


"""simple docstring"""







from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    UpperCAmelCase__       (lowerCAmelCase_ = ""   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_ = "IMDb_Top_250_Movies.csv"   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				get_imdb_top_aaa_movies()
					with open(lowerCAmelCase_  ,						"w"  ,						newline=""   ) as out_file:
										__SCREAMING_SNAKE_CASE						   =				csv.writer(lowerCAmelCase_   )
										writer.writerow(["Movie title", "IMDb rating"]   )
										for title, rating in movies.items():
															writer.writerow([title, rating]   )


if __name__ == "__main__":
		write_movies()




"""simple docstring"""







import math
import random




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value   ))


# Initial Value
a__				:							Tuple						=     0.02




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(2 * (random.randint(1  ,						100   )) - 1   )

					for _ in range(lowerCAmelCase_   ):
										# Forward propagation
										__SCREAMING_SNAKE_CASE						   =				sigmoid_function(INITIAL_VALUE * weight   )
										# How much did we miss?
										__SCREAMING_SNAKE_CASE						   =				(expected / 100) - layer_a
										# Error delta
										__SCREAMING_SNAKE_CASE						   =				layer_1_error * sigmoid_function(lowerCAmelCase_  ,						lowerCAmelCase_   )
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 100


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__				:							List[str]						=     int(input('''Expected value: '''))
		a__				:							str						=     int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
