res/map.txt

@@ -1,14 +1,14 @@
 MP--H---------------------
-RORRRRRRRRRRRRRRRRR---G---
-SZ--G------G------R---GGG-
-----G------G------R---G---
--RRRG------GGGGGGGRRRRR---
+RRRRRRRRRRRRRRRRRRR---G---
+SZ--R------G------R---GGG-
+----R------G------R---G---
+-RRRR------GGGGGGGRRRRR---
 -R--G-------------R-------
--R--GGGGGGGGGRRRRRRH------
+-R--GGGGGGGGGGGGGGRH------
 -R--G---G---GH----R-------
--R--G---G---G-----RRRRRRR-
+-R--G---G---GH----RRRRRRR-
 -R--G-------H-----R-------
 -R--G-----G----G--R-------
 -R--G-----G----G--R-------
--RRRRRRRRRRRRRRRRRRRRRRR--
+-RRRRRRRRRRRRRRRRRRRORRR--
 --------------------------

src/agent.py

@@ -1,27 +1,6 @@
 import random
 import pygame as pg
 import heapq
-from decisiontree import decision
-from trashtype import trash_type_def
-import genetics
-
-HOUSE_NUMBER = {'House_number': 0}
-
-
-class Node:
-    def __init__(self, state, parent, cost):
-        self.state = state
-        self.parent = parent
-        self.cost = cost
-
-    def __lt__(self, other):
-        return self.cost < other.cost
-
-
-class State:
-    def __init__(self, position, house_list):
-        self.position = position
-        self.house_list = house_list
 
 
 def vector_to_tuple(vector):
@@ -31,111 +10,10 @@ def vector_to_tuple(vector):
 
 class HousePOI:
     def __init__(self):
-        from simulation import Interface
-        self.season = Interface.season.season
-        self.day = Interface.day.day
-        self.truck_fullness = None
-        self.payment = None
-        self.bin_fullness = None
-        self.trash_type = None
-        self.trash = 1
-        self.dump_fullness = 0
+        self.bins = None
 
-
-    def discover_bins(self, bin_fullness, trash_type, payment):
-        from simulation import Interface
-
-        season_autumn = 0
-        season_spring = 0
-        season_summer = 0
-        season_winter = 0
-        day_friday = 0
-        day_monday = 0
-        day_wednesday = 0
-        trash_types_glass = 0
-        trash_types_biological = 0
-        trash_types_paper = 0
-        trash_types_plastic = 0
-        truck_fullness = 0
-
-        guessed_trash_type = trash_type_def(trash_type)
-
-        self.truck_fullness = Interface.truck_fullness.fullness
-        self.payment = payment
-        self.bin_fullness = bin_fullness
-        self.trash_type = guessed_trash_type[0]
-
-        if self.season == 'autumn':
-            season_autumn = 1
-        if self.season == 'spring':
-            season_spring = 1
-        if self.season == 'summer':
-            season_summer = 1
-        if self.season == 'winter':
-            season_winter = 1
-        if self.day == 'friday':
-            day_friday = 1
-        if self.day == 'monday':
-            day_monday = 1
-        if self.day == 'wednesday':
-            day_wednesday = 1
-        if self.trash_type == 'glass':
-            trash_types_glass = 1
-            truck_fullness = self.truck_fullness["glass"]
-        if self.trash_type == 'biological':
-            trash_types_biological = 1
-            truck_fullness = self.truck_fullness["biological"]
-        if self.trash_type == 'paper':
-            trash_types_paper = 1
-            truck_fullness = self.truck_fullness["paper"]
-        if self.trash_type == 'plastic':
-            trash_types_plastic = 1
-            truck_fullness = self.truck_fullness["plastic"]
-        if truck_fullness < 1:
-            truck_fullness = 0
-        if truck_fullness == 1:
-            truck_fullness = 1
-        dec_tree = {'dump_fullness': [self.dump_fullness],
-                    'truck_fullness': [truck_fullness],
-                    'trash': [self.trash],
-                    'payment': [payment],
-                    'bin_fullness': [bin_fullness],
-                    'trash_types_glass': [trash_types_glass],
-                    'trash_types_mixed': [trash_types_biological],
-                    'trash_types_paper': [trash_types_paper],
-                    'trash_types_plastic': [trash_types_plastic],
-                    'season_autumn': [season_autumn],
-                    'season_spring': [season_spring],
-                    'season_summer': [season_summer],
-                    'season_winter': [season_winter],
-                    'day_friday': [day_friday],
-                    'day_monday': [day_monday],
-                    'day_wednesday': [day_wednesday]
-                    }
-        outcome = decision(dec_tree)
-        if outcome == 1:
-            Interface.truck_fullness.fullness[trash_type] += 0.25  # 0.25
-
-        debug(dec_tree, outcome, trash_type, guessed_trash_type[0], guessed_trash_type[1], self.season, self.day, self.bin_fullness,
-              self.truck_fullness)
-
-
-def debug(dec_tree, outcome, trash_type, guessed_trash_type, filename, season, day, bin_fullness, truck_fullness):
-    HOUSE_NUMBER['House_number'] += 1
-    print("Domek nr: " + str(HOUSE_NUMBER['House_number']))
-    if outcome == 1:
-        print("Odebrano śmieci")
-    if outcome == 0:
-        print("Nie odebrano śmieci")
-    print("Pora: " + season)
-    print("Dzień: " + day)
-    print("Typ śmieci: " + trash_type)
-    print("Nazwa pliku: " + trash_type + str(filename))
-    print("Zaobserwowane: " + guessed_trash_type)
-    print("Zapełnienie domu: " + str(bin_fullness))
-    print(truck_fullness)
-    print(dec_tree)
-    print("####################################################################################")
+    def discover_bins(self, bins):
+        self.bins = bins
 
 
 class Agent:
@@ -148,44 +26,44 @@ class Agent:
         self.fullness = None
         self.weights = {}
         self.orientation = 90
-        self.genetic_house_list = None
+
 
