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
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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'