refactor #26
5
.gitignore
vendored
5
.gitignore
vendored
@ -1,2 +1,5 @@
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__pycache__/
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.idea/
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.idea/
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tree.png
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dataset/
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dataset.zip
|
49
Climate.py
Normal file
49
Climate.py
Normal file
@ -0,0 +1,49 @@
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#THESE DICTIONARIES ARE USED FOR DISPLAY AND FOR DOCUMENTATION PURPOSES
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seasons={
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0:"zima",
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1:"wiosna",
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2:"lato",
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3:"jesien"}
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time={
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0:"rano",
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1:"poludnie",
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2:"wieczor",
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3:"noc"}
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rain={
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0:"brak",
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1:"lekki deszcz",
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2:"normalny deszcz",
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3:"ulewa"
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}
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temperature={
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0:"bardzo zimno",
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1:"zimno",
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2:"przecietnie",
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3:"cieplo",
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4:"upal",}
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def getNextSeason(season):
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if(season==3):
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return 0
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else:
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return season+1
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def getNextTime(currentTime):
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if(currentTime==3):
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return 0
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else:
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return currentTime+1
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def getAmount(type):
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if(type=="seasons"):
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return len(seasons)
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if(type=="rain"):
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return len(rain)
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if(type=="time"):
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return len(time)
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if(type=="temperature"):
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return len(temperature)
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47
Condition.py
Normal file
47
Condition.py
Normal file
@ -0,0 +1,47 @@
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import random
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import Climate
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import Ui
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class Condition:
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def __init__(self):
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self.season=self.setRandomSeason()
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self.currentTime=self.setRandomTime()
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self.rain=self.setRandomRain()
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self.temperature=self.setRandomRain()
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self.clock=0
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def setRandomSeason(self):
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return self.randomizer(Climate.getAmount("seasons"))
