Merge pull request 'Merge master with genetic_algorithms branch' (#29) from genetic_algorithms into master

Reviewed-on: #29

.gitignore

@@ -1,2 +1,5 @@
 __pycache__/
-.idea/
+.idea/
+tree.png
+dataset/
+dataset.zip

AStar.py

@@ -0,0 +1,284 @@
+"""
+f(n) = g(n) + h(n)
+g(n) = dotychczasowy koszt -> dodać currentCost w Node lub brać koszt na nowo przy oddtawrzaniu ścieżki
+h(n) = abs(state['x'] - goalTreassure[0]) + abs(state['y'] - goalTreassure[1]) -> odległość Manhatan -> można zrobić jeszcze drugą wersje gdzie mnoży się razy 5.5 ze wzgledu na średni koszt przejścia
+Należy zaimplementować kolejkę priorytetową oraz zaimplementować algorytm przeszukiwania grafu stanów z uwzględnieniem kosztu za pomocą przerobienia algorytmu przeszukiwania grafu stanów
+"""
+import random
+import pygame
+import Node
+import BFS
+from displayControler import NUM_X, NUM_Y
+from Pole import stoneList
+from queue import PriorityQueue
+
+
+def getRandomGoalTreasure():
+    while True:
+        goalTreasure = (random.randint(0, NUM_X - 1), random.randint(0, NUM_Y - 1)) # Współrzędne celu
+        if goalTreasure not in stoneList:
+            break
+    return goalTreasure
+
+
+def heuristic(state, goal):
+    # Oblicz odległość Manhattanowską między aktualnym stanem a celem
+    manhattan_distance = abs(state['x'] - goal[0]) + abs(state['y'] - goal[1])
+    return manhattan_distance
+
+
+'''def get_cost_for_plant(plant_name):
+    plant_costs = {
+        "pszenica": 7,
+        "kukurydza": 9,
+        "ziemniak": 2,
+        "slonecznik": 5,
+        "borowka": 3,
+        "winogrono": 4,
+        "mud": 15,
+        "dirt": 0,
+    }
+    if plant_name in plant_costs:
+        return plant_costs[plant_name]
+    else:
+        # Jeśli nazwa rośliny nie istnieje w słowniku, zwróć domyślną wartość
+        return 0
+'''
+
+def A_star(istate, pole, goalTreasure):
+    # goalTreasure = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
+    # #jeśli chcemy używać random musimy wykreslić sloty z kamieniami, ponieważ tez mogą się wylosować i wtedy traktor w ogóle nie rusza
+    #lub zrobić to jakoś inaczej, np. funkcja szukająca najmniej nawodnionej rośliny
+    
+    # przeniesione wyżej do funkcji getRandomGoalTreasure, wykorzystywana jest w App.py
+    # while True:
+    #     goalTreasure = (random.randint(0, NUM_X - 1), random.randint(0, NUM_Y - 1)) # Współrzędne celu
+    #     if goalTreasure not in stoneList:
+    #         break
+
+    fringe = PriorityQueue()  # Kolejka priorytetowa dla wierzchołków do rozpatrzenia
+    explored = []  # Lista odwiedzonych stanów
+    obrot = 1
+
+    # Tworzenie węzła początkowego
+    x = Node.Node(istate)
+    x.g = 0
+    x.h = heuristic(x.state, goalTreasure)
+    fringe.put((x.g + x.h, x))  # Dodanie węzła do kolejki
+    total_cost = 0
+
+    while not fringe.empty():
+        _, elem = fringe.get()  # Pobranie węzła z najniższym priorytetem
+
+        if BFS.goalTest3(elem.state, goalTreasure):  # Sprawdzenie, czy osiągnięto cel
+            path = []
+            cost_list=[]
+            while elem.parent is not None:  # Odtworzenie ścieżki
+                path.append([elem.parent, elem.action])
+                elem = elem.parent
+            for node, action in path:
+                # Obliczanie kosztu ścieżki dla każdego pola i wyświetlanie
+                plant_cost = get_plant_name_and_cost_from_coordinates(node.state['x'],node.state['y'], pole)
+                if action == "left" or action == "right":  # Liczenie kosztu tylko dla pól nie będących obrotami
+                    total_cost += obrot
+                    cost_list.append(obrot)
+                else:
+                    total_cost += plant_cost
+                    cost_list.append(plant_cost)
+            return path,cost_list,total_cost
+
+        explored.append(elem.state)
+
+        for resp in succ3A(elem.state):
+            child_state = resp[1]
+            if child_state not in explored:
+                child = Node.Node(child_state)
+                child.parent = elem
+                child.action = resp[0]
+
+                # Pobranie nazwy rośliny z danego slotu na podstawie współrzędnych
+                plant_cost = get_plant_name_and_cost_from_coordinates(child_state['x'], child_state['y'], pole)
+                # Pobranie kosztu dla danej rośliny
+                #plant_cost = get_cost_for_plant(plant_name)
+
+                if child.action == "left" or child.action == "right":
+                    child.g = elem.g + obrot
+                else:
+                    child.g = elem.g + plant_cost
+
+                # Obliczenie heurystyki dla dziecka
+                child.h = heuristic(child.state, goalTreasure)
+
+                in_fringe = False
+                for priority, item in fringe.queue:
+                    if item.state == child.state:
+                        in_fringe = True
+                        if priority > child.g + child.h:
+                            # Jeśli znaleziono węzeł w kolejce o gorszym priorytecie, zastąp go nowym
+                            fringe.queue.remove((priority, item))
+                            fringe.put((child.g + child.h, child))
+                            break
+
+                if not in_fringe:
+                    # Jeśli stan dziecka nie jest w kolejce, dodaj go do kolejki
+                    fringe.put((child.g + child.h, child))
+
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                quit()
+
+    return False
+
+
+def get_plant_name_and_cost_from_coordinates(x, y, pole):
+    if (x, y) in pole.slot_dict:  # Sprawdzenie, czy podane współrzędne znajdują się na polu
+        slot = pole.slot_dict[(x, y)]  # Pobranie slotu na podstawie współrzędnych
+        if slot.plant:  # Sprawdzenie, czy na slocie znajduje się roślina
+            return slot.plant.stan.koszt  # Zwrócenie nazwy rośliny na slocie
+        else:
+            return 0  # jeśli na slocie nie ma rośliny
+    else:
+        return 0  #  jeśli podane współrzędne są poza polem
+
+
+#to ogólnie identyczna funkcja jak w BFS ale nie chciałam tam ruszać, żeby przypadkiem nie zapsuć do BFS,
+#tylko musiałam dodac sprawdzenie kolizji, bo traktor brał sloty z Y których nie ma na planszy
+def succ3A(state):
+    resp = []
+    if state["direction"] == "N":
+        if state["y"] > 0 and (state['x'], state["y"] - 1) not in stoneList:
+            resp.append(["forward", {'x': state["x"], 'y': state["y"]-1, 'direction': state["direction"]}])
+        resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
+        resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
+    elif state["direction"] == "S":
+        if state["y"] < NUM_Y - 1 and (state['x'], state["y"] + 1) not in stoneList:
+            resp.append(["forward", {'x': state["x"], 'y': state["y"]+1, 'direction': state["direction"]}])
+        resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
+        resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
+    elif state["direction"] == "E":
+        if state["x"] < NUM_X - 1 and (state['x'] + 1, state["y"]) not in stoneList:
+            resp.append(["forward", {'x': state["x"]+1, 'y': state["y"], 'direction': state["direction"]}])
+        resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "S"}])
+        resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "N"}])
+    else: #state["direction"] == "W"
+        if state["x"] > 0 and (state['x'] - 1, state["y"]) not in stoneList:
+            resp.append(["forward", {'x': state["x"]-1, 'y': state["y"], 'direction': state["direction"]}])
+        resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "N"}])
+        resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "S"}])
+
+    return resp
+
+
+
+
+def heuristic2(state, goal):
+    # Oblicz odległość Manhattanowską między aktualnym stanem a celem
+    manhattan_distance = (abs(state['x'] - goal[0]) + abs(state['y'] - goal[1])) * 2.5
+    return manhattan_distance
+
+
+def A_star2(istate, pole, goalTreasure):
+    # goalTreasure = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
+    # #jeśli chcemy używać random musimy wykreslić sloty z kamieniami, ponieważ tez mogą się wylosować i wtedy traktor w ogóle nie rusza
+    #lub zrobić to jakoś inaczej, np. funkcja szukająca najmniej nawodnionej rośliny
+    
+    # przeniesione wyżej do funkcji getRandomGoalTreasure, wykorzystywana jest w App.py
+    # while True:
+    #     goalTreasure = (random.randint(0, NUM_X - 1), random.randint(0, NUM_Y - 1)) # Współrzędne celu
+    #     if goalTreasure not in stoneList:
+    #         break
+    fringe = PriorityQueue()  # Kolejka priorytetowa dla wierzchołków do rozpatrzenia
+    explored = []  # Lista odwiedzonych stanów
+    obrot = 1
+
+    # Tworzenie węzła początkowego
+    x = Node.Node(istate)
+    x.g = 0
+    x.h = heuristic2(x.state, goalTreasure)
+    fringe.put((x.g + x.h, x))  # Dodanie węzła do kolejki
+    total_cost=0
+
+    while not fringe.empty():
+        _, elem = fringe.get()  # Pobranie węzła z najniższym priorytetem
+
+        if BFS.goalTest3(elem.state, goalTreasure):  # Sprawdzenie, czy osiągnięto cel
+            path = []
+            cost_list=[]
+            while elem.parent is not None:  # Odtworzenie ścieżki
+                path.append([elem.parent, elem.action])
+                elem = elem.parent
+            for node, action in path:
+                # Obliczanie kosztu ścieżki dla każdego pola i wyświetlanie
+                plant_cost = get_plant_name_and_cost_from_coordinates(node.state['x'],node.state['y'], pole)
+                if action == "left" or action == "right":  # Liczenie kosztu tylko dla pól nie będących obrotami
+                    total_cost += obrot
+                    cost_list.append(obrot)
+                else:
+                    total_cost += plant_cost
+                    cost_list.append(plant_cost)
+            return path,cost_list,total_cost
+
+        explored.append(elem.state)
+
+        for resp in succ3A(elem.state):
+            child_state = resp[1]
+            if child_state not in explored:
+                child = Node.Node(child_state)
+                child.parent = elem
+                child.action = resp[0]
+
+                # Pobranie nazwy rośliny z danego slotu na podstawie współrzędnych
+                plant_cost = get_plant_name_and_cost_from_coordinates(child_state['x'], child_state['y'], pole)
+
+                if child.action == "left" or child.action == "right":
+                    child.g = elem.g + obrot
+                else:
+                    child.g = elem.g + plant_cost
+                # Obliczenie heurystyki dla dziecka
+                child.h = heuristic2(child.state, goalTreasure)
+
+                in_fringe = False
+                for priority, item in fringe.queue:
+                    if item.state == child.state:
+                        in_fringe = True
+                        if priority > child.g + child.h:
+                            # Jeśli znaleziono węzeł w kolejce o gorszym priorytecie, zastąp go nowym
+                            fringe.queue.remove((priority, item))
+                            fringe.put((child.g + child.h, child))
+                            break
+
+                if not in_fringe:
+                    # Jeśli stan dziecka nie jest w kolejce, dodaj go do kolejki
+                    fringe.put((child.g + child.h, child))
+
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                quit()
+
+    return False
+
+"""
+TO TEST SPEED OF ASTAR
+
+test_speed = False
+
+            if test_speed:
+                time1 = 0
+                time2 = 0
+                cost1 = 0
+                cost2 = 0
+                for i in range(500):
+                    print(i)
+                    start = time.time()
+                    aStarRoot, cost_list, total_cost = AStar.A_star({'x': 0, 'y': 0, 'direction': "E"}, pole, goalTreasure)
+                    end = time.time()
+                    time1 += end - start
+                    cost1 += total_cost
+                    start = time.time()
+                    aStarRoot2, cost_list, total_cost = AStar.A_star2({'x': 0, 'y': 0, 'direction': "E"}, pole, goalTreasure)
+                    end = time.time()
+                    time2 += end - start
+                    cost2 += total_cost
+                print(time1, time2)
+                print(float(cost1 / 1000), float(cost2 / 1000))
+"""

