Попытка перейти к регрессии #1

generate.py

@@ -1,36 +1,4 @@
-import random
-class Dot:
-	def __init__(self, x: float, y: float) -> None:
-		self.x = x
-		self.y = y
-		self.classification = float(((x**2 + y**2)**0.5)>=0.5)
+import classes
 
-	def get_tup(self) -> tuple:
-		return (self.x, self.y, self.classification)
-	
-	def __str__(self) -> str:
-		return f"({self.x}, {self.y})"
-	
-	def __repr__(self) -> str:
-		return f"({self.x}, {self.y}, {self.classification})"
-
-class Dataset:
-	def __init__(self, train: list[Dot], test: list[Dot]) -> None:
-		self.train = train
-		self.test = test
-	
-	def __str__(self) -> str: 
-		return f"Train: {str(self.train)}\nTest: {str(self.test)}"
-	
-	def __repr__(self) -> str: 
-		return f"Train: {self.train}\nTest: {self.test}"
-	
-def generate_data() -> Dot: 
-	return Dot(random.uniform(-1.0, 1.0), random.uniform(-1.0, 1.0))
-
-def generate_dataset(N = 1000) -> Dataset:
-	return Dataset([generate_data() for i in range(N//5*4)], [generate_data() for i in range(N//5)])
-
-if __name__ == "__main__":
-	data = generate_dataset(10)
-	print(data)
+def generate_dataset(N=1000):
+	return classes.DataSet(N)