


"""simple docstring"""


from collections import Counter

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

_snake_case          =							datasets.load_iris()

_snake_case          =							np.array(data["data"])
_snake_case          =							np.array(data["target"])
_snake_case          =							data["target_names"]

_snake_case      ,					_snake_case      ,					_snake_case      ,					_snake_case          =							train_test_split(X, y)



def   snake_case						(				_a:     Optional[Any]		,   _a:     List[Any]							)->		Union[str, Any]:
      '''simple docstring'''


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



def   snake_case						(				_a:     List[Any]		,   _a:     List[Any]		,   _a:     Optional[Any]		,   _a:     List[str]		,   _a:     List[Any]=5							)->		Optional[Any]:
      '''simple docstring'''


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


def   snake_case						(				_a:     list[list[float]]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for data in source_data:
            for i, el in enumerate(_a							):
                  if len(_a							) < i + 1:
                        data_lists.append([]							)
                  data_lists[i].append(float(_a							)							)
      return data_lists



def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for dlist, weight in zip(_a		,   _a							):
            lowerCamelCase__           =		min(_a							)
            lowerCamelCase__           =		max(_a							)

            lowerCamelCase__           =		[]
            # for weight 0 score is 1 - actual score
            if weight == 0:
                  for item in dlist:
                        try:
                              score.append(1 - ((item - mind) / (maxd - mind))							)
                        except ZeroDivisionError:
                              score.append(1							)

            elif weight == 1:
                  for item in dlist:
                        try:
                              score.append((item - mind) / (maxd - mind)							)
                        except ZeroDivisionError:
                              score.append(0							)

        # weight not 0 or 1
            else:
                  lowerCamelCase__           =		F'Invalid weight of {weight:f} provided'
                  raise ValueError(_a							)

            score_lists.append(_a							)

      return score_lists



def   snake_case						(				_a:     list[list[float]]							)->		list[float]:
      '''simple docstring'''


      lowerCamelCase__           =		[0 for i in range(len(score_lists[0]							)							)]

      for slist in score_lists:
            for j, ele in enumerate(_a							):
                  lowerCamelCase__           =		final_scores[j] + ele

      return final_scores






def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		get_data(_a							)
      lowerCamelCase__           =		calculate_each_score(_a		,   _a							)
      lowerCamelCase__           =		generate_final_scores(_a							)

      # append scores to source data
      for i, ele in enumerate(_a							):
            source_data[i].append(_a							)

      return source_data



