"""simple docstring"""







from collections import Counter

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

_a  :  str=				datasets.load_iris()

_a  :  List[Any]=				np.array(data["data"])
_a  :  Optional[Any]=				np.array(data["target"])
_a  :  Dict=				data["target_names"]

_a  :  Any=				train_test_split(X, y)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       Optional[Any]						)							->    Tuple:

 '''simple docstring'''





 return np.linalg.norm(np.array(lowerCamelCase__						) - np.array(lowerCamelCase__						)						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       Dict=5						)							->    int:

 '''simple docstring'''





 __snake_case    :							List[Any]   	=				zip(lowerCamelCase__    ,							lowerCamelCase__						)
 # List of distances of all points from the point to be classified
 __snake_case    :							int   	=				[]
 for data_point in data:
  __snake_case    :							Optional[Any]   	=				euclidean_distance(data_point[0]    ,							lowerCamelCase__						)
  distances.append((distance, data_point[1])						)
 # Choosing 'k' points with the least distances.
 __snake_case    :							Union[str, Any]   	=				[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    :							Dict   	=				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]))






"""simple docstring"""







import math
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				len(UpperCAmelCase_						)
	__snake_case    :							List[Any]   	=				int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
	__snake_case    :							Any   	=				0
	while arr[min(UpperCAmelCase_    ,							UpperCAmelCase_						) - 1] < x:
		__snake_case    :							Tuple   	=				step
		step += int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
		if prev >= n:
			return -1

	while arr[prev] < x:
		__snake_case    :							Union[str, Any]   	=				prev + 1
		if prev == min(UpperCAmelCase_    ,							UpperCAmelCase_						):
			return -1
	if arr[prev] == x:
		return prev
	return -1


if __name__ == "__main__":
	_a  :  str=				input("Enter numbers separated by a comma:\n").strip()
	_a  :  int=				[int(item) for item in user_input.split(",")]
	_a  :  Optional[Any]=				int(input("Enter the number to be searched:\n"))
	_a  :  Tuple=				jump_search(arr, x)
	if res == -1:
		print("Number not found!")
	else:
		print(f'''Number {x} is at index {res}''')





