


"""simple docstring"""







from collections import Counter

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

a__    :							Union[str, Any]            =					datasets.load_iris()

a__    :							Any            =					np.array(data['''data'''])
a__    :							Dict            =					np.array(data['''target'''])
a__    :							Optional[int]            =					data['''target_names''']

a__    :							Dict            =					train_test_split(X, y)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=5       ):




					'''simple docstring'''


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







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "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]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "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:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

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

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

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

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
