splitting data_set and getting correctnes

src/classes/concept.py

@@ -7,4 +7,21 @@ class Concept(Enum):
 	STOP = auto()
 	RECHTS_ABBIEGEN = auto()
 	LINKS_ABBIEGEN = auto()
-	RECHTS_VOR_LINKS = auto()
+	RECHTS_VOR_LINKS = auto()
+
+	def identify_by_str(name: str):
+		match name:
+			case 'VORFAHRT_GEWAEHREN':
+				return Concept.VORFAHRT_GEWAEHREN
+			case 'VORFAHRT_STRASSE':
+				return Concept.VORFAHRT_STRASSE
+			case 'STOP':
+				return Concept.STOP
+			case 'RECHTS_ABBIEGEN': 
+				return Concept.RECHTS_ABBIEGEN
+			case 'LINKS_ABBIEGEN':
+				return Concept.LINKS_ABBIEGEN
+			case 'RECHTS_VOR_LINKS':
+				return Concept.RECHTS_VOR_LINKS
+			case _:
+				return Concept.UNKNOWN

src/classes/learner.py

@@ -6,12 +6,15 @@ from classes.concept import Concept
 from classes.feature_vector import FeatureVector
 
 class Learner:
+
+	def __init__(self, k_paramater: int) -> None:
+		self.k_paramter = k_paramater
+
 	# The training method, that changes the internal state of the learner such that 
 	# it will classify examples of a similar set (i.e. the testSet better.
 	# 
 	# @param trainingSet contains feature vectors and corresponding concepts 
 	# to provide experience to learn from  
-
 	def learn(self, path_to_training_set: str):
 		training_set = []
 		with open(path_to_training_set, mode='r' ,newline='') as csv_file:
@@ -45,8 +48,7 @@ class Learner:
 			distances.append((single_fv.get_concept(), single_dist))
 
 		sorted_distances = sorted(distances, key=lambda tuple: tuple[1])
-		k_nearest = 3
-		interested_distances = sorted_distances[:k_nearest]
+		interested_distances = sorted_distances[:self.k_paramter]
 
 		for interested_fv in interested_distances:
 			concept = self.string_to_enum(Concept, interested_fv[0])
@@ -76,8 +78,13 @@ class Learner:
 			print(f"Error: {e}")
 			return None
 		
-	def analyse(self, result, k):
-		sorted_dict_result = {key: value for key, value in sorted(result.items(), key=lambda item: item[1])}
+	def analyse(self, result, expectedConcept: Concept):
+		sorted_dict_result = {key: value for key, value in sorted(result.items(), reverse=True, key=lambda item: item[1])}
 		for key, amount in sorted_dict_result.items():
-			probability = (amount/k) * 100
-			print(f" Probability of {key} is {probability}%")
+			probability = (amount/self.k_paramter) * 100
+			print(f" Probability of {key} is {probability}%")
+
+		if next(iter(sorted_dict_result)) == expectedConcept:
+			return 1
+		else:
+			return 0