kod alfa

.idea/workspace.xml

@@ -3,10 +3,34 @@
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       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/definitions.py" beforeDir="false" afterPath="$PROJECT_DIR$/definitions.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/plant.py" beforeDir="false" afterPath="$PROJECT_DIR$/plant.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/py.py" beforeDir="false" afterPath="$PROJECT_DIR$/py.py" afterDir="false" />
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@@ -37,6 +61,7 @@
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+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_08_01.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_08_01.png" afterDir="false" />
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@@ -46,6 +71,16 @@
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+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_01.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_01.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_02.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_02.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_03.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_03.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_04.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_04.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_05.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_05.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_06.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_06.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_07.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_07.png" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_08.png" beforeDir="false" afterPath="$PROJECT_DIR$/resources/neural_network/tiles/tile_09_08.png" afterDir="false" />
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     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -217,6 +252,11 @@
       <workItem from="1622904041533" duration="226000" />
       <workItem from="1622904271971" duration="12000" />
       <workItem from="1622921561418" duration="3872000" />
+      <workItem from="1622976899995" duration="1243000" />
+      <workItem from="1622989814058" duration="2000" />
+      <workItem from="1623575428375" duration="4887000" />
+      <workItem from="1623582818543" duration="8926000" />
+      <workItem from="1623594481441" duration="3000" />
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     <servers />
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@@ -228,35 +268,35 @@
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+    <state width="1879" height="295" key="GridCell.Tab.0.right" timestamp="1623594478725">
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-    <state width="1879" height="295" key="GridCell.Tab.0.right/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622925953140" />
+    <state width="1879" height="295" key="GridCell.Tab.0.right/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1623594478725" />
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@@ -278,11 +318,15 @@
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+    <state x="615" y="209" key="com.intellij.openapi.editor.actions.MultiplePasteAction$ClipboardContentChooser/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1623589256933" />
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@@ -303,6 +347,6 @@
     <SUITE FILE_PATH="coverage/SmartCart$copy.coverage" NAME="copy Coverage Results" MODIFIED="1622503170538" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
     <SUITE FILE_PATH="coverage/SmartCart$glue.coverage" NAME="glue Coverage Results" MODIFIED="1622501306368" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
     <SUITE FILE_PATH="coverage/SmartTractor$py.coverage" NAME="py Coverage Results" MODIFIED="1622469837941" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
-    <SUITE FILE_PATH="coverage/SmartCart$py.coverage" NAME="py Coverage Results" MODIFIED="1622925873783" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/SmartCart$py.coverage" NAME="py Coverage Results" MODIFIED="1623592389826" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
   </component>
 </project>

