.vs/ProjectSettings.json

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+{
+  "CurrentProjectSetting": null
+}

.vs/VSWorkspaceState.json

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+{
+  "ExpandedNodes": [
+    "",
+    "\\decisionTree"
+  ],
+  "SelectedNode": "\\decisionTree\\decisionTree.sav",
+  "PreviewInSolutionExplorer": false
+}

decisionTree/database.txt

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decisionTree/decissions.txt

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decisionTree/generator.py

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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')

decisionTree/learning_set.txt

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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']
+['rainy', 'planted', 'good', 'too strong', 'snow', 'dry', 'healthy', 'morning']
+['rainy', 'planted', 'good', 'good', 'snow', 'dry', 'rot', 'night']
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+['rainy', 'planted', 'good', 'good', 'snow', 'moist', 'rot', 'night']
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+['rainy', 'not planted', 'bad', 'too strong', 'snow', 'moist', 'healthy', 'noon']
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+['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']
+['clear', 'planted', 'bad', 'good', 'snow', 'dry', 'rot', 'morning']
+['rainy', 'not planted', 'good', 'good', 'snow', 'dry', 'healthy', 'night']
+['clear', 'not planted', 'bad', 'too strong', 'no snow', 'dry', 'healthy', 'night']
+['rainy', 'not planted', 'bad', 'good', 'no snow', 'moist', 'healthy', 'sunset']
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+['rainy', 'planted', 'bad', 'good', 'no snow', 'moist', 'rot', 'morning']
+['rainy', 'planted', 'bad', 'good', 'snow', 'dry', 'healthy', 'sunset']
+['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']

decisionTree/treemaker.py

@@ -0,0 +1,76 @@
+
+
+#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)