BFS.py

@@ -3,6 +3,7 @@ import random
 import pygame
 import Node
 from displayControler import NUM_X, NUM_Y
+from Pole import stoneList
 
 
 def goalTest1(hIndex):
@@ -93,31 +94,31 @@ def BFS1(istate):
 
 
 
-def goalTest2(state, goalTreassure):
+def goalTest3(state, goalTreassure):
     if state["x"] == goalTreassure[0] and state["y"] == goalTreassure[1]:
             return True
     return False
 
 
-def succ2(state):
+def succ3(state):
     resp = []
     if state["direction"] == "N":
-        if state["y"] > 0:
+        if state["y"] > 0 and (state['x'], state["y"] - 1) not in stoneList:
             resp.append(["forward", {'x': state["x"], 'y': state["y"]-1, 'direction': state["direction"]}])
         resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
         resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
     elif state["direction"] == "S":
-        if state["y"] < NUM_Y:
+        if state["y"] < NUM_Y - 1 and (state['x'], state["y"] + 1) not in stoneList:
             resp.append(["forward", {'x': state["x"], 'y': state["y"]+1, 'direction': state["direction"]}])
         resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
         resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
     elif state["direction"] == "E":
-        if state["x"] < NUM_X:
+        if state["x"] < NUM_X - 1 and (state['x'] + 1, state["y"]) not in stoneList:
             resp.append(["forward", {'x': state["x"]+1, 'y': state["y"], 'direction': state["direction"]}])
         resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "S"}])
         resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "N"}])
     else: #state["zwrot"] == "W"
-        if state["x"] > 0:
+        if state["x"] > 0  and (state['x'] - 1, state["y"]) not in stoneList:
             resp.append(["forward", {'x': state["x"]-1, 'y': state["y"], 'direction': state["direction"]}])
         resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "N"}])
         resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "S"}])
@@ -125,15 +126,19 @@ def succ2(state):
     return resp
 
 
-def check2(tab, state):
+def check3(tab, state):
     for i in tab:
         if i.state == state:
             return False
     return True
 
 
-def BFS2(istate):
-    goalTreassuere = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
+def BFS3(istate,GT):
+    randomGT=False
+    if(randomGT==True):
+        goalTreassuere = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
+    else:
+        goalTreassuere=GT
     print(goalTreassuere)
     fringe = []
     explored = []
@@ -148,7 +153,7 @@ def BFS2(istate):
 
         elem = fringe.pop(0)
 
-        if goalTest2(elem.state, goalTreassuere):
+        if goalTest3(elem.state, goalTreassuere):
             x = elem
             tab = []
             while x.parent != None:
@@ -158,8 +163,8 @@ def BFS2(istate):
 
         explored.append(elem)
 
-        for resp in succ2(elem.state):
-            if check2(fringe, resp[1]) and check2(explored, resp[1]):
+        for resp in succ3(elem.state):
+            if check3(fringe, resp[1]) and check3(explored, resp[1]):
                 x = Node.Node(resp[1])
                 x.parent = elem
                 x.action = resp[0]

Climate.py

@@ -0,0 +1,49 @@
+#THESE DICTIONARIES ARE USED FOR DISPLAY AND FOR DOCUMENTATION PURPOSES
+
+seasons={
+            0:"zima",
+            1:"wiosna",
+            2:"lato",
+            3:"jesien"}
+
+time={
+            0:"rano",
+            1:"poludnie",
+            2:"wieczor", 
+            3:"noc"}
+
+rain={
+    0:"brak",
+    1:"lekki deszcz",
+    2:"normalny deszcz",
+    3:"ulewa"
+}
+
+temperature={
+                0:"bardzo zimno",
+                1:"zimno",
+                2:"przecietnie",
+                3:"cieplo",
+                4:"upal",}
+
+def getNextSeason(season):
+    if(season==3):
+        return 0
+    else:
+        return season+1
+    
+def getNextTime(currentTime):
+    if(currentTime==3):
+        return 0
+    else:
+        return currentTime+1
+    
+def getAmount(type):
+    if(type=="seasons"):
+        return len(seasons)
+    if(type=="rain"):
+        return len(rain)
+    if(type=="time"):
+        return len(time)
+    if(type=="temperature"):
+        return len(temperature)

Condition.py

@@ -0,0 +1,47 @@
+import random
+import Climate
+import Ui
+
+class Condition:
+    def __init__(self):
+        self.season=self.setRandomSeason()
+        self.currentTime=self.setRandomTime()
+        self.rain=self.setRandomRain()
+        self.temperature=self.setRandomRain()
+        self.clock=0
+
+    def setRandomSeason(self):
+        return self.randomizer(Climate.getAmount("seasons"))
+        
+    def setRandomTime(self):
+        return self.randomizer(Climate.getAmount("time"))
+    
+    def setRandomRain(self):
+        return self.randomizer(Climate.getAmount("rain"))
+
+    def setRandomTemperature(self):
+        return self.randomizer(Climate.getAmount("temperature"))
+
+    def randomizer(self,max):
+        return random.randint(0,max-1)
+    
+    def cycle(self):
+        if(self.clock==11):
+            self.currentTime=0
+            self.rain=self.setRandomRain()
+            self.temperature=self.setRandomTemperature()
+            self.season=Climate.getNextSeason(self.season)
+            self.clock=0
+            return
+        else:
+            self.currentTime=Climate.getNextTime(self.currentTime)
+            self.rain=self.setRandomRain()
+            self.temperature=self.setRandomTemperature()
+            self.clock=self.clock+1
+
+    def return_condition(self):
+        return [self.temperature,self.rain,self.season,self.currentTime]
+
+
+    def getCondition(self):
+        return ([Climate.temperature[self.temperature],Climate.rain[self.rain],Climate.seasons[self.season],Climate.time[self.currentTime]])

Data/dataTree2.csv

@@ -0,0 +1,248 @@
+plant_water_level,growth,disease,fertility,tractor_water_level,temperature,rain,season,current_time,action
+1,20,0,40,60,2,0,2,1,1
+20,40,0,40,60,2,0,2,1,1
+87,20,0,40,60,2,0,2,1,0
+27,43,1,40,60,2,0,2,1,0
+89,56,1,40,60,2,1,1,1,0
+67,100,1,37,55,1,3,3,3,0
+67,40,1,87,90,4,0,1,0,0
+1,20,0,40,60,2,0,0,1,0
+20,40,0,40,60,2,0,0,1,0
+87,20,0,56,45,2,0,0,2,0
+27,43,1,40,60,2,0,0,3,0
+89,56,1,40,89,2,1,0,1,0
+67,100,1,37,55,1,3,0,3,0
+67,40,1,87,90,4,0,0,0,0
+1,100,0,45,20,2,0,2,1,0
+20,100,0,40,34,0,1,2,0,0
+87,100,0,56,60,2,0,1,1,0
+27,100,0,89,67,1,2,2,2,0
+89,100,0,40,60,2,1,1,1,0
+76,100,0,37,55,1,3,3,3,0
+67,100,0,87,90,4,0,1,0,0
+1,20,0,40,0,2,0,2,1,0
+20,40,0,40,0,2,0,2,1,0
+87,20,0,40,0,2,0,2,1,0
+27,43,1,40,0,2,0,2,1,0
+89,56,1,40,0,2,1,1,1,0
+67,100,1,37,0,1,3,3,3,0
+67,40,1,87,0,4,0,1,0,0
+1,20,0,40,0,2,0,0,1,0
+20,40,0,40,0,2,0,0,1,0
+87,20,0,56,0,2,0,0,2,0
+27,43,1,40,0,2,0,0,3,0
+89,56,1,40,0,2,1,0,1,0
+67,100,1,37,0,1,3,0,3,0
+67,40,1,87,0,4,0,0,0,0
+1,100,0,45,0,2,0,2,1,0
+20,100,0,40,0,0,1,2,0,0
+87,100,0,56,0,2,0,1,1,0
+27,100,0,89,0,1,2,2,2,0
+89,100,0,40,0,2,1,1,1,0
+76,100,0,37,0,1,3,3,3,0
+67,100,0,87,0,4,0,1,0,0
+1,45,0,56,44,2,1,1,1,1
+20,55,0,43,34,2,0,2,2,1
+15,23,0,23,26,2,1,3,3,1
+45,67,0,12,67,3,0,1,0,1
+59,88,0,34,87,3,0,2,1,1
+32,32,0,32,90,3,0,3,2,1
+44,43,0,19,27,2,0,1,3,1
+33,11,0,28,76,2,0,2,0,1
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Drzewo.py