definitions.py

@@ -5,8 +5,8 @@ pygame.init()
 BLOCK_SIZE = 60
 BEETROOT = pygame.image.load(os.path.join('resources/images', 'beetroot.png'))
 BEETROOT = pygame.transform.scale(BEETROOT, (BLOCK_SIZE, BLOCK_SIZE))
-BEETROOTS_ADULT_COST = 4
+BEETROOTS_ADULT_COST = None
-BEETROOTS_GROW_COST = 10
+BEETROOTS_GROW_COST = None
 BEETROOTS_GROW_TIME = 5
 BEETROOTS_MAXIMUM_STATE = BEETROOTS_GROW_TIME * 3 + 1
 BEETROOTS_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'beetroots_stage_0.png'))
@@ -19,8 +19,8 @@ BEETROOTS_STAGE_3 = pygame.image.load(os.path.join('resources/images', 'beetroot
 BEETROOTS_STAGE_3 = pygame.transform.scale(BEETROOTS_STAGE_3, (BLOCK_SIZE, BLOCK_SIZE))
 CARROT = pygame.image.load(os.path.join('resources/images', 'carrot.png'))
 CARROT = pygame.transform.scale(CARROT, (BLOCK_SIZE, BLOCK_SIZE))
-CARROTS_ADULT_COST = 4
+CARROTS_ADULT_COST = None
-CARROTS_GROW_COST = 10
+CARROTS_GROW_COST = None
 CARROTS_GROW_TIME = 5
 CARROTS_MAXIMUM_STATE = CARROTS_GROW_TIME * 3 + 1
 CARROTS_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'carrots_stage_0.png'))
@@ -46,34 +46,34 @@ CART_DIRECTION_WEST = 4
 CART_FERTILIZER = 2
 CART_FUEL = 200
 CART_AMOUNT_OF_SEEDS_EACH_TYPE = 20
-CART_MAXIMUM_COLLECTED_PLANTS = 80
+CART_MAXIMUM_COLLECTED_PLANTS = 200
 CART_WATER_LEVEL = 40
 DIRT = pygame.image.load(os.path.join('resources/images', 'dirt.png'))
 DIRT = pygame.transform.scale(DIRT, (BLOCK_SIZE, BLOCK_SIZE))
-DIRT_COST = 2
+DIRT_COST = None
 FARMLAND_DRY = pygame.image.load(os.path.join('resources/images', 'farmland_dry.png'))
 FARMLAND_DRY = pygame.transform.scale(FARMLAND_DRY, (BLOCK_SIZE, BLOCK_SIZE))
-FARMLAND_DRY_COST = 3
+FARMLAND_DRY_COST = None
 FARMLAND_WET = pygame.image.load(os.path.join('resources/images', 'farmland_wet.png'))
 FARMLAND_WET = pygame.transform.scale(FARMLAND_WET, (BLOCK_SIZE, BLOCK_SIZE))
-FARMLAND_WET_COST = 1
+FARMLAND_WET_COST = None
 FLOWER_DANDELION = pygame.image.load(os.path.join('resources/images', 'flower_dandelion.png'))
 FLOWER_DANDELION = pygame.transform.scale(FLOWER_DANDELION, (BLOCK_SIZE, BLOCK_SIZE))
-FLOWER_DANDELION_COST = 5
+FLOWER_DANDELION_COST = None
 FLOWER_DANDELION_GROW_PROBABILITY = 0.01
 FLOWER_DANDELION_MAXIMUM_STATE = 1
 FONT_COLOR = (255, 255, 255)
 FONT_SIZE = 23
 FONT = pygame.font.Font(os.path.join('resources/font', 'Minecraft.ttf'), FONT_SIZE)
-FPS = 1
+FPS = 10
 HEIGHT_AMOUNT, WIDTH_AMOUNT = 10, 10
 HEIGHT_MAP, WIDTH_MAP = BLOCK_SIZE * HEIGHT_AMOUNT, BLOCK_SIZE * WIDTH_AMOUNT
 HEIGHT, WIDTH = HEIGHT_MAP + BLOCK_SIZE, WIDTH_MAP
 IMAGE_SIZE_NEURAL_NETWORK = 16
 POTATO = pygame.image.load(os.path.join('resources/images', 'potato.png'))
 POTATO = pygame.transform.scale(POTATO, (BLOCK_SIZE, BLOCK_SIZE))
-POTATOES_ADULT_COST = 4
+POTATOES_ADULT_COST = None
-POTATOES_GROW_COST = 10
+POTATOES_GROW_COST = None
 POTATOES_GROW_TIME = 5
 POTATOES_MAXIMUM_STATE = POTATOES_GROW_TIME * 3 + 1
 POTATOES_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'potatoes_stage_0.png'))
@@ -90,11 +90,11 @@ SPONGE_WET = pygame.image.load(os.path.join('resources/images', 'sponge_wet.png'
 SPONGE_WET = pygame.transform.scale(SPONGE_WET, (BLOCK_SIZE, BLOCK_SIZE))
 STATION = pygame.image.load(os.path.join('resources/images', 'rail_normal.png'))
 STATION = pygame.transform.scale(STATION, (BLOCK_SIZE, BLOCK_SIZE))
-STATION_COST = 6
+STATION_COST = None
 WHEAT = pygame.image.load(os.path.join('resources/images', 'wheat.png'))
 WHEAT = pygame.transform.scale(WHEAT, (BLOCK_SIZE, BLOCK_SIZE))
-WHEAT_ADULT_COST = 4
+WHEAT_ADULT_COST = None
-WHEAT_GROW_COST = 10
+WHEAT_GROW_COST = None
 WHEAT_GROW_TIME = 5
 WHEAT_MAXIMUM_STATE = WHEAT_GROW_TIME * 7 + 1
 WHEAT_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_0.png'))

