garbage.py

@@ -32,9 +32,10 @@ class Garbage:
         self.does_din = does_din
 
 
-class RecognizedGarbage(Garbage):
+class RecognizedGarbage:
     garbage_type: GarbageType
+    src: Garbage
 
     def __init__(self, src: Garbage, garbage_type: GarbageType) -> None:
-        super().__init__(src.img)
         self.garbage_type = garbage_type
+        self.src = src

garbageTruck.py

@@ -1,6 +1,6 @@
 from typing import List, Tuple
 from agentOrientation import AgentOrientation
-from garbage import RecognizedGarbage
+from garbage import GarbageType, RecognizedGarbage
 from gameContext import GameContext
 
 class GarbageTruck:
@@ -22,19 +22,19 @@ class GarbageTruck:
         
 
     def sort_garbage(self, RecognizedGarbage) -> None:
-        if RecognizedGarbage.garbage_type == 0:
+        if RecognizedGarbage.garbage_type == GarbageType.PAPER:
             self.paper.append(RecognizedGarbage)
 
-        elif RecognizedGarbage.garbage_type == 1:
+        elif RecognizedGarbage.garbage_type == GarbageType.PLASTIC_AND_METAL:
             self.plastic_and_metal.append(RecognizedGarbage)
 
-        elif RecognizedGarbage.garbage_type == 3:
+        elif RecognizedGarbage.garbage_type == GarbageType.GLASS:
             self.glass.append(RecognizedGarbage)
 
-        elif RecognizedGarbage.garbage_type == 4:
+        elif RecognizedGarbage.garbage_type == GarbageType.BIO:
             self.bio.append(RecognizedGarbage)
 
-        elif RecognizedGarbage.garbage_type == 5:
+        elif RecognizedGarbage.garbage_type == GarbageType.MIXED:
             self.mixed.append(RecognizedGarbage)
 
     def render(self, game_context: GameContext) -> None:

machine_learning/decisionTree.py

@@ -18,32 +18,78 @@ def _read_training_data() -> TrainingData:
         line_attributes = values[:-1]
         line_class = values[-1]
         attributes.append(line_attributes)
-        classes.append(line_class)
+        classes.append(line_class.strip())
     return TrainingData(attributes, classes)
 
+def _attributes_to_floats(attributes: list[str]) -> list[float]:
+    output: list[float] = []
+    if attributes[0] == 'Longitiudonal':
+        output.append(0)
+    elif attributes[0] == 'Round':
+        output.append(1)
+    elif attributes[0] == 'Flat':
+        output.append(2)
+    elif attributes[0] == 'Irregular':
+        output.append(3)
+    
+    if attributes[1] == 'Low':
+        output.append(0)
+    elif attributes[1] == 'Medium':
+        output.append(1)
+    elif attributes[1] == 'High':
+        output.append(2)
+    
+
+    if attributes[2] == "Yes":
+        output.append(0)
+    else:
+        output.append(1)
+    
+    if attributes[3] == 'Low':
+        output.append(0)
+    elif attributes[3] == 'Medium':
+        output.append(1)
+    elif attributes[3] == 'High':
+        output.append(2)
+
+    if attributes[4] == 'Low':
+        output.append(0)
+    elif attributes[4] == 'Medium':
+        output.append(1)
+    elif attributes[4] == 'High':
+        output.append(2)
+
+    if attributes[5] == 'Transparent':
+        output.append(0)
+    elif attributes[5] == 'Light':
+        output.append(1)
+    elif attributes[5] == 'Dark':
+        output.append(2)
+    elif attributes[5] == "Colorful":
+        output.append(3)
+
+    if attributes[6] == 'Low':
+        output.append(0)
+    elif attributes[6] == 'Medium':
+        output.append(1)
+    elif attributes[6] == 'High':
+        output.append(2)
+
+    if attributes[7] == "Yes":
+        output.append(0)
+    else:
+        output.append(1)
+    return output
+
+
 
 trainning_data = _read_training_data()
 
