


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







def     lowerCAmelCase_					( __lowerCamelCase = ""     ):
							__snake_case		:      Dict										=       url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
							__snake_case		:      Dict										=       BeautifulSoup(requests.get(__lowerCamelCase     ).text							,     "html.parser"     )
							__snake_case		:      str										=       soup.find_all("td"							,     attrs="titleColumn"     )
							__snake_case		:      Dict										=       soup.find_all("td"							,     class_="ratingColumn imdbRating"     )
							return {
							    title.a.text: float(rating.strong.text     )
							    for title, rating in zip(__lowerCamelCase							,     __lowerCamelCase     )
							}







def     lowerCAmelCase_					( __lowerCamelCase = "IMDb_Top_250_Movies.csv"     ):
							__snake_case		:      Any										=       get_imdb_top_aaa_movies()
							with open(__lowerCamelCase							,     "w"							,     newline=""     ) as out_file:
														__snake_case		:      List[str]										=       csv.writer(__lowerCamelCase     )
														writer.writerow(["Movie title", "IMDb rating"]     )
														for title, rating in movies.items():
																					writer.writerow([title, rating]     )


if __name__ == "__main__":
			write_movies()





from collections import Counter

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

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

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

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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       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]))

