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treelearn

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Tomasz Adamczyk 2021-05-16 15:36:53 +02:00
parent dea6adc796
commit 4b73064f4e
5 changed files with 31 additions and 21 deletions
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25
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__pycache__/definitions.cpython-37.pyc
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__pycache__/treelearn.cpython-37.pyc
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9
py.py
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@ -20,7 +20,8 @@ def main():
tractor1 = tractor.Tractor(amount_of_seeds_dict, collected_plants_dict, definitions.TRACTOR_DIRECTION_NORTH, fertilizer_dict, definitions.TRACTOR_FUEL, definitions.TRACTOR_WATER_LEVEL, 0, 0) tractor1 = tractor.Tractor(amount_of_seeds_dict, collected_plants_dict, definitions.TRACTOR_DIRECTION_NORTH, fertilizer_dict, definitions.TRACTOR_FUEL, definitions.TRACTOR_WATER_LEVEL, 0, 0)
tractor1_rect = pygame.Rect(tractor1.get_x(), tractor1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE) tractor1_rect = pygame.Rect(tractor1.get_x(), tractor1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
clock = pygame.time.Clock() clock = pygame.time.Clock()
treelearn.treelearn() tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
run = True run = True
while run: #pętla główna programu while run: #pętla główna programu
clock.tick(definitions.FPS) clock.tick(definitions.FPS)
@ -31,12 +32,16 @@ def main():
if not move_list and plant.Plant.if_any_mature_plant(map1) is True: #jeżeli są jakieś ruchy do wykonania w move_list oraz istnieje jakaś dojrzała roślina if not move_list and plant.Plant.if_any_mature_plant(map1) is True: #jeżeli są jakieś ruchy do wykonania w move_list oraz istnieje jakaś dojrzała roślina
istate = graph.Istate(tractor1.get_direction(), tractor1.get_x() / definitions.BLOCK_SIZE, tractor1.get_y() / definitions.BLOCK_SIZE) #stan początkowy traktora (jego orientacja oraz jego aktualne współrzędne) istate = graph.Istate(tractor1.get_direction(), tractor1.get_x() / definitions.BLOCK_SIZE, tractor1.get_y() / definitions.BLOCK_SIZE) #stan początkowy traktora (jego orientacja oraz jego aktualne współrzędne)
#move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać, graph #move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać, graph
move_list = (astar.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, istate), astar.f, map1)) #lista z ruchami, które należy po kolei wykonać, astar if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
move_list = (astar.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, istate), astar.f, map1)) #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([], [], istate, graph.succ, (0, 0))) #lista z ruchami, które należy po kolei wykonać, graphsearch
elif move_list: #jeżeli move_list nie jest pusta elif move_list: #jeżeli move_list nie jest pusta
tractor1.handle_movement(move_list.pop(0), tractor1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy tractor1.handle_movement(move_list.pop(0), tractor1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
else: else:
tractor1.handle_random_movement(tractor1_rect) #wykonuj losowe ruchy tractor1.handle_random_movement(tractor1_rect) #wykonuj losowe ruchy
tractor1.do_work(map1, station1, tractor1_rect) #wykonaj pracę na danym polu tractor1.do_work(map1, station1, tractor1_rect) #wykonaj pracę na danym polu
decision = treelearn.make_decision(tree, tractor1.get_all_amount_of_seeds(), tractor1.get_all_collected_plants(), tractor1.get_all_fertilizer(), tractor1.get_fuel(), tractor1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)
plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin
pygame.quit() pygame.quit()
if __name__ == "__main__": if __name__ == "__main__":

18
treelearn.py
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@ -5,16 +5,20 @@ import pandas
import pydotplus import pydotplus
from sklearn import tree from sklearn import tree
from sklearn.tree import DecisionTreeClassifier from sklearn.tree import DecisionTreeClassifier
def treelearn(): def treelearn(): #zwraca utworzone drzewo decyzyjne
df = pandas.read_csv(os.path.join('resources', 'data.csv')) df = pandas.read_csv(os.path.join('resources', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv
features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level'] features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
x = df[features] x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo
y = df['back to station'] y = df['back to station'] #podjęte decyzje
dtree = DecisionTreeClassifier() dtree = DecisionTreeClassifier() #tworzy obiekt drzewa
dtree = dtree.fit(x, y) dtree = dtree.fit(x, y) #uczy drzewo
data = tree.export_graphviz(dtree, out_file=None, feature_names=features) data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
graph = pydotplus.graph_from_dot_data(data) graph = pydotplus.graph_from_dot_data(data)
graph.write_png(os.path.join('resources', 'mydecisiontree.png')) graph.write_png(os.path.join('resources', 'mydecisiontree.png'))
img = pltimg.imread(os.path.join('resources', 'mydecisiontree.png')) img = pltimg.imread(os.path.join('resources', 'mydecisiontree.png'))
imgplot = plt.imshow(img) imgplot = plt.imshow(img)
plt.show() plt.show() #wyświetl drzewo decyzyjne
return dtree
def make_decision(tree, amount_of_seeds, collected_plants, fertilizer, fuel, water_level): #zwraca decyzję o powrocie do stacji (0 : NIE, 1 : TAK)
decision = tree.predict([[amount_of_seeds, collected_plants, fertilizer, fuel, water_level]]) #podejmij decyzję na podstawie aktualnych parametrów traktora o powrocie do stacji lub nie
return decision