refactor #26
13
App.py
13
App.py
@ -11,6 +11,7 @@ import BFS
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import AStar
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import random
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import Condition
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import Drzewo
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bfs1_flag=False
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bfs2_flag=False #Change this lines to show different bfs implementation
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@ -19,7 +20,7 @@ Astar = False
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Astar2 = False
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if bfs3_flag or Astar or Astar2:
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Pole.stoneFlag = True
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TreeFlag=True
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pygame.init()
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show_console=True
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@ -36,7 +37,7 @@ ui=Ui.Ui(screen)
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traktor_slot = pole.get_slot_from_cord((0, 0))
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traktor = Tractor.Tractor(traktor_slot, screen, Osprzet.opryskiwacz,clock,bfs2_flag)
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condition=Condition.Condition()
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drzewo=Drzewo.Drzewo()
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def init_demo(): #Demo purpose
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old_info=""
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@ -115,10 +116,14 @@ def init_demo(): #Demo purpose
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else:
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print_to_console("Nie można znaleźć ścieżki A*") # Wyświetl komunikat, jeśli nie znaleziono ścieżki
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if(TreeFlag):
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drzewo.treeLearn()
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drzewo.plotTree()
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print("Decyzja to: ",drzewo.makeDecision([[10,60,0,1,0,20,1,20]]))
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#do moves and condtion cycles
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start_flag=False
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# demo_move()
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condition.cycle()
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condition.cycle() #powinno zostac wrzucone razem z getCondition do ruchu traktora. Aktualnie tutaj by zobaczyc czy dziala
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condition.getCondition()
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old_info=get_info(old_info)
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for event in pygame.event.get():
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3
Data/dataTree.csv
Normal file
3
Data/dataTree.csv
Normal file
@ -0,0 +1,3 @@
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plant_water_lever,tractor_water_lever,weather,season,current_time,growth,disease,fertility,action
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80,60,0,1,0,20,1,20,0
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20,60,0,1,0,20,1,20,1
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25
Drzewo.py
Normal file
25
Drzewo.py
Normal file
@ -0,0 +1,25 @@
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from sklearn import tree as skltree
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import pandas,os
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import matplotlib.pyplot as plt
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atributes=['plant_water_lever','tractor_water_lever','weather','season','current_time','growth','disease','fertility'] #Columns in CSV file should be in the same order
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class Drzewo:
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def __init__(self):
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self.tree=self.treeLearn()
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def treeLearn(self):
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csvdata=pandas.read_csv('Data/dataTree.csv')
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x=csvdata[atributes]
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decision=csvdata['action']
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self.tree=skltree.DecisionTreeClassifier()
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self.tree=self.tree.fit(x,decision)
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def plotTree(self):
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plt.figure()
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skltree.plot_tree(self.tree,filled=True,feature_names=atributes)
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plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych")
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plt.show()
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def makeDecision(self,values):
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action=self.tree.predict(values) #0- nie podlewac, 1-podlewac
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return action
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@ -1,6 +1,7 @@
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Required packages:
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pygame,matplotlib,sklearn
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pygame,matplotlib,sklearn,pandas
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How to install:
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pip install pygame
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pip install matplotlib
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pip install scikit-learn
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pip install scikit-learn
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pip install pandas
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