


"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def      lowerCamelCase       (     _UpperCamelCase		:      np.ndarray       ,			_UpperCamelCase		:      np.ndarray							)		->       float:

							'''simple docstring'''



							return math.sqrt(sum(pow(a - b       ,			2							) for a, b in zip(_UpperCamelCase       ,			_UpperCamelCase							)							)							)
def      lowerCamelCase       (     _UpperCamelCase		:      np.ndarray       ,			_UpperCamelCase		:      np.ndarray							)		->       list[list[list[float] | float]]:

							'''simple docstring'''



							if dataset.ndim != value_array.ndim:
														__UpperCAmelCase   :						List[Any]      							=					(
														    """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]:
																					__UpperCAmelCase   :						Optional[Any]      							=					(
																					    """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:
														__UpperCAmelCase   :						Union[str, Any]      							=					(
														    """Input data have different datatype... """
														    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
														)
														raise TypeError(_UpperCamelCase							)

							__UpperCAmelCase   :						str      							=					[]

							for value in value_array:
														__UpperCAmelCase   :						Any      							=					euclidean(_UpperCamelCase       ,			dataset[0]							)
														__UpperCAmelCase   :						List[str]      							=					dataset[0].tolist()

														for dataset_value in dataset[1:]:
																					__UpperCAmelCase   :						Dict      							=					euclidean(_UpperCamelCase       ,			_UpperCamelCase							)

																					if dist > temp_dist:
																												__UpperCAmelCase   :						Any      							=					temp_dist
																												__UpperCAmelCase   :						Optional[Any]      							=					dataset_value.tolist()

														answer.append([vector, dist]							)

							return answer
def      lowerCamelCase       (     _UpperCamelCase		:      np.ndarray       ,			_UpperCamelCase		:      np.ndarray							)		->       float:

							'''simple docstring'''



							return np.dot(_UpperCamelCase       ,			_UpperCamelCase							) / (norm(_UpperCamelCase							) * norm(_UpperCamelCase							))


if __name__ == "__main__":
							import doctest

							doctest.testmod()




"""simple docstring"""






import re
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       str:

							'''simple docstring'''



							if len(re.findall("""[ATCG]"""       ,			_UpperCamelCase							)							) != len(_UpperCamelCase							):
														raise ValueError("""Invalid Strand"""							)

							return dna.translate(dna.maketrans("""ATCG"""       ,			"""TAGC"""							)							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
