.gitignore

@@ -57,6 +57,8 @@ docs/_build/
 
 Pipfile
 Pipfile.lock
-
+decision_tree
-# output
+decision_tree.pdf
-/out
+Source.gv.pdf
+Source.gv
+decision_tree.txt

README.MD

@@ -41,14 +41,8 @@
 
 ---
 
-- [x] **Drzewa decyzyjne: wymagania dot. trzeciego przyrostu**
+- [ ] **Drzewa decyzyjne: wymagania dot. trzeciego przyrostu**
-  - [x] Należy wykorzystać algorytm ID3 (tj. schemat indukcyjnego uczenia drzewa decyzyjnego oraz procedurę wyboru atrybutu o największym przyroście informacji) lub któreś z jego uogólnień.
+  - [ ] Należy wykorzystać algorytm ID3 (tj. schemat indukcyjnego uczenia drzewa decyzyjnego oraz procedurę wyboru atrybutu o największym przyroście informacji) lub któreś z jego uogólnień.
-  - [x] Należy przygotować zbiór uczący złożony z co najmniej 200 przykładów.
+  - [ ] Należy przygotować zbiór uczący złożony z co najmniej 200 przykładów.
   - [x] Decyzja stanowiąca cel uczenia powinna zostać opisana przynajmniej ośmioma atrybutami.
-  - [x] Powinna pojawić się opcja podglądu wyuczonego drzewa (np. w logach lub w pliku z graficzną reprezentacją drzewa).
+  - [ ] Powinna pojawić się opcja podglądu wyuczonego drzewa (np. w logach lub w pliku z graficzną reprezentacją drzewa).
-
----
-
-- [ ] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
-  - [ ] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
-  - [ ] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.

TreeConcept.py

@@ -0,0 +1,38 @@
+from sklearn import tree
+import pandas as pd #for manipulating the csv data
+import numpy as np
+import graphviz
+import os
+os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
+
+#importing the dataset from the disk
+train_data_m=np.genfromtxt("dataset/converted_dataset.csv", delimiter=",",skip_header=1);
+
+# Separate the attributes and labels
+X_train = [data[:-1] for data in train_data_m]
+y_train = [data[-1] for data in train_data_m]
+
+# Create the decision tree classifier using the ID3 algorithm
+clf = tree.DecisionTreeClassifier(criterion='entropy')
+#clf = tree.DecisionTreeClassifier(criterion='gini')
+
+# Train the decision tree on the training data
+clf.fit(X_train, y_train)
+
+# Visualize the trained decision tree
+tree_text = tree.export_text(clf,feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'])
+with open('decision_tree.txt', 'w') as f:
+    f.write(tree_text) # Save the visualization as a text file
+dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True,rounded=True)
+graph = graphviz.Source(dot_data)
+graph.render("decision_tree")  # Save the visualization as a PDF file
+
+# Test the decision tree with a new example
+#Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
+new_example = [2, 0, 1, 1, 1 ,2, 0]
+predicted_label = clf.predict([new_example])
+if predicted_label[0]>0:
+    result="YES"
+else:
+    result="NO"
+print("Predicted Label:", result)

agent.py

@@ -11,16 +11,17 @@ SCREEN_SIZE = [800, 800]
 SQUARE_SIZE = 40
 
 waiter = Waiter([0, 0], 0, SQUARE_SIZE, SCREEN_SIZE)
-kitchen = Kitchen([0, 0], 0, SQUARE_SIZE, SCREEN_SIZE)
+objects = [
-objects = []
+    Kitchen([0, 0], 0, SQUARE_SIZE, SCREEN_SIZE)
+]
 
 for i in range(150):
 
     pos = [0, 0]
 
