


from __future__ import annotations

from math import ceil, floor, sqrt







def     lowerCAmelCase_					( __lowerCamelCase = 2_0_0_0_0_0_0     ):
							__snake_case		:      list[int]										=       [0]
							__snake_case		:      int

							for idx in range(1							,     ceil(sqrt(target * 2     ) * 1.1     )     ):
														triangle_numbers.append(triangle_numbers[-1] + idx     )

							# we want this to be as close as possible to target
							__snake_case		:      int										=       0
							# the area corresponding to the grid that gives the product closest to target
							__snake_case		:      int										=       0
							# an estimate of b, using the quadratic formula
							__snake_case		:      float
							# the largest integer less than b_estimate
							__snake_case		:      int
							# the largest integer less than b_estimate
							__snake_case		:      int
							# the triangle number corresponding to b_floor
							__snake_case		:      int
							# the triangle number corresponding to b_ceil
							__snake_case		:      int

							for idx_a, triangle_a in enumerate(triangle_numbers[1:]							,     1     ):
														__snake_case		:      Any										=       (-1 + sqrt(1 + 8 * target / triangle_a     )) / 2
														__snake_case		:      List[str]										=       floor(__lowerCamelCase     )
														__snake_case		:      Optional[Any]										=       ceil(__lowerCamelCase     )
														__snake_case		:      Optional[int]										=       triangle_numbers[b_floor]
														__snake_case		:      Any										=       triangle_numbers[b_ceil]

														if abs(target - triangle_b_first_guess * triangle_a     ) < abs(
														    target - best_product     ):
																					__snake_case		:      Any										=       triangle_b_first_guess * triangle_a
																					__snake_case		:      List[Any]										=       idx_a * b_floor

														if abs(target - triangle_b_second_guess * triangle_a     ) < abs(
														    target - best_product     ):
																					__snake_case		:      str										=       triangle_b_second_guess * triangle_a
																					__snake_case		:      Optional[Any]										=       idx_a * b_ceil

							return area


if __name__ == "__main__":
			print(f'''{solution() = }''')





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       Counter(__lowerCamelCase     ).most_common(1     )[0][0]
							return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