geneticalgorithm.py

@@ -0,0 +1,180 @@
+import astar
+import cart
+import definitions
+import graph
+import map
+import os
+import pickle
+import plant
+import pygame
+import random
+import station
+import treelearn
+def create_genetic_algorithm():
+    if os.path.exists("resources/genetic_algorithm/optimal.pkl"): #jeżeli drzewo jest zapisane w pliku to odczytaj
+        astar_costs = pickle.load(open(os.path.join('resources/genetic_algorithm', "optimal.pkl"), "rb"))
+        definitions.BEETROOTS_ADULT_COST = astar_costs[0]
+        definitions.BEETROOTS_GROW_COST = astar_costs[1]
+        definitions.CARROTS_ADULT_COST = astar_costs[2]
+        definitions.CARROTS_GROW_COST = astar_costs[3]
+        definitions.DIRT_COST = astar_costs[4]
+        definitions.FARMLAND_DRY_COST = astar_costs[5]
+        definitions.FARMLAND_WET_COST = astar_costs[6]
+        definitions.FLOWER_DANDELION_COST = astar_costs[7]
+        definitions.POTATOES_ADULT_COST = astar_costs[8]
+        definitions.POTATOES_GROW_COST = astar_costs[9]
+        definitions.STATION_COST = astar_costs[10]
+        definitions.WHEAT_ADULT_COST = astar_costs[11]
+        definitions.WHEAT_GROW_COST = astar_costs[12]
+        print(definitions.BEETROOTS_ADULT_COST)
+        print(definitions.BEETROOTS_GROW_COST)
+    else:
+        astar_costs = [definitions.BEETROOTS_ADULT_COST, definitions.BEETROOTS_GROW_COST, definitions.CARROTS_ADULT_COST, definitions.CARROTS_GROW_COST, definitions.DIRT_COST, definitions.FARMLAND_DRY_COST, definitions.FARMLAND_WET_COST, definitions.FLOWER_DANDELION_COST, definitions.POTATOES_ADULT_COST, definitions.POTATOES_GROW_COST, definitions.STATION_COST, definitions.WHEAT_ADULT_COST, definitions.WHEAT_GROW_COST]
+        astar_costs = make_generations(astar_costs)
+        pickle.dump(astar_costs, open(os.path.join('resources/genetic_algorithm', "optimal.pkl"), "wb"))
+
+def fitness(astar_costs):
+    ans = solution(astar_costs)
+    if ans == 0:
+        return 0
+    else:
+        return 1 / ans
+def solution(astar_costs):
+    definitions.BEETROOTS_ADULT_COST = astar_costs[0]
+    definitions.BEETROOTS_GROW_COST = astar_costs[1]
+    definitions.CARROTS_ADULT_COST = astar_costs[2]
+    definitions.CARROTS_GROW_COST = astar_costs[3]
+    definitions.DIRT_COST = astar_costs[4]
+    definitions.FARMLAND_DRY_COST = astar_costs[5]
+    definitions.FARMLAND_WET_COST = astar_costs[6]
+    definitions.FLOWER_DANDELION_COST = astar_costs[7]
+    definitions.POTATOES_ADULT_COST = astar_costs[8]
+    definitions.POTATOES_GROW_COST = astar_costs[9]
+    definitions.STATION_COST = astar_costs[10]
+    definitions.WHEAT_ADULT_COST = astar_costs[11]
+    definitions.WHEAT_GROW_COST = astar_costs[12]
+    #tworzenie podstawowych obiektów
+    map1 = map.Map([])
+    map1.create_base_map()
+    move_list = ["rotate_left", "move", "move", "move", "move", "move", "move", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "move", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "rotate_left", "move", "rotate_left", "rotate_left", "rotate_left", "move"] #początkowe ruchy
+    amount_of_seeds_dict = {"beetroot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "carrot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "potato": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "wheat": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE}
+    collected_plants_dict_cart = {"beetroot": 0, "carrot": 0, "potato": 0, "wheat": 0}
+    collected_plants_dict_station = {"beetroot": 0, "carrot": 0, "potato": 0, "wheat": 0}
+    fertilizer_dict = {"beetroot": definitions.CART_FERTILIZER, "carrot": definitions.CART_FERTILIZER, "potato": definitions.CART_FERTILIZER, "wheat": definitions.CART_FERTILIZER}
+    station1 = station.Station(collected_plants_dict_station)
+    cart1 = cart.Cart(amount_of_seeds_dict, collected_plants_dict_cart, definitions.CART_DIRECTION_WEST, fertilizer_dict, definitions.CART_FUEL, definitions.CART_WATER_LEVEL, 0 * definitions.BLOCK_SIZE, 0 * definitions.BLOCK_SIZE)
+    cart1_rect = pygame.Rect(cart1.get_x(), cart1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
+    tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
+    decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
+    grow_flower_dandelion = False
+    random_movement = False
+    for run in range(1000): #liczba ruchów wózka
+        if not move_list: #jeżeli są jakieś ruchy do wykonania w move_list
+            grow_flower_dandelion = True
+            istate = graph.Istate(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE) #stan początkowy wózka (jego orientacja oraz jego aktualne miejsce)
+            if plant.Plant.if_any_mature_plant(map1) is True: #jeżeli istnieje jakaś dojrzała roślina
+                random_movement = False
+                if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
