





"""simple docstring"""





from collections import Counter

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

_UpperCamelCase							=      datasets.load_iris()

_UpperCamelCase							=      np.array(data['data'])
_UpperCamelCase							=      np.array(data['target'])
_UpperCamelCase							=      data['''target_names''']

_UpperCamelCase							=      train_test_split(X, y)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







							return np.linalg.norm(np.array(_A   ) - np.array(_A   )   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]=5   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[str]         =zip(_A    ,						_A   )
							# List of distances of all points from the point to be classified
							__lowerCamelCase      :      Union[str, Any]         =[]
							for data_point in data:
														__lowerCamelCase      :      Optional[Any]         =euclidean_distance(data_point[0]    ,						_A   )
														distances.append((distance, data_point[1])   )
							# Choosing 'k' points with the least distances.
							__lowerCamelCase      :      Tuple         =[i[1] for i in sorted(_A   )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__lowerCamelCase      :      Any         =Counter(_A   ).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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments




