kNN implemented with analyse

src/classes/feature_vector.py

@@ -0,0 +1,96 @@
+from typing import Any, Self
+import numpy as np
+import os
+import pickle
+
+from classes.concept import Concept
+from classes.feature import Feature
+
+
+class FeatureVector:
+
+	def __init__(self, concept: Concept, features: dict = {}, features_list: list = None, loaded: bool = False) -> None:
+		if loaded == False:
+			self.loaded: bool = False
+			self.features: dict = features
+			self.concept: Concept = concept
+		else:
+			self.loaded: bool = True
+			self.concept: Concept = concept
+			self.features_list: list = features_list
+
+
+	def _get_values_of_feature(self, feature: Feature) -> list[int]:
+		try:
+			feature_data = self.features[feature]
+		except KeyError:
+			print("missing key: ", feature)
+			return [-1]
+
+		ret = []
+		if feature == Feature.OVERALL_COLOR_PERCENTAGE:
+			feature_data: dict
+			for val in feature_data.values():
+				ret.append(val)
+			return ret
+		
+		elif feature == Feature.RASTER_COLOR_PERCENTAGE:
+			feature_data: list[list[dict]]
+			
+			for feature_data1d in feature_data:
+				for data in feature_data1d:
+					for val in data.values():
+						ret.append(val)
+			return ret
+		
+		elif feature == Feature.CORNERS:
+			ret.append(feature_data)
+			return ret
+		elif feature == Feature.EDGES:
+			ret.append(feature_data)
+			return ret
+		elif feature == Feature.COUNTOURS:
+			ret.append(feature_data)
+			return ret
+
+
+	def add_feature(self, key: Feature, value: Any) -> None:
+		self.features.update({key: value})
+
+
+	def get_vector(self) -> list:
+		if self.loaded:
+			return self.features_list
+		
+		ret = []
+		for feature in Feature:
+			ret = ret + self._get_values_of_feature(feature)
+		return ret
+
+
+	def get_concept(self) -> Concept:
+		return self.concept
+
+
+	def get_num_features(self) -> int:
+		return len(self.features)
+
+
+	def get_feature_value(self, key: str) -> Any:
+		return self.features[key]
+	
+
+	def get_features(self) -> Any:
+		return self.features
+	
+
+	def save(self, path: os.path) -> None:
+		with open(path, "wb") as write:
+			pickle.dump(self, write, pickle.HIGHEST_PROTOCOL)
+
+
+	def load(path: os.path) -> Self:
+		with open(path, "rb") as read:
+			self: FeatureVector = pickle.load(read)
+		return self
+