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generowanie drzewa decyzyjnego

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This commit is contained in:
Tomasz Adamczyk 2021-05-15 22:41:07 +02:00
parent 424794e53e
commit 7309c28361
7 changed files with 147 additions and 113 deletions
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36
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@ -3,7 +3,8 @@
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2
py.py
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@ -5,6 +5,7 @@ import map
import plant
import pygame
import station
import treelearn
import tractor
pygame.display.set_caption("Smart Tractor")
def main():
@ -19,6 +20,7 @@ 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_rect = pygame.Rect(tractor1.get_x(), tractor1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
clock = pygame.time.Clock()
treelearn.treelearn()
run = True
while run: #pętla główna programu
clock.tick(definitions.FPS)

202
resources/data.csv
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@ -1,101 +1,101 @@
#;amount of seeds;collected plants;fertilizer;fuel;water level;back to station
1;10;79;8;129;12;1
2;39;195;8;125;1;1
3;68;5;5;2;3;1
4;30;70;4;162;24;0
5;49;121;0;158;37;0
6;62;139;4;112;9;0
7;24;154;4;138;14;1
8;45;151;3;68;4;1
9;44;96;0;2;30;1
10;78;17;6;146;24;0
11;38;170;3;91;9;1
12;40;85;3;40;4;1
13;47;38;0;54;32;0
14;36;179;6;77;31;1
15;3;22;6;73;23;1
16;4;46;3;160;17;1
17;48;27;8;43;0;1
18;9;16;1;164;12;1
19;9;14;0;19;3;1
20;48;126;0;68;21;0
21;35;185;7;81;3;1
22;75;21;8;153;6;1
23;32;136;5;91;23;0
24;47;167;7;123;17;0
25;76;32;3;114;35;0
26;80;60;6;5;0;1
27;40;15;1;50;24;0
28;62;3;6;6;21;1
29;10;159;8;199;24;1
30;8;149;2;49;1;1
31;76;55;1;45;3;1
32;3;29;5;111;8;1
33;15;14;7;118;7;1
34;56;112;4;87;30;0
35;1;168;4;40;27;1
36;65;149;4;40;34;0
37;21;190;4;82;17;1
38;66;83;1;155;6;1
39;19;165;7;3;2;1
40;38;92;3;2;16;1
41;18;141;0;6;11;1
42;30;192;6;85;20;1
43;54;30;0;172;30;0
44;66;25;0;79;4;1
45;43;142;1;29;9;1
46;0;48;0;58;32;1
47;6;105;1;83;13;1
48;22;175;7;117;24;1
49;31;95;3;146;30;0
50;72;64;0;33;23;0
51;41;113;87;31;31;0
52;45;54;4;87;9;1
53;39;107;5;200;1;1
54;38;167;6;112;32;0
55;63;103;0;56;18;0
56;73;66;8;36;10;1
57;3;67;1;174;10;1
58;59;182;8;4;12;1
59;72;24;4;60;17;0
60;26;3;4;67;35;0
61;66;153;5;19;9;1
62;74;155;5;86;13;1
63;75;71;8;179;9;1
64;67;101;3;65;38;0
65;46;192;6;39;4;1
66;58;109;7;11;3;1
67;25;30;0;156;37;0
68;25;115;1;150;7;1
69;38;195;3;185;12;1
70;39;41;5;168;32;0
71;45;163;1;98;32;1
72;18;107;0;150;39;0
73;72;131;1;188;6;1
74;64;134;3;121;28;0
75;76;193;5;181;35;1
76;42;151;4;8;40;1
