






"""simple docstring"""



from collections import Counter

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

A_    =							datasets.load_iris()

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

A_    ,	A_    ,	A_    ,	A_    =							train_test_split(X, y)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Any      )		->Any:
						return np.linalg.norm(np.array(UpperCAmelCase__      ) - np.array(UpperCAmelCase__      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[Any]=5      )		->Dict:
						A__						:  Any		  =  zip(UpperCAmelCase__,						UpperCAmelCase__      )
						# List of distances of all points from the point to be classified
						A__						:  Dict		  =  []
						for data_point in data:
												A__						:  List[Any]		  =  euclidean_distance(data_point[0],						UpperCAmelCase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						A__						:  Tuple		  =  [i[1] for i in sorted(UpperCAmelCase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						A__						:  Optional[Any]		  =  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 __future__ import annotations






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													A__  ,  A__						:  List[str]		  =  text, pattern
													A__  ,  A__						:  List[str]		  =  len(snake_case							), len(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if char == self.pattern[i]:
																									return i
													return -1


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						int							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if self.pattern[i] != self.text[current_pos + i]:
																									return current_pos + i
													return -1


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  []
													for i in range(self.textLen - self.patLen + 1							):
																			A__						:  List[Any]		  =  self.mismatch_in_text(snake_case							)
																			if mismatch_index == -1:
																									positions.append(snake_case							)
																			else:
																									A__						:  Dict		  =  self.match_in_pattern(self.text[mismatch_index]							)
																									A__						:  Union[str, Any]		  =  (
																									    mismatch_index - match_index
																									)  # shifting index lgtm [py/multiple-definition]
													return positions




A_    =							'''ABAABA'''
A_    =							'''AB'''
A_    =							BoyerMooreSearch(text, pattern)
A_    =							bms.bad_character_heuristic()

if len(positions) == 0:
						print('''No match found''')
else:
						print('''Pattern found in following positions: ''')
						print(positions)