-    while any([o.compare_pos(pos) for o in objects]) or pos == [0, 0]:
+    while any([o.compare_pos(pos) for o in objects]):
-        pos = [random.randint(1, SCREEN_SIZE[0]/SQUARE_SIZE - 1),
+        pos = [random.randint(1, SCREEN_SIZE[0]/SQUARE_SIZE),
-               random.randint(1, SCREEN_SIZE[0]/SQUARE_SIZE - 1)]
+               random.randint(1, SCREEN_SIZE[0]/SQUARE_SIZE)]
 
     if (random.randint(0, 1)):
         objects.append(Block(pos, 0, SQUARE_SIZE, SCREEN_SIZE))
@@ -29,7 +30,7 @@ for i in range(150):
 
 user = UserController(waiter)
 state = StateController(waiter)
-engine = Engine(SCREEN_SIZE, SQUARE_SIZE, kitchen, user, state)
+engine = Engine(SCREEN_SIZE, SQUARE_SIZE, user, state)
 
 for o in objects:
     engine.subscribe(o)

dataset/Dataset.csv

@@ -0,0 +1,201 @@
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+0,1,1,0,1,1,0,0
+0,1,1,1,0,1,1,0
+0,0,1,0,1,2,0,0
+1,0,0,0,1,2,1,0
+0,1,0,1,0,1,1,0
+2,1,0,1,0,1,1,0
+2,1,1,0,1,0,1,0
+2,1,1,0,0,2,1,0
+2,1,1,1,1,2,0,1
+2,1,0,0,1,0,0,0
+3,1,0,1,1,2,0,1
+1,0,1,1,0,0,1,0
+1,1,0,1,0,2,1,0
+2,1,1,1,1,2,1,0
+3,1,1,1,1,0,0,0
+2,1,0,0,0,2,1,0
+2,1,0,1,0,2,0,0
+3,0,0,1,0,2,1,0
+0,0,0,1,1,1,0,0
+0,0,1,0,0,2,0,0
+3,0,1,1,0,2,1,0
+2,0,1,1,0,0,1,0
+2,1,0,0,0,0,0,0
+3,0,0,0,1,0,0,0
+3,1,1,0,0,1,0,0
+3,1,1,0,0,0,1,0
+0,0,1,1,1,0,1,0
+3,0,0,0,0,0,1,0
+2,0,0,0,1,0,0,0
+1,1,0,1,0,1,0,0
+1,1,0,1,1,2,1,0
+3,1,0,1,0,2,1,0
+2,1,0,1,1,1,1,0
+0,0,0,1,0,2,1,0
+2,0,1,0,0,1,1,0
+0,0,0,1,0,0,1,0
+3,0,0,1,0,1,0,0
+2,0,1,0,1,0,1,0
+2,1,1,1,1,1,0,1
+3,1,1,0,0,1,1,0
+3,1,1,1,0,1,0,0
+1,1,1,0,1,1,0,0
+2,0,1,0,0,2,1,0
+2,0,1,0,1,1,1,0
+3,0,0,0,1,2,0,0
+0,1,1,0,0,2,1,0
+1,0,1,0,0,0,1,0
+0,1,1,1,1,0,1,0
+2,0,1,1,0,1,0,0
+2,0,0,0,0,2,0,0
+2,1,1,1,0,1,0,0
+0,1,1,1,1,1,0,0
+1,0,1,0,1,0,0,0

dataset/datasetConverter.py

@@ -1,33 +1,30 @@
 import csv
 
-
+def convert_dataset(input_file, output_file):
-def convertDataset():
     attributes = {
-        1: ["low", "high"],
+        1: ["empty", "low", "medium", "high"],
-        2: ["far", "close"],
+        2: ["empty", "full"],
-        3: ['bad', 'good', 'undefined'],
+        3: ["none", "available"],
-        4: ["no", "yes"],
+        4: ["none", "available"],
-        5: ["no", "yes"],
+        5: ["unavailable", "available"],
-        6: ["no", "yes"],
+        6: ["low", "medium", "high"],
-        7: ["empty", "new order", "waiting for dish", "have a dish"],
+        7: ["no", "yes"],
-        8: ["no", "yes"],
+        8: ["no", "yes"]
-        9: ["high priority", "low priority", "return to kitchen"]
     }
-
+    
     # Create a mapping dictionary for attribute values
     mapping = {}
     for attr, values in attributes.items():
         mapping[attr] = {value: index for index, value in enumerate(values)}
-
+    
     converted_dataset = []
-
+    
     # Read the input CSV file
-    with open("out/rawDataset.csv", "r") as csvfile:
+    with open(input_file, "r") as csvfile:
         reader = csv.reader(csvfile)
-
+        
         header = next(reader)  # Skip the header row
-        converted_dataset.append(header)
+        
-
         # Convert the data rows
         for row in reader:
             converted_row = []
@@ -37,15 +34,21 @@ def convertDataset():
                     converted_value = mapping[i + 1][value]
                 else:
                     converted_value = value
-
+                
                 converted_row.append(converted_value)
-
+            
             converted_dataset.append(converted_row)
-
+    
     # Write the converted dataset to a new CSV file
-    with open("out/dataset.csv", "w", newline="") as csvfile:
+    with open(output_file, "w", newline="") as csvfile:
         writer = csv.writer(csvfile)
-
+        
+        # Write the header row
+        #writer.writerow(header)
+        
         # Write the converted data rows
         for row in converted_dataset:
             writer.writerow(row)
+
+# Example usage:
+convert_dataset("dataset/Dataset.csv", "dataset/converted_dataset.csv")

