added DecisionTree and not working implementaion

.gitignore

@@ -1,5 +1,6 @@
 
 .vsc
+.vscode
 .venv
 
 data

src/classes/decision_tree.py

@@ -0,0 +1,56 @@
+from typing import Self
+import csv
+import ast
+
+from classes.concept import Concept
+from classes.feature_vector import FeatureVector
+
+class DecisionTree:
+
+	def __init__(self, children: dict = {}, parent: Self = None, concept: Concept = Concept.UNKNOWN, depth: int = 0) -> None:
+		self.children: dict = children # (number, DecisionTree)
+		self.parent: Self = parent
+		self.concept: Concept = concept
+		self.depth: int = depth
+	
+
+	# TODO
+	# IMPLEMENTATION IS NOT WORKING
+
+	def classify(self, feature_vector: FeatureVector) -> tuple[Concept, int]:
+		if len(self.children) == 0:
+			return self.concept, self.depth
+		
+		feature_value = feature_vector.features_list[self.depth] # get feature value of vector based on current depth
+		
+		try:
+			new_decision_tree: Self = self.children[feature_value] # get sub decition tree for feature
+		except KeyError:
+			return Concept.UNKNOWN, self.depth
+		
+		return new_decision_tree.classify(feature_vector) # classify new desicion tree
+
+	
+	def learn(self, training_set: list[FeatureVector]) -> None:
+		i = 0
+		while not self._finished(i, len(training_set)*2):
+			vector: FeatureVector = training_set[i % len(training_set)]
+			concept, depth = self.classify(vector)
+			print(f"concept is {vector.concept}, was classified as {concept} in depth: {depth}")
+			if concept == Concept.UNKNOWN:
+				self.concept = vector.concept
+				i+=1
+
+			elif concept == vector.concept:
+				i+=1
+
+			else:
+				self.concept = Concept.UNKNOWN
+				self.children.update({vector.features_list[depth]: DecisionTree(concept=vector.concept, parent=self, depth=depth+1, children={})})
+			
+			
+	def _finished(self, i: int, num_training_data: int):
+		# TODO implement proper finished condition
+		if i < num_training_data:
+			return False
+		return True

src/main.py

@@ -2,8 +2,12 @@ import os
 import ast
 import csv
 
-from classes.learner import Learner
 from classes.concept import Concept
+from classes.feature_vector import FeatureVector
+
+
+def knn():
+    from classes.learner import Learner
 
     training = os.path.abspath(os.path.join(__file__, "..", "training.csv"))
     testing = os.path.abspath(os.path.join(__file__, "..", "testing.csv"))
@@ -24,4 +28,23 @@ for test_feature_vector in testing_data:
         distances = learner.classify(ast.literal_eval(test_feature_vector[1]))
         cnt += learner.analyse(distances, Concept.identify_by_str(test_feature_vector[0].split('.')[1]))
 
-print(f"Correctnes: {(cnt / len(testing_data)) * 100}%")
+    print(f"Correctness: {(cnt / len(testing_data)) * 100}%")
+
+
+if __name__ == "__main__":
+
+    from classes.decision_tree import DecisionTree
+
+    training = os.path.abspath(os.path.join(__file__, "..", "training.csv"))
+    testing = os.path.abspath(os.path.join(__file__, "..", "testing.csv"))
+
+    testing_data = []
+    with open(testing, 'r') as csv_file:
+        reader = csv.reader(csv_file, delimiter=";")
+        next(reader)
+        for row in reader:
+            fv = FeatureVector(concept = Concept.identify_by_str(row[1].split(".")[1]), features_list = ast.literal_eval(row[2]), loaded = True)
+            testing_data.append(fv)
+
+    tree = DecisionTree()
+    tree.learn(testing_data)