         # utworzenie grafu dróg
-        roads_pos = [vector_to_tuple(pos) for pos in
-                     self.simulation.state.road_pos_g + self.simulation.state.road_pos_r]
+        roads_pos = [vector_to_tuple(pos) for pos in self.simulation.state.road_pos_g + self.simulation.state.road_pos_r]
         roads_pos.sort()
         for index, pos in enumerate(roads_pos):
             if pos[0] < 0 or pos[0] >= 27 or pos[1] < 0 or pos[1] >= 14:
                 continue
             for another_pos in roads_pos[index:]:
-                if pos == another_pos or another_pos[0] < 0 or another_pos[0] >= 27 or another_pos[1] < 0 or \
-                        another_pos[1] >= 14:
+                if pos == another_pos or  another_pos[0] < 0 or another_pos[0] >= 27 or another_pos[1] < 0 or another_pos[1] >= 14:
                     continue
-                if ((abs(pos[0] - another_pos[0]) == 1 and abs(pos[1] - another_pos[1]) == 0) or (
-                        abs(pos[0] - another_pos[0]) == 0 and abs(pos[1] - another_pos[1]) == 1)):
+                if ((abs(pos[0] - another_pos[0]) == 1 and abs(pos[1] - another_pos[1]) == 0) or (abs(pos[0] - another_pos[0]) == 0 and abs(pos[1] - another_pos[1]) == 1)):
                     if pos not in self.graph.keys():
                         self.graph[pos] = set()
                     if another_pos not in self.graph.keys():
                         self.graph[another_pos] = set()
-                    if another_pos in self.simulation.state.road_pos_r:
-                        weight = 1
+                    if  another_pos in self.simulation.state.road_pos_r:
+                        weight = 2
                     elif another_pos in self.simulation.state.road_pos_g:
-                        weight = 100
+                        weight = 3
                     else:
                         weight = 1
                     self.graph[pos].add(another_pos)
                     self.graph[another_pos].add(pos)
-                    self.weights[(pos, another_pos)] = weight
+                    self.weights[(pos,another_pos)] = weight
                     self.weights[(another_pos, pos)] = weight
 
+                 
+                    
+
         # dołączenie domów i składowisk do grafu dróg
         entities = self.simulation.state.entities
         for entity in entities:
             entity_pos = vector_to_tuple(entity.position)
-            for neighbour_pos in [(entity_pos[0] - 1, entity_pos[1]),
-                                  (entity_pos[0] + 1, entity_pos[1]),
-                                  (entity_pos[0], entity_pos[1] - 1),
-                                  (entity_pos[0], entity_pos[1] + 1)]:
+            for neighbour_pos in [(entity_pos[0]-1, entity_pos[1]),
+                                  (entity_pos[0]+1, entity_pos[1]),
+                                  (entity_pos[0], entity_pos[1]-1),
+                                  (entity_pos[0], entity_pos[1]+1)]:
                 if neighbour_pos[0] < 0 or neighbour_pos[0] >= 27 or neighbour_pos[1] < 0 or neighbour_pos[1] >= 14:
                     continue
                 if neighbour_pos in roads_pos:
@@ -199,9 +77,10 @@ class Agent:
                 self.dumps[entity.trash_type] = vector_to_tuple(entity.position)
             if entity.entity_type == 'house':
                 self.houses[vector_to_tuple(entity.position)] = HousePOI()
+        
+        self.path = self.A_star()
 
-        self.path = self.a_star()
-        pass
+       
 
     def update(self):
         entities = self.simulation.state.entities
@@ -211,132 +90,141 @@ class Agent:
                 self.fullness = entity.fullness
                 self.orientation = entity.orientation
 
+    
     def decide_move(self):
-        if self.path:
-            move_a = self.path[0]
-            move = (move_a[0] - self.current_pos[0], move_a[1] - self.current_pos[1])
-
-            if self.orientation == 0:
-                if move[0] == 0:
-                    if move[1] == -1:
-                        self.path.pop(0)
-                        return pg.Vector2(move)
-                    else:
-                        return 180
-                elif move[1] == 0:
-                    if move[0] == -1:
-                        return 270
-                    else:
-                        return 90
-            elif self.orientation == 90:
-                if move[0] == 0:
-                    if move[1] == -1:
-                        return 0
-                    else:
-                        return 180
-                elif move[1] == 0:
-                    if move[0] == -1:
-                        return 270
-                    else:
-                        self.path.pop(0)
-                        return pg.Vector2(move)
-            elif self.orientation == 180:
-                if move[0] == 0:
-                    if move[1] == -1:
-                        return 0
-                    else:
-                        self.path.pop(0)
-                        return pg.Vector2(move)
-                elif move[1] == 0:
-                    if move[0] == -1:
-                        return 270
-                    else:
-                        return 90
-            else:
-                if move[0] == 0:
-                    if move[1] == -1:
-                        return 0
-                    else:
-                        return 180
-                elif move[1] == 0:
-                    if move[0] == -1:
-                        self.path.pop(0)
-                        return pg.Vector2(move)
-                    else:
-                        return 90
+        
+          
+        
+        move = self.path.pop
+    
+        if self.orientation == 0:
+            if move[0] != 0:
+                if move[0] == 1:
+                    return 90
+                else:
+                    return 270
+            elif move[1] != 0:
+                if move[1] == 1:
+                    return pg.Vector2(move)
+                else:
+                    return 180
+        elif self.orientation == 90:
+            if move[0] != 0:
+                if move[0] == 1:
+                    return pg.Vector2(move)
+                else:
+                    return 270
+            elif move[1] != 0:
+                if move[1] == 1:
+                    return 0
+                else:
+                    return 180
+        elif self.orientation == 180:
+            if move[0] != 0:
+                if move[0] == 1:
+                    return 90
+                else:
+                    return 270
+            elif move[1] != 0:
+                if move[1] == 1:
+                    return 0
+                else:
+                    return pg.Vector2(move)
         else:
-            return pg.Vector2(0, 0)
+            if move[0] != 0:
+                if move[0] == 1:
+                    return 90
+                else:
+                    return pg.Vector2(move)
+            elif move[1] != 0:
+                if move[1] == 0:
+                    return 0
+                else:
+                    return 180
+    
+    def heuristic(self, start_pos, end_pos):
+        return abs((end_pos[0] - start_pos[0])) + abs((end_pos[1] - start_pos[1]))
 
