



from collections import Counter

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

A:     List[str]										=	datasets.load_iris()

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

A:     Tuple										=	train_test_split(X, y)

def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	Union[str, Any]		):
 return np.linalg.norm(np.array(UpperCamelCase		) - np.array(UpperCamelCase		)		)

def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	Any=5		):
 UpperCAmelCase			:      Union[str, Any]			=			zip(UpperCamelCase    ,	UpperCamelCase		)
 # List of distances of all points from the point to be classified
 UpperCAmelCase			:      Any			=			[]
 for data_point in data:
  UpperCAmelCase			:      Optional[Any]			=			euclidean_distance(data_point[0]    ,	UpperCamelCase		)
  distances.append((distance, data_point[1])		)
 # Choosing 'k' points with the least distances.
 UpperCAmelCase			:      Tuple			=			[i[1] for i in sorted(UpperCamelCase		)[:k]]
 # Most commonly occurring class among them
 # is the class into which the point is classified
 UpperCAmelCase			:      Optional[int]			=			Counter(UpperCamelCase		).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"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


A:     Dict										=	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

      A:     int										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