 X = trainning_data.attributes
 Y = trainning_data.classes
 
-le_shape = LabelEncoder()
-le_flexibility = LabelEncoder()
-le_color = LabelEncoder()
-
-le_shape.fit([x[0] for x in X])
-le_flexibility.fit([x[3] for x in X])
-le_color.fit([x[4] for x in X])
-
-X_encoded = np.array([
-    [le_shape.transform([x[0]])[0], x[1], x[2], le_flexibility.transform([x[3]])[0], le_color.transform([x[4]])[0]]
-    for x in X
-])
-
-encoder = OneHotEncoder(categories='auto', sparse=False)
-X_encoded = encoder.fit_transform(X_encoded)
 
 model = tree.DecisionTreeClassifier()
-model.fit(X_encoded, Y)
+encoded = [_attributes_to_floats(x) for x in X]
-
+dtc = model.fit(encoded, Y)
 joblib.dump(model, 'model.pkl')

movement.py

@@ -1,5 +1,9 @@
+import joblib
+from sklearn.calibration import LabelEncoder
 from agentActionType import AgentActionType
 import time
+from garbage import GarbageType, RecognizedGarbage
+from garbageCan import GarbageCan
 from turnCar import turn_left_orientation, turn_right_orientation
 from garbageTruck import GarbageTruck
 from typing import Tuple, Dict
@@ -20,9 +24,92 @@ def collect_garbage(game_context: GameContext) -> None:
         move_dust_car(path, game_context)
         next_position = calculate_next_position(game_context.dust_car)
         game_context.grid[next_position] = GridCellType.VISITED_GARBAGE_CAN
-        game_context.city.cans_dict[next_position].is_visited = True
+        can = game_context.city.cans_dict[next_position]
+        can.is_visited = True
+        _recognize_garbage(game_context.dust_car, can)
     pass
 
+def _recognize_garbage(dust_car: GarbageTruck, can: GarbageCan) -> None:
+    loaded_model = joblib.load('machine_learning/model.pkl')
+    for garbage in can.garbage:
+        attributes = [garbage.shape, garbage.flexibility, garbage.does_smell, garbage.weight, garbage.size, garbage.color, garbage.softness, garbage.does_din]
+        encoded = attributes_to_floats(attributes)
+        predicted_class = loaded_model.predict([encoded])[0]
+        garbage_type: GarbageType = None
+        if predicted_class == 'PAPER':
+            garbage_type = GarbageType.PAPER
+        elif predicted_class == 'PLASTIC_AND_METAL':
+            garbage_type = GarbageType.PLASTIC_AND_METAL
+        elif garbage_type == 'GLASS':
+            garbage_type = GarbageType.GLASS
+        elif predicted_class == 'BIO' :
+            garbage_type = GarbageType.BIO
+        elif predicted_class == 'MIXED':
+            garbage_type = GarbageType.MIXED
+        print(predicted_class)
+        recognized_garbage = RecognizedGarbage(garbage, garbage_type)
+        dust_car.sort_garbage(recognized_garbage)
+
+def attributes_to_floats(attributes: list[str]) -> list[float]:
+    output: list[float] = []
+    if attributes[0] == 'Longitiudonal':
+        output.append(0)
+    elif attributes[0] == 'Round':
+        output.append(1)
+    elif attributes[0] == 'Flat':
+        output.append(2)
+    elif attributes[0] == 'Irregular':
+        output.append(3)
+    
+    if attributes[1] == 'Low':
+        output.append(0)
+    elif attributes[1] == 'Medium':
+        output.append(1)
+    elif attributes[1] == 'High':
+        output.append(2)
+    
+
+    if attributes[2] == "Yes":
+        output.append(0)
+    else:
+        output.append(1)
+    
+    if attributes[3] == 'Low':
+        output.append(0)
+    elif attributes[3] == 'Medium':
+        output.append(1)
+    elif attributes[3] == 'High':
+        output.append(2)
+
+    if attributes[4] == 'Low':
+        output.append(0)
+    elif attributes[4] == 'Medium':
+        output.append(1)
+    elif attributes[4] == 'High':
+        output.append(2)
+
+    if attributes[5] == 'Transparent':
+        output.append(0)
+    elif attributes[5] == 'Light':
+        output.append(1)
+    elif attributes[5] == 'Dark':
+        output.append(2)
+    elif attributes[5] == "Colorful":
+        output.append(3)
+
+    if attributes[6] == 'Low':
+        output.append(0)
+    elif attributes[6] == 'Medium':
+        output.append(1)
+    elif attributes[6] == 'High':
+        output.append(2)
+
+    if attributes[7] == "Yes":
+        output.append(0)
+    else:
+        output.append(1)
+    return output
+
 
 def move_dust_car(actions: list[AgentActionType], game_context: GameContext) -> None:
     for action in actions:

startup.py

@@ -57,7 +57,7 @@ def create_garbage_pieces() -> List[Garbage]:
         for line in lines[1:]:
             param = line.strip().split(',')
             garbage_pieces.append(
-                Garbage('img', param[0], param[1], param[2], param[3], param[4], param[5], param[6], param[7]))
+                Garbage('img', param[0], param[1], param[2], param[3], param[4], param[5], param[6], param[7].strip()))
     return garbage_pieces