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ID3 - drzewo decyzyjne #2

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s473599 merged 1 commits from ID3 into master 2023-05-24 18:28:18 +02:00
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.vs/ProjectSettings.json Normal file
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{
"CurrentProjectSetting": null
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.vs/VSWorkspaceState.json Normal file
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{
"ExpandedNodes": [
"",
"\\decisionTree"
],
"SelectedNode": "\\decisionTree\\decisionTree.sav",
"PreviewInSolutionExplorer": false
}

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decisionTree/database.txt Normal file
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decisionTree/decisionTree.sav Normal file
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decisionTree/generator.py Normal file
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import random
# Generowanie unikalnej losowej linii tekstu
def generate_unique_line(existing_lines):
while True:
line = ''.join(random.choice(['0', '1']) for _ in range(7))
last_digit = random.choice(['1', '2', '3', '0'])
line += last_digit
if line not in existing_lines:
return line
# Generowanie 200 unikalnych linii tekstu
lines = []
while len(lines) < 200:
line = generate_unique_line(lines)
lines.append(line)
# Zapisywanie linii tekstu do pliku
with open('decisionTree/database.txt', 'w') as file:
for line in lines:
file.write(line + '\n')

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decisionTree/learning_set.txt Normal file
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['rainy', 'planted', 'good', 'good', 'no snow', 'moist', 'healthy', 'noon']
['rainy', 'planted', 'bad', 'too strong', 'snow', 'moist', 'rot', 'morning']
['clear', 'not planted', 'bad', 'good', 'snow', 'moist', 'healthy', 'noon']
['rainy', 'planted', 'good', 'good', 'snow', 'dry', 'healthy', 'sunset']
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['clear', 'not planted', 'bad', 'good', 'no snow', 'dry', 'healthy', 'noon']
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['clear', 'planted', 'bad', 'too strong', 'snow', 'moist', 'healthy', 'morning']
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['clear', 'planted', 'good', 'good', 'no snow', 'dry', 'healthy', 'sunset']
['clear', 'planted', 'good', 'too strong', 'no snow', 'dry', 'rot', 'noon']
['rainy', 'planted', 'good', 'good', 'snow', 'moist', 'rot', 'night']
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['clear', 'planted', 'bad', 'too strong', 'no snow', 'moist', 'rot', 'night']
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['clear', 'planted', 'good', 'good', 'no snow', 'moist', 'healthy', 'noon']
['rainy', 'not planted', 'bad', 'too strong', 'no snow', 'moist', 'healthy', 'noon']
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['clear', 'planted', 'good', 'good', 'no snow', 'dry', 'healthy', 'morning']
['clear', 'not planted', 'good', 'too strong', 'snow', 'moist', 'healthy', 'night']
['rainy', 'planted', 'bad', 'good', 'no snow', 'dry', 'rot', 'noon']
['rainy', 'not planted', 'bad', 'too strong', 'no snow', 'dry', 'rot', 'sunset']
['clear', 'not planted', 'good', 'too strong', 'snow', 'dry', 'healthy', 'night']
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['rainy', 'not planted', 'good', 'good', 'snow', 'dry', 'healthy', 'night']
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['rainy', 'planted', 'bad', 'too strong', 'no snow', 'dry', 'healthy', 'sunset']
['clear', 'planted', 'good', 'too strong', 'no snow', 'dry', 'rot', 'sunset']
['clear', 'not planted', 'good', 'good', 'no snow', 'dry', 'rot', 'night']
['clear', 'not planted', 'bad', 'too strong', 'no snow', 'moist', 'healthy', 'night']
['clear', 'planted', 'good', 'too strong', 'no snow', 'moist', 'rot', 'noon']
['rainy', 'not planted', 'bad', 'too strong', 'no snow', 'moist', 'rot', 'night']
['clear', 'planted', 'bad', 'too strong', 'no snow', 'dry', 'healthy', 'night']
['rainy', 'planted', 'bad', 'good', 'snow', 'moist', 'rot', 'morning']
['clear', 'not planted', 'bad', 'too strong', 'snow', 'moist', 'rot', 'sunset']
['clear', 'not planted', 'bad', 'too strong', 'no snow', 'moist', 'healthy', 'noon']
['rainy', 'not planted', 'good', 'good', 'no snow', 'moist', 'healthy', 'noon']
['clear', 'planted', 'bad', 'too strong', 'snow', 'moist', 'healthy', 'noon']

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decisionTree/treemaker.py Normal file
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#from sklearn.datasets import load_iris
from sklearn.tree import export_text
from sklearn.tree import DecisionTreeClassifier
import joblib
X1 = []
view = []
with open("decisionTree/database.txt", 'r') as f:
for line in f:
line = line.strip()
test_list = [int(i) for i in line]
x = []
if line[0] == "0":
x.append("clear")
else:
x.append("rainy")
if line[1] == "0":
x.append("not planted")
else:
x.append("planted")
if line[2] == "0":
x.append("bad")
else:
x.append("good")
if line[3] == "0":
x.append("good")
else:
x.append("too strong")
if line[4] == "0":
x.append("no snow")
else:
x.append("snow")
if line[5] == "0":
x.append("dry")
else:
x.append("moist")
if line[6] == "0":
x.append("healthy")
else:
x.append("rot")
if line[7] == "0":
x.append("morning")
elif line[7] == "1":
x.append("noon")
elif line[7] == "2":
x.append("sunset")
else:
x.append("night")
view.append(x)
X1.append(test_list)
f = open("decisionTree/learning_set.txt", "w") #zapisuje atrybuty s³ownie
for i in view:
f.write(str(i)+"\n")
f.close()
Y1 = []
with open("decisionTree/decissions.txt", 'r') as f: #czyta decyzje
for line in f:
line = line.strip()
test = int(line)
Y1.append(test)
dataset = X1
decision = Y1
labels = ['Rain','Plant','Temperature','Sun','Snow','Moisture','Rotten','Time']
model = DecisionTreeClassifier(random_state=0, max_depth=20).fit(dataset, decision)
filename = 'decisionTree/decisionTree.sav'
print("Model trained")
print("Decision tree:")
print(export_text(model, feature_names=labels))
joblib.dump(model, filename)