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def setRandomTime(self):
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return self.randomizer(Climate.getAmount("time"))
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def setRandomRain(self):
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return self.randomizer(Climate.getAmount("rain"))
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def setRandomTemperature(self):
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return self.randomizer(Climate.getAmount("temperature"))
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def randomizer(self,max):
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return random.randint(0,max-1)
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def cycle(self):
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if(self.clock==11):
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self.currentTime=0
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self.rain=self.setRandomRain()
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self.temperature=self.setRandomTemperature()
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self.season=Climate.getNextSeason(self.season)
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self.clock=0
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return
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else:
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self.currentTime=Climate.getNextTime(self.currentTime)
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self.rain=self.setRandomRain()
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self.temperature=self.setRandomTemperature()
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self.clock=self.clock+1
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def return_condition(self):
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return [self.temperature,self.rain,self.season,self.currentTime]
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def getCondition(self):
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return ([Climate.temperature[self.temperature],Climate.rain[self.rain],Climate.seasons[self.season],Climate.time[self.currentTime]])
|
9219
Data/dataTree.csv
Normal file
9219
Data/dataTree.csv
Normal file
File diff suppressed because it is too large
Load Diff
248
Data/dataTree2.csv
Normal file
248
Data/dataTree2.csv
Normal file
@ -0,0 +1,248 @@
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plant_water_level,growth,disease,fertility,tractor_water_level,temperature,rain,season,current_time,action
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1,20,0,40,60,2,0,2,1,1
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||||
20,40,0,40,60,2,0,2,1,1
|
||||
87,20,0,40,60,2,0,2,1,0
|
||||
27,43,1,40,60,2,0,2,1,0
|
||||
89,56,1,40,60,2,1,1,1,0
|
||||
67,100,1,37,55,1,3,3,3,0
|
||||
67,40,1,87,90,4,0,1,0,0
|
||||
1,20,0,40,60,2,0,0,1,0
|
||||
20,40,0,40,60,2,0,0,1,0
|
||||
87,20,0,56,45,2,0,0,2,0
|
||||
27,43,1,40,60,2,0,0,3,0
|
||||
89,56,1,40,89,2,1,0,1,0
|
||||
67,100,1,37,55,1,3,0,3,0
|
||||
67,40,1,87,90,4,0,0,0,0
|
||||
1,100,0,45,20,2,0,2,1,0
|
||||
20,100,0,40,34,0,1,2,0,0
|
||||
87,100,0,56,60,2,0,1,1,0
|
||||
27,100,0,89,67,1,2,2,2,0
|
||||
89,100,0,40,60,2,1,1,1,0
|
||||
76,100,0,37,55,1,3,3,3,0
|
||||
67,100,0,87,90,4,0,1,0,0
|
||||
1,20,0,40,0,2,0,2,1,0
|
||||
20,40,0,40,0,2,0,2,1,0
|
||||
87,20,0,40,0,2,0,2,1,0
|
||||
27,43,1,40,0,2,0,2,1,0
|
||||
89,56,1,40,0,2,1,1,1,0
|
||||
67,100,1,37,0,1,3,3,3,0
|
||||
67,40,1,87,0,4,0,1,0,0
|
||||
1,20,0,40,0,2,0,0,1,0
|
||||
20,40,0,40,0,2,0,0,1,0
|
||||
87,20,0,56,0,2,0,0,2,0
|
||||
27,43,1,40,0,2,0,0,3,0
|
||||
89,56,1,40,0,2,1,0,1,0
|
||||
67,100,1,37,0,1,3,0,3,0
|
||||
67,40,1,87,0,4,0,0,0,0
|
||||
1,100,0,45,0,2,0,2,1,0
|
||||
20,100,0,40,0,0,1,2,0,0
|
||||
87,100,0,56,0,2,0,1,1,0
|
||||
27,100,0,89,0,1,2,2,2,0
|
||||
89,100,0,40,0,2,1,1,1,0
|
||||
76,100,0,37,0,1,3,3,3,0
|
||||
67,100,0,87,0,4,0,1,0,0
|
||||
1,45,0,56,44,2,1,1,1,1
|
||||
20,55,0,43,34,2,0,2,2,1
|
||||
15,23,0,23,26,2,1,3,3,1
|
||||
45,67,0,12,67,3,0,1,0,1
|
||||
59,88,0,34,87,3,0,2,1,1
|
||||
32,32,0,32,90,3,0,3,2,1
|
||||
44,43,0,19,27,2,0,1,3,1
|
||||
33,11,0,28,76,2,0,2,0,1
|
||||
54,90,0,44,5,3,0,3,1,1
|
||||
21,76,0,50,25,3,1,1,2,1
|
||||
29,64,0,38,36,2,0,2,3,1
|
||||
11,54,0,65,44,3,1,1,2,1
|
||||
23,55,0,34,43,3,0,2,1,1
|
||||
51,32,0,32,62,3,1,3,3,1
|
||||
54,76,0,21,76,2,0,1,2,1
|
||||
95,88,0,43,78,2,0,2,1,0
|
||||
23,23,0,23,9,2,0,3,3,1
|
