treelearn
This commit is contained in:
Tomasz Adamczyk
parent
dea6adc796
commit
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9
py.py
9
py.py
@ -20,7 +20,8 @@ def main():
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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)
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tractor1_rect = pygame.Rect(tractor1.get_x(), tractor1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
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clock = pygame.time.Clock()
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treelearn.treelearn()
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tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
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decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
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run = True
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while run: #pętla główna programu
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clock.tick(definitions.FPS)
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@ -31,12 +32,16 @@ def main():
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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
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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)
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#move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać, graph
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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
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if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
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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
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else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy
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move_list = (graph.graphsearch([], [], istate, graph.succ, (0, 0))) #lista z ruchami, które należy po kolei wykonać, graphsearch
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elif move_list: #jeżeli move_list nie jest pusta
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tractor1.handle_movement(move_list.pop(0), tractor1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
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else:
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tractor1.handle_random_movement(tractor1_rect) #wykonuj losowe ruchy
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tractor1.do_work(map1, station1, tractor1_rect) #wykonaj pracę na danym polu
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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)
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plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin
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pygame.quit()
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if __name__ == "__main__":
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18
treelearn.py
18
treelearn.py
@ -5,16 +5,20 @@ import pandas
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import pydotplus
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from sklearn import tree
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from sklearn.tree import DecisionTreeClassifier
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def treelearn():
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df = pandas.read_csv(os.path.join('resources', 'data.csv'))
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def treelearn(): #zwraca utworzone drzewo decyzyjne
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df = pandas.read_csv(os.path.join('resources', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv
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features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
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x = df[features]
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y = df['back to station']
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dtree = DecisionTreeClassifier()
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dtree = dtree.fit(x, y)
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x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo
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y = df['back to station'] #podjęte decyzje
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dtree = DecisionTreeClassifier() #tworzy obiekt drzewa
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dtree = dtree.fit(x, y) #uczy drzewo
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data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
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graph = pydotplus.graph_from_dot_data(data)
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graph.write_png(os.path.join('resources', 'mydecisiontree.png'))
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img = pltimg.imread(os.path.join('resources', 'mydecisiontree.png'))
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imgplot = plt.imshow(img)
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plt.show()
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plt.show() #wyświetl drzewo decyzyjne
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return dtree
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def make_decision(tree, amount_of_seeds, collected_plants, fertilizer, fuel, water_level): #zwraca decyzję o powrocie do stacji (0 : NIE, 1 : TAK)
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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
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return decision
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