dataset/datasetGenerator.py

@@ -0,0 +1,57 @@
+import random
+import csv
+
+attributes = {
+    1: ["empty", "low", "medium", "high"],
+    2: ["empty", "full"],
+    3: ["none", "available"],
+    4: ["none", "available"],
+    5: ["unavailable", "available"],
+    6: ["low", "medium", "high"],
+    7: ["no", "yes"],
+    8: ["no", "yes"]
+}
+
+dataset = []
+while len(dataset) < 200:
+    data = {}
+    
+    # Generate random values for attributes 1-7
+    for attr in range(1, 8):
+        data[attr] = random.choice(attributes[attr])
+    
+    # Apply the rules to determine the value of attribute 8
+    if data[1] in ["empty", "low"]:
+        data[8] = "no"
+    elif data[2] == "full" and data[4] == "none":
+        data[8] = "no"
+    elif data[2] == "empty" and data[3] == "none":
+        data[8] = "no"
+    elif data[3] == "none" and data[4] == "none":
+        data[8] = "no"
+    elif data[5] == "unavailable":
+        data[8] = "no"
+    elif data[6] == "low":
+        data[8] = "no"
+    elif data[7] == "yes":
+        data[8] = "no"
+    else:
+        data[8] = "yes"
+    
+    # Check if the generated data already exists in the dataset
+    if data not in dataset:
+        dataset.append(data)
+
+# Print the generated dataset
+for data in dataset:
+    print(data)
+with open("dataset/Dataset.csv", "w", newline="") as csvfile:
+    writer = csv.writer(csvfile)
+    
+    # Write the header row
+    writer.writerow(["Battery Charge", "Fullness", "Ready orders", "Waiting tables","Availability", "Cleanliness", "Error", "To go"])
+    
+    # Write the data rows
+    for data in dataset:
+        row = [data[attr] for attr in range(1, 9)]
+        writer.writerow(row)

requirements.txt

@@ -1,5 +1,2 @@
 -i https://pypi.org/simple
 pygame==2.3.0
-pandas
-scikit-learn
-graphviz

src/Engine.py

@@ -1,24 +1,18 @@
 import time
 import pygame
-from .obj.Goal import Goal
 from .obj.Object import Object
 from .obj.Waiter import Waiter
-from .obj.Kitchen import Kitchen
 from .UserController import UserController
 from .StateController import StateController
-from .decisionTree.TreeConcept import TreeEngine
-from queue import PriorityQueue
 
 
 class Engine:
 
-    def __init__(self, screen_size, square_size, kitchen: Kitchen, user: UserController, state: StateController):
+    def __init__(self, screen_size, square_size, user: UserController, state: StateController):
         pygame.display.set_caption('Waiter Agent')
 
         self.action_clock = 0
-        self.tree = TreeEngine()
 
-        self.kitchen: Kitchen = kitchen
         self.user: Waiter = user
         self.state: StateController = state
         self.screen_size: list[int] = screen_size
@@ -65,7 +59,6 @@ class Engine:
             if self.goals:
                 self.state.graphsearch(self)
             self.user.handler(self)
-            self.predict()
         else:
             # went path
 
@@ -82,13 +75,8 @@ class Engine:
             # waiter interaction
 
             for o in self.objects:
-                if self.user.obj.chechNeighbor(o):
-                    o.updateState(self.action_clock)
                 if o.compare_pos(self.user.obj.position):
-                    o.action(self.user.obj, self.action_clock)
+                    o.action(self.user.obj)
-
-            if self.kitchen.compare_pos(self.user.obj.position):
-                self.kitchen.action(self.user.obj, self.action_clock)
 
             time.sleep(0.5)
 