@@ -0,0 +1,29 @@
+from sklearn import tree as skltree
+import pandas,os
+import matplotlib.pyplot as plt
+
+atributes=['plant_water_level','growth','disease','fertility','tractor_water_level','temperature','rain','season','current_time'] #Columns in CSV file has to be in the same order
+class Drzewo:
+    def __init__(self):
+        self.tree=self.treeLearn()
+
+    def treeLearn(self):
+        csvdata=pandas.read_csv('Data/dataTree2.csv')
+        #csvdata = pandas.read_csv('Data/dataTree2.csv')
+        x=csvdata[atributes]
+        decision=csvdata['action']
+        self.tree=skltree.DecisionTreeClassifier()
+        self.tree=self.tree.fit(x.values,decision)
+
+    def plotTree(self):
+        plt.figure(figsize=(20,30))
+        skltree.plot_tree(self.tree,filled=True,feature_names=atributes)
+        plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych: ")
+        plt.savefig('tree.png')
+        #plt.show()
+    def makeDecision(self,values):
+        action=self.tree.predict([values]) #0- nie podlewac, 1-podlewac
+        if(action==[0]):
+            return "Nie"
+        if(action==[1]):
+            return "Tak"

GeneticAccuracy.py

@@ -0,0 +1,139 @@
+import json
+import random
+
+from displayControler import NUM_Y, NUM_X
+
+iterat = 2500
+population = 120
+roulette = True
+
+plants = ['corn', 'potato', 'tomato', 'carrot']
+initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
+yield_reduction = {
+    'corn': {'corn': -4.5, 'potato': -3, 'tomato': -7, 'carrot': -7},
+    'potato': {'corn': -7, 'potato': -5, 'tomato': -10, 'carrot': -6},
+    'tomato': {'corn': -4, 'potato': -5, 'tomato': -7, 'carrot': -7},
+    'carrot': {'corn': -11, 'potato': -5, 'tomato': -4, 'carrot': -7}
+}
+yield_reduction2 = {
+    'corn': {'corn': None, 'potato': -4, 'tomato': -2, 'carrot': -4},
+    'potato': {'corn': None, 'potato': -5, 'tomato': -5, 'carrot': -2},
+    'tomato': {'corn': -5, 'potato': -3, 'tomato': -7, 'carrot': None},
+    'carrot': {'corn': -3, 'potato': -6, 'tomato': -4, 'carrot': -9}
+}
+yield_multiplier = {'corn': 1.25, 'potato': 1.17, 'tomato': 1.22, 'carrot': 1.13}
+yield_multiplier2 = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.15}
+
+
+def calculate_yields(garden):
+    rows = len(garden)
+    cols = len(garden[0])
+
+    total_yields = 0
+
+    for i in range(rows):
+        for j in range(cols):
+            plant = garden[i][j]
+            yield_count = initial_yields[plant]
+
+            # Sprawdzanie sąsiadów
+            neighbors = [
+                (i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
+            ]
+
+            for ni, nj in neighbors:
+                if 0 <= ni < rows and 0 <= nj < cols:
+                    neighbor_plant = garden[ni][nj]
+                    yield_count += yield_reduction[plant][neighbor_plant]
+
+            yield_count *= yield_multiplier[plant]
+            total_yields += yield_count
+
+    return total_yields
+
+
+def calculate_yields2(garden):
+    rows = len(garden)
+    cols = len(garden[0])
+
+    total_yields = 0
+
+    for i in range(rows):
+        for j in range(cols):
+            plant = garden[i][j]
+            yield_count = initial_yields[plant]
+
+            # Sprawdzanie sąsiadów
+            neighbors = [
+                (i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
+            ]
+            neighbor_flag = False
+            for ni, nj in neighbors:
+                if 0 <= ni < rows and 0 <= nj < cols:
+                    neighbor_plant = garden[ni][nj]
+
+                    if yield_reduction2[plant][neighbor_plant] is not None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
+                        yield_count += yield_reduction2[plant][neighbor_plant]
+                    else:
+                        neighbor_flag = True
+
+            if not neighbor_flag:
+                yield_count *= yield_multiplier2[plant]
+                total_yields += yield_count
+
+    return total_yields
+
+
+def generate_garden(rows=20, cols=12):
+    return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
+
+
+def generate_garden_with_yields(t, rows=NUM_Y, cols=NUM_X):
+    garden = generate_garden(rows, cols)
+    if t == 1:
+        total_yields = calculate_yields(garden)
+    else:
+        total_yields = calculate_yields2(garden)
+    return [garden, total_yields]
+
+
+def generate():
+    s1 = 0
+    s2 = 0
+    n = 150
+    for i in range(n):
+        x = generate_garden_with_yields(1)
+        s1 += x[1]
+        y = generate_garden_with_yields(2)
+        s2 += y[1]
+    return [s1/n, s2/n]
+
+
+data = generate()
+# print(data)
+
+# Odczyt z pliku
+with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
+    garden_data = json.load(file)
+
+# print("Odczytane dane ogrodu:")
+# for row in garden_data:
+#     print(row)
+
+print("Wygenerowane przy pomocy GA: ", calculate_yields(garden_data))
+print(f"Przeciętny ogród wygenerowany randomowo ma {data[0]} plonów")
+print("Uśredniony przyrost plonów (ile razy więcej plonów): ", calculate_yields(garden_data)/data[0])
+
+
+
+# Odczyt z pliku
+with open(f'pole2_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
+    garden_data2 = json.load(file)
+
+# print("Odczytane dane ogrodu:")
+# for row in garden_data2:
+#     print(row)
+
+print("Wygenerowane: przy pomocy GA2", calculate_yields2(garden_data2))
+print(f"Przeciętny ogród wygenerowany randomowo ma {data[1]} plonów")
+print("Uśredniony przyrost plonów (ile razy więcej plonów): ", calculate_yields2(garden_data2)/data[1])

GeneticAlgorithm.py

@@ -0,0 +1,208 @@
+import copy
+import json
+import random
+from displayControler import NUM_X, NUM_Y
+
+# Definiowanie stałych dla roślin i plonów
+plants = ['corn', 'potato', 'tomato', 'carrot']
+initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
+yield_reduction = {
+    'corn': {'corn': -4.5, 'potato': -3, 'tomato': -7, 'carrot': -7},
+    'potato': {'corn': -7, 'potato': -5, 'tomato': -10, 'carrot': -6},
+    'tomato': {'corn': -4, 'potato': -5, 'tomato': -7, 'carrot': -7},
+    'carrot': {'corn': -11, 'potato': -5, 'tomato': -4, 'carrot': -7}
+}
+yield_multiplier = {'corn': 1.25, 'potato': 1.17, 'tomato': 1.22, 'carrot': 1.13}
+
+
+# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
+def generate_garden(rows=20, cols=12):
+    return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
+
+
+# Funkcja do obliczania liczby plonów
+def calculate_yields(garden):
+    rows = len(garden)
+    cols = len(garden[0])
+
+    total_yields = 0
+
+    for i in range(rows):
+        for j in range(cols):
+            plant = garden[i][j]
+            yield_count = initial_yields[plant]
+
+            # Sprawdzanie sąsiadów
+            neighbors = [
+                (i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
+            ]
+
+            for ni, nj in neighbors:
+                if 0 <= ni < rows and 0 <= nj < cols:
+                    neighbor_plant = garden[ni][nj]
+                    yield_count += yield_reduction[plant][neighbor_plant]
+
+            yield_count *= yield_multiplier[plant]
+            total_yields += yield_count
+
+    return total_yields
+
+
+# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
+def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
+    garden = generate_garden(rows, cols)
+    total_yields = calculate_yields(garden)
+    return [garden, total_yields]
+
+
+# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
+def line():
+    path = []
+    flag = False
+    x = random.randint(4, 8)
+    position = (0, x)
+    path.append(position)
+    while not flag:  # wybór punktu dopóki nie wybierze się skrajnego
+        # prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
+        p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
+        w = [0.6, 0.2, 0.2]
+        position2 = random.choices(p, w)[0]
+        if position2 not in path:  # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
+            path.append(position2)
+            position = position2
+        if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y:  # sprawdzenie czy osiągnięto skrajny punkt
+            flag = True
+    info = []  # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
+    for i in range(len(path) - 1):
+        if path[i + 1][0] - path[i][0] == 1:
+            info.append(NUM_Y - path[i][1])
+    if len(info) < NUM_X:  # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
+        if path[-1:][0][1] == 0:
+            x = NUM_Y
+        else:
+            x = 0
+        while len(info) < NUM_X:
+            info.append(x)
+    # return path, info
+    return info
+
+
+# Funkcja do generowania potomstwa
+def divide_gardens(garden1, garden2):
+    info = line()
+    new_garden1 = [[] for _ in range(NUM_Y)]
+    new_garden2 = [[] for _ in range(NUM_Y)]
+    for i in range(NUM_X):
+        for j in range(NUM_Y):
+            # do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
+            if j < info[i]:
+                new_garden1[j].append(garden1[j][i])
+                new_garden2[j].append(garden2[j][i])
+            else:
+                new_garden1[j].append(garden2[j][i])
+                new_garden2[j].append(garden1[j][i])
+
+    return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
+
+
+# Funkcja do mutacji danej planszy/ogrodu
+def mutation(garden, not_used):
+    new_garden = copy.deepcopy(garden)
+    for i in range(NUM_X):
+        x = random.randint(0, 11)  # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
+        other_plants = [plant for plant in plants if plant != new_garden[x][i]]
+        new_garden[x][i] = random.choice(other_plants)
+    return [new_garden, calculate_yields(new_garden)]
+
+
+# Funkcja do generowania pierwszego pokolenia
+def generate(n):
+    generation = []
+    for i in range(n * 3):
+        generation.append(generate_garden_with_yields())
+    generation.sort(reverse=True, key=lambda x: x[1])
+    return generation[:n]
+
+
+# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
+def sum_yields(x):
+    s = 0
+    for i in range(len(x)):
+        s += x[i][1]
+    return s
+
+
+if __name__ == '__main__':
+    roulette = True
+    attemps = 150
+    iterat = 2500
+    population = 120
+    best = []
+    for a in range(attemps):
+        generation = generate(population)
+        print(generation[0][1])
+        for i in range(iterat):  # ile iteracji - nowych pokoleń
+            print(a, i)
+            new_generation = generation[:(population // 7)]  # dziedziczenie x najlepszych osobników
+            j = 0
+            while j < (
+                    population - (
+                    population // 7)):  # dobór reszty osobników do pełnej liczby populacji danego pokolenia
+                if roulette:  # zasada ruletki -> "2 rzuty kulką"
+                    s = sum_yields(generation)  # suma wszystkich plnów całego pokolenia
+                    z = []
+                    if s == 0:  # wtedy każdy osobnik ma takie same szanse
+                        z.append(random.randint(0, population - 1))
+                        z.append(random.randint(0, population - 1))
+                    else:
+                        weights = []  # wagi prawdopodobieństwa dla każdego osobnika generacji
+                        pos = []  # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
+                        for i in range(population):
+                            weights.append(generation[i][1] / s)
+                            pos.append(i)
+                        z.append(random.choices(pos, weights)[0])  # wybranie osobnika według wag prawdopodobieństwa
+                        z.append(random.choices(pos, weights)[0])  # wybranie osobnika według wag prawdopodobieństwa
+                else:  # metoda rankingu
+                    z = random.sample(range(0, int(population // 1.7)), 2)
+
+                # krzyzowanie 90% szans, mutacja 10% szans
+                function = [divide_gardens, mutation]
+                weight = [0.9, 0.1]
+                fun = random.choices(function, weight)[0]
+                h = fun(generation[z[0]][0], generation[z[1]][0])
+                if len(h[0]) == 2:
+                    new_generation.append(h[0])
+                    new_generation.append(h[1])
+                    j += 2
+                else:
+                    new_generation.append(h)
+                    j += 1
+
+            new_generation.sort(reverse=True, key=lambda x: x[1])  # sortowanie malejąco listy według wartości plonów
+            generation = new_generation[:population]
+
+        best.append(generation[0])
+
+    best.sort(reverse=True, key=lambda x: x[1])
+
+    # Zapis do pliku
+    # for i in range(len(best)):
+    #     print(best[i][1], calculate_yields(best[i][0]))
+    #
+    #
+    # with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'w') as file:  # zapis planszy/ogrodu do pliku json
+    #     json.dump(best[0][0], file, indent=4)
+    #
+    # print("Dane zapisane do pliku")
+
+    # Odczyt z pliku
+    # with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
+    #     garden_data = json.load(file)
+    #
+    # print("Odczytane dane ogrodu:")
+    # for row in garden_data:
+    #     print(row)
+    #
+    # print(calculate_yields(garden_data))
+    # if best[0][0] == garden_data:
+    #     print("POPRAWNE: ", calculate_yields(garden_data), calculate_yields(best[0][0]))