-    def successors(self, state):
+    def weight_cost(self, start_pos, end_pos):  
+        return self.weights[(start_pos, end_pos)]
+
+    def get_move_cost(self, start_pos, end_pos):
+        return self.heuristic(start_pos, end_pos) + self.weight_cost(start_pos, end_pos)
+
+    def get_start_state(self):
+        entities = self.simulation.state.entities
+        # orientation - self.orientation
+        position = self.current_pos
+        house_list = tuple(self.houses)
+        start_state = (position, house_list)
+        return start_state
+
+    def get_end_state(self):
+        position = (0,1)
+        house_list = ()
+        end_state = (position, house_list)
+        return end_state
+
+    def succesor(self, state):
         successors_pos = self.graph[state[0]]
         house_list = state[1]
-        successors = []
+        successors = ()
         for pos in successors_pos:
-            if len(house_list) > 0:
-                if house_list[0] == pos:
-                    house_list = list(house_list)
-                    house_list.pop(0)
-                    house_list = tuple(house_list)
+            if pos in house_list:
+                house_list = list(house_list)
+                house_list.remove(pos)
+                house_list = tuple(house_list)
 
-            successors.append((pos, tuple(house_list)))
+                successors = list(successors)
+                successors.append((pos, tuple(house_list)))
+                successors = tuple(successors)
+            else:
+                successors = list(successors)
+                successors.append((pos, tuple(house_list)))
+                successors = tuple(successors)
 
         return successors
 
-    def cost(self, current_node, succ_state):
-        if succ_state[0] not in current_node.state[1]:
-            cost = current_node.cost + self.weights[current_node.state[0], succ_state[0]]
-        else:
-            cost = float(1)
-        return cost
 
-    def heuristic(self, succ_state, goal_state):
-        position = succ_state[0]
-        if len(succ_state[1]) != 0:
-            estimated_cost = 0
-            for house in succ_state[1]:
-                estimated_cost += abs(position[0] - house[0]) + abs(position[1] - house[1])
-        else:
-            goal_position = goal_state[0]
-            estimated_cost = abs(position[0] - goal_position[0]) + abs(position[1] - goal_position[1])
-        return estimated_cost
-
-    def a_star(self):
+    def A_star(self):
         fringe = []
         explored = set()
-        self.genetic_house_list = genetics.genetic_algorithm(sorted(self.houses))
-        initial_state = (self.current_pos, tuple(self.genetic_house_list))
-        goal_state = (self.current_pos, ())
-        start_node = Node(initial_state, None, 0)
+        istate = self.get_start_state()
+        goaltest = self.get_end_state()
+        node = (istate, None)
 
-        heapq.heappush(fringe, start_node)
+        heapq.heappush(fringe, (self.heuristic(istate[0], goaltest[0]), node))
 
         while fringe:
-            current_node = heapq.heappop(fringe)
+            _, el = heapq.heappop(fringe)
+            elem = el[0]
+            parent = el[1]
 
-            if current_node.state == goal_state:
+            if elem == goaltest:
                 actions = []
-                while current_node:
-                    actions.append(current_node.state[0])
-                    current_node = current_node.parent
-                c = (4, 0) in actions
-                return list(reversed(actions))
+                while parent is not None:
+                    actions.append(elem[0])
+                    elem = parent
+                actions.reverse()
+                return actions
 
-            explored.add(current_node.state)
+            explored.add(elem)
 
-            succ_states = self.successors(current_node.state)
+            succs = self.succesor(elem)
+            for succ in succs:
 
-            for succ_state in succ_states:
-                if succ_state in explored:
-                    continue
+                node = (succ, elem)
+                p = self.heuristic(succ[0], goaltest[0]) + self.weight_cost(elem[0], succ[0])
 
-                g = self.cost(current_node, succ_state)
-                h = self.heuristic(succ_state, goal_state)
-                succ_cost = g + h
-                succ_node = Node(succ_state, current_node, succ_cost)
+                if succ not in explored and not any(tup[1][0] == succ for tup in fringe):
+                    heapq.heappush(fringe, (p,node))
+                elif any(tup[1] == succ for tup in fringe):
+                    i = next(i for node in enumerate(fringe) if node[1][0] == succ)
+                    if fringe[i][0] > p:
+                        fringe[i] = (p,node)
+        return False 
+        
 
-                heapq.heappush(fringe, succ_node)
-
-        return None
 
     def discover(self):
         if self.current_pos in self.houses.keys():
@@ -345,7 +233,5 @@ class Agent:
                 if entity.entity_type == 'house' and vector_to_tuple(entity.position) == self.current_pos:
                     current_house = entity
                     break
-            if current_house is not None and current_house.visited == False:
-                current_house.visited = True
-                self.houses[self.current_pos].discover_bins(current_house.bins[0], current_house.bins[1],
-                                                            current_house.bins[2])
+            if current_house is not None:
+                self.houses[self.current_pos].discover_bins(current_house.bins)

src/decisiontree.py

@@ -1,22 +0,0 @@
-import pandas as pd
-from sklearn import tree
-from sklearn.tree import DecisionTreeClassifier
-
-
-data = pd.read_csv('tree_data.csv')
-data_encoded = pd.get_dummies(data, columns=['trash_types', 'season', 'day'])
-
-X = data_encoded.drop('decision', axis=1)
-y = data_encoded['decision']
-
-clf = DecisionTreeClassifier()
-decision_tree = clf.fit(X, y)
-
-with open('./tree_in_txt.txt', "w") as file:
-    file.write(tree.export_text(decision_tree, feature_names=X.columns.tolist()))
-
-
-def decision(conditions):
-    new_conditions = pd.DataFrame(conditions)
-    new_decision = decision_tree.predict(new_conditions)
-    return new_decision