77;32;95;1;185;40;0
78;31;106;4;30;14;0
79;75;37;7;175;0;1
80;58;70;0;163;36;0
81;74;112;6;133;7;1
82;46;72;0;175;35;0
83;42;68;6;188;3;1
84;1;127;5;16;13;1
85;16;38;8;92;15;0
86;22;82;3;69;29;0
87;17;68;4;0;5;1
88;22;172;4;11;17;1
89;14;115;5;67;20;0
90;18;29;7;135;20;0
91;52;140;7;20;39;1
92;0;139;5;49;35;1
93;31;170;6;93;26;1
94;56;78;4;47;10;1
95;71;6;1;116;3;1
96;11;61;3;39;27;0
97;73;152;8;126;1;1
98;21;57;4;0;29;1
99;68;6;3;170;38;0
100;13;38;0;50;34;0
amount of seeds,collected plants,fertilizer,fuel,water level,back to station
10,79,8,129,12,1
39,195,8,125,1,1
68,5,5,2,3,1
30,70,4,162,24,0
49,121,0,158,37,0
62,139,4,112,9,0
24,154,4,138,14,1
45,151,3,68,4,1
44,96,0,2,30,1
78,17,6,146,24,0
38,170,3,91,9,1
40,85,3,40,4,1
47,38,0,54,32,0
36,179,6,77,31,1
3,22,6,73,23,1
4,46,3,160,17,1
48,27,8,43,0,1
9,16,1,164,12,1
9,14,0,19,3,1
48,126,0,68,21,0
35,185,7,81,3,1
75,21,8,153,6,1
32,136,5,91,23,0
47,167,7,123,17,0
76,32,3,114,35,0
80,60,6,5,0,1
40,15,1,50,24,0
62,3,6,6,21,1
10,159,8,199,24,1
8,149,2,49,1,1
76,55,1,45,3,1
3,29,5,111,8,1
15,14,7,118,7,1
56,112,4,87,30,0
1,168,4,40,27,1
65,149,4,40,34,0
21,190,4,82,17,1
66,83,1,155,6,1
19,165,7,3,2,1
38,92,3,2,16,1
18,141,0,6,11,1
30,192,6,85,20,1
54,30,0,172,30,0
66,25,0,79,4,1
43,142,1,29,9,1
0,48,0,58,32,1
6,105,1,83,13,1
22,175,7,117,24,1
31,95,3,146,30,0
72,64,0,33,23,0
41,113,87,31,31,0
45,54,4,87,9,1
39,107,5,200,1,1
38,167,6,112,32,0
63,103,0,56,18,0
73,66,8,36,10,1
3,67,1,174,10,1
59,182,8,4,12,1
72,24,4,60,17,0
26,3,4,67,35,0
66,153,5,19,9,1
74,155,5,86,13,1
75,71,8,179,9,1
67,101,3,65,38,0
46,192,6,39,4,1
58,109,7,11,3,1
25,30,0,156,37,0
25,115,1,150,7,1
38,195,3,185,12,1
39,41,5,168,32,0
45,163,1,98,32,1
18,107,0,150,39,0
72,131,1,188,6,1
64,134,3,121,28,0
76,193,5,181,35,1
42,151,4,8,40,1
32,95,1,185,40,0
31,106,4,30,14,0
75,37,7,175,0,1
58,70,0,163,36,0
74,112,6,133,7,1
46,72,0,175,35,0
42,68,6,188,3,1
1,127,5,16,13,1
16,38,8,92,15,0
22,82,3,69,29,0
17,68,4,0,5,1
22,172,4,11,17,1
14,115,5,67,20,0
18,29,7,135,20,0
52,140,7,20,39,1
0,139,5,49,35,1
31,170,6,93,26,1
56,78,4,47,10,1
71,6,1,116,3,1
11,61,3,39,27,0
73,152,8,126,1,1
21,57,4,0,29,1
68,6,3,170,38,0
13,38,0,50,34,0
1 # amount of seeds collected plants fertilizer fuel water level back to station
2 1 10 79 8 129 12 1
3 2 39 195 8 125 1 1
4 3 68 5 5 2 3 1
5 4 30 70 4 162 24 0
6 5 49 121 0 158 37 0
7 6 62 139 4 112 9 0
8 7 24 154 4 138 14 1
9 8 45 151 3 68 4 1
10 9 44 96 0 2 30 1
11 10 78 17 6 146 24 0
12 11 38 170 3 91 9 1
13 12 40 85 3 40 4 1
14 13 47 38 0 54 32 0
15 14 36 179 6 77 31 1
16 15 3 22 6 73 23 1
17 16 4 46 3 160 17 1
18 17 48 27 8 43 0 1
19 18 9 16 1 164 12 1