GeneticAlgorithm2.py

@@ -0,0 +1,213 @@
+import copy
+import json
+import random
+from displayControler import NUM_X, NUM_Y
+
+# Definiowanie stałych dla roślin i plonów
+plants = ['corn', 'potato', 'tomato', 'carrot']
+initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
+yield_reduction = {
+    'corn': {'corn': None, 'potato': -4, 'tomato': -2, 'carrot': -4},
+    'potato': {'corn': None, 'potato': -5, 'tomato': -5, 'carrot': -2},
+    'tomato': {'corn': -5, 'potato': -3, 'tomato': -7, 'carrot': None},
+    'carrot': {'corn': -3, 'potato': -6, 'tomato': -4, 'carrot': -9}
+}
+yield_multiplier = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.15}
+
+
+# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
+def generate_garden(rows=20, cols=12):
+    return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
+
+
+# Funkcja do obliczania liczby plonów
+def calculate_yields(garden):
+    rows = len(garden)
+    cols = len(garden[0])
+
+    total_yields = 0
+
+    for i in range(rows):
+        for j in range(cols):
+            plant = garden[i][j]
+            yield_count = initial_yields[plant]
+
+            # Sprawdzanie sąsiadów
+            neighbors = [
+                (i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
+            ]
+            neighbor_flag = False
+            for ni, nj in neighbors:
+                if 0 <= ni < rows and 0 <= nj < cols:
+                    neighbor_plant = garden[ni][nj]
+
+                    if yield_reduction[plant][neighbor_plant] is not None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
+                        yield_count += yield_reduction[plant][neighbor_plant]
+                    else:
+                        neighbor_flag = True
+
+            if not neighbor_flag:
+                yield_count *= yield_multiplier[plant]
+                total_yields += yield_count
+
+    return total_yields
+
+
+# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
+def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
+    garden = generate_garden(rows, cols)
+    total_yields = calculate_yields(garden)
+    return [garden, total_yields]
+
+
+# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
+def line():
+    path = []
+    flag = False
+    x = random.randint(4, 8)
+    position = (0, x)
+    path.append(position)
+    while not flag:  # wybór punktu dopóki nie wybierze się skrajnego
+        # prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
+        p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
+        w = [0.6, 0.2, 0.2]
+        position2 = random.choices(p, w)[0]
+        if position2 not in path:  # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
+            path.append(position2)
+            position = position2
+        if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y:  # sprawdzenie czy osiągnięto skrajny punkt
+            flag = True
+    info = []  # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
+    for i in range(len(path) - 1):
+        if path[i + 1][0] - path[i][0] == 1:
+            info.append(NUM_Y - path[i][1])
+    if len(info) < NUM_X:  # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
+        if path[-1:][0][1] == 0:
+            x = NUM_Y
+        else:
+            x = 0
+        while len(info) < NUM_X:
+            info.append(x)
+    # return path, info
+    return info
+
+
+# Funkcja do generowania potomstwa
+def divide_gardens(garden1, garden2):
+    info = line()
+    new_garden1 = [[] for _ in range(NUM_Y)]
+    new_garden2 = [[] for _ in range(NUM_Y)]
+    for i in range(NUM_X):
+        for j in range(NUM_Y):
+            # do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
+            if j < info[i]:
+                new_garden1[j].append(garden1[j][i])
+                new_garden2[j].append(garden2[j][i])
+            else:
+                new_garden1[j].append(garden2[j][i])
+                new_garden2[j].append(garden1[j][i])
+
+    return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
+
+
+# Funkcja do mutacji danej planszy/ogrodu
+def mutation(garden, not_used):
+    new_garden = copy.deepcopy(garden)
+    for i in range(NUM_X):
+        x = random.randint(0, 11)  # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
+        other_plants = [plant for plant in plants if plant != new_garden[x][i]]
+        new_garden[x][i] = random.choice(other_plants)
+    return [new_garden, calculate_yields(new_garden)]
+
+
+# Funkcja do generowania pierwszego pokolenia
+def generate(n):
+    generation = []
+    for i in range(n * 3):
+        generation.append(generate_garden_with_yields())
+    generation.sort(reverse=True, key=lambda x: x[1])
+    return generation[:n]
+
+
+# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
+def sum_yields(x):
+    s = 0
+    for i in range(len(x)):
+        s += x[i][1]
+    return s
+
+
+if __name__ == '__main__':
+    roulette = True
+    attemps = 20
+    iterat = 2500
+    population = 120
+    best = []
+    for a in range(attemps):
+        generation = generate(population)
+        print(generation[0][1])
+        for i in range(iterat):  # ile iteracji - nowych pokoleń
+            print(a, i)
+            new_generation = generation[:(population // 7)]  # dziedziczenie x najlepszych osobników
+            j = 0
+            while j < (
+                    population - (
+                    population // 7)):  # dobór reszty osobników do pełnej liczby populacji danego pokolenia
+                if roulette:  # zasada ruletki -> "2 rzuty kulką"
+                    s = sum_yields(generation)  # suma wszystkich plnów całego pokolenia
+                    z = []
+                    if s == 0:  # wtedy każdy osobnik ma takie same szanse
+                        z.append(random.randint(0, population - 1))
+                        z.append(random.randint(0, population - 1))
+                    else:
+                        weights = []  # wagi prawdopodobieństwa dla każdego osobnika generacji
+                        pos = []  # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
+                        for i in range(population):
+                            weights.append(generation[i][1] / s)
+                            pos.append(i)
+                        z.append(random.choices(pos, weights)[0])  # wybranie osobnika według wag prawdopodobieństwa
+                        z.append(random.choices(pos, weights)[0])  # wybranie osobnika według wag prawdopodobieństwa
+                else:  # metoda rankingu
+                    z = random.sample(range(0, int(population // 1.7)), 2)
+
+                # krzyzowanie 90% szans, mutacja 10% szans
+                function = [divide_gardens, mutation]
+                weight = [0.9, 0.1]
+                fun = random.choices(function, weight)[0]
+                h = fun(generation[z[0]][0], generation[z[1]][0])
+                if len(h[0]) == 2:
+                    new_generation.append(h[0])
+                    new_generation.append(h[1])
+                    j += 2
+                else:
+                    new_generation.append(h)
+                    j += 1
+
+            new_generation.sort(reverse=True, key=lambda x: x[1])  # sortowanie malejąco listy według wartości plonów
+            generation = new_generation[:population]
+
+        best.append(generation[0])
+
+    best.sort(reverse=True, key=lambda x: x[1])
+
+    # Zapis do pliku
+    # for i in range(len(best)):
+    #     print(best[i][1], calculate_yields(best[i][0]))
+    #
+    #
+    # with open(f'pole2_pop{population}_iter{iterat}_{roulette}.json', 'w') as file:  # zapis planszy/ogrodu do pliku json
+    #     json.dump(best[0][0], file, indent=4)
+    #
+    # print("Dane zapisane do pliku")
+    #
+    # Odczyt z pliku
+    # with open(f'pole2_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
+    #     garden_data = json.load(file)
+    #
+    # print("Odczytane dane ogrodu:")
+    # for row in garden_data:
+    #     print(row)
+    #
+    # print(calculate_yields(garden_data))
+    # if best[0][0] == garden_data:
+    #     print("POPRAWNE: ", calculate_yields(garden_data), calculate_yields(best[0][0]))