src/genetics.py

@@ -1,83 +0,0 @@
-import random
-
-POPULATION_SIZE = 100
-MUTATION_RATE = 0.01
-NUM_GENERATIONS = 100
-
-
-
-def generate_individual(houses):
-    return random.sample(houses, len(houses))
-
-def generate_population(houses, size):
-    return [generate_individual(houses) for _ in range(size)]
-
-def calculate_distance(house1, house2):
-    x1, y1 = house1
-    x2, y2 = house2
-    return abs(x1 - x2) + abs(y1 - y2)
-
-def calculate_total_distance(houses):
-    total_distance = 0
-    for i in range(len(houses) - 1):
-        total_distance += calculate_distance(houses[i], houses[i+1])
-    total_distance += calculate_distance(houses[-1], houses[0])  # Zamknięcie cyklu
-    return total_distance
-
-def select_parents(population, num_parents):
-    parents = []
-    for _ in range(num_parents):
-        tournament = random.sample(population, 5)
-        winner = min(tournament, key=calculate_total_distance)
-        parents.append(winner)
-    return parents
-
-def crossover(parent1, parent2):
-    child1 = [None] * len(parent1)
-    child2 = [None] * len(parent1)
-    start_index = random.randint(0, len(parent1) - 1)
-    end_index = random.randint(start_index, len(parent1) - 1)
-    child1[start_index:end_index+1] = parent1[start_index:end_index+1]
-    child2[start_index:end_index+1] = parent2[start_index:end_index+1]
-
-    for i in range(len(parent1)):
-        if parent2[i] not in child1:
-            for j in range(len(parent2)):
-                if child1[j] is None:
-                    child1[j] = parent2[i]
-                    break
-
-    for i in range(len(parent1)):
-        if parent1[i] not in child2:
-            for j in range(len(parent1)):
-                if child2[j] is None:
-                    child2[j] = parent1[i]
-                    break
-
-    return child1, child2
-
-def mutate(individual):
-    index1, index2 = random.sample(range(len(individual)), 2)
-    individual[index1], individual[index2] = individual[index2], individual[index1]
-
-def genetic_algorithm(houses):
-    population = generate_population(houses, POPULATION_SIZE)
-
-    for _ in range(NUM_GENERATIONS):
-        fitness_scores = [calculate_total_distance(individual) for individual in population]
-
-        parents = select_parents(population, 2)
-
-        new_population = []
-        for i in range(POPULATION_SIZE // 2):
-            child1, child2 = crossover(parents[0], parents[1])
-            mutate(child1)
-            mutate(child2)
-            new_population.extend([child1, child2])
-
-        population = new_population
-
-    best_individual = min(population, key=calculate_total_distance)
-
-    return best_individual
-

src/simulation.py

@@ -1,6 +1,5 @@
 from pathlib import Path
 from random import randint
-import random
 
 import pygame as pg
 from agent import Agent
@@ -14,9 +13,9 @@ HOUSE_SPRITES = {0: HOUSE_WITHOUT_TRASH_SPRITE,
                  1: HOUSE_WITH_TRASH_SPRITE}
 
 TRUCK_SPRITE_R = pg.Vector2(2, 0)
-TRUCK_SPRITE_D = pg.Vector2(2, 1)
-TRUCK_SPRITE_L = pg.Vector2(2, 2)
-TRUCK_SPRITE_U = pg.Vector2(2, 3)
+TRUCK_SPRITE_D = pg.Vector2(2,1)
+TRUCK_SPRITE_L = pg.Vector2(2,2)
+TRUCK_SPRITE_U = pg.Vector2(2,3)
 
 PAPER_DUMP_SPRITE = pg.Vector2(3, 0)
 PLASTIC_DUMP_SPRITE = pg.Vector2(3, 1)
@@ -27,24 +26,7 @@ DUMP_SPRITES = {'paper': PAPER_DUMP_SPRITE,
                 'plastic': PLASTIC_DUMP_SPRITE,
                 'mixed': MIXED_DUMP_SPRITE}
 
-TRASH_TYPES = ['paper', 'plastic', 'glass', 'biological']
-DAYS = ['friday', 'monday', 'wednesday']
-SEASONS = ['autumn', 'spring', 'summer', 'winter']
-
-
-class Season:
-    def __init__(self, season):
-        self.season = season
-
-
-class Day:
-    def __init__(self, day):
-        self.day = day
-
-
-class TruckFullness:
-    def __init__(self, fullness):
-        self.fullness = fullness
+TRASH_TYPES = ['paper', 'plastic', 'glass', 'mixed']
 
 
 class Entity:
@@ -58,9 +40,12 @@ class TruckEntity(Entity):
         super().__init__(state, position)
         self.entity_type = 'truck'
         self.tile = TRUCK_SPRITE_R
-        self.fullness = None
+        self.fullness = {'paper': 0,
+                         'glass': 0,
+                         'plastic': 0,
+                         'mixed': 0}
         self.orientation = 90
-
+    
     def rotate_img(self, orientation):
         self.orientation = orientation
         if orientation == 0:
@@ -91,11 +76,13 @@ class TruckEntity(Entity):
 
 
 class Bin:
-    def __init__(self, items):
-        self.items = items
+    def __init__(self, size, trash_type):
+        self.size = size
+        self.trash_type = trash_type
+        self.fullness = randint(0, 100)
 
