



from collections import Counter

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

UpperCamelCase__ =						datasets.load_iris()

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

UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ =						train_test_split(X, y)






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      str:
						return np.linalg.norm(np.array(lowercase__   ) - np.array(lowercase__   )   )






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__=5   ) ->      List[Any]:
						__lowercase					=				zip(lowercase__						,       lowercase__   )
						# List of distances of all points from the point to be classified
						__lowercase					=				[]
						for data_point in data:
												__lowercase					=				euclidean_distance(data_point[0]						,       lowercase__   )
												distances.append((distance, data_point[1])   )
						# Choosing 'k' points with the least distances.
						__lowercase					=				[i[1] for i in sorted(lowercase__   )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__lowercase					=				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]))










def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						__lowercase					=				(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
						# formula for sum of series
						return total






def  UpperCAmelCase__    (    ) ->      List[str]:
						print(sum_of_series(1						,       1						,       10   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