||||
44,34,0,91,72,3,0,1,0,1
|
||||
33,11,0,82,67,3,0,2,2,1
|
||||
45,9,0,44,50,2,0,3,3,1
|
||||
21,67,0,50,52,2,1,1,0,1
|
||||
92,46,0,83,63,3,0,2,1,0
|
||||
20,55,1,43,34,0,0,2,2,0
|
||||
15,23,1,23,26,0,1,3,3,0
|
||||
45,67,1,12,67,0,0,1,0,0
|
||||
59,88,1,34,87,0,0,2,1,0
|
||||
32,32,0,32,90,0,0,3,2,0
|
||||
44,43,0,19,27,4,0,1,3,0
|
||||
33,11,0,28,76,4,0,2,0,0
|
||||
54,90,0,44,5,4,0,3,1,0
|
||||
21,76,0,50,25,4,1,1,2,0
|
||||
29,64,0,38,36,4,0,2,3,0
|
||||
11,54,0,65,44,0,1,1,2,0
|
||||
23,55,0,34,43,0,0,2,1,0
|
||||
51,32,0,32,62,0,1,3,3,0
|
||||
80,76,1,39,7,3,0,1,0,0
|
||||
98,77,0,15,91,1,3,2,3,0
|
||||
3,48,1,73,41,2,2,0,3,0
|
||||
20,15,1,97,87,4,1,2,1,0
|
||||
93,6,0,37,0,0,1,0,1,0
|
||||
4,31,0,1,5,2,3,1,2,0
|
||||
42,52,0,33,19,3,2,3,0,0
|
||||
76,43,0,77,18,4,0,0,3,0
|
||||
31,13,1,21,42,0,1,2,3,0
|
||||
96,65,1,63,35,1,3,3,2,0
|
||||
29,39,0,40,37,3,3,0,0,0
|
||||
82,53,0,55,9,0,1,3,2,0
|
||||
21,35,0,58,1,1,2,2,0,0
|
||||
92,98,0,69,16,3,0,0,1,0
|
||||
34,23,0,95,2,2,3,0,3,0
|
||||
36,28,0,62,22,0,1,1,1,0
|
||||
66,88,1,10,85,3,1,2,3,0
|
||||
53,51,0,79,90,2,2,3,2,0
|
||||
9,74,0,60,4,4,1,2,3,1
|
||||
17,0,0,38,58,1,2,3,0,0
|
||||
12,76,0,50,25,3,1,1,2,1
|
||||
92,64,0,38,36,2,0,2,3,0
|
||||
11,54,0,65,44,3,1,1,2,1
|
||||
32,55,0,34,43,3,0,2,1,1
|
||||
15,32,0,32,62,3,1,3,3,1
|
||||
45,76,0,21,76,2,0,1,2,1
|
||||
59,88,0,43,78,2,0,2,1,1
|
||||
32,23,0,23,9,2,0,3,3,1
|
||||
14,34,0,91,72,3,0,1,0,1
|
||||
13,11,0,82,67,3,0,2,2,1
|
||||
45,9,0,44,50,2,0,3,3,1
|
||||
21,67,0,50,52,2,1,1,0,1
|
||||
92,46,0,83,63,3,0,2,1,0
|
||||
2,40,1,34,43,1,3,2,2,0
|
||||
51,32,1,32,62,2,1,3,3,0
|
||||
54,76,1,21,76,3,0,1,0,0
|
||||
98,38,0,50,44,4,0,1,0,0
|
||||
63,7,0,93,79,2,0,2,1,1
|
||||
91,59,0,94,24,4,0,3,2,0
|
||||
11,49,0,54,76,2,0,1,3,1
|
||||
33,31,0,59,39,3,0,1,3,1
|
||||
28,50,0,26,0,4,0,2,2,0
|
||||
54,83,0,36,0,3,0,2,1,0
|
||||
49,78,0,68,0,2,0,3,2,0
|
||||
59,21,0,43,100,1,0,3,2,1
|
||||
1,30,0,52,100,2,0,0,3,0
|
||||
60,9,0,40,40,3,0,0,3,0
|
||||
85,94,0,87,85,4,0,1,3,0
|
||||
79,68,0,56,90,1,0,2,2,1
|
||||
75,22,0,25,95,1,0,3,2,1
|
||||
100,51,0,33,12,0,0,2,2,0
|
||||
90,70,0,71,81,0,0,2,1,0
|
||||
47,26,0,6,78,4,0,1,1,1
|
||||
14,89,0,70,18,4,0,1,0,1
|
||||
99,19,0,74,91,2,0,3,0,0
|
||||
18,48,0,15,32,2,0,3,0,1
|
||||
5,57,0,14,34,0,1,1,3,1
|
||||
22,67,0,9,5,0,1,2,2,0
|
||||
95,81,0,46,86,1,1,3,1,0
|
||||
39,65,0,84,0,1,1,0,0,0
|
||||
84,75,0,30,0,2,1,1,1,0
|
||||
86,41,0,2,67,2,1,2,2,0
|
||||
64,53,0,53,47,1,1,3,3,1
|
||||
69,61,0,0,73,2,1,0,0,0
|
||||
94,40,1,0,18,3,1,1,2,0
|
||||
62,82,1,20,50,4,1,2,3,0
|
||||
57,1,1,17,92,0,1,3,2,0
|
||||
80,35,1,58,45,0,0,3,1,0
|
||||
30,47,1,8,47,1,0,2,1,0
|
||||
82,32,0,99,39,1,3,1,3,0
|
||||
20,84,0,0,51,2,3,2,3,0
|
||||
42,88,0,0,54,2,2,2,0,0
|
||||
66,45,0,91,10,3,2,1,0,0
|
||||
81,14,0,19,55,3,0,1,2,1
|
||||
74,37,0,88,78,4,0,3,2,1
|
||||
89,99,0,100,60,4,0,3,3,0
|
||||
15,20,0,45,11,0,0,1,3,1
|
||||
92,28,0,85,90,2,0,1,1,0
|
||||
55,4,0,13,95,2,0,2,1,1
|
||||
2,6,0,35,0,2,0,2,0,0
|
||||
61,56,0,90,0,2,0,3,0,0
|
||||
76,11,0,61,10,3,0,3,1,1
|
||||
26,80,0,57,9,3,0,1,2,1
|
||||
40,44,0,81,8,3,0,2,3,1
|
||||
50,66,0,23,7,3,0,3,0,1
|
||||
48,15,0,77,6,2,0,0,1,0
|
||||
11,54,0,65,44,3,3,1,2,0
|
||||
23,55,0,34,43,3,3,2,1,0
|
||||
51,32,0,32,62,3,3,3,3,0
|
||||
54,76,0,21,76,2,3,1,2,0
|
||||
95,88,0,43,78,2,3,2,1,0
|
||||
23,23,0,23,9,2,3,3,3,0
|
||||
44,34,0,91,72,3,3,1,0,0
|
||||
33,11,0,82,67,3,3,2,2,0
|
||||
45,9,0,44,50,2,3,3,3,0
|
||||
21,67,0,50,52,2,3,1,0,0
|
||||
92,46,0,83,63,3,3,2,1,0
|
||||
20,55,1,43,34,0,3,2,2,0
|
||||
15,23,1,23,26,0,3,3,3,0
|
||||
45,67,1,12,67,0,3,1,0,0
|
||||
59,88,1,34,87,0,3,2,1,0
|
||||
32,32,0,32,90,0,3,3,2,0
|
||||
1,60,0,55,11,0,1,0,0,1
|
||||
2,70,0,44,12,1,1,0,1,1
|
||||
3,44,0,11,13,2,1,0,2,1
|
||||
4,55,0,34,66,3,0,0,3,1
|
||||
5,66,0,90,77,0,0,1,2,1
|
||||
6,22,0,89,88,0,0,2,2,1
|
||||
7,1,0,45,9,0,1,2,3,1
|
||||
8,2,0,34,22,3,1,2,3,1
|
||||
9,3,0,56,34,3,1,0,1,1
|
||||
10,6,0,78,5,3,0,3,1,1
|
||||
11,8,0,36,67,2,0,0,0,1
|
||||
12,59,0,57,23,2,1,1,0,1
|
||||
13,67,0,29,34,1,1,0,1,1
|
||||
14,20,0,30,90,1,1,2,2,1
|
||||
15,21,0,66,89,0,1,3,3,1
|
||||
44,100,0,91,72,3,3,1,0,0
|
||||
33,100,0,82,67,3,3,2,2,0
|
||||
45,100,0,44,50,2,3,3,3,0
|
||||
21,100,0,50,52,2,3,1,0,0
|
||||
92,100,0,83,63,3,3,2,1,0
|
||||
20,100,1,43,34,0,3,2,2,0
|
||||
15,100,1,23,26,0,3,3,3,0
|
||||
45,100,1,12,67,0,3,1,0,0
|
||||
59,100,1,34,87,0,3,2,1,0
|
||||
32,100,0,32,90,0,3,3,2,0
|
||||
1,100,0,55,11,0,1,0,0,0
|
||||
2,100,0,44,12,1,1,0,1,0