@@ -105,7 +93,6 @@ class Engine:
         for o in self.objects:
             o.blit(self.screen)
 
-        self.kitchen.blit(self.screen)
         self.user.obj.blit(self.screen)
 
         for f in self.state.fringe.queue:
@@ -114,72 +101,7 @@ class Engine:
         for s in self.state.path:
             s.blit(self.screen)
 
-        if self.goals:
-            self.goals[-1].blit(self.screen)
-
         pygame.display.flip()
 
     def appendGoalPosition(self, position):
-        self.goals.append(Goal(position, self.square_size, self.screen_size))
+        self.goals.append(position)
-
-    def predict(self):
-
-        goal_queue = PriorityQueue()
-
-        for o in self.objects:
-
-            condition = o.agent_role in [
-                "table",
-                "order",
-                "wait",
-                "done"
-            ]
-
-            if not condition or o.compare_pos(self.user.obj.position):
-                continue
-
-            medium_dist = (self.screen_size[0] // self.square_size) // 2
-
-            dataset = [
-                # battery
-                self.user.obj.battery_status(),
-                # high | low |
-
-                # distance between kitchen and object
-                0 if o.distance_to(self.kitchen.position) > medium_dist else 1,
-                # far | close |
-
-                # mood 
-                o.get_mood(self.action_clock),
-                # undefined | good | bad |
-
-                # basket is empty
-                1 if self.user.obj.basket_is_empty() else 0,
-                # yes | no |
-
-                # dish is ready
-                1 if o.dish_is_ready(self.action_clock) else 0,
-                # yes | no |
-
-                # dish in basket
-                1 if self.user.obj.dish_in_basket(o) else 0,
-                # yes | no |
-
-                # status
-                o.get_state_number(),
-                # empty | new order | waiting for dish | have a dish |
-
-                # is actual
-                1 if o.isActual() else 0,
-                # yes | no |
-            ]
-
-            p = self.tree.make_predict(dataset)
-            goal_queue.put((p, o.position))
-
-        if goal_queue.queue:
-            priority, goal = goal_queue.queue[0]
-            if priority == 2:
-                self.appendGoalPosition(self.kitchen.position)
-            else:
-                self.appendGoalPosition(goal)

src/StateController.py

@@ -15,10 +15,9 @@ class StateController:
         self.explored.clear()
         self.fringe = PriorityQueue()
 
-    def build_path(self, goal_state, engine):
+    def build_path(self, goal_state):
         total_cost = goal_state.cost
         self.path.append(goal_state)
-        engine.goals.pop()
         while self.path[-1].parent.agent_role not in ["blank", "waiter"]:
             self.path.append(self.path[-1].parent)
             total_cost += self.path[-1].cost
@@ -29,7 +28,7 @@ class StateController:
     def graphsearch(self, engine):            # A*
         print("Search path")
 
-        self.goal = list(engine.goals[-1].position)
+        self.goal = list(engine.goals.pop())
 
         self.reset()
 
@@ -37,10 +36,13 @@ class StateController:
 
         self.fringe.put(start)
 
-        while self.fringe.queue and not self.path:
+        while self.fringe and not self.path:
             self.explored.append(self.fringe.get())
-            if self.goal_test(engine):
+
-                return
+            if self.explored[-1].position == self.goal:
+                goal_state = self.explored[-1]
+                self.reset()
+                return self.build_path(goal_state)
 
             self.succ(self.explored[-1].front(), engine)
             self.succ(self.explored[-1].left(), engine)
@@ -49,10 +51,6 @@ class StateController:
             engine.redraw()
 
         self.reset()
-        for o in engine.objects:
-            o.compare_pos(engine.goals[-1].position)
-            o.agent_role = "block"
-        engine.goals.pop()
 
         print("Not found")
 
