
'''simple docstring'''
from collections import Counter

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

A__   :		str 		=datasets.load_iris()

A__   :		Dict 		=np.array(data['data'])
A__   :		Tuple 		=np.array(data['target'])
A__   :		Tuple 		=data['target_names']

A__		,      A__		,      A__		,      A__   :		int 		=train_test_split(X, y)







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  Tuple )				->       str:





  """simple docstring"""

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  List[Any]=5 )				->       Any:





  """simple docstring"""

  __A		:     str												=		zip(_lowercase			,     _lowercase )
  # List of distances of all points from the point to be classified
  __A		:     Optional[int]												=		[]
  for data_point in data:
    __A		:     Dict												=		euclidean_distance(data_point[0]			,     _lowercase )
    distances.append((distance, data_point[1]) )
  # Choosing 'k' points with the least distances.
  __A		:     Any												=		[i[1] for i in sorted(_lowercase )[:k]]
  # Most commonly occurring class among them
  # is the class into which the point is classified
  __A		:     int												=		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'''
A__   :		int 		=8.3_1_4_4_6_2  # Unit - J mol-1 K-1







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()