|
||||
3,100,0,11,13,2,1,0,2,0
|
||||
4,100,0,34,66,3,0,0,3,0
|
||||
5,100,0,90,77,0,0,1,2,0
|
||||
6,100,0,89,88,0,0,2,2,0
|
||||
7,100,0,45,9,0,1,2,3,0
|
||||
8,100,0,34,22,3,1,2,3,0
|
||||
9,100,0,56,34,3,1,0,1,0
|
||||
10,100,0,78,5,3,0,3,1,0
|
||||
11,100,0,36,67,2,0,0,0,0
|
||||
12,100,0,57,23,2,1,1,0,0
|
||||
13,100,0,29,34,1,1,0,1,0
|
||||
14,100,0,30,90,1,1,2,2,0
|
||||
15,100,0,66,89,0,1,3,3,0
|
||||
1,6,0,5,10,4,1,1,3,1
|
||||
2,7,0,4,20,4,1,2,2,1
|
||||
3,4,0,11,30,4,1,3,1,1
|
||||
4,5,0,43,5,2,0,1,2,1
|
||||
5,6,0,9,17,2,0,2,1,1
|
||||
6,2,0,98,18,4,0,3,1,1
|
||||
7,11,0,54,19,4,1,0,2,1
|
||||
8,20,0,43,22,4,1,1,1,1
|
||||
9,30,0,65,43,4,1,2,3,1
|
||||
10,60,0,87,50,1,0,3,3,1
|
||||
11,80,0,63,76,1,0,0,2,1
|
||||
12,95,0,75,32,1,1,1,1,1
|
||||
13,76,0,30,43,2,1,2,0,1
|
||||
14,2,0,92,9,2,1,3,0,1
|
||||
1,6,0,5,10,4,3,1,3,0
|
||||
2,7,0,4,20,4,3,2,2,0
|
||||
3,4,0,11,30,4,3,3,1,0
|
||||
4,5,0,43,5,2,3,1,2,0
|
||||
5,6,0,9,17,2,3,2,1,0
|
||||
6,2,0,98,18,4,3,3,1,0
|
||||
7,11,0,54,19,4,3,0,2,0
|
||||
8,20,0,43,22,4,3,1,1,0
|
||||
9,30,0,65,43,4,3,2,3,0
|
||||
10,60,0,87,50,1,3,3,3,0
|
||||
11,80,0,63,76,1,3,0,2,0
|
||||
12,95,0,75,32,1,3,1,1,0
|
||||
13,76,0,30,43,2,3,2,0,0
|
||||
14,2,0,92,9,2,3,3,0,0
|
|
29
Drzewo.py
Normal file
29
Drzewo.py
Normal file
@ -0,0 +1,29 @@
|
||||
from sklearn import tree as skltree
|
||||
import pandas,os
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
atributes=['plant_water_level','growth','disease','fertility','tractor_water_level','temperature','rain','season','current_time'] #Columns in CSV file has to be in the same order
|
||||
class Drzewo:
|
||||
def __init__(self):
|
||||
self.tree=self.treeLearn()
|
||||
|
||||
def treeLearn(self):
|
||||
csvdata=pandas.read_csv('Data/dataTree.csv')
|
||||
#csvdata = pandas.read_csv('Data/dataTree2.csv')
|
||||
x=csvdata[atributes]
|
||||
decision=csvdata['action']
|
||||
self.tree=skltree.DecisionTreeClassifier()
|
||||
self.tree=self.tree.fit(x.values,decision)
|
||||
|
||||
def plotTree(self):
|
||||
plt.figure(figsize=(20,30))
|
||||
skltree.plot_tree(self.tree,filled=True,feature_names=atributes)
|
||||
plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych: ")
|
||||
plt.savefig('tree.png')
|
||||
#plt.show()
|
||||
def makeDecision(self,values):
|
||||
action=self.tree.predict([values]) #0- nie podlewac, 1-podlewac
|
||||
if(action==[0]):
|
||||
return "Nie"
|
||||
if(action==[1]):
|
||||
return "Tak"
|
29
Image.py
29
Image.py
@ -1,6 +1,8 @@
|
||||
import pygame
|
||||
import displayControler as dCon
|
||||
import random
|
||||
import neuralnetwork
|
||||
import os
|
||||
|
||||
class Image:
|
||||
def __init__(self):
|
||||
@ -37,7 +39,7 @@ class Image:
|
||||
self.gasStation_image=pygame.transform.scale(gasStation,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
|
||||
|
||||
def return_random_plant(self):
|
||||
x=random.randint(0,7)
|
||||
x=random.randint(0,5) #disabled dirt and mud generation
|
||||
keys=list(self.plants_image_dict.keys())
|
||||
plant=keys[x]
|
||||
return (plant,self.plants_image_dict[plant])
|
||||
@ -53,3 +55,28 @@ class Image:
|
||||
|
||||
def return_gasStation(self):
|
||||
return self.gasStation_image
|
||||
|
||||
# losowanie zdjęcia z testowego datasetu bez powtórzeń
|
||||
imagePathList = []
|
||||
def getRandomImageFromDataBase():
|
||||
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)
|
||||
|
||||
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
|
||||
|
19
Pole.py
19
Pole.py
@ -6,6 +6,8 @@ import time
|
||||
import Ui
|
||||
import math
|
||||
import random
|
||||
import neuralnetwork
|
||||
import Image
|
||||
|
||||
stoneList = [(3,3), (3,4), (3,5), (3,6), (4,6), (5,6), (6,6), (7,6), (8,6), (9,6), (10,6), (11,6), (12,6), (13,6), (14,6), (15,6), (16,6), (16,7), (16,8), (16,9)]
|
||||
stoneFlag = False
|
||||
@ -30,7 +32,7 @@ class Pole:
|
||||
return self.slot_dict
|
||||
|
||||
#Draw grid and tractor (new one)
|
||||
def draw_grid(self):
|
||||
def draw_grid(self, nn=False):
|
||||
for x in range(0,dCon.NUM_X): #Draw all cubes in X axis
|
||||
for y in range(0,dCon.NUM_Y): #Draw all cubes in Y axis
|
||||
new_slot=Slot.Slot(x,y,Colors.BROWN,self.screen,self.image_loader) #Creation of empty slot
|
||||
@ -44,19 +46,22 @@ class Pole:
|
||||
for i in stoneList:
|
||||
st=self.slot_dict[i]
|
||||
st.set_stone_image()
|
||||
if self.gasStation[0] != -1:
|
||||
st=self.slot_dict[self.gasStation]
|
||||
st.set_gasStation_image()
|
||||
if self.gasStation[0] != -1:
|