-    def __getitem__(self, index):
-        return self.items[index]
+    def __str__(self):
+        return self.trash_type + " bin"
 
 
 class HouseEntity(Entity):
@@ -103,8 +90,7 @@ class HouseEntity(Entity):
         super().__init__(state, position)
         self.tile = HOUSE_SPRITES[0]
         self.entity_type = 'house'
-        self.visited = False
-        self.bins = Bin([randint(0, 1), random.choice(TRASH_TYPES), randint(0, 1)])
+        self.bins = [Bin(randint(1, 2), trash_type) for trash_type in TRASH_TYPES]
 
 
 class DumpEntity(Entity):
@@ -139,7 +125,7 @@ class SimulationState:
                     if tile == "R":
                         self.road_pos_r.append(pg.Vector2(x, y))
                     if tile == "G":
-                        self.road_pos_g.append(pg.Vector2(x, y))
+                        self.road_pos_g.append(pg.Vector2(x,y))
                     if tile == "H":
                         self.houses_pos.append(pg.Vector2(x, y))
                         self.entities.append(HouseEntity(self, pg.Vector2(x, y)))
@@ -179,7 +165,7 @@ class Layer:
         self.sim = sim
         self.texture_atlas = pg.image.load(texture_file)
 
-    def renderTile(self, surface, position, tile, orientation=90, rotate=False):
+    def renderTile(self, surface, position, tile, orientation = 90, rotate=False):
         # pozycja na ekranie
         sprite_pos = position.elementwise() * self.sim.cell_size
 
@@ -189,6 +175,7 @@ class Layer:
                           int(pos_in_atlas.y),
                           self.sim.cell_size.x,
                           self.sim.cell_size.y)
+    
 
         # render
         surface.blit(self.texture_atlas, sprite_pos, texture)
@@ -224,13 +211,6 @@ class StructureLayer(Layer):
 
 
 class Interface:
-    truck_fullness = TruckFullness({'paper': 0,
-                                    'glass': 0,
-                                    'plastic': 0,
-                                    'biological': 0})
-    season = Season(random.choice(SEASONS))
-    day = Day(random.choice(DAYS))
-
     def __init__(self):
         pg.init()
 
@@ -244,8 +224,8 @@ class Interface:
         # rendering
         self.cell_size = pg.Vector2(64, 64)
         texture_file = Path(r"..\res\tiles.png")
-        self.layers = [StructureLayer(self, texture_file, self.state, self.state.road_pos_r, ROAD_SPRITE_R),
-                       StructureLayer(self, texture_file, self.state, self.state.road_pos_g, ROAD_SPRITE_G),
+        self.layers = [StructureLayer(self, texture_file, self.state, self.state.road_pos_r, ROAD_SPRITE_R ),
+                       StructureLayer(self, texture_file, self.state, self.state.road_pos_g, ROAD_SPRITE_G ),
                        EntityLayer(self, texture_file, self.state, self.state.entities)]
 
         # okno
@@ -277,16 +257,16 @@ class Interface:
                 if self.debug_mode:
                     if event.key == pg.K_RIGHT:
                         self.move_truck.x = 1
-
+                        
                     if event.key == pg.K_LEFT:
                         self.move_truck.x = -1
-
+                        
                     if event.key == pg.K_DOWN:
                         self.move_truck.y = 1
-
+                        
                     if event.key == pg.K_UP:
                         self.move_truck.y = -1
-
+                        
                     if event.key == pg.K_d:
                         self.agent.discover()
 
@@ -296,10 +276,9 @@ class Interface:
     def update(self):
         self.state.update(self.move_truck)
         self.agent.update()
-        self.agent.discover()
-        '''if isinstance(self.move_truck, int):
+        if isinstance(self.move_truck, int):
             self.state.update(self.move_truck)
-            self.agent.update()'''
+            self.agent.update()
 
     def render(self):
         if not self.debug_mode:
@@ -319,5 +298,5 @@ class Interface:
                 self.processAgentInput()
             self.update()
             self.render()
-            self.clock.tick(50)
-        pg.quit()
+            self.clock.tick(24)
+        pg.quit()

src/trashtype.py

@@ -1,48 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-import PIL
-import random
-from PIL import Image
-
-
-def trash_type_def(trash_type):
-    img_num = 0
-    img_height, img_width = 180, 180
-
-    if trash_type == 'biological':
-        img_num = random.randint(1, 985)
-    if trash_type == 'glass':
-        img_num = random.randint(1, 501)
-    if trash_type == 'paper':
-        img_num = random.randint(1, 594)
-    if trash_type == 'plastic':
-        img_num = random.randint(1, 482)
-
-    filename = trash_type + str(img_num)
-    class_names = ['biological', 'glass', 'paper', 'plastic']
-    trash_classification_model = tf.keras.models.load_model('../trash-classification/m_with_data_augmentation.h5')
-    test_image_path = "../trash-classification/trash/test/"+trash_type+"/"+filename+".jpg"
-
-    img = tf.keras.utils.load_img(
-        test_image_path, target_size=(img_height, img_width)
-    )
-    img_array = tf.keras.utils.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0)
-
-    predictions = trash_classification_model.predict(img_array)
-    score = tf.nn.softmax(predictions[0])
-
-    name = class_names[np.argmax(score)]
-
-    file_path = "../trash-classification/trash/test/"+trash_type+"/"+filename+".jpg"
-    img_show = Image.open(file_path)
-    img_numpy_format = np.asarray(img_show)
-    plt.imshow(img_numpy_format)
-    plt.suptitle(filename)
-    plt.draw()
-    plt.pause(1)
-    plt.close()
-
-    return [name, img_num]

