
'''simple docstring'''


from collections import Counter

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

__lowercase							:   int					=     datasets.load_iris()

__lowercase							:   int					=     np.array(data['''data'''])
__lowercase							:   Optional[Any]					=     np.array(data['''target'''])
__lowercase							:   Optional[Any]					=     data['''target_names''']

__lowercase				,		__lowercase				,		__lowercase				,		__lowercase							:   Dict					=     train_test_split(X, y)




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

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




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=5				)     ->							Optional[Any]:
					'''simple docstring'''

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




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							dict[str, float]:
					'''simple docstring'''

					if (voltage, current, resistance).count(0				) != 1:
										raise ValueError('''One and only one argument must be 0'''				)
					if resistance < 0:
										raise ValueError('''Resistance cannot be negative'''				)
					if voltage == 0:
										return {"voltage": float(current * resistance				)}
					elif current == 0:
										return {"current": voltage / resistance}
					elif resistance == 0:
										return {"resistance": voltage / current}
					else:
										raise ValueError('''Exactly one argument must be 0'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


