Dodano do resources plik dataset.csv oraz skrypt decision_tree.py odpowiadający za stworzenie drzewo_decyzyjne.png

.idea/Traktor AI.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.9" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (Traktor)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
-</project>

source/decision_tree.py

@@ -0,0 +1,42 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+import matplotlib.pyplot as plt
+dtype_dict = {
+    'season': str,
+    'anomalies': str,
+    'weather': str,
+    'temp': int,
+    'water': int,
+    'nutri': int,
+    'pests': str,
+    'weeds': str,
+    'type': str,
+    'ripeness': int,
+    'target_column': str
+}
+# Wczytaj dane z pliku CSV
+data = pd.read_csv('resources/dataset.csv', header=0, dtype=dtype_dict)
+#print(data)
+X = data.drop('target_column', axis=1)
+y = data['target_column']
+X = pd.get_dummies(X)
+
+print(X)
+print(y)
+
+# Podział danych na zbiór treningowy i testowy
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Dopasowanie modelu drzewa decyzyjnego
+model = DecisionTreeClassifier()
+model.fit(X_train, y_train)
+
+fig = plt.figure(figsize=(25,20))
+_ = plot_tree(model, feature_names=X.columns, class_names=model.classes_, filled=True)
+plt.savefig('drzewo_decyzyjne.png')
+plt.show()
+
+
+

source/ground.py

@@ -20,7 +20,7 @@ class Dirt:
         elif i == 4:
             self.weed = True
             self.pest = True
-        elif i == 5:
+        elif i == 5 or i == 6 or i == 7:
             self.obstacle = True
 
 # add init, getters,setters

source/resources/dataset.csv

@@ -0,0 +1,301 @@
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