20 19 9 14 0 19 3 1
21 20 48 126 0 68 21 0
22 21 35 185 7 81 3 1
23 22 75 21 8 153 6 1
24 23 32 136 5 91 23 0
25 24 47 167 7 123 17 0
26 25 76 32 3 114 35 0
27 26 80 60 6 5 0 1
28 27 40 15 1 50 24 0
29 28 62 3 6 6 21 1
30 29 10 159 8 199 24 1
31 30 8 149 2 49 1 1
32 31 76 55 1 45 3 1
33 32 3 29 5 111 8 1
34 33 15 14 7 118 7 1
35 34 56 112 4 87 30 0
36 35 1 168 4 40 27 1
37 36 65 149 4 40 34 0
38 37 21 190 4 82 17 1
39 38 66 83 1 155 6 1
40 39 19 165 7 3 2 1
41 40 38 92 3 2 16 1
42 41 18 141 0 6 11 1
43 42 30 192 6 85 20 1
44 43 54 30 0 172 30 0
45 44 66 25 0 79 4 1
46 45 43 142 1 29 9 1
47 46 0 48 0 58 32 1
48 47 6 105 1 83 13 1
49 48 22 175 7 117 24 1
50 49 31 95 3 146 30 0
51 50 72 64 0 33 23 0
52 51 41 113 87 31 31 0
53 52 45 54 4 87 9 1
54 53 39 107 5 200 1 1
55 54 38 167 6 112 32 0
56 55 63 103 0 56 18 0
57 56 73 66 8 36 10 1
58 57 3 67 1 174 10 1
59 58 59 182 8 4 12 1
60 59 72 24 4 60 17 0
61 60 26 3 4 67 35 0
62 61 66 153 5 19 9 1
63 62 74 155 5 86 13 1
64 63 75 71 8 179 9 1
65 64 67 101 3 65 38 0
66 65 46 192 6 39 4 1
67 66 58 109 7 11 3 1
68 67 25 30 0 156 37 0
69 68 25 115 1 150 7 1
70 69 38 195 3 185 12 1
71 70 39 41 5 168 32 0
72 71 45 163 1 98 32 1
73 72 18 107 0 150 39 0
74 73 72 131 1 188 6 1
75 74 64 134 3 121 28 0
76 75 76 193 5 181 35 1
77 76 42 151 4 8 40 1
78 77 32 95 1 185 40 0
79 78 31 106 4 30 14 0
80 79 75 37 7 175 0 1
81 80 58 70 0 163 36 0
82 81 74 112 6 133 7 1
83 82 46 72 0 175 35 0
84 83 42 68 6 188 3 1
85 84 1 127 5 16 13 1
86 85 16 38 8 92 15 0
87 86 22 82 3 69 29 0
88 87 17 68 4 0 5 1
89 88 22 172 4 11 17 1
90 89 14 115 5 67 20 0
91 90 18 29 7 135 20 0
92 91 52 140 7 20 39 1
93 92 0 139 5 49 35 1
94 93 31 170 6 93 26 1
95 94 56 78 4 47 10 1
96 95 71 6 1 116 3 1
97 96 11 61 3 39 27 0
98 97 73 152 8 126 1 1
99 98 21 57 4 0 29 1
100 99 68 6 3 170 38 0
101 100 13 38 0 50 34 0

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treelearn.py Normal file
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@ -0,0 +1,20 @@
import matplotlib.image as pltimg
import matplotlib.pyplot as plt
import os
import pandas
import pydotplus
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
def treelearn():
df = pandas.read_csv(os.path.join('resources', 'data.csv'))
features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
x = df[features]
y = df['back to station']
dtree = DecisionTreeClassifier()
dtree = dtree.fit(x, y)
data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
graph = pydotplus.graph_from_dot_data(data)
graph.write_png(os.path.join('resources', 'mydecisiontree.png'))
img = pltimg.imread('mydecisiontree.png')
imgplot = plt.imshow(img)
plt.show()