src/tree.py

@@ -1,67 +0,0 @@
-import csv
-import random
-import pandas as pd
-
-truck_fullness = [0, 1]
-trash_types = ['paper', 'plastic', 'glass', 'mixed']
-payment = [0, 1]
-bin_fullness = [0, 1]
-day = ['monday', 'wednesday', 'friday']
-season = ['spring', 'summer', 'autumn', 'winter']
-dump_fullness = [0, 1]
-trash = [0, 1]
-# spring: monday = plastic, wednesday = glass, friday = biological & paper
-# summer: monday = glass, wednesday = biological & plastic, friday = paper
-# autumn: monday = biological & plastic, wednesday = glass, friday = paper
-# winter: monday = paper, wednesday = biological & plastic, friday = glass
-
-with open('tree_data.csv', 'w', newline='') as csvfile:
-    writer = csv.writer(csvfile)
-
-    writer.writerow(
-        ['dump_fullness', 'truck_fullness', 'trash', 'payment', 'bin_fullness', 'trash_types', 'season', 'day',
-         'decision'])
-    rows = set()
-
-    positives = [(0, 0, 1, 1, 1, 'glass', 'spring', 'wednesday', 1),
-                 (0, 0, 1, 1, 1, 'glass', 'summer', 'monday', 1),
-                 (0, 0, 1, 1, 1, 'glass', 'autumn', 'wednesday', 1),
-                 (0, 0, 1, 1, 1, 'glass', 'winter', 'friday', 1),
-
-                 (0, 0, 1, 1, 1, 'paper', 'spring', 'friday', 1),
-                 (0, 0, 1, 1, 1, 'paper', 'summer', 'friday', 1),
-                 (0, 0, 1, 1, 1, 'paper', 'autumn', 'friday', 1),
-                 (0, 0, 1, 1, 1, 'paper', 'winter', 'monday', 1),
-
-                 (0, 0, 1, 1, 1, 'plastic', 'spring', 'monday', 1),
-                 (0, 0, 1, 1, 1, 'plastic', 'summer', 'wednesday', 1),
-                 (0, 0, 1, 1, 1, 'plastic', 'autumn', 'monday', 1),
-                 (0, 0, 1, 1, 1, 'plastic', 'winter', 'wednesday', 1),
-
-                 (0, 0, 1, 1, 1, 'mixed', 'spring', 'friday', 1),
-                 (0, 0, 1, 1, 1, 'mixed', 'summer', 'wednesday', 1),
-                 (0, 0, 1, 1, 1, 'mixed', 'autumn', 'monday', 1),
-                 (0, 0, 1, 1, 1, 'mixed', 'winter', 'wednesday', 1)]
-
-    for positive in positives:
-        rows.add(positive)
-
-    while len(rows) < 200:
-        rand_1 = random.choice(dump_fullness)
-        rand_2 = random.choice(truck_fullness)
-        rand_3 = random.choice(trash)
-        rand_4 = random.choice(payment)
-        rand_5 = random.choice(bin_fullness)
-        rand_6 = random.choice(trash_types)
-        rand_7 = random.choice(season)
-        rand_8 = random.choice(day)
-
-        rows.add((rand_1, rand_2, rand_3, rand_4, rand_5, rand_6, rand_7, rand_8, 0))
-
-    rows_tuple = tuple(rows)
-
-    for elem in rows_tuple:
-        writer.writerow(elem)
-
-dt = pd.read_csv('tree_data.csv')
-print(dt.head())