+                    move_list = (astar.graphsearch([], astar.f, [], plant.Plant.get_closest_mature_plant(istate, map1), istate, map1, graph.succ))  #lista z ruchami, które należy po kolei wykonać, astar
+                else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy
+                    move_list = (graph.graphsearch([], [], (0, 0), istate, graph.succ)) #lista z ruchami, które należy po kolei wykonać, graphsearch
+            else:
+                random_movement = True
+        elif move_list: #jeżeli move_list nie jest pusta
+            cart1.handle_movement(cart1_rect, move_list.pop(0)) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
+        if random_movement is True:
+            cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy
+        if grow_flower_dandelion is True:
+            plant.Plant.grow_flower_dandelion(map1) #losuj urośnięcie kwiatka dandeliona
+        cart1.do_work(cart1_rect, map1, station1) #wykonaj pracę na danym polu
+        decision = treelearn.make_decision(cart1.get_all_amount_of_seeds(), cart1.get_all_collected_plants(), cart1.get_all_fertilizer(), cart1.get_fuel(), tree, cart1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)
+        plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin
+
+
+
+    print("Ile zebrał: ", station1.get_collected_plants("beetroot") + station1.get_collected_plants("carrot") + station1.get_collected_plants("potato") + station1.get_collected_plants("wheat"))
+    return station1.get_collected_plants("beetroot") + station1.get_collected_plants("carrot") + station1.get_collected_plants("potato") + station1.get_collected_plants("wheat")
+
+
+
+def make_generations(astar_costs):
+    solutions = []
+    ans = []
+    for s in range(10): #liczba osobników zerowej generacji
+        solutions.append((random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10)))
+    for i in range(10): #liczba generacji
+        ranked_solutions = []
+        for s in solutions:
+            ranked_solutions.append((fitness(s), s))
+        ranked_solutions.sort()
+        print(f"=== Gen {i + 1} best solution ===")
+        #print(f"=== Gen {i} best solutions ===")
+        print(ranked_solutions[0])
+        ans.append(ranked_solutions[0])
+        print(ans)
+
+        #można dodać warunek stopu
+
+        best_solutions = ranked_solutions[:4] #najlepsze 4 osobniki w danej generacji
+        elements1 = []
+        elements2 = []
+        elements3 = []
+        elements4 = []
+        elements5 = []
+        elements6 = []
+        elements7 = []
+        elements8 = []
+        elements9 = []
+        elements10 = []
+        elements11 = []
+        elements12 = []
+        elements13 = []
+        for s in best_solutions:
+            elements1.append(s[1][0])
+            elements2.append(s[1][1])
+            elements3.append(s[1][2])
+            elements4.append(s[1][3])
+            elements5.append(s[1][4])
+            elements6.append(s[1][5])
+            elements7.append(s[1][6])
+            elements8.append(s[1][7])
+            elements9.append(s[1][8])
+            elements10.append(s[1][9])
+            elements11.append(s[1][10])
+            elements12.append(s[1][11])
+            elements13.append(s[1][12])
+
+        new_gen = []
+        for i in range(10):            # liczba osobników w kolejnej generacji
+            e1 = random.choice(elements1) * random.uniform(0.99, 1.01)          # mutacje
+            e2 = random.choice(elements2) * random.uniform(0.99, 1.01)
+            e3 = random.choice(elements3) * random.uniform(0.99, 1.01)
+            e4 = random.choice(elements4) * random.uniform(0.99, 1.01)
+            e5 = random.choice(elements5) * random.uniform(0.99, 1.01)
+            e6 = random.choice(elements6) * random.uniform(0.99, 1.01)
+            e7 = random.choice(elements7) * random.uniform(0.99, 1.01)
+            e8 = random.choice(elements8) * random.uniform(0.99, 1.01)
+            e9 = random.choice(elements9) * random.uniform(0.99, 1.01)
+            e10 = random.choice(elements10) * random.uniform(0.99, 1.01)
+            e11 = random.choice(elements11) * random.uniform(0.99, 1.01)
+            e12 = random.choice(elements12) * random.uniform(0.99, 1.01)
+            e13 = random.choice(elements13) * random.uniform(0.99, 1.01)
+
+            new_gen.append((e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13))
+
+        solutions = new_gen
+    ans.sort()
+    astar_costs[0] = ans[0][1][0]
+    astar_costs[1] = ans[0][1][1]
+    astar_costs[2] = ans[0][1][2]
+    astar_costs[3] = ans[0][1][3]
+    astar_costs[4] = ans[0][1][4]
+    astar_costs[5] = ans[0][1][5]
+    astar_costs[6] = ans[0][1][6]
+    astar_costs[7] = ans[0][1][7]
+    astar_costs[8] = ans[0][1][8]
+    astar_costs[9] = ans[0][1][9]
+    astar_costs[10] = ans[0][1][10]
+    astar_costs[11] = ans[0][1][11]
+    astar_costs[12] = ans[0][1][12]
+    return astar_costs