||||
st=self.slot_dict[self.gasStation]
|
||||
st.set_gasStation_image()
|
||||
|
||||
def randomize_colors(self):
|
||||
def randomize_colors(self, nn = False):
|
||||
pygame.display.update()
|
||||
time.sleep(3)
|
||||
#self.ui.render_text("Randomizing Crops")
|
||||
for coordinates in self.slot_dict:
|
||||
if(coordinates==(0,0) or coordinates in stoneList or coordinates == self.gasStation):
|
||||
if(stoneFlag):
|
||||
if( coordinates in stoneList or coordinates == self.gasStation ):
|
||||
continue
|
||||
if(coordinates==(0,0)):
|
||||
continue
|
||||
else:
|
||||
self.slot_dict[coordinates].set_random_plant()
|
||||
self.slot_dict[coordinates].set_random_plant(nn)
|
||||
|
||||
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)
|
||||
|
@ -110,8 +110,14 @@ class Roslina:
|
||||
def return_stan(self):
|
||||
return self.stan
|
||||
|
||||
def return_stan_for_tree(self):
|
||||
return self.stan.return_stan_for_tree()
|
||||
|
||||
def get_hydrate_stats(self):
|
||||
return self.stan.return_hydrate()
|
||||
|
||||
def report_status(self):
|
||||
return f"Nazwa rosliny: {self.nazwa} "+self.stan.report_all()
|
||||
return f"Nazwa rosliny: {self.nazwa} "+self.stan.report_all()
|
||||
|
||||
def return_status_tree(self):
|
||||
return self.stan.return_stan_for_tree()
|
27
Slot.py
27
Slot.py
@ -16,14 +16,20 @@ class Slot:
|
||||
self.field=pygame.Rect(self.x_axis*dCon.CUBE_SIZE,self.y_axis*dCon.CUBE_SIZE,dCon.CUBE_SIZE,dCon.CUBE_SIZE)
|
||||
self.image_loader=image_loader
|
||||
self.garage_image=None
|
||||
self.label = None
|
||||
self.imagePath = None
|
||||
|
||||
def draw(self):
|
||||
pygame.draw.rect(self.screen,Colors.BROWN,self.field,0) #Draw field
|
||||
pygame.draw.rect(self.screen,Colors.BLACK,self.field,BORDER_THICKNESS) #Draw border
|
||||
pygame.display.update()
|
||||
|
||||
def redraw_image(self):
|
||||
self.mark_visited()
|
||||
def redraw_image(self, destroy = True):
|
||||
if destroy:
|
||||
self.mark_visited()
|
||||
else:
|
||||
self.screen.blit(self.plant_image, (self.x_axis * dCon.CUBE_SIZE, self.y_axis * dCon.CUBE_SIZE))
|
||||
pygame.draw.rect(self.screen, Colors.BLACK, self.field, BORDER_THICKNESS)
|
||||
|
||||
def mark_visited(self):
|
||||
plant,self.plant_image=self.image_loader.return_plant('road')
|
||||
@ -34,9 +40,14 @@ class Slot:
|
||||
self.plant=color
|
||||
self.draw()
|
||||
|
||||
def set_random_plant(self):
|
||||
(plant_name,self.plant_image)=self.random_plant()
|
||||
self.plant=Roslina.Roslina(plant_name)
|
||||
def set_random_plant(self, nn=False):
|
||||
if not nn:
|
||||
(plant_name,self.plant_image)=self.random_plant()
|
||||
self.plant=Roslina.Roslina(plant_name)
|
||||
else:
|
||||
self.plant_image, self.label, self.imagePath = self.random_plant_dataset()
|
||||
# print(self.plant_image)
|
||||
self.plant=Roslina.Roslina(self.label)
|
||||
self.set_image()
|
||||
|
||||
def set_image(self):
|
||||
@ -62,6 +73,8 @@ class Slot:
|
||||
|
||||
def random_plant(self): #Probably will not be used later only for demo purpouse
|
||||
return self.image_loader.return_random_plant()
|
||||
def random_plant_dataset(self):
|
||||
return Image.getRandomImageFromDataBase()
|
||||
|
||||
def return_plant(self):
|
||||
return self.plant
|
||||
@ -82,4 +95,6 @@ class Slot:
|
||||
self.plant.stan.nawodnienie=random.randint(61,100)
|
||||
elif(index==-1):
|
||||
pass
|
||||
|
||||
|
||||
def return_stan_for_tree(self):
|
||||
return self.plant.return_stan_for_tree()
|
20
Stan.py
20
Stan.py
@ -5,7 +5,7 @@ class Stan:
|
||||
nawodnienie = None #[int] 0-100 (0-60: trzeba podlać), spada w zaleznosci od rosliny: aktualizowane bedzie "w tle"
|
||||
zyznosc = None #[int] 0-100 (0-60: trzeba użyźnić), spada w zaleznosci od rosliny: aktualizowane bedzie "w tle"
|
||||
wzrost = None #[int] 0-100 (75-100: scinanie), wzrasta w zaleznosci od rosliny: aktualizowane bedzie "w tle"
|
||||
choroba = None #[string] brak, grzyb, bakteria, pasożyt
|
||||
choroba = None #[int] brak-0,choroba-1
|
||||
akcja = None #[Akcja]
|
||||
koszt = None #[int] 0-15, im więcej tym trudniej wjechać
|
||||
|
||||
@ -24,7 +24,7 @@ class Stan:
|
||||
self.nawodnienie=random.randint(0,100)
|
||||
self.zyznosc=random.randint(0,100)
|
||||
self.wzrost=random.randint(0,100)
|
||||
self.choroba=random.choice(["brak","grzyb","bakteria","pasozyt"])
|
||||
self.choroba=random.randint(0,1)
|
||||
|
||||
def checkStan(self):
|
||||
# sprawdza stan rośliny i podejmuje akcje jeśli potrzebna
|
||||
@ -47,6 +47,20 @@ class Stan:
|
||||
|
