





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	UpperCamelCase__(       UpperCamelCase__			:       np.ndarray ,     UpperCamelCase__			:       np.ndarray  )->float:
       return math.sqrt(sum(pow(a - b ,     2  ) for a, b in zip(UpperCamelCase__ ,     UpperCamelCase__  )  )  )


def 	UpperCamelCase__(       UpperCamelCase__			:       np.ndarray ,     UpperCamelCase__			:       np.ndarray  )->list[list[list[float] | float]]:

       if dataset.ndim != value_array.ndim:
              A__											=						(
                  '''Wrong input data\'s dimensions... '''
                  f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
              )
              raise ValueError(UpperCamelCase__  )

       try:
              if dataset.shape[1] != value_array.shape[1]:
                     A__											=						(
                         '''Wrong input data\'s shape... '''
                         f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
                     )
                     raise ValueError(UpperCamelCase__  )
       except IndexError:
              if dataset.ndim != value_array.ndim:
                     raise TypeError('''Wrong shape'''  )

       if dataset.dtype != value_array.dtype:
              A__											=						(
                  '''Input data have different datatype... '''
                  f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
              )
              raise TypeError(UpperCamelCase__  )

       A__											=						[]

       for value in value_array:
              A__											=						euclidean(UpperCamelCase__ ,     dataset[0]  )
              A__											=						dataset[0].tolist()

              for dataset_value in dataset[1:]:
                     A__											=						euclidean(UpperCamelCase__ ,     UpperCamelCase__  )

                     if dist > temp_dist:
                            A__											=						temp_dist
                            A__											=						dataset_value.tolist()

              answer.append([vector, dist]  )

       return answer


def 	UpperCamelCase__(       UpperCamelCase__			:       np.ndarray ,     UpperCamelCase__			:       np.ndarray  )->float:
       return np.dot(UpperCamelCase__ ,     UpperCamelCase__  ) / (norm(UpperCamelCase__  ) * norm(UpperCamelCase__  ))


if __name__ == "__main__":
       import doctest

       doctest.testmod()












import numpy as np


def 	UpperCamelCase__(       UpperCamelCase__			:       np.array  )->np.array:
       return (2 / (1 + np.exp(-2 * vector  ))) - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod()





