preparation to neural network implementation

.gitignore

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

README.MD

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

@@ -1,38 +0,0 @@
-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

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

dataset/Dataset.csv

@@ -1,201 +0,0 @@
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dataset/converted_dataset.csv

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-2,1,1,1,0,0,0,0
-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/datasetGenerator.py

@@ -1,57 +0,0 @@
-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

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

src/Engine.py

@@ -1,18 +1,24 @@
 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, user: UserController, state: StateController):
+    def __init__(self, screen_size, square_size, kitchen: Kitchen, 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
@@ -59,6 +65,7 @@ class Engine:
             if self.goals:
                 self.state.graphsearch(self)
             self.user.handler(self)
+            self.predict()
         else:
             # went path
 
@@ -75,8 +82,13 @@ 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)
+                    o.action(self.user.obj, self.action_clock)
+
+            if self.kitchen.compare_pos(self.user.obj.position):
+                self.kitchen.action(self.user.obj, self.action_clock)
 
             time.sleep(0.5)
 
@@ -93,6 +105,7 @@ 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:
@@ -101,7 +114,72 @@ 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(position)
+        self.goals.append(Goal(position, self.square_size, self.screen_size))
+
+    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

@@ -15,9 +15,10 @@ class StateController:
         self.explored.clear()
         self.fringe = PriorityQueue()
 
-    def build_path(self, goal_state):
+    def build_path(self, goal_state, engine):
         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
@@ -28,7 +29,7 @@ class StateController:
     def graphsearch(self, engine):            # A*
         print("Search path")
 
-        self.goal = list(engine.goals.pop())
+        self.goal = list(engine.goals[-1].position)
 
         self.reset()
 
@@ -36,13 +37,10 @@ class StateController:
 
         self.fringe.put(start)
 
-        while self.fringe and not self.path:
+        while self.fringe.queue and not self.path:
             self.explored.append(self.fringe.get())
-
+            if self.goal_test(engine):
-            if self.explored[-1].position == self.goal:
+                return
-                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)
@@ -51,6 +49,10 @@ 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")
 
@@ -90,3 +92,19 @@ 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

@@ -9,11 +9,3 @@ 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

@@ -0,0 +1,70 @@
+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/datasetConverter.py

@@ -1,30 +1,33 @@
 import csv
 
-def convert_dataset(input_file, output_file):
+
+def convertDataset():
     attributes = {
-        1: ["empty", "low", "medium", "high"],
+        1: ["low", "high"],
-        2: ["empty", "full"],
+        2: ["far", "close"],
-        3: ["none", "available"],
+        3: ['bad', 'good', 'undefined'],
-        4: ["none", "available"],
+        4: ["no", "yes"],
-        5: ["unavailable", "available"],
+        5: ["no", "yes"],
-        6: ["low", "medium", "high"],
+        6: ["no", "yes"],
-        7: ["no", "yes"],
+        7: ["empty", "new order", "waiting for dish", "have a dish"],
-        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(input_file, "r") as csvfile:
+    with open("out/rawDataset.csv", "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 = []
@@ -34,21 +37,15 @@ def convert_dataset(input_file, output_file):
                     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(output_file, "w", newline="") as csvfile:
+    with open("out/dataset.csv", "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")

src/decisionTree/datasetGenerator.py

@@ -0,0 +1,111 @@
+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

@@ -1,33 +0,0 @@
-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

@@ -0,0 +1,9 @@
+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):
+    def action(self, waiter, current_time):
-        waiter.combine_orders()
+        waiter.combine_orders(current_time)
         waiter.recharge()

src/obj/Object.py

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

src/obj/Table.py

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

src/obj/Waiter.py

@@ -18,106 +18,28 @@ 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 combine_orders(self):
+    def basket_is_empty(self) -> bool:
+        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()
+            dish.set_done(current_time)
             self.basket.append(dish)
             self.basket_size -= 1
             self.memory_size += 1
 
-    def deliver_dish(self, table):
+    def deliver_dish(self, table, current_time):
         if table in self.basket:
-            table.reset()
+            table.reset(current_time)
             self.basket.remove(table)
             self.basket_size += 1
 
@@ -130,21 +52,16 @@ class Waiter(Object):
     def memory_is_full(self) -> bool:
         return self.memory_size == 0
 
-    def collect_order(self, table):
+    def collect_order(self, table, current_time):
         if self.memory_is_full():
             return
         if table.agent_role == "order":
-            table.set_wait()
+            table.set_wait(current_time)
             self.memory.append(table)
             self.memory_size -= 1
 
-    def battary_status(self) -> str:
+    def battery_status(self) -> int:
-        if self.battery >= 200:
+        return 1 if self.battery >= 100 else 0
-            return "hight"
-        elif self.battery >= 100:
-            return "medium"
-        else:
-            return "low"
 
     def recharge(self):
         self.battery = 300
@@ -160,3 +77,10 @@ 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