GeneticAlgorithm3.py

@@ -0,0 +1,211 @@
+import copy
+import json
+import random
+from displayControler import NUM_X, NUM_Y
+
+# Definiowanie stałych dla roślin i plonów
+plants = ['corn', 'potato', 'tomato', 'carrot']
+initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
+yield_reduction = {
+    'corn': {'corn': None, 'potato': 0, 'tomato': 0, 'carrot': 0},
+    'potato': {'corn': None, 'potato': 0, 'tomato': 0, 'carrot': 0},
+    'tomato': {'corn': 0, 'potato': 0, 'tomato': 0, 'carrot': None},
+    'carrot': {'corn': 0, 'potato': 0, 'tomato': 0, 'carrot': 0}
+}
+yield_multiplier = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.13}
+
+
+# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
+def generate_garden(rows=20, cols=12):
+    return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
+
+
+# Funkcja do obliczania liczby plonów
+def calculate_yields(garden):
+    rows = len(garden)
+    cols = len(garden[0])
+
+    total_yields = 0
+
+    for i in range(rows):
+        for j in range(cols):
+            plant = garden[i][j]
+
+            # Sprawdzanie sąsiadów
+            neighbors = [
+                (i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
+            ]
+            neighbor_flag = False
+            for ni, nj in neighbors:
+                if 0 <= ni < rows and 0 <= nj < cols:
+                    neighbor_plant = garden[ni][nj]
+
+                    if yield_reduction[plant][neighbor_plant] is None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
+                        neighbor_flag = True
+
+            if not neighbor_flag:
+                total_yields += 1
+
+    return total_yields
+
+
+# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
+def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
+    garden = generate_garden(rows, cols)
+    total_yields = calculate_yields(garden)
+    return [garden, total_yields]
+
+
+# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
+def line():
+    path = []
+    flag = False
+    x = random.randint(4, 8)
+    position = (0, x)
+    path.append(position)
+    while not flag:  # wybór punktu dopóki nie wybierze się skrajnego
+        # prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
+        p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
+        w = [0.6, 0.2, 0.2]
+        position2 = random.choices(p, w)[0]
+        if position2 not in path:  # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
+            path.append(position2)
+            position = position2
+        if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y:  # sprawdzenie czy osiągnięto skrajny punkt
+            flag = True
+    info = []  # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
+    for i in range(len(path) - 1):
+        if path[i + 1][0] - path[i][0] == 1:
+            info.append(NUM_Y - path[i][1])
+    if len(info) < NUM_X:  # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
+        if path[-1:][0][1] == 0:
+            x = NUM_Y
+        else:
+            x = 0
+        while len(info) < NUM_X:
+            info.append(x)
+    # return path, info
+    return info
+
+
+# Funkcja do generowania potomstwa
+def divide_gardens(garden1, garden2):
+    info = line()
+    new_garden1 = [[] for _ in range(NUM_Y)]
+    new_garden2 = [[] for _ in range(NUM_Y)]
+    for i in range(NUM_X):
+        for j in range(NUM_Y):
+            # do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
+            if j < info[i]:
+                new_garden1[j].append(garden1[j][i])
+                new_garden2[j].append(garden2[j][i])
+            else:
+                new_garden1[j].append(garden2[j][i])
+                new_garden2[j].append(garden1[j][i])
+
+    return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
+
+
+# Funkcja do mutacji danej planszy/ogrodu
+def mutation(garden, not_used):
+    new_garden = copy.deepcopy(garden)
+    for i in range(NUM_X):
+        x = random.randint(0, 11)  # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
+        other_plants = [plant for plant in plants if plant != new_garden[x][i]]
+        new_garden[x][i] = random.choice(other_plants)
+    return [new_garden, calculate_yields(new_garden)]
+
+
+# Funkcja do generowania pierwszego pokolenia
+def generate(n):
+    generation = []
+    for i in range(n * 3):
+        generation.append(generate_garden_with_yields())
+    generation.sort(reverse=True, key=lambda x: x[1])
+    return generation[:n]
+
+
+# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
+def sum_yields(x):
+    s = 0
+    for i in range(len(x)):
+        s += x[i][1]
+    return s
+
+
+if __name__ == '__main__':
+    roulette = True
+    attemps = 1
+    population = 120
+    best = []
+    iter = 0
+    for a in range(attemps):
+        generation = generate(population)
+        print(generation[0][1])
+        while generation[0][1] != NUM_X*NUM_Y:  # ile iteracji - nowych pokoleń
+            iter += 1
+            print(iter)
+            print(generation[0][1])
+            new_generation = generation[:(population // 7)]  # dziedziczenie x najlepszych osobników
+            j = 0
+            while j < (
+                    population - (
+                    population // 7)):  # dobór reszty osobników do pełnej liczby populacji danego pokolenia
+                if roulette:  # zasada ruletki -> "2 rzuty kulką"
+                    s = sum_yields(generation)  # suma wszystkich plnów całego pokolenia
+                    z = []
+                    if s == 0:  # wtedy każdy osobnik ma takie same szanse
+                        z.append(random.randint(0, population - 1))
+                        z.append(random.randint(0, population - 1))
+                    else:
+                        weights = []  # wagi prawdopodobieństwa dla każdego osobnika generacji
+                        pos = []  # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
+                        for i in range(population):
+                            weights.append(generation[i][1] / s)
+                            pos.append(i)
+                        z.append(random.choices(pos, weights)[0])  # wybranie osobnika według wag prawdopodobieństwa
+                        z.append(random.choices(pos, weights)[0])  # wybranie osobnika według wag prawdopodobieństwa
+                else:  # metoda rankingu
+                    z = random.sample(range(0, int(population // 1.7)), 2)
+
+                # krzyzowanie 90% szans, mutacja 10% szans
+                function = [divide_gardens, mutation]
+                weight = [0.9, 0.1]
+                fun = random.choices(function, weight)[0]
+                h = fun(generation[z[0]][0], generation[z[1]][0])
+                if len(h[0]) == 2:
+                    new_generation.append(h[0])
+                    new_generation.append(h[1])
+                    j += 2
+                else:
+                    new_generation.append(h)
+                    j += 1
+
+            new_generation.sort(reverse=True, key=lambda x: x[1])  # sortowanie malejąco listy według wartości plonów
+            generation = new_generation[:population]
+
+        best.append(generation[0])
+
+    best.sort(reverse=True, key=lambda x: x[1])
+
+    # Zapis do pliku
+    # for i in range(len(best)):
+    #     print(best[i][1], calculate_yields(best[i][0]))
+    #
+    #
+    # with open(f'pole3_pop{population}_{iter}_{roulette}.json', 'w') as file:  # zapis planszy/ogrodu do pliku json
+    #     json.dump(best[0][0], file, indent=4)
+    #
+    # print("Dane zapisane do pliku")
+    #
+    # Odczyt z pliku
+    # with open(f'pole3_pop{population}_{iter}_{roulette}.json', 'r') as file:
+    #     garden_data = json.load(file)
+    #
+    # print("Odczytane dane ogrodu:")
+    # for row in garden_data:
+    #     print(row)
+    #
+    # print(calculate_yields(garden_data))
+    # if best[0][0] == garden_data:
+    #     print("POPRAWNE: ", calculate_yields(garden_data), calculate_yields(best[0][0]))

Image.py

@@ -1,29 +1,45 @@
 import pygame
 import displayControler as dCon
 import random
+import neuralnetwork
+import os
 
 class Image:
     def __init__(self):
         self.plants_image_dict={}
         self.tractor_image=None
         self.garage_image=None
+        self.stone_image=None
+        self.gasStation_image=None
     def load_images(self):
-        files_plants={0:"borowka",
+        files_plants={
+            0:"borowka",
             1:"kukurydza",
             2:"pszenica",
             3:"slonecznik",
             4:"winogrono",
-            5:"ziemniak"}
+            5:"ziemniak",
+            6:"dirt",
+            7:"mud",
+            8:"road"}
         for index in files_plants:
-            plant_image=pygame.image.load("images/plants/"+files_plants[index]+".jpg")
+            if index >= 6:
+                plant_image = pygame.image.load("images/" + files_plants[index] + ".jpg")
+            else:
+                plant_image=pygame.image.load("images/plants/"+files_plants[index]+".jpg")
             plant_image=pygame.transform.scale(plant_image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
             self.plants_image_dict[files_plants[index]]=plant_image
         tractor_image=pygame.image.load("images/traktor.png")
         tractor_image=pygame.transform.scale(tractor_image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
         garage=pygame.image.load("images/garage.png")
         self.garage_image=pygame.transform.scale(garage,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
+        stone=pygame.image.load("images/stone.png")
+        self.stone_image=pygame.transform.scale(stone,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
+        gasStation=pygame.image.load("images/gasStation.png")
+        self.gasStation_image=pygame.transform.scale(gasStation,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
+
     def return_random_plant(self):
-        x=random.randint(0,5)
+        x=random.randint(0,5) #disabled dirt and mud generation
         keys=list(self.plants_image_dict.keys())
         plant=keys[x]
         return (plant,self.plants_image_dict[plant])
@@ -32,4 +48,57 @@ class Image:
         return (plant_name,self.plants_image_dict[plant_name])
     
     def return_garage(self):
-        return self.garage_image
+        return self.garage_image
+
+    def return_stone(self):
+        return self.stone_image
+    
+    def return_gasStation(self):
+        return self.gasStation_image
+
+# losowanie zdjęcia z testowego datasetu bez powtórzeń
+imagePathList = []
+def getRandomImageFromDataBase():
+    label = random.choice(neuralnetwork.labels)
+    folderPath = f"dataset/test/{label}"
+    files = os.listdir(folderPath)
+    random_image = random.choice(files)
+    imgPath = os.path.join(folderPath, random_image)
+
+    while imgPath in imagePathList:
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                quit()
+        label = random.choice(neuralnetwork.labels)
+        folderPath = f"dataset/test/{label}"
+        files = os.listdir(folderPath)
+        random_image = random.choice(files)
+        imgPath = os.path.join(folderPath, random_image)
+
+    imagePathList.append(imgPath)
+
+    image = pygame.image.load(imgPath)
+    image=pygame.transform.scale(image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
+    return image, label, imgPath
+
+def getSpedifiedImageFromDatabase(label):
+    folderPath = f"dataset/test/{label}"
+    files = os.listdir(folderPath)
+    random_image = random.choice(files)
+    imgPath = os.path.join(folderPath, random_image)
+
+    while imgPath in imagePathList:
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                quit()
+        label = random.choice(neuralnetwork.labels)
+        folderPath = f"dataset/test/{label}"
+        files = os.listdir(folderPath)
+        random_image = random.choice(files)
+        imgPath = os.path.join(folderPath, random_image)
+
+    imagePathList.append(imgPath)
+
+    image = pygame.image.load(imgPath)
+    image=pygame.transform.scale(image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
+    return image, label, imgPath

Node.py

@@ -6,3 +6,8 @@ class Node:
     def __init__(self, state):
         self.state = state
 
+    def __lt__(self, other):
+        """
+        Definicja metody __lt__ (less than), która jest wymagana do porównywania obiektów typu Node.
+        """
+        return self.g + self.h < other.g + other.h