||||
def return_hydrate(self):
|
||||
return self.nawodnienie
|
||||
|
||||
|
||||
def return_disease(self):
|
||||
return self.choroba
|
||||
|
||||
def return_disease_as_string(self):
|
||||
if(self.choroba==0):
|
||||
return "Zdrowa"
|
||||
if(self.choroba==1):
|
||||
return "Chora"
|
||||
|
||||
def return_stan_for_tree(self):
|
||||
return [self.nawodnienie,self.wzrost,self.choroba,self.zyznosc]
|
||||
|
||||
def report_all(self):
|
||||
return f"Nawodnienie: {self.nawodnienie} Zyznosc: {self.zyznosc} Wzrost: {self.wzrost} Choroba: {self.choroba} Koszt wejścia: {self.koszt}"
|
||||
return f"Nawodnienie: {self.nawodnienie} Zyznosc: {self.zyznosc} Wzrost: {self.wzrost} Choroba: {self.return_disease_as_string()}"
|
||||
|
||||
|
100
Tractor.py
100
Tractor.py
@ -7,6 +7,16 @@ import displayControler as dCon
|
||||
import Slot
|
||||
import Osprzet
|
||||
import Node
|
||||
import Condition
|
||||
import Drzewo
|
||||
import neuralnetwork as nn
|
||||
|
||||
condition=Condition.Condition()
|
||||
drzewo=Drzewo.Drzewo()
|
||||
|
||||
format_string = "{:<25}{:<25}{:<25}{:<10}{:<10}{:<10}{:<25}{:<15}{:<20}{:<10}{:<15}"
|
||||
format_string_nn="{:<10}{:<20}{:<20}{:<15}{:<20}"
|
||||
|
||||
|
||||
tab = [-1, 0, 0, 0, 0, 1, 1, 1, 1, 1,
|
||||
1, 1, 1, 1, 1, 0, 1, 0, 1, 1,
|
||||
@ -39,7 +49,7 @@ class Tractor:
|
||||
self.clock=clock
|
||||
self.slot_hydrate_dict={}
|
||||
self.bfs2_flag=bfs2_flag
|
||||
|
||||
self.waterLevel=random.randint(0,100)
|
||||
|
||||
def draw_tractor(self):
|
||||
self.screen.blit(self.current_tractor_image, (self.slot.x_axis * dCon.CUBE_SIZE, self.slot.y_axis * dCon.CUBE_SIZE))
|
||||
@ -57,6 +67,24 @@ class Tractor:
|
||||
self.current_tractor_image = self.tractor_images[self.direction]
|
||||
self.draw_tractor()
|
||||
|
||||
def tree_move(self, pole):
|
||||
drzewo.treeLearn()
|
||||
drzewo.plotTree()
|
||||
self.snake_move_irrigation(pole, drzewo)
|
||||
|
||||
def get_attributes(self):
|
||||
slot_attributes=self.slot.return_stan_for_tree()
|
||||
climate_attributes=condition.return_condition()
|
||||
attributes=[]
|
||||
attributes=attributes+slot_attributes+[self.waterLevel]+climate_attributes
|
||||
return attributes
|
||||
|
||||
def get_attributes_for_print(self):
|
||||
slot_attributes=self.slot.return_plant().return_status_tree()
|
||||
climate_attributes=condition.getCondition()
|
||||
slot_attributes=slot_attributes+[self.waterLevel]
|
||||
return slot_attributes+climate_attributes
|
||||
|
||||
def turn_right(self):
|
||||
# zmiana kierunku w prawo
|
||||
direction_map = {
|
||||
@ -69,7 +97,7 @@ class Tractor:
|
||||
self.current_tractor_image = self.tractor_images[self.direction]
|
||||
self.draw_tractor()
|
||||
|
||||
def move_forward(self, pole):
|
||||
def move_forward(self, pole, destroy = True):
|
||||
next_slot_coordinates = None
|
||||
if self.direction == Tractor.DIRECTION_EAST:
|
||||
next_slot_coordinates = (self.slot.x_axis + 1, self.slot.y_axis)
|
||||
@ -85,12 +113,13 @@ class Tractor:
|
||||
self.current_tractor_image = self.tractor_images[self.direction]
|
||||
|
||||
# sprawdzenie czy następny slot jest dobry
|
||||
self.do_move_if_valid(pole,next_slot_coordinates)
|
||||
self.do_move_if_valid(pole,next_slot_coordinates, destroy)
|
||||
self.clock.tick(10)
|
||||
|
||||
def do_move_if_valid(self,pole, next_slot_coordinates):
|
||||
def do_move_if_valid(self,pole, next_slot_coordinates, destroy = True):
|
||||
if next_slot_coordinates and pole.is_valid_move(next_slot_coordinates):
|
||||
next_slot = pole.get_slot_from_cord(next_slot_coordinates)
|
||||
self.slot.redraw_image()
|
||||
self.slot.redraw_image(destroy)
|
||||
self.slot = next_slot
|
||||
self.draw_tractor()
|
||||
return True
|
||||
@ -136,6 +165,63 @@ class Tractor:
|
||||
self.snake_move(pole,x,y)
|
||||
|
||||
|
||||
def snake_move_irrigation(self, pole, drzewo):
|
||||
headers=['Wspolrzedne','Czy podlac','Poziom nawodnienia','Wzrost','Choroba','Zyznosc','Poziom wody w traktorze','Temperatura','Opady','Pora Roku','Aktualny czas']
|
||||
print(format_string.format(*headers))
|
||||
initPos = (self.slot.x_axis, self.slot.y_axis)
|
||||
counter = 0
|
||||
for i in range(initPos[1], dCon.NUM_Y):
|
||||
for j in range(initPos[0], dCon.NUM_X):
|
||||
attributes=self.get_attributes()
|
||||
decision = drzewo.makeDecision(attributes)
|
||||
self.pretty_print_tree([str("({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis),decision,*self.get_attributes_for_print()])
|
||||
if decision == "Tak":
|
||||
