
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 		_UpperCAmelCase (						UpperCamelCase:  List[Any]      ,				UpperCamelCase:  int	):






	"""simple docstring"""


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






def 		_UpperCAmelCase (						UpperCamelCase:  Optional[Any]      ,				UpperCamelCase:  int      ,				UpperCamelCase:  Union[str, Any]      ,				UpperCamelCase:  int      ,				UpperCamelCase:  List[str]=5	):






	"""simple docstring"""


	__lowerCAmelCase    =				zip(UpperCamelCase_      ,				UpperCamelCase_	)
	# List of distances of all points from the point to be classified
	__lowerCAmelCase    =				[]
	for data_point in data:
		__lowerCAmelCase    =				euclidean_distance(data_point[0]      ,				UpperCamelCase_	)
		distances.append((distance, data_point[1])	)
	# Choosing 'k' points with the least distances.
	__lowerCAmelCase    =				[i[1] for i in sorted(UpperCamelCase_	)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	__lowerCAmelCase    =				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'''
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class 					__lowerCAmelCase   (   unittest.TestCase				,			lowercase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		load_tool("text-classification"	)
													self.tool.setup()
													A_			    =		load_tool("text-classification" ,  remote=lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.remote_tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.remote_tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)





