
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Any		):






     '''simple docstring'''
     return math.sqrt(sum(pow(a - b       ,    2		) for a, b in zip(lowerCAmelCase_       ,    lowerCAmelCase_		)		)		)



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       int		):






     '''simple docstring'''



     if dataset.ndim != value_array.ndim:
          UpperCAmelCase__							: Tuple 							=						(
              '''Wrong input data\'s dimensions... '''
              F"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
          )
          raise ValueError(lowerCAmelCase_		)

     try:
          if dataset.shape[1] != value_array.shape[1]:
               UpperCAmelCase__							: Union[str, Any] 							=						(
                   '''Wrong input data\'s shape... '''
                   F"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
               )
               raise ValueError(lowerCAmelCase_		)
     except IndexError:
          if dataset.ndim != value_array.ndim:
               raise TypeError("Wrong shape"		)

     if dataset.dtype != value_array.dtype:
          UpperCAmelCase__							: Optional[Any] 							=						(
              '''Input data have different datatype... '''
              F"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
          )
          raise TypeError(lowerCAmelCase_		)

     UpperCAmelCase__							: Union[str, Any] 							=						[]

     for value in value_array:
          UpperCAmelCase__							: Tuple 							=						euclidean(lowerCAmelCase_       ,    dataset[0]		)
          UpperCAmelCase__							: Optional[Any] 							=						dataset[0].tolist()

          for dataset_value in dataset[1:]:
               UpperCAmelCase__							: Optional[Any] 							=						euclidean(lowerCAmelCase_       ,    lowerCAmelCase_		)

               if dist > temp_dist:
                    UpperCAmelCase__							: List[Any] 							=						temp_dist
                    UpperCAmelCase__							: str 							=						dataset_value.tolist()

          answer.append([vector, dist]		)

     return answer



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     return np.dot(lowerCAmelCase_       ,    lowerCAmelCase_		) / (norm(lowerCAmelCase_		) * norm(lowerCAmelCase_		))


if __name__ == "__main__":
    import doctest

    doctest.testmod()





"""simple docstring"""




import math



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5       ,    int(math.sqrt(lowerCAmelCase		) + 1		)       ,    6		):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True



def 							a__ (				lowerCAmelCase	:       int = 1_0001		):






     '''simple docstring'''

     try:
          UpperCAmelCase__							: List[str] 							=						int(lowerCAmelCase		)
     except (TypeError, ValueError):
          raise TypeError("Parameter nth must be int or castable to int."		) from None
     if nth <= 0:
          raise ValueError("Parameter nth must be greater than or equal to one."		)
     UpperCAmelCase__							: list[int] 							=						[]
     UpperCAmelCase__							: str 							=						2
     while len(lowerCAmelCase		) < nth:
          if is_prime(lowerCAmelCase		):
               primes.append(lowerCAmelCase		)
               num += 1
          else:
               num += 1
     return primes[len(lowerCAmelCase		) - 1]


if __name__ == "__main__":
    print(f"""{solution() = }""")