self.slot.irrigatePlant()
|
||||
counter += 1
|
||||
condition.cycle()
|
||||
pygame.time.delay(50)
|
||||
self.waterLevel=random.randint(0,100)
|
||||
#condition.getCondition()
|
||||
self.move_forward(pole, False)
|
||||
if i % 2 == 0 and i != dCon.NUM_Y - 1:
|
||||
self.turn_right()
|
||||
self.move_forward(pole, False)
|
||||
self.turn_right()
|
||||
elif i != dCon.NUM_Y - 1:
|
||||
self.turn_left()
|
||||
self.move_forward(pole, False)
|
||||
self.turn_left()
|
||||
print("podlanych slotów: ", str(counter))
|
||||
|
||||
def snake_move_predict_plant(self, pole, model):
|
||||
headers=['Coords','Real plant','Predicted plant','Result','Fertilizer']
|
||||
print(format_string_nn.format(*headers))
|
||||
initPos = (self.slot.x_axis, self.slot.y_axis)
|
||||
count = 0
|
||||
for i in range(initPos[1], dCon.NUM_Y):
|
||||
for j in range(initPos[0], dCon.NUM_X):
|
||||
for event in pygame.event.get():
|
||||
if event.type == pygame.QUIT:
|
||||
quit()
|
||||
if self.slot.imagePath != None:
|
||||
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]))
|
||||
if self.slot.label != predictedLabel:
|
||||
self.slot.mark_visited()
|
||||
count += 1
|
||||
self.move_forward(pole, False)
|
||||
if i % 2 == 0 and i != dCon.NUM_Y - 1:
|
||||
self.turn_right()
|
||||
self.move_forward(pole, False)
|
||||
self.turn_right()
|
||||
elif i != dCon.NUM_Y - 1:
|
||||
self.turn_left()
|
||||
self.move_forward(pole, False)
|
||||
self.turn_left()
|
||||
print(f"Dobrze nawiezionych roślin: {20*12-count}, źle nawiezionych roślin: {count}")
|
||||
|
||||
def snake_move(self,pole,x,y):
|
||||
next_slot_coordinates=(x,y)
|
||||
if(self.do_move_if_valid(pole,next_slot_coordinates)):
|
||||
@ -180,9 +266,11 @@ class Tractor:
|
||||
print("- Typ:", akcja.typ)
|
||||
else:
|
||||
print("Brak akcji przypisanych do tego sprzętu.")
|
||||
|
||||
def pretty_print_tree(self,attributes):
|
||||
print(format_string.format(*attributes))
|
||||
def irrigateSlot(self):
|
||||
try:
|
||||
self.slot.irrigatePlant()
|
||||
except:
|
||||
pass
|
||||
|
||||
|
2
main.py
2
main.py
@ -1,3 +1,3 @@
|
||||
import App
|
||||
|
||||
App.init(demo=True)#DEMO=TRUE WILL INIT DEMO MODE WITH RANDOM COLOR GEN
|
||||
App.init(demo=True)#DEMO=TRUE WILL INIT DEMO MODE WITH RANDOM COLOR GEN
|
||||
|
BIN
model_2_crops.pth
Normal file
BIN
model_2_crops.pth
Normal file
Binary file not shown.
BIN
model_500_hidden.pth
Normal file
BIN
model_500_hidden.pth
Normal file
Binary file not shown.
114
neuralnetwork.py
Normal file
114
neuralnetwork.py
Normal file
@ -0,0 +1,114 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision import datasets
|
||||
from torchvision.transforms import Compose, Lambda, ToTensor
|
||||
import torchvision.transforms as transforms
|
||||
import matplotlib.pyplot as plt
|
||||
from PIL import Image
|
||||
import random
|
||||
|
||||
imageSize = (128, 128)
|
||||
labels = ['carrot','corn', 'potato', 'tomato'] # musi być w kolejności alfabetycznej
|
||||
fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa', labels[2]: 'superfosfat', labels[3]:'obornik kurzy'}
|
||||
#labels = ['corn','tomato'] #uncomment this two lines for 2 crops only
|
||||
#fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa'}
|
||||
torch.manual_seed(42)
|
||||
|
||||
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
device = torch.device("cpu")
|
||||
# device = torch.device("mps") if torch.backends.mps.is_available() else torch.device('cpu')
|
||||
# print(device)
|
||||
|
||||
def getTransformation():
|
||||
transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
|
||||
transforms.Resize(imageSize),
|
||||
Lambda(lambda x: x.flatten())])
|
||||
return transform
|
||||
|
||||
def getDataset(train=True):
|
||||
transform = getTransformation()
|
||||
if (train):
|
||||
trainset = datasets.ImageFolder(root='dataset/train', transform=transform)
|
||||
return trainset
|
||||
else:
|
||||
testset = datasets.ImageFolder(root='dataset/test', transform=transform)
|
||||
return testset
|
||||
|
||||
|
||||
def train(model, dataset, n_iter=100, batch_size=256):
|
||||
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
|
||||
criterion = nn.NLLLoss()
|
||||
dl = DataLoader(dataset, batch_size=batch_size)
|
||||
model.train()
|
||||
for epoch in range(n_iter):
|
||||
for images, targets in dl:
|
||||
optimizer.zero_grad()
|
||||
out = model(images.to(device))
|
||||
loss = criterion(out, targets.to(device))
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
if epoch % 10 == 0:
|