@@ -92,19 +90,3 @@ class StateController:
 
             if state.cost_so_far < fringe.cost_so_far:
                 fringe.replace(state)
-
-    def goal_test(self, engine) -> bool:
-        if self.explored[-1].position == self.goal:
-            self.__is_goal__(self.explored[-1], engine)
-            return True
-
-        for fringe in self.fringe.queue:
-            if fringe.position == self.goal:
-                self.__is_goal__(fringe, engine)
-                return True
-
-        return False
-
-    def __is_goal__(self, goal_state, engine):
-        self.reset()
-        self.build_path(goal_state, engine)

src/UserController.py

@@ -9,3 +9,11 @@ class UserController:
         for event in pygame.event.get():
             if event.type == pygame.QUIT:
                 engine.quit()
+            elif event.type == pygame.MOUSEBUTTONDOWN:
+                pos = pygame.mouse.get_pos()
+                pos = [pos[0] // engine.square_size,
+                       pos[1] // engine.square_size]
+                # for o in engine.objects:
+                #    if o.compare_pos(pos):
+                #        o.set_order(engine.action_clock)
+                engine.appendGoalPosition(pos)

src/decisionTree/TreeConcept.py

@@ -1,70 +0,0 @@
-import time
-from sklearn import tree
-import numpy as np
-import graphviz
-from src.decisionTree.datasetGenerator import generateRawDataset
-from src.decisionTree.datasetConverter import convertDataset
-
-
-class TreeEngine():
-    def __init__(self):
-        generateRawDataset()
-        convertDataset()
-
-        # importing the dataset from the disk
-        train_data_m = np.genfromtxt(
-            "out/dataset.csv", delimiter=",", skip_header=1)
-
-        # Separate the attributes and labels
-        self.X_train = [data[:-1] for data in train_data_m]
-        self.y_train = [data[-1] for data in train_data_m]
-
-        # Create the decision tree classifier using the ID3 algorithm
-        self.clf = tree.DecisionTreeClassifier(
-            criterion='entropy', splitter="best")
-        # clf = tree.DecisionTreeClassifier(criterion='gini')
-
-        # Train the decision tree on the training data
-        self.clf.fit(self.X_train, self.y_train)
-
-        self.exportText()
-        self.exportPdf()
-
-    def exportText(self):
-        # Visualize the trained decision tree
-        tree_text = tree.export_text(self.clf, feature_names=[
-            "Battery level",
-            "Distance between kitchen and table",
-            "Customers mood",
-            "Basket is empty",
-            "Dish is ready",
-            "Dish in basket",
-            "Table status",
-            "Is actual",
-        ])
-
-        with open('out/decision_tree.txt', 'w') as f:
-            f.write(tree_text)  # Save the visualization as a text file
-
-    def exportPdf(self):
-        dot_data = tree.export_graphviz(self.clf, out_file=None, feature_names=[
-            "Battery level",
-            "Distance between kitchen and table",
-            "Customers mood",
-            "Basket is empty",
-            "Dish is ready",
-            "Dish in basket",
-            "Table status",
-            "Is actual",
-        ], class_names=[
-            'High priority',
-            'Low priority',
-            'Return to kitchen',
-        ], filled=True, rounded=True)
-
-        graph = graphviz.Source(dot_data)
-        # Save the visualization as a PDF file
-        graph.render("out/decision_tree")
-
-    def make_predict(self, dataset):
-        return self.clf.predict([dataset])