plant.py

@@ -1,4 +1,5 @@
 import definitions
+import graph
 import random
 class Plant:
     def __init__(self, name, state):
@@ -13,6 +14,55 @@ class Plant:
     def set_state(self, state):
         self.state = state
     @staticmethod
+    def get_closest_mature_plant(istate, map):  # TO DO, pobiera współrzędne najbliższej dojrzałej rośliny od miejsca, w którym znajduje się traktor
+        x = None  #x'owa pola
+        y = None  #y'kowa  pola
+        min = None
+        for i in range(definitions.WIDTH_AMOUNT):
+            for j in range(definitions.HEIGHT_AMOUNT):
+                field = map.fields[i][j]
+                if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
+                    if min is None:
+                        x = i
+                        y = j
+                        min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+                    else:
+                        if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
+                            x = i
+                            y = j
+                            min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+                elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
+                    if min is None:
+                        x = i
+                        y = j
+                        min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+                    else:
+                        if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
+                            x = i
+                            y = j
+                            min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+                elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE:
+                    if min is None:
+                        x = i
+                        y = j
+                        min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+                    else:
+                        if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
+                            x = i
+                            y = j
+                            min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+                elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE:
+                    if min is None:
+                        x = i
+                        y = j
+                        min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+                    else:
+                        if len((graph.graphsearch([], [], (x, y), istate, graph.succ))) < min:
+                            x = i
+                            y = j
+                            min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
+        return x, y
+    @staticmethod
     def grow_flower_dandelion(map): #metoda statyczna, losująca czy na danym polu ma urosnąć kwiat dandelion
         for i in range(definitions.WIDTH_AMOUNT):
             for j in range(definitions.HEIGHT_AMOUNT):
@@ -35,3 +85,17 @@ class Plant:
                     field.get_plant().set_state(field.get_plant().get_state() + 1)
                 elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.WHEAT_MAXIMUM_STATE:
                     field.get_plant().set_state(field.get_plant().get_state() + 1)
+    @staticmethod
+    def if_any_mature_plant(map): #sprawdza czy na polu występuje choć jedna dojrzała roślina, jeśli tak zwraca prawdę, w przeciwnym razie zwraca fałsz
+        for i in range(definitions.WIDTH_AMOUNT):
+            for j in range(definitions.HEIGHT_AMOUNT):
+                field = map.get_fields()[i][j]
+                if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
+                    return True
+                elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
+                    return True
+                elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE:
+                    return True
+                elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE:
+                    return True
+        return False

py.py

@@ -1,6 +1,7 @@
 import astar
 import cart
 import definitions
+import geneticalgorithm
 import graph
 import image_slicer
 import map
@@ -28,6 +29,7 @@ def main():
     clock = pygame.time.Clock()
     tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
     decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
+    geneticalgorithm.create_genetic_algorithm() #stworzenie algorytmu genetycznego
     classes, model = neuralnetwork.create_neural_network() #uczenie sieci neuronowej
     grow_flower_dandelion = False
     random_movement = False