src/tree_data.csv

@@ -1,201 +0,0 @@
-dump_fullness,truck_fullness,trash,payment,bin_fullness,trash_types,season,day,decision
-0,0,0,1,0,glass,winter,wednesday,0
-1,1,1,0,1,mixed,spring,monday,0
-0,1,1,1,0,plastic,winter,monday,0
-1,0,1,1,0,glass,spring,monday,0
-1,1,1,0,0,paper,spring,wednesday,0
-0,1,0,1,1,plastic,spring,monday,0
-1,1,1,1,1,mixed,autumn,friday,0
-0,0,1,0,1,glass,autumn,friday,0
-1,0,0,1,1,glass,spring,monday,0
-0,0,0,0,0,paper,spring,friday,0
-1,1,0,1,0,paper,summer,friday,0
-1,1,1,0,0,mixed,spring,wednesday,0
-1,0,1,1,0,mixed,autumn,monday,0
-0,0,1,0,1,mixed,summer,wednesday,0
-1,0,1,0,0,glass,spring,monday,0
-1,1,1,0,0,paper,autumn,friday,0
-0,0,1,0,1,paper,summer,friday,0
-1,0,0,1,1,glass,autumn,wednesday,0
-1,1,1,1,0,glass,winter,wednesday,0
-0,0,0,0,0,plastic,summer,monday,0
-0,0,1,1,1,mixed,autumn,friday,0
-1,0,1,0,0,mixed,summer,friday,0
-1,0,0,1,0,paper,winter,friday,0
-1,1,1,0,1,paper,winter,wednesday,0
-0,0,1,1,1,glass,summer,wednesday,0
-1,0,0,0,0,glass,summer,friday,0
-0,0,0,1,0,glass,summer,wednesday,0
-1,0,1,1,0,paper,spring,friday,0
-1,1,1,1,1,paper,autumn,monday,0
-1,0,0,1,0,glass,winter,wednesday,0
-1,0,1,0,1,paper,spring,wednesday,0
-0,1,0,1,0,mixed,winter,wednesday,0
-1,1,1,1,1,paper,winter,friday,0
-1,0,1,0,0,glass,autumn,wednesday,0
-0,1,0,0,1,plastic,autumn,monday,0
-0,0,1,1,1,glass,winter,friday,1
-0,1,0,1,1,paper,spring,monday,0
-1,0,0,0,1,plastic,spring,monday,0
-0,0,1,1,1,mixed,autumn,monday,1
-0,1,1,0,1,glass,spring,monday,0
-0,0,1,1,0,paper,autumn,wednesday,0
-0,0,0,1,1,plastic,spring,friday,0
-1,0,1,1,0,plastic,summer,monday,0
-1,1,1,1,1,plastic,winter,friday,0
-0,0,0,1,1,mixed,autumn,friday,0
-1,0,1,0,0,paper,spring,wednesday,0
-0,0,0,1,1,glass,spring,monday,0
-0,1,1,0,0,plastic,summer,wednesday,0
-1,0,1,0,0,glass,summer,friday,0
-0,1,0,0,0,glass,winter,wednesday,0
-0,0,0,1,0,paper,summer,monday,0
-1,1,0,0,1,plastic,summer,friday,0
-1,0,0,0,0,plastic,spring,monday,0
-1,0,1,1,0,plastic,autumn,friday,0
-1,1,1,1,0,mixed,autumn,wednesday,0
-0,0,0,0,1,paper,autumn,monday,0
-0,0,1,1,0,glass,winter,friday,0
-0,1,0,1,0,glass,summer,wednesday,0
-1,1,0,0,0,plastic,summer,monday,0
-0,1,0,1,0,paper,spring,monday,0
-0,0,1,1,1,mixed,winter,wednesday,1
-0,0,1,0,0,plastic,spring,monday,0
-1,1,0,0,1,mixed,winter,monday,0
-0,0,1,1,1,mixed,summer,wednesday,1
-0,1,0,0,1,paper,winter,friday,0
-0,0,1,1,1,plastic,winter,monday,0
-1,0,1,0,0,glass,winter,monday,0
-0,0,1,0,0,mixed,summer,monday,0
-1,1,1,0,1,mixed,spring,friday,0
-1,0,1,1,1,mixed,spring,monday,0
-1,1,0,1,1,glass,autumn,friday,0
-0,1,0,0,1,plastic,spring,monday,0
-0,1,1,0,1,plastic,spring,monday,0
-0,0,1,1,1,plastic,autumn,monday,1
-1,0,0,1,0,glass,spring,wednesday,0
-1,1,1,0,0,plastic,autumn,monday,0
-0,1,0,1,1,plastic,spring,friday,0
-0,1,0,1,1,glass,autumn,wednesday,0
-1,0,0,0,1,mixed,winter,friday,0
-1,0,1,1,0,plastic,spring,monday,0
-0,0,1,0,1,plastic,summer,friday,0
-1,1,0,0,0,glass,spring,wednesday,0
-0,1,1,1,1,mixed,winter,wednesday,0
-1,1,0,1,0,mixed,winter,friday,0
-0,0,1,1,1,paper,spring,friday,1
-0,1,1,0,1,mixed,spring,friday,0
-1,1,1,0,0,glass,winter,monday,0
-1,1,1,0,1,glass,spring,monday,0
-1,0,0,1,1,plastic,spring,monday,0
-0,0,1,1,1,paper,autumn,friday,1
-0,0,0,0,1,paper,autumn,wednesday,0
-1,1,1,0,1,plastic,summer,monday,0
-0,1,1,1,1,paper,spring,monday,0
-0,1,1,1,1,plastic,spring,monday,0
-0,1,1,0,1,glass,autumn,wednesday,0
-0,0,1,1,1,paper,summer,friday,1
-1,1,1,1,1,mixed,winter,wednesday,0
-0,1,1,0,0,mixed,spring,friday,0
-1,1,0,0,0,paper,autumn,friday,0
-1,1,0,1,1,paper,winter,friday,0
-1,1,1,1,0,mixed,winter,monday,0
-1,1,0,0,0,plastic,summer,wednesday,0
-0,0,0,0,0,mixed,winter,wednesday,0
-1,1,1,1,0,paper,spring,monday,0
-1,1,1,0,0,plastic,winter,wednesday,0
-0,0,0,1,1,paper,summer,friday,0
-0,1,0,0,0,glass,autumn,friday,0
-0,1,0,0,0,plastic,spring,monday,0
-1,1,0,1,0,paper,spring,monday,0
-0,1,0,0,1,plastic,spring,wednesday,0
-0,1,0,1,0,mixed,spring,monday,0
-1,0,1,1,1,paper,summer,friday,0
-0,1,1,1,0,mixed,winter,friday,0
-0,0,1,0,0,plastic,autumn,friday,0
-1,1,0,1,0,paper,spring,friday,0
-1,0,1,0,1,mixed,winter,wednesday,0
-0,0,0,0,0,glass,autumn,monday,0
-1,0,1,1,1,mixed,autumn,friday,0
-1,1,0,1,1,paper,spring,monday,0
-1,1,1,0,0,glass,winter,wednesday,0
-0,0,1,1,1,glass,autumn,wednesday,1
-0,0,0,1,1,glass,spring,friday,0
-1,0,1,0,1,paper,winter,monday,0
-0,0,1,1,1,paper,winter,monday,1
-0,1,1,0,0,glass,summer,wednesday,0
-0,0,1,1,0,glass,spring,wednesday,0
-0,0,1,1,0,paper,spring,wednesday,0
-1,0,0,0,0,paper,summer,wednesday,0
-1,1,0,0,1,glass,autumn,monday,0
-1,1,0,0,1,paper,spring,monday,0
-0,0,0,1,1,plastic,spring,monday,0
-0,1,1,0,1,plastic,autumn,monday,0
-0,0,0,1,0,plastic,spring,wednesday,0
-0,1,1,1,0,glass,autumn,monday,0
-1,0,0,0,1,glass,summer,monday,0
-1,0,0,0,0,paper,winter,wednesday,0
-0,0,1,1,1,glass,summer,monday,1
-1,1,0,1,0,glass,summer,monday,0
-0,1,1,1,1,glass,winter,monday,0
-0,0,0,1,1,glass,winter,wednesday,0
-0,0,1,0,1,mixed,winter,friday,0
-0,1,0,1,1,paper,winter,monday,0
-0,0,1,1,1,mixed,spring,friday,1
-0,0,1,1,0,glass,winter,monday,0
-1,0,0,0,1,paper,summer,monday,0
-1,0,0,0,1,plastic,summer,wednesday,0
-0,1,1,1,1,plastic,winter,friday,0
-0,0,1,0,0,paper,autumn,friday,0
-0,0,1,1,1,plastic,summer,wednesday,1
-1,0,1,0,1,paper,winter,wednesday,0
-0,0,0,0,0,plastic,spring,wednesday,0
-1,0,1,0,0,glass,winter,wednesday,0
-1,0,1,1,1,plastic,summer,wednesday,0
-0,1,1,0,1,paper,summer,wednesday,0
-0,1,0,1,0,paper,winter,wednesday,0
-0,0,1,1,1,plastic,spring,monday,1
-0,1,1,1,0,paper,winter,wednesday,0
-0,1,1,1,0,glass,autumn,friday,0
-1,1,1,0,0,plastic,spring,monday,0
-0,0,1,0,1,mixed,spring,wednesday,0
-0,1,1,0,0,glass,summer,friday,0
-1,1,0,1,0,plastic,winter,monday,0
-0,0,0,1,0,paper,winter,wednesday,0
-1,0,1,0,1,plastic,summer,friday,0
-1,0,0,0,1,mixed,autumn,friday,0
-0,0,0,1,1,plastic,winter,friday,0
-1,0,0,0,0,plastic,winter,monday,0
-1,0,0,1,0,plastic,winter,friday,0
-1,0,0,0,1,glass,spring,monday,0
-0,0,0,1,1,plastic,autumn,friday,0
-0,1,0,1,1,glass,spring,friday,0
-0,0,0,1,1,mixed,spring,wednesday,0
-1,1,1,1,0,paper,autumn,wednesday,0
-0,0,1,1,1,mixed,winter,friday,0
-0,1,0,0,1,paper,summer,monday,0
-0,0,1,1,1,paper,summer,wednesday,0
-1,0,1,0,1,paper,autumn,wednesday,0
-0,1,1,1,1,mixed,spring,friday,0
-0,0,0,1,1,glass,winter,friday,0
-0,1,0,0,0,glass,winter,monday,0
-0,0,1,0,1,glass,autumn,wednesday,0
-0,0,0,1,0,plastic,summer,friday,0
-1,1,1,1,0,mixed,spring,wednesday,0
-1,0,1,0,1,plastic,autumn,friday,0
-0,1,1,0,1,mixed,autumn,monday,0
-0,1,0,1,1,plastic,winter,wednesday,0
-0,1,1,0,1,mixed,winter,friday,0
-1,0,0,0,1,plastic,summer,friday,0
-1,0,1,0,1,mixed,spring,friday,0
-1,1,1,1,0,paper,summer,monday,0
-1,0,1,0,1,paper,winter,friday,0
-0,1,1,0,1,paper,summer,friday,0
-0,0,1,1,1,plastic,winter,wednesday,1
-0,0,1,1,1,mixed,spring,monday,0
-0,1,1,0,1,paper,winter,wednesday,0
-1,1,1,1,0,plastic,spring,friday,0
-1,0,1,1,0,paper,summer,friday,0
-1,1,0,0,1,glass,autumn,friday,0
-0,0,1,1,1,glass,spring,wednesday,1
-1,1,0,1,1,paper,summer,friday,0

