Merge pull request 'Merge genetic_algorithms with final_show branch' (#28) from final_show into genetic_algorithms

Reviewed-on: #28

App.py

@@ -10,6 +10,7 @@ import Ui
 import BFS
 import AStar
 import neuralnetwork
+import json
 
 
 bfs1_flag=False
@@ -20,8 +21,9 @@ Astar2 = False
 if bfs3_flag or Astar or Astar2:
    Pole.stoneFlag = True
 TreeFlag=False
-nnFlag=True
+nnFlag=False
 newModel=False
+finalFlag = True
 
 pygame.init()
 show_console=True
@@ -43,7 +45,15 @@ def init_demo(): #Demo purpose
     old_info=""
     traktor.draw_tractor()
     time.sleep(2)
-    pole.randomize_colors(nnFlag)
+    if not finalFlag:
+        pole.randomize_colors(nnFlag)
+    else:
+        population = 120
+        iterat = 2500
+        roulette = True
+        with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
+            garden_data = json.load(file)
+        pole.setPlantsByList(garden_data)
     traktor.draw_tractor()
     start_flag=True
     while True:
@@ -75,7 +85,7 @@ def init_demo(): #Demo purpose
                 print_to_console("Traktor porusza sie obliczona sciezka BFS")
                 traktor.move_by_root(bfsRoot2, pole, [traktor.irrigateSlot])
             if(bfs3_flag):
-                bfsRoot3 = BFS.BFS3({'x': 0, 'y': 0, 'direction': "E"})
+                bfsRoot3 = BFS.BFS3({'x': 0, 'y': 0, 'direction': "E"},goalTreasure)
                 #displayControler: NUM_X: 20, NUM_Y: 12 (skarb) CHANGE THIS IN DCON BY HAND!!!!!!!!
                 bfsRoot3.reverse()
                 print_to_console("Traktor porusza sie obliczona sciezka BFS")
@@ -128,11 +138,16 @@ def init_demo(): #Demo purpose
                     print_to_console("sieć nuronowa nauczona")
                     print('model został wygenerowany')
                 else:
-                    model = neuralnetwork.loadModel('model.pth')
+                    model = neuralnetwork.loadModel('model_500_hidden.pth')
                     print_to_console("model został załądowny")
                 testset = neuralnetwork.getDataset(False)
                 print(neuralnetwork.accuracy(model, testset))
-                traktor.snake_move_predict_plant(pole, model)
+                traktor.snake_move_predict_plant(pole, model, headers=['Coords','Real plant','Predicted plant','Result','Fertilizer'], actions=[traktor.fertilize_slot])
+            if(finalFlag):
+                pass
+                model = neuralnetwork.loadModel('model_500_hidden.pth')
+                Tractor.drzewo.treeLearn()
+                traktor.snake_move_predict_plant(pole, model, headers=['Coords','Real plant','Predicted plant','Result','Water decision'], actions=[traktor.irigate_slot_NN])
             start_flag=False
         # demo_move()
         old_info=get_info(old_info)

BFS.py

@@ -133,8 +133,12 @@ def check3(tab, state):
     return True
 
 
-def BFS3(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 = []

Drzewo.py

@@ -8,7 +8,7 @@ class Drzewo:
         self.tree=self.treeLearn()
 
     def treeLearn(self):
-        csvdata=pandas.read_csv('Data/dataTree.csv')
+        csvdata=pandas.read_csv('Data/dataTree2.csv')
         #csvdata = pandas.read_csv('Data/dataTree2.csv')
         x=csvdata[atributes]
         decision=csvdata['action']

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

@@ -136,7 +136,7 @@ if __name__ == '__main__':
     roulette = True
     attemps = 150
     iterat = 2500
-    population = 100
+    population = 120
     best = []
     for a in range(attemps):
         generation = generate(population)

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

@@ -80,3 +80,25 @@ def getRandomImageFromDataBase():
     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

Pole.py

@@ -62,6 +62,14 @@ class Pole:
                 continue
             else:
                 self.slot_dict[coordinates].set_random_plant(nn)
+    def setPlantsByList(self, plantList):
+        pygame.display.update()
+        time.sleep(3)
+        for coordinates in self.slot_dict:
+            if(coordinates==(0,0)):
+                continue
+            else:
+                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)

Slot.py

@@ -49,6 +49,11 @@ class Slot:
             # 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):
         if self.plant_image is None:
@@ -75,6 +80,8 @@ class Slot:
         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

Tractor.py

@@ -30,6 +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'),
@@ -193,8 +194,7 @@ class Tractor:
                 self.turn_left()
         print("podlanych slotów: ", str(counter))
 
-    def snake_move_predict_plant(self, pole, model):
-        headers=['Coords','Real plant','Predicted plant','Result','Fertilizer']
+    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
@@ -207,9 +207,11 @@ class Tractor:
                     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]))
+                    # 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()
+                        # self.slot.mark_visited()
                         count += 1
                 self.move_forward(pole, False)
             if i % 2 == 0 and i != dCon.NUM_Y - 1:
@@ -220,7 +222,19 @@ class Tractor:
                 self.turn_left()
                 self.move_forward(pole, False)
                 self.turn_left()
-        print(f"Dobrze nawiezionych roślin: {20*12-count}, źle nawiezionych roślin: {count}")
+        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)

neuralnetwork.py

@@ -77,7 +77,7 @@ def saveModel(model, path):
 def loadModel(path):
     print("Loading model")
     model = getModel()
-    model.load_state_dict(torch.load(path))
+    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):

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"
+    ]
+]

readme.txt

@@ -4,4 +4,21 @@ Required packages:
         pip install pygame
         pip install matplotlib
         pip install scikit-learn
-        pip install pandas
+        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)