Implement brutal scheduler

2 files changed, 62 insertions, 5 deletions

diff --git a/src/scheduler_brutal.py b/src/scheduler_brutal.py
index d1c98b5..f41d203 100644
--- a/src/scheduler_brutal.py
+++ b/src/scheduler_brutal.py
@@ -2,6 +2,7 @@
 """
 
 from card import Card
+from random import shuffle
 
 HISTORY_DEPTH = 8
 
@@ -14,7 +15,7 @@ class SchedulerBrutal:
         for id, card in self._cards.items():
             history = state.get(id, [None] * HISTORY_DEPTH)
 
-            # adjust history if depth has changed
+            # Adjust history if depth has changed
             if len(history) > HISTORY_DEPTH:
                 history = history[-HISTORY_DEPTH:]
             elif len(history) < HISTORY_DEPTH:
@@ -22,11 +23,11 @@ class SchedulerBrutal:
 
             self._state[id] = history
 
-    def practice(self, size: int) -> dict:
-        pass
+    def practice(self, size: int) -> list[int]:
+        return self._schedule(size)
 
-    def test(self, size: int) -> dict:
-        pass
+    def test(self, size: int) -> list[int]:
+        return self._schedule(size)
 
     def update(self, results: dict[int, int]) -> None:
         # Add card result to sliding window, or None if card was not shown
@@ -35,3 +36,33 @@ class SchedulerBrutal:
 
     def getState(self) -> dict:
         return self._state
+
+    # Consolidation index is a measure of how well the card has been memorised
+    @staticmethod
+    def _consolidationIndex(history: list, weights: range) -> float:
+        relevant_history = [(h, w) for h, w in zip(history, weights) if h is not None]
+        weighted_history = sum([h * w for h, w in relevant_history])
+        total_weights = sum([w for h, w in relevant_history])
+        return weighted_history / total_weights if total_weights > 0 else 0.0
+
+    # Exposure index is a measure of how much and how recently a card has been shown
+    @staticmethod
+    def _exposureIndex(history: list) -> float:
+        return sum([i + 1 for i, h in enumerate(history) if h is not None])
+
+    def _schedule(self, size: int) -> list[int]:
+        weights = range(10, 10 + HISTORY_DEPTH)
+        cards = [id for id, card in self._cards.items()]
+
+        # First sort by consolidation index
+        cards.sort(key=lambda id: SchedulerBrutal._consolidationIndex(self._state[id], weights))
+
+        # Next sort by exposure index
+        cards.sort(key=lambda id: SchedulerBrutal._exposureIndex(self._state[id]))
+
+        # Return least exposed and least consolidated cards, shuffled
+        cards = cards[0:size]
+
+        shuffle(cards)
+
+        return cards
diff --git a/src/scheduler_brutal_unittest.py b/src/scheduler_brutal_unittest.py
index adfb417..779d2c3 100644
--- a/src/scheduler_brutal_unittest.py
+++ b/src/scheduler_brutal_unittest.py
@@ -7,6 +7,32 @@ from card import Card
 scheduler_brutal.HISTORY_DEPTH = 3
 
 #--------------------------------------------------------------------------
+# Scheduling behaviour
+#--------------------------------------------------------------------------
+def test_scheduling():
+    cards = {id: Card("", "") for id in range(0, 10)}
+    state = {
+        0: [1, 1, 1],
+        1: [0, 0, 0],
+        2: [0, 0, 1],
+        3: [1, 0, 0],
+
+        4: [None, None, 1   ],
+        5: [None, 1,    None],
+        6: [1,    None, None],
+        7: [None, None, 0   ],
+        8: [0,    0,    None],
+        9: [None, None, None],
+    }
+
+    expected_priority = [9, 6, 5, 7, 8, 4, 1, 3, 2, 0]
+
+    uut = UUT(cards, state)
+
+    for i in range(0, len(expected_priority)):
+        assert set(uut.practice(i + 1)) == set(expected_priority[0:i + 1])
+
+#--------------------------------------------------------------------------
 # State update
 #--------------------------------------------------------------------------
 def test_stateUpdate():