Pole.py

@@ -5,13 +5,20 @@ import pygame
 import time
 import Ui
 import math
+import random
+import neuralnetwork
+import Image
+
+stoneList = [(3,3), (3,4), (3,5), (3,6), (4,6), (5,6), (6,6), (7,6), (8,6), (9,6), (10,6), (11,6), (12,6), (13,6), (14,6), (15,6), (16,6), (16,7), (16,8), (16,9)]
+stoneFlag = False
 
 class Pole:
-    def __init__(self,screen,image_loader):
+    def __init__(self,screen,image_loader, gasStation = (-1, -1)):
         self.screen=screen
         self.slot_dict={} #Slot are stored in dictionary with key being a Tuple of x and y coordinates so top left slot key is (0,0) and value is slot object
         self.ui=Ui.Ui(screen)
         self.image_loader=image_loader
+        self.gasStation=gasStation
 
     def get_slot_from_cord(self,coordinates):
         (x_axis,y_axis)=coordinates
@@ -23,9 +30,9 @@ class Pole:
 
     def get_slot_dict(self): #returns whole slot_dict
         return self.slot_dict
-    
+
     #Draw grid and tractor (new one)
-    def draw_grid(self):
+    def draw_grid(self, nn=False):
         for x in range(0,dCon.NUM_X): #Draw all cubes in X axis
             for y in range(0,dCon.NUM_Y): #Draw all cubes in Y axis
                 new_slot=Slot.Slot(x,y,Colors.BROWN,self.screen,self.image_loader) #Creation of empty slot 
@@ -35,16 +42,34 @@ class Pole:
             slot_dict[coordinates].draw()
         garage=self.slot_dict[(0,0)]
         garage.set_garage_image()
+        if stoneFlag:
+            for i in stoneList:
+                st=self.slot_dict[i]
+                st.set_stone_image()
+            if self.gasStation[0] != -1:
+                st=self.slot_dict[self.gasStation]
+                st.set_gasStation_image()
     
-    def randomize_colors(self):
+    def randomize_colors(self, nn = False):
+        pygame.display.update()
+        time.sleep(3)
+        #self.ui.render_text("Randomizing Crops")
+        for coordinates in self.slot_dict:
+            if(stoneFlag):
+                if( coordinates in stoneList or coordinates == self.gasStation ):
+                    continue
+            if(coordinates==(0,0)):
+                continue
+            else:
+                self.slot_dict[coordinates].set_random_plant(nn)
+    def setPlantsByList(self, plantList):
         pygame.display.update()
         time.sleep(3)
-        self.ui.render_text("Randomizing Crops")
         for coordinates in self.slot_dict:
             if(coordinates==(0,0)):
                 continue
             else:
-                self.slot_dict[coordinates].set_random_plant()
+                self.slot_dict[coordinates].set_specifided_plant(plantList[coordinates[1]][coordinates[0]])
 
     def change_color_of_slot(self,coordinates,color): #Coordinates must be tuple (x,y) (left top slot has cord (0,0) ), color has to be from defined in Colors.py or custom in RGB value (R,G,B)
         self.get_slot_from_cord(coordinates).color_change(color)
@@ -64,4 +89,6 @@ class Pole:
             if(mouse_y<dCon.NUM_Y):
                 collided=self.get_slot_from_cord((mouse_x,mouse_y))
                 return collided.print_status()
-        return ""
+        return ""
+
+

Roslina.py

@@ -32,20 +32,49 @@ class Roslina:
         - słonecznik: +3
         - borówka: +5
         - winogrono: +4
-    """
-
-
+        
+    Koszt (0-15):
+        - pszenica: 7
+        - kukurydza: 9
+        - ziemniak: 2
+        - słonecznik: 5
+        - borówka: 3
+        - winogrono: 4
+        - szuter (ścieżka): 0
+        - błoto: 15
+            
+    
     def __init__(self, nazwa, stan, srodek):
         self.nazwa = nazwa
         self.stan = stan
         self.srodek = srodek
+    """
 
+    def __init__(self, nazwa):
+        self.nazwa = nazwa
+        self.stan = Stan.Stan()
+        if nazwa == "dirt":
+            self.stan.koszt = 0
+            self.stan.nawodnienie = 100
+        elif nazwa == "mud":
+            self.stan.koszt = 15
+            self.stan.nawodnienie = 100
+        else:
+            self.stan.set_random()
+            if nazwa == "pszenica":
+                self.stan.koszt = 7
+            elif nazwa == "kukurydza":
+                self.stan.koszt = 9
+            elif nazwa == "ziemniak":
+                self.stan.koszt = 2
+            elif nazwa == "slonecznik":
+                self.stan.koszt = 5
+            elif nazwa == "borowka":
+                self.stan.koszt = 3
+            else:  # winogrono
+                self.stan.koszt = 4
+        self.srodek = None
 
-    def __init__(self,nazwa):
-        self.nazwa=nazwa
-        self.stan=Stan.Stan()
-        self.stan.set_random()
-        self.srodek=None
 
     def checkSrodek(self):
         #może wykorzystać AI do porównywania zdjęć
@@ -81,8 +110,14 @@ class Roslina:
     def return_stan(self):
         return self.stan
     
+    def return_stan_for_tree(self):
+        return self.stan.return_stan_for_tree()
+    
     def get_hydrate_stats(self):
         return self.stan.return_hydrate()
     
     def report_status(self):
-        return f"Nazwa rosliny: {self.nazwa} "+self.stan.report_all()
+        return f"Nazwa rosliny: {self.nazwa} "+self.stan.report_all()
+    
+    def return_status_tree(self):
+        return self.stan.return_stan_for_tree()

Slot.py

@@ -16,22 +16,43 @@ class Slot:
         self.field=pygame.Rect(self.x_axis*dCon.CUBE_SIZE,self.y_axis*dCon.CUBE_SIZE,dCon.CUBE_SIZE,dCon.CUBE_SIZE)
         self.image_loader=image_loader
         self.garage_image=None
+        self.label = None
+        self.imagePath = None
 
     def draw(self):
         pygame.draw.rect(self.screen,Colors.BROWN,self.field,0) #Draw field
         pygame.draw.rect(self.screen,Colors.BLACK,self.field,BORDER_THICKNESS) #Draw border
         pygame.display.update()
     
-    def redraw_image(self):
-        self.set_image()
+    def redraw_image(self, destroy = True):
+        if destroy:
+            self.mark_visited()
+        else:
+            self.screen.blit(self.plant_image, (self.x_axis * dCon.CUBE_SIZE, self.y_axis * dCon.CUBE_SIZE))
+            pygame.draw.rect(self.screen, Colors.BLACK, self.field, BORDER_THICKNESS)
+
+    def mark_visited(self):
+        plant,self.plant_image=self.image_loader.return_plant('road')
+        self.screen.blit(self.plant_image, (self.x_axis * dCon.CUBE_SIZE, self.y_axis * dCon.CUBE_SIZE))
+        pygame.draw.rect(self.screen, Colors.BLACK, self.field, BORDER_THICKNESS)
 
     def color_change(self,color):
         self.plant=color
         self.draw()
 
-    def set_random_plant(self):
-        (plant_name,self.plant_image)=self.random_plant()
-        self.plant=Roslina.Roslina(plant_name)
+    def set_random_plant(self, nn=False):
+        if not nn:
+            (plant_name,self.plant_image)=self.random_plant()
+            self.plant=Roslina.Roslina(plant_name)
+        else:
+            self.plant_image, self.label, self.imagePath = self.random_plant_dataset()
+            # print(self.plant_image)
+            self.plant=Roslina.Roslina(self.label)
+        self.set_image()
+    
+    def set_specifided_plant(self, plant):
+        self.plant_image, self.label, self.imagePath = self.specified_plant_dataset(plant)
+        self.plant=Roslina.Roslina(self.label)
         self.set_image()
 
     def set_image(self):
@@ -45,9 +66,22 @@ class Slot:
         self.screen.blit(self.plant_image, (self.x_axis * dCon.CUBE_SIZE, self.y_axis * dCon.CUBE_SIZE))
         pygame.draw.rect(self.screen, Colors.BLACK, self.field, BORDER_THICKNESS)
 
+    def set_stone_image(self):
+        self.plant_image=self.image_loader.return_stone()
+        self.screen.blit(self.plant_image, (self.x_axis * dCon.CUBE_SIZE, self.y_axis * dCon.CUBE_SIZE))
+        pygame.draw.rect(self.screen, Colors.BLACK, self.field, BORDER_THICKNESS)
+    
+    def set_gasStation_image(self):
+        self.plant_image=self.image_loader.return_gasStation()
+        self.screen.blit(self.plant_image, (self.x_axis * dCon.CUBE_SIZE, self.y_axis * dCon.CUBE_SIZE))
+        pygame.draw.rect(self.screen, Colors.BLACK, self.field, BORDER_THICKNESS)
 
     def random_plant(self): #Probably will not be used later only for demo purpouse
         return self.image_loader.return_random_plant()
+    def random_plant_dataset(self):
+        return Image.getRandomImageFromDataBase()
+    def specified_plant_dataset(self, plant):
+        return Image.getSpedifiedImageFromDatabase(plant)
     
     def return_plant(self):
         return self.plant
@@ -57,6 +91,7 @@ class Slot:
 
     def print_status(self):
         return f"wspolrzedne: (X:{self.x_axis} Y:{self.y_axis}) "+self.plant.report_status()
+
     def irrigatePlant(self):
         self.plant.stan.nawodnienie = 100
 
@@ -67,4 +102,6 @@ class Slot:
             self.plant.stan.nawodnienie=random.randint(61,100)
         elif(index==-1):
             pass
-    
+    
+    def return_stan_for_tree(self):
+        return self.plant.return_stan_for_tree()

Stan.py

@@ -5,8 +5,9 @@ class Stan:
     nawodnienie = None #[int] 0-100 (0-60: trzeba podlać), spada w zaleznosci od rosliny: aktualizowane bedzie "w tle"
     zyznosc = None #[int] 0-100 (0-60: trzeba użyźnić), spada w zaleznosci od rosliny: aktualizowane bedzie "w tle"
     wzrost = None #[int] 0-100 (75-100: scinanie), wzrasta w zaleznosci od rosliny: aktualizowane bedzie "w tle"
-    choroba = None #[string] brak, grzyb, bakteria, pasożyt
+    choroba = None #[int] brak-0,choroba-1
     akcja = None #[Akcja]
+    koszt = None #[int] 0-15, im więcej tym trudniej wjechać
 
 
 