src/tree_in_txt.txt

@@ -1,52 +0,0 @@
-|--- payment <= 0.50
-|   |--- class: 0
-|--- payment >  0.50
-|   |--- trash <= 0.50
-|   |   |--- class: 0
-|   |--- trash >  0.50
-|   |   |--- bin_fullness <= 0.50
-|   |   |   |--- class: 0
-|   |   |--- bin_fullness >  0.50
-|   |   |   |--- truck_fullness <= 0.50
-|   |   |   |   |--- dump_fullness <= 0.50
-|   |   |   |   |   |--- trash_types_mixed <= 0.50
-|   |   |   |   |   |   |--- season_summer <= 0.50
-|   |   |   |   |   |   |   |--- day_monday <= 0.50
-|   |   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |   |   |   |--- day_monday >  0.50
-|   |   |   |   |   |   |   |   |--- season_winter <= 0.50
-|   |   |   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |   |   |   |   |--- season_winter >  0.50
-|   |   |   |   |   |   |   |   |   |--- trash_types_plastic <= 0.50
-|   |   |   |   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |   |   |   |   |   |--- trash_types_plastic >  0.50
-|   |   |   |   |   |   |   |   |   |   |--- class: 0
-|   |   |   |   |   |   |--- season_summer >  0.50
-|   |   |   |   |   |   |   |--- day_wednesday <= 0.50
-|   |   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |   |   |   |--- day_wednesday >  0.50
-|   |   |   |   |   |   |   |   |--- trash_types_plastic <= 0.50
-|   |   |   |   |   |   |   |   |   |--- class: 0
-|   |   |   |   |   |   |   |   |--- trash_types_plastic >  0.50
-|   |   |   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |   |--- trash_types_mixed >  0.50
-|   |   |   |   |   |   |--- day_wednesday <= 0.50
-|   |   |   |   |   |   |   |--- season_winter <= 0.50
-|   |   |   |   |   |   |   |   |--- season_spring <= 0.50
-|   |   |   |   |   |   |   |   |   |--- day_friday <= 0.50
-|   |   |   |   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |   |   |   |   |   |--- day_friday >  0.50
-|   |   |   |   |   |   |   |   |   |   |--- class: 0
-|   |   |   |   |   |   |   |   |--- season_spring >  0.50
-|   |   |   |   |   |   |   |   |   |--- day_monday <= 0.50
-|   |   |   |   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |   |   |   |   |   |--- day_monday >  0.50
-|   |   |   |   |   |   |   |   |   |   |--- class: 0
-|   |   |   |   |   |   |   |--- season_winter >  0.50
-|   |   |   |   |   |   |   |   |--- class: 0
-|   |   |   |   |   |   |--- day_wednesday >  0.50
-|   |   |   |   |   |   |   |--- class: 1
-|   |   |   |   |--- dump_fullness >  0.50
-|   |   |   |   |   |--- class: 0
-|   |   |   |--- truck_fullness >  0.50
-|   |   |   |   |--- class: 0