src/decisionTree/datasetGenerator.py

@@ -1,111 +0,0 @@
-import csv
-from pathlib import Path
-
-
-def generateRawDataset():
-    battery_values = ['high', 'low']
-    distance_values = ['far', 'close']
-    mood_values = ['undefined', 'good', 'bad']
-    status_values = ['empty', 'new order', 'waiting for dish', 'have a dish']
-    other = ['no', 'yes']
-
-    training_data = []
-    training_data.append([
-        "Battery level",
-        "Distance between kitchen and table",
-        "Customers mood",
-        "Basket is empty",
-        "Dish is ready",
-        "Dish in basket",
-        "Table status",
-        "Is actual",
-        "Priority"
-    ])
-
-    for battery in battery_values:
-        for distance in distance_values:
-            for mood in mood_values:
-                for basket in other:
-                    for ready in other:
-                        for dish in other:
-                            for status in status_values:
-                                for actual in other:
-
-                                    dataset = buildDataset(
-                                        battery,
-                                        distance,
-                                        mood,
-                                        basket,
-                                        ready,
-                                        dish,
-                                        status,
-                                        actual
-                                    )
-
-                                    training_data.append(dataset)
-
-    Path("out/").mkdir(parents=True, exist_ok=True)
-    with open('out/rawDataset.csv', 'w', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerows(training_data)
-
-
-def buildDataset(b, d, m, e, r, i, s, a) -> list:
-    dataset = [
-        b,          # battery
-        d,          # distance
-        m,          # mood
-        e,          # basket is empty
-        r,          # ready
-        i,          # dish in basket
-        s,          # status
-        a,          # actual
-    ]
-
-    dataset.append(getPriority(dataset))
-
-    return dataset
-
-
-def getPriority(dataset) -> str:
-    PRIORITY = {
-        'high': 'high priority',
-        'low': 'low priority',
-        'kitchen': 'return to kitchen'
-    }
-
-    BATTERY_LEVEL = dataset[0]
-    DISTANCE_TO_OBJECT = dataset[1]
-    MOOD = dataset[2]
-    BASKET_IS_EMPTY = dataset[3]
-    DISH_IS_READY = dataset[4]
-    DISH_IN_BASKET = dataset[5]
-    STATUS_OF_TABLE = dataset[6]
-    IS_ACTUAL = dataset[7]
-
-    def mood_and_distance() -> str:
-        if MOOD == "undefined":
-            return PRIORITY['kitchen']
-        elif MOOD == "good":
-            return PRIORITY['low']
-        elif DISTANCE_TO_OBJECT == "far":
-            return PRIORITY['high']
-        else:
-            return PRIORITY['low']
-
-    if BATTERY_LEVEL == "low":
-        return PRIORITY['kitchen']
-    elif IS_ACTUAL == "no":
-        return PRIORITY['high']
-    elif STATUS_OF_TABLE == "empty" or STATUS_OF_TABLE == "have a dish":
-        return PRIORITY['kitchen']
-    elif STATUS_OF_TABLE == "new order":
-        return mood_and_distance()
-    elif BASKET_IS_EMPTY == "yes":
-        return PRIORITY['kitchen']
-    elif DISH_IS_READY == "no":
-        return PRIORITY['kitchen']
-    elif DISH_IN_BASKET == "no":
-        return PRIORITY['kitchen']
-    else:
-        return mood_and_distance()

src/decisionTree/dataset_generator.py

@@ -0,0 +1,33 @@
+import csv
+import random
+
+battery_values = ['high', 'medium', 'low']
+distance_values = ['far', 'medium', 'close']
+mood_values = ['good', 'medium', 'bad']
+other = ['yes', 'no']
+
+
+training_data = []
+
+for _ in range(200):
+    battery = random.choice(battery_values)
+    distance = random.choice(distance_values)
+    mood = random.choice(mood_values)
+    memory = random.randint(0,4)
+    dishes_held = random.randint(0,4)
+    """empty_basket = random.choice(other)
+    if empty_basket == 'yes':
+        dish_in_basket = 'no'
+    else:
+        dish_in_basket = random.choice(other)
+    dish_in_basket = random.choice(other)"""
+    waiting_for_order = random.choice(other)
+    waiting_for_dish = random.choice(other)
+
+    example = [battery, distance, mood, memory, dishes_held, waiting_for_order, waiting_for_dish]
+    training_data.append(example)
+
+with open('data.csv', 'w', newline='') as file:
+    writer = csv.writer(file)
+    writer.writerows(training_data)
+

src/obj/Goal.py

@@ -1,9 +0,0 @@
-from src.obj.Object import Object
-
-
-class Goal(Object):
-    def __init__(self, position, square_size, screen_size):
-        super().__init__("goal", position, 0, square_size, screen_size)
-
-    def collide_test(self, waiter: Object) -> bool:
-        return waiter.position == self.position

src/obj/Kitchen.py

@@ -5,6 +5,6 @@ class Kitchen(Object):
     def __init__(self, position, orientation, square_size, screen_size):
         super().__init__("kitchen", position, orientation, square_size, screen_size)
 