@@ -23,7 +24,7 @@ class Stan:
         self.nawodnienie=random.randint(0,100)
         self.zyznosc=random.randint(0,100)
         self.wzrost=random.randint(0,100)
-        self.choroba=random.choice(["brak","grzyb","bakteria","pasozyt"])
+        self.choroba=random.randint(0,1)
 
     def checkStan(self):
         # sprawdza stan rośliny i podejmuje akcje jeśli potrzebna
@@ -46,6 +47,20 @@ class Stan:
         
     def return_hydrate(self):
         return self.nawodnienie
+    
+
+    def return_disease(self):
+        return self.choroba   
+    
+    def return_disease_as_string(self):
+        if(self.choroba==0):
+            return "Zdrowa"
+        if(self.choroba==1):
+            return "Chora"       
         
+    def return_stan_for_tree(self):
+        return [self.nawodnienie,self.wzrost,self.choroba,self.zyznosc]
+    
     def report_all(self):
-        return f"Nawodnienie: {self.nawodnienie} Zyznosc: {self.zyznosc} Wzrost: {self.wzrost} Choroba: {self.choroba}"
+        return f"Nawodnienie: {self.nawodnienie} Zyznosc: {self.zyznosc} Wzrost: {self.wzrost} Choroba: {self.return_disease_as_string()}"
+    

Tractor.py

@@ -1,10 +1,22 @@
 import time
 import pygame
 import random
+
+import Pole
 import displayControler as dCon
 import Slot
 import Osprzet
 import Node
+import Condition
+import Drzewo
+import neuralnetwork as nn
+
+condition=Condition.Condition()
+drzewo=Drzewo.Drzewo()
+
+format_string = "{:<25}{:<25}{:<25}{:<10}{:<10}{:<10}{:<25}{:<15}{:<20}{:<10}{:<15}"
+format_string_nn="{:<10}{:<20}{:<20}{:<15}{:<20}"
+
 
 tab = [-1, 0, 0, 0, 0, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
@@ -18,6 +30,7 @@ class Tractor:
     DIRECTION_SOUTH = 'S'
     DIRECTION_WEST = 'W'
     DIRECTION_EAST = 'E'
+
     def __init__(self,slot,screen, osprzet,clock,bfs2_flag):
         self.tractor_images = {
             Tractor.DIRECTION_NORTH: pygame.transform.scale(pygame.image.load('images/traktorN.png'),
@@ -37,7 +50,7 @@ class Tractor:
         self.clock=clock
         self.slot_hydrate_dict={}
         self.bfs2_flag=bfs2_flag
-
+        self.waterLevel=random.randint(0,100)
 
     def draw_tractor(self):
         self.screen.blit(self.current_tractor_image, (self.slot.x_axis * dCon.CUBE_SIZE, self.slot.y_axis * dCon.CUBE_SIZE))
@@ -55,6 +68,24 @@ class Tractor:
         self.current_tractor_image = self.tractor_images[self.direction]
         self.draw_tractor()
 
+    def tree_move(self, pole):
+        drzewo.treeLearn()
+        drzewo.plotTree()
+        self.snake_move_irrigation(pole, drzewo)
+    
+    def get_attributes(self): 
+        slot_attributes=self.slot.return_stan_for_tree()
+        climate_attributes=condition.return_condition()
+        attributes=[]
+        attributes=attributes+slot_attributes+[self.waterLevel]+climate_attributes
+        return attributes
+    
+    def get_attributes_for_print(self):
+        slot_attributes=self.slot.return_plant().return_status_tree()
+        climate_attributes=condition.getCondition()
+        slot_attributes=slot_attributes+[self.waterLevel]
+        return slot_attributes+climate_attributes
+
     def turn_right(self):
         # zmiana kierunku w prawo
         direction_map = {
@@ -67,7 +98,7 @@ class Tractor:
         self.current_tractor_image = self.tractor_images[self.direction]
         self.draw_tractor()
 
-    def move_forward(self, pole):
+    def move_forward(self, pole, destroy = True):
         next_slot_coordinates = None
         if self.direction == Tractor.DIRECTION_EAST:
             next_slot_coordinates = (self.slot.x_axis + 1, self.slot.y_axis)
@@ -83,12 +114,13 @@ class Tractor:
             self.current_tractor_image = self.tractor_images[self.direction]
 
         # sprawdzenie czy następny slot jest dobry
-        self.do_move_if_valid(pole,next_slot_coordinates)
+        self.do_move_if_valid(pole,next_slot_coordinates, destroy)
+        self.clock.tick(10)
 
-    def do_move_if_valid(self,pole, next_slot_coordinates):
+    def do_move_if_valid(self,pole, next_slot_coordinates, destroy = True):
         if next_slot_coordinates and pole.is_valid_move(next_slot_coordinates):
             next_slot = pole.get_slot_from_cord(next_slot_coordinates)
-            self.slot.redraw_image()
+            self.slot.redraw_image(destroy)
             self.slot = next_slot
             self.draw_tractor()
             return True
@@ -134,16 +166,87 @@ class Tractor:
                         self.snake_move(pole,x,y)
 
 
+    def snake_move_irrigation(self, pole, drzewo):
+        headers=['Wspolrzedne','Czy podlac','Poziom nawodnienia','Wzrost','Choroba','Zyznosc','Poziom wody w traktorze','Temperatura','Opady','Pora Roku','Aktualny czas']
+        print(format_string.format(*headers))
+        initPos = (self.slot.x_axis, self.slot.y_axis)
+        counter = 0
+        for i in range(initPos[1], dCon.NUM_Y):
+            for j in range(initPos[0], dCon.NUM_X):
+                attributes=self.get_attributes()
+                decision = drzewo.makeDecision(attributes)
+                self.pretty_print_tree([str("({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis),decision,*self.get_attributes_for_print()])
+                if decision == "Tak":
+                    self.slot.irrigatePlant()
+                    counter += 1
+                condition.cycle()
+                pygame.time.delay(50)
+                self.waterLevel=random.randint(0,100)
+                #condition.getCondition()
+                self.move_forward(pole, False)
+            if i % 2 == 0 and i != dCon.NUM_Y - 1:
+                self.turn_right()
+                self.move_forward(pole, False)
+                self.turn_right()
+            elif i != dCon.NUM_Y - 1:
+                self.turn_left()
+                self.move_forward(pole, False)
+                self.turn_left()
+        print("podlanych slotów: ", str(counter))
+
+    def snake_move_predict_plant(self, pole, model, headers, actions = None):
+        print(format_string_nn.format(*headers))
+        initPos = (self.slot.x_axis, self.slot.y_axis)
+        count = 0
+        for i in range(initPos[1], dCon.NUM_Y):
+            for j in range(initPos[0], dCon.NUM_X):
+                for event in pygame.event.get():
+                    if event.type == pygame.QUIT:
+                        quit()
+                if self.slot.imagePath != None:
+                    predictedLabel = nn.predictLabel(self.slot.imagePath, model)
+
+                    #print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real:", self.slot.label, "predicted:", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect", 'nawożę za pomocą:', nn.fertilizer[predictedLabel])
+                    # print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",nn.fertilizer[predictedLabel]))
+                    for a in actions:
+                        a(predictedLabel)
+                    if self.slot.label != predictedLabel:
+                        # self.slot.mark_visited()
+                        count += 1
+                self.move_forward(pole, False)
+            if i % 2 == 0 and i != dCon.NUM_Y - 1:
+                self.turn_right()
+                self.move_forward(pole, False)
+                self.turn_right()
+            elif i != dCon.NUM_Y - 1:
+                self.turn_left()
+                self.move_forward(pole, False)
+                self.turn_left()
+        print(f"Dobrze rozpoznanych roślin: {20*12-count}, źle rozpoznanych roślin: {count}")
+    
+    def fertilize_slot(self, predictedLabel):
+        print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",nn.fertilizer[predictedLabel]))
+        if self.slot.label != predictedLabel:
+            self.slot.mark_visited()
+
+    def irigate_slot_NN(self, predictedLabel):
+        attributes=self.get_attributes()
+        decision = drzewo.makeDecision(attributes)
+        print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",decision))
+        condition.cycle()
+        self.waterLevel = random.randint(0, 100)
+
     def snake_move(self,pole,x,y):
         next_slot_coordinates=(x,y)
         if(self.do_move_if_valid(pole,next_slot_coordinates)):
-            if x == 0 and y == 0:
-                hydrateIndex = -1
-            elif pole.get_slot_from_cord((x,y)).get_hydrate_stats() < 60:
-                hydrateIndex = 0
-            else:
-                hydrateIndex = 1
-            self.slot_hydrate_dict[(x,y)]= hydrateIndex #Budowanie slownika slotow z poziomem nawodnienia dla traktorka
+            if (x,y) not in Pole.stoneList:
+                if x == 0 and y == 0:
+                    hydrateIndex = -1
+                elif pole.get_slot_from_cord((x,y)).get_hydrate_stats() < 60:
+                    hydrateIndex = 0
+                else:
+                    hydrateIndex = 1
+                self.slot_hydrate_dict[(x,y)]= hydrateIndex #Budowanie slownika slotow z poziomem nawodnienia dla traktorka
         self.clock.tick(10)
         for event in pygame.event.get():
             if event.type == pygame.QUIT:
@@ -177,9 +280,11 @@ class Tractor:
                 print("- Typ:", akcja.typ)
         else:
             print("Brak akcji przypisanych do tego sprzętu.")
+
+    def pretty_print_tree(self,attributes):
+        print(format_string.format(*attributes))
     def irrigateSlot(self):
         try:
             self.slot.irrigatePlant()
         except:
             pass
-

