self._entries:
-                # for some reason both entry do not exist.
+                # for some reason both entries do not exist.
                 self._logger.warning("Checking relation between rows: both entries are not defined.")
                 continue
             elif entry1 not in self._entries or entry2 not in self._entries:

tests/learning_algorithm_test.py

@@ -79,16 +79,16 @@ class TestLearningAlgorithm2:
 
     def table_row_equality_test(self):
         self._Hminus, self._Hplus = self.L1.run()
-        equal = self.L1._table._rowEquality(('delta',), ('b',), True)
+        equal = self.L1._table._moreSpecificRow(('delta',), ('b',), True)
         assert_equal(equal, True)
         # Actually, next test might fail if _noEffectLimit in learning.py is small
-        equal = self.L1._table._rowEquality(('a',), ('b',), True)
+        equal = self.L1._table._moreSpecificRow(('a',), ('b',), True)
         assert_equal(equal, False)
         # Test also other equality check
-        equal = self.L1._table._rowEquality(('delta',), ('b',))
+        equal = self.L1._table._moreSpecificRow(('delta',), ('b',))
         assert_equal(equal, True)
         # Actually, next test might fail if _noEffectLimit in learning.py is small
-        equal = self.L1._table._rowEquality(('a',), ('b',))
+        equal = self.L1._table._moreSpecificRow(('a',), ('b',))
         assert_equal(equal, False)
 
         assert_not_in(('b',),self.L1._table._rowsInS)