-    def action(self, waiter, current_time):
+    def action(self, waiter):
-        waiter.combine_orders(current_time)
+        waiter.combine_orders()
         waiter.recharge()

src/obj/Object.py

@@ -57,6 +57,3 @@ class Object:
 
     def action(self, obj):
         pass
-
-    def updateState(self, current_time):
-        pass

src/obj/Table.py

@@ -4,59 +4,29 @@ from src.obj.Object import Object
 
 class Table(Object):
     def __init__(self, position, orientation, square_size, screen_size):
-        super().__init__("table", position, orientation, square_size, screen_size)
+        super().__init__("order", position, orientation, square_size, screen_size)
-        self.waiting_time = 0
+        self.order_time = 0
-        self.cooking_time = 0
+        self.customers = 0
-        self.is_actual = False
+        # roles = ["table", "order", "wait", "done"]
 
-    def isActual(self):
+    def reset(self):
-        return self.is_actual
+        self.change_role("table")
-
+        self.order_time = 0
-    def updateState(self, current_time):
+        self.customers = 0
-        if self.is_actual:
-            return
-        self.is_actual = True
-        # here must be neural network choise
-        new_role = random.choice(["table", "order", "wait", "done"])
-        self.change_role(new_role, current_time)
-
-        if self.agent_role == "table":
-            return
-        elif self.agent_role == "wait":
-            self.cooking_time = random.randint(0, 300)
-
-    def dish_is_ready(self, current_time):
-        return current_time - self.waiting_time > self.cooking_time
-
-    def get_state_number(self) -> int:
-        roles = {
-            "table": 0,
-            "order": 1,
-            "wait": 2,
-            "done": 3
-        }
-
-        return roles[self.agent_role]
-
-    def change_role(self, new_role, current_time):
-        self.waiting_time = current_time
-        return super().change_role(new_role)
-
-    def reset(self, current_time):
-        self.is_actual = False
-        self.change_role("table", current_time)
 
     def set_order(self, current_time):
         if self.agent_role == "table":
-            self.change_role("order", current_time)
+            self.change_role("order")
+            self.customers = random.randint(1, 6)
+            self.order_time = current_time
 
-    def set_wait(self, current_time):
+    def set_wait(self):
         if self.agent_role == "order":
-            self.change_role("wait", current_time)
+            self.change_role("wait")
 
-    def set_done(self, current_time):
+    def set_done(self):
         if self.agent_role == "wait":
-            self.change_role("done", current_time)
+            self.change_role("done")
 
     def is_order(self):
         return self.agent_role == "order"
@@ -67,18 +37,26 @@ class Table(Object):
     def is_done(self):
         return self.agent_role == "done"
 
+    def waiting_for_dish(self):
+        return self.agent_role in ["wait", "done"]
+
     def get_customers_count(self) -> int:
         return self.customers
 
-    def get_mood(self, current_time) -> int:  # перапісаць
+    def get_mood(self, current_time) -> str:
         if self.agent_role == "table":
-            return 2  # undefined
+            return None
 
-        diff = current_time - self.waiting_time
+        diff = current_time - self.order_time
-        return 0 if diff >= 300 else 1  # 0 - bad; 1 - good
+        if diff < 200:
+            return "good"
+        elif diff < 400:
+            return "medium"
+        else:
+            return "bad"
 
-    def action(self, waiter, current_time):
+    def action(self, waiter):
         if self.is_order():
-            waiter.collect_order(self, current_time)
+            waiter.collect_order(self)
         elif self.is_done():
-            waiter.deliver_dish(self, current_time)
+            waiter.deliver_dish(self)

src/obj/Waiter.py

@@ -18,28 +18,106 @@ class Waiter(Object):
         self.orientation = copy.copy(state.orientation)
         self.basket = copy.copy(state.basket)
         self.battery -= state.cost
+        # self.calcTree()
         return state
 