||||
print('epoch: %3d loss: %.4f' % (epoch, loss))
|
||||
return model
|
||||
|
||||
def accuracy(model, dataset):
|
||||
model.eval()
|
||||
correct = sum([(model(images.to(device)).argmax(dim=1) == targets.to(device)).sum()
|
||||
for images, targets in DataLoader(dataset, batch_size=256)])
|
||||
return correct.float() / len(dataset)
|
||||
|
||||
def getModel():
|
||||
hidden_size = 500
|
||||
model = nn.Sequential(
|
||||
nn.Linear(imageSize[0] * imageSize[1] * 3, hidden_size),
|
||||
nn.ReLU(),
|
||||
nn.Linear(hidden_size, len(labels)),
|
||||
nn.LogSoftmax(dim=-1)
|
||||
).to(device)
|
||||
return model
|
||||
|
||||
def saveModel(model, path):
|
||||
print("Saving model")
|
||||
torch.save(model.state_dict(), path)
|
||||
|
||||
def loadModel(path):
|
||||
print("Loading model")
|
||||
model = getModel()
|
||||
model.load_state_dict(torch.load(path))
|
||||
return model
|
||||
|
||||
def trainNewModel(n_iter=100, batch_size=256):
|
||||
trainset = getDataset(True)
|
||||
model = getModel()
|
||||
model = train(model, trainset)
|
||||
return model
|
||||
|
||||
def predictLabel(imagePath, model):
|
||||
image = Image.open(imagePath).convert("RGB")
|
||||
image = preprocess_image(image)
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
model.to(device)
|
||||
with torch.no_grad():
|
||||
model.eval() # Ustawienie modelu w tryb ewaluacji
|
||||
output = model(image)
|
||||
|
||||
# Znalezienie indeksu klasy o największej wartości prawdopodobieństwa
|
||||
predicted_class = torch.argmax(output).item()
|
||||
return labels[predicted_class]
|
||||
|
||||
# Znalezienie indeksu klasy o największej wartości prawdopodobieństwa
|
||||
predicted_class = torch.argmax(output).item()
|
||||
return labels[predicted_class]
|
||||
|
||||
|
||||
def preprocess_image(image):
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
transform = getTransformation()
|
||||
image = transform(image).unsqueeze(0) # Add batch dimension
|
||||
image = image.to(device) # Move the image tensor to the same device as the model
|
||||
return image
|
||||
|
||||
|
7
readme.txt
Normal file
7
readme.txt
Normal file
@ -0,0 +1,7 @@
|
||||
Required packages:
|
||||
pygame,matplotlib,sklearn,pandas
|
||||
How to install:
|
||||
pip install pygame
|
||||
pip install matplotlib
|
||||
pip install scikit-learn
|
||||
pip install pandas
|
BIN
testModels/modelCPUdataset2.pth
Normal file
BIN
testModels/modelCPUdataset2.pth
Normal file
Binary file not shown.
BIN
testModels/modelCPUdataset2_500.pth
Normal file
BIN
testModels/modelCPUdataset2_500.pth
Normal file
Binary file not shown.
BIN
testModels/modelMPS.pth
Normal file
BIN
testModels/modelMPS.pth
Normal file
Binary file not shown.
BIN
testModels/modelMPS650.pth
Normal file
BIN
testModels/modelMPS650.pth
Normal file
Binary file not shown.
BIN
testModels/modelMPS_AL.pth
Normal file
BIN
testModels/modelMPS_AL.pth
Normal file
Binary file not shown.
87
treeData.py
Normal file
87
treeData.py
Normal file
@ -0,0 +1,87 @@
|
||||
tab = []
|
||||
def iter(odp, i):
|
||||
if i == 0:
|
||||
j = ""
|
||||
for k in range(0, 101, 28):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 1:
|
||||
j = ""
|
||||
for k in range(0, 101, 28):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 2:
|
||||
j = ""
|
||||
for k in range(2):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 3:
|
||||
j = ""
|
||||
for k in range(0, 101, 34):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 4:
|
||||
j = ""
|
||||
for k in range(0, 101, 40):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 5:
|
||||
j = ""
|
||||
for k in range(0, 4, 2):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 6:
|
||||
j = ""
|
||||
for k in range(0, 4, 2):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 7:
|
||||
j = ""
|
||||
for k in range(0, 4, 2):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 8:
|
||||
j=""
|
||||
for k in range(4):
|
||||
odp += f"{k},"
|
||||
odp = iter(odp, i + 1)
|
||||
j = str(k)
|
||||
odp = odp[:-(len(j) + 1)]
|
||||
return odp
|
||||
if i == 9:
|
||||
global licznik
|
||||
x = odp.split(",")
|
||||
if (int(x[0]) > 60 or int(x[1]) > 90 or int(x[2]) == 1 or int(x[3]) > 70 or int(x[4]) <= 10 or int(x[5]) == 0 or int(x[5]) == 4 or int(x[6]) == 2 or int(x[6]) == 3 or int(x[7]) == 0 or int(x[8]) == 1):
|
||||
odp += "0"
|
||||
else:
|
||||
odp += "1"
|
||||
print(odp)
|
||||
tab.append(odp)
|
||||
licznik += 1
|
||||
return odp[:-1]
|
||||
|
||||
iter("", 0)
|
Loading…
Reference in New Issue
Block a user