Ui.py

@@ -25,6 +25,7 @@ class Ui:
 
     def render_text_to_console(self,string_to_print):
         font=pygame.font.Font(self.font,self.font_size)
+        string_to_print=str(string_to_print)
         self.break_string_to_console(string_to_print)
         for string in self.to_print:
             text=font.render(string,True,Colors.BLACK,Colors.WHITE)

displayControler.py

@@ -1,6 +1,6 @@
 CUBE_SIZE = 64
-NUM_X = 6
-NUM_Y = 3
+NUM_X = 20
+NUM_Y = 12
 
 #returns true if tractor can move to specified slot
 def isValidMove(x, y):

main.py

@@ -1,3 +1,3 @@
 import App
 
-App.init(demo=True)#DEMO=TRUE WILL INIT DEMO MODE WITH RANDOM COLOR GEN
+App.init(demo=True)#DEMO=TRUE WILL INIT DEMO MODE WITH RANDOM COLOR GEN

neuralnetwork.py

@@ -0,0 +1,114 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import Compose, Lambda, ToTensor
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+from PIL import Image
+import random
+
+imageSize = (128, 128)
+labels = ['carrot','corn', 'potato', 'tomato'] # musi być w kolejności alfabetycznej 
+fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa', labels[2]: 'superfosfat', labels[3]:'obornik kurzy'}
+#labels = ['corn','tomato'] #uncomment this two lines for 2 crops only
+#fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa'}
+torch.manual_seed(42)
+
+#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cpu")
+# device = torch.device("mps") if torch.backends.mps.is_available() else torch.device('cpu')
+# print(device)
+
+def getTransformation():
+    transform=transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        transforms.Resize(imageSize),
+        Lambda(lambda x: x.flatten())])
+    return transform
+
+def getDataset(train=True):
+    transform = getTransformation()
+    if (train):
+        trainset = datasets.ImageFolder(root='dataset/train', transform=transform)
+        return trainset
+    else:
+        testset = datasets.ImageFolder(root='dataset/test', transform=transform)
+        return testset
+
+
+def train(model, dataset, n_iter=100, batch_size=256):
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+    criterion = nn.NLLLoss()
+    dl = DataLoader(dataset, batch_size=batch_size)
+    model.train()
+    for epoch in range(n_iter):
+        for images, targets in dl:
+            optimizer.zero_grad()
+            out = model(images.to(device))
+            loss = criterion(out, targets.to(device))
+            loss.backward()
+            optimizer.step()
+        if epoch % 10 == 0:
+            print('epoch: %3d loss: %.4f' % (epoch, loss))
+    return model
+
+def accuracy(model, dataset):
+    model.eval()
+    correct = sum([(model(images.to(device)).argmax(dim=1) == targets.to(device)).sum()
+    for images, targets in DataLoader(dataset, batch_size=256)])
+    return correct.float() / len(dataset)
+
+def getModel():
+    hidden_size = 500
+    model = nn.Sequential(
+        nn.Linear(imageSize[0] * imageSize[1] * 3, hidden_size),
+        nn.ReLU(),
+        nn.Linear(hidden_size, len(labels)),
+        nn.LogSoftmax(dim=-1)
+        ).to(device)
+    return model
+    
+def saveModel(model, path):
+    print("Saving model")
+    torch.save(model.state_dict(), path)
+
+def loadModel(path):
+    print("Loading model")
+    model = getModel()
+    model.load_state_dict(torch.load(path, map_location=torch.device('cpu'))) # musiałem tutaj dodać to ładowanie z mapowaniem na cpu bo u mnie CUDA nie działa wy pewnie możecie to usunąć
+    return model
+
+def trainNewModel(n_iter=100, batch_size=256):
+    trainset = getDataset(True)
+    model = getModel()
+    model = train(model, trainset)
+    return model
+
+def predictLabel(imagePath, model):
+    image = Image.open(imagePath).convert("RGB")
+    image = preprocess_image(image)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    with torch.no_grad():
+        model.eval()  # Ustawienie modelu w tryb ewaluacji
+        output = model(image)
+
+    # Znalezienie indeksu klasy o największej wartości prawdopodobieństwa
+    predicted_class = torch.argmax(output).item()
+    return labels[predicted_class]
+
+    # Znalezienie indeksu klasy o największej wartości prawdopodobieństwa
+    predicted_class = torch.argmax(output).item()
+    return labels[predicted_class]
+
+
+def preprocess_image(image):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    transform = getTransformation()
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+    image = image.to(device)  # Move the image tensor to the same device as the model
+    return image
+
+

pole2_pop120_iter2500_True.json

@@ -0,0 +1,266 @@
+[
+    [
+        "tomato",
+        "corn",
+        "tomato",
+        "tomato",
+        "corn",
+        "tomato",
+        "tomato",
+        "corn",
+        "carrot",
+        "potato",
+        "potato",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato"
+    ],
+    [
+        "corn",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn"
+    ],
+    [
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "corn",
+        "carrot",
+        "potato",
+        "carrot",
+        "potato",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato"
+    ],
+    [
+        "potato",
+        "potato",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "carrot",
+        "corn",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn"
+    ],
+    [
+        "potato",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato"
+    ],
+    [
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn"
+    ],
+    [
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato"
+    ],
+    [
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn"
+    ],
+    [
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "carrot",
+        "corn",
+        "tomato"
+    ],
+    [
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "carrot",
+        "potato",
+        "tomato",
+        "potato"
+    ],
+    [
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "carrot",
+        "corn",
+        "carrot",
+        "corn",
+        "carrot",
+        "potato",
+        "potato",
+        "tomato",
+        "potato"
+    ],
+    [
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "potato",
+        "potato",
+        "carrot",
+        "corn",
+        "tomato"
+    ]
+]

pole3_pop120_365_True.json

@@ -0,0 +1,266 @@
+[
+    [
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "potato",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "potato",
+        "potato",
+        "carrot"
+    ],
+    [
+        "tomato",
+        "potato",
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "carrot",
+        "carrot",
+        "carrot",
+        "carrot",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "potato"
+    ],
+    [
+        "corn",
+        "carrot",
+        "corn",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "carrot",
+        "corn",
+        "carrot",
+        "potato",
+        "potato",
+        "tomato"
+    ],
+    [
+        "tomato",
+        "potato",
+        "carrot",
+        "corn",
+        "tomato",
+        "corn",
+        "carrot",
+        "carrot",
+        "corn",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "corn",
+        "carrot",
+        "carrot",
+        "potato",
+        "potato",
+        "tomato",
+        "tomato"
+    ],
+    [
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot",
+        "corn",
+        "carrot",
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "tomato",
+        "tomato",
+        "potato",
+        "potato"
+    ],
+    [
+        "potato",
+        "tomato",
+        "potato",
+        "potato",
+        "tomato",
+        "corn",
+        "carrot",
+        "potato",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot"
+    ],
+    [
+        "potato",
+        "potato",
+        "tomato",
+        "tomato",
+        "corn",
+        "carrot",
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato"
+    ],
+    [
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "potato",
+        "tomato",
+        "tomato",
+        "potato",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "corn",
+        "carrot"
+    ],
+    [
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "carrot",
+        "carrot",
+        "carrot",
+        "potato",
+        "tomato",
+        "potato",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "carrot",
+        "potato",
+        "tomato",
+        "corn"
+    ],
+    [
+        "potato",
+        "tomato",
+        "tomato",
+        "potato",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "corn",
+        "tomato",
+        "potato",
+        "tomato"
+    ],
+    [
+        "potato",
+        "tomato",
+        "corn",
+        "tomato",
+        "potato",
+        "potato",
+        "potato",
+        "potato",
+        "carrot",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "corn",
+        "tomato",
+        "potato"
+    ],
+    [
+        "potato",
+        "potato",
+        "carrot",
+        "corn",
+        "carrot",
+        "potato",
+        "potato",
+        "potato",
+        "carrot",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "carrot",
+        "potato",
+        "carrot",
+        "corn",
+        "carrot",
+        "corn",
+        "tomato"
+    ]
+]

pole_pop120_iter2500_True.json

@@ -0,0 +1,266 @@
+[
+    [
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "potato",
+        "potato",
+        "carrot",
+        "potato"
+    ],
+    [
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "potato",
+        "potato",
+        "potato"
+    ],
+    [
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "potato",
+        "carrot"
+    ],
+    [
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato"
+    ],
+    [
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot"
+    ],
+    [
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato"
+    ],
+    [
+        "corn",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "potato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot"
+    ],
+    [
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato"
+    ],
+    [
+        "carrot",
+        "potato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot"
+    ],
+    [
+        "potato",
+        "potato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato"
+    ],
+    [
+        "potato",
+        "carrot",
+        "tomato",
+        "corn",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "corn",
+        "corn",
+        "tomato",
+        "carrot",
+        "carrot",
+        "tomato",
+        "carrot"
+    ],
+    [
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "corn",
+        "corn",
+        "tomato",
+        "corn",
+        "tomato",
+        "carrot",
+        "tomato",
+        "tomato",
+        "carrot",
+        "tomato"
+    ]
+]

readme.txt

@@ -0,0 +1,24 @@
+Required packages:
+    pygame,matplotlib,sklearn,pandas
+    How to install:
+        pip install pygame
+        pip install matplotlib
+        pip install scikit-learn
+        pip install pandas
+How to run:
+    For BFS3:
+        -in App.py: -change bfs3_flag to True (other flags need to be disabled) -ensure that in App.py in BFS3 you give goalTreasure
+        -in Image.py change range in function return_random_plant to (0,5)
+    For Astar:
+        in App.py change Astar to True (other flags need to be disabled)
+        -in Image.py change range in function return_random_plant to (0,7)
+    For Astar2:
+        -in App.py change Astar2 flag to True (other flags need to be disabled)
+        -in Image.py change range in function return_random_plant to (0,7)
+    For Tree:
+        -in App.py change TreeFlag to True (other flags need to be disabled)
+        -in Image.py change range in function return_random_plant to (0,5)
+    For neuralnetwork:
+        -in App.py change nnFlag to True (other flags need to be disabled)
+    For final_show (neuralnetwork+tree+genetic algorithm)
+        -in App.py change finalFlag to True (other flags need to be disabled)

treeData.py

@@ -0,0 +1,87 @@
+tab = []
+def iter(odp, i):
+    if i == 0:
+        j = ""
+        for k in range(0, 101, 28):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 1:
+        j = ""
+        for k in range(0, 101, 28):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 2:
+        j = ""
+        for k in range(2):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 3:
+        j = ""
+        for k in range(0, 101, 34):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 4:
+        j = ""
+        for k in range(0, 101, 40):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 5:
+        j = ""
+        for k in range(0, 4, 2):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 6:
+        j = ""
+        for k in range(0, 4, 2):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 7:
+        j = ""
+        for k in range(0, 4, 2):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 8:
+        j=""
+        for k in range(4):
+            odp += f"{k},"
+            odp = iter(odp, i + 1)
+            j = str(k)
+            odp = odp[:-(len(j) + 1)]
+        return odp
+    if i == 9:
+        global licznik
+        x = odp.split(",")
+        if (int(x[0]) > 60 or int(x[1]) > 90 or int(x[2]) == 1 or int(x[3]) > 70 or int(x[4]) <= 10 or int(x[5]) == 0 or int(x[5]) == 4 or int(x[6]) == 2 or int(x[6]) == 3 or int(x[7]) == 0 or int(x[8]) == 1):
+            odp += "0"
+        else:
+            odp += "1"
+        print(odp)
+        tab.append(odp)
+        licznik += 1
+        return odp[:-1]
+
+iter("", 0)