     def dish_in_basket(self, table) -> bool:
         return table in self.basket
 
+    '''    
+    def calcTree(self):
+        from sklearn import tree
+        import pandas as pd  # for manipulating the csv data
+        import numpy as np
+        import os
+
+        # importing the dataset from the disk
+        train_data_m = np.genfromtxt(
+            "dataset/converted_dataset.csv", delimiter=",", skip_header=1)
+
+        X_train = [data[:-1] for data in train_data_m]
+        y_train = [data[-1] for data in train_data_m]
+        # Create the decision tree classifier using the ID3 algorithm
+        clf = tree.DecisionTreeClassifier(criterion='entropy')
+
+        # Train the decision tree on the training data
+        clf.fit(X_train, y_train)
+
+        # Visualize the trained decision tree
+        tree_text = tree.export_text(clf, feature_names=[
+                                     'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
+        with open('decision_tree.txt', 'w') as f:
+            f.write(tree_text)  # Save the visualization as a text file
+
+        # Test the decision tree with a new example
+        # Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
+        new_example = [self.battery, 0, self.orderReadiness,
+                       self.waitingTables, self.availability, self.cleanliness, self.error]
+        predicted_label = clf.predict([new_example])
+        if predicted_label[0] > 0:
+            result = "YES"
+        else:
+            result = "NO"
+        print("Predicted Label:", result)
+
+    def calcTreePDF(self):
+        from sklearn import tree
+        import pandas as pd  # for manipulating the csv data
+        import numpy as np
+        import graphviz
+        import os
+        os.environ["PATH"] += os.pathsep + \
+            'C:/Program Files (x86)/Graphviz/bin/'
+
+        # importing the dataset from the disk
+        train_data_m = np.genfromtxt(
+            "dataset/converted_dataset.csv", delimiter=",", skip_header=1)
+
+        X_train = [data[:-1] for data in train_data_m]
+        y_train = [data[-1] for data in train_data_m]
+        # Create the decision tree classifier using the ID3 algorithm
+        clf = tree.DecisionTreeClassifier(criterion='entropy')
+
+        # Train the decision tree on the training data
+        clf.fit(X_train, y_train)
+
+        # Visualize the trained decision tree
+        tree_text = tree.export_text(clf, feature_names=[
+                                     'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
+        with open('decision_tree.txt', 'w') as f:
+            f.write(tree_text)  # Save the visualization as a text file
+        dot_data = tree.export_graphviz(clf, out_file=None, feature_names=[
+                                        'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True, rounded=True)
+        graph = graphviz.Source(dot_data)
+        graph.render("decision_tree")  # Save the visualization as a PDF file
+
+        # Test the decision tree with a new example
+        # Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
+        new_example = [self.battery, 0, self.orderReadiness,
+                       self.waitingTables, self.availability, self.cleanliness, self.error]
+        predicted_label = clf.predict([new_example])
+        if predicted_label[0] > 0:
+            result = "YES"
+        else:
+            result = "NO"
+        print("Predicted Label:", result)
+        
+    '''
+
     def basket_is_full(self) -> bool:
         return self.basket_size == 0
 
-    def basket_is_empty(self) -> bool:
+    def combine_orders(self):
-        return self.basket_size == 4
-
-    def combine_orders(self, current_time):
         while not self.basket_is_full() and not self.memory_is_empty():
             dish = self.memory.pop()
-            dish.set_done(current_time)
+            dish.set_done()
             self.basket.append(dish)
             self.basket_size -= 1
             self.memory_size += 1
 
-    def deliver_dish(self, table, current_time):
+    def deliver_dish(self, table):
         if table in self.basket:
-            table.reset(current_time)
+            table.reset()
             self.basket.remove(table)
             self.basket_size += 1
 
@@ -52,16 +130,21 @@ class Waiter(Object):
     def memory_is_full(self) -> bool:
         return self.memory_size == 0
 
-    def collect_order(self, table, current_time):
+    def collect_order(self, table):
         if self.memory_is_full():
             return
         if table.agent_role == "order":
-            table.set_wait(current_time)
+            table.set_wait()
             self.memory.append(table)
             self.memory_size -= 1
 
-    def battery_status(self) -> int:
+    def battary_status(self) -> str:
-        return 1 if self.battery >= 100 else 0
+        if self.battery >= 200:
+            return "hight"
+        elif self.battery >= 100:
+            return "medium"
+        else:
+            return "low"
 
     def recharge(self):
         self.battery = 300
@@ -77,10 +160,3 @@ class Waiter(Object):
             self.position[0] += self.orientation - 2    # x (-1 or +1)
         else:                                           # y (0 or 2)
             self.position[1] += self.orientation - 1    # y (-1 or +1)
-
-    def chechNeighbor(self, n, r=1):
-
-        cond_x = abs(self.position[0] - n.position[0]) <= r
-        cond_y = abs(self.position[1] - n.position[1]) <= r
-
-        return cond_x and cond_y