






"""simple docstring"""
import numpy as np
import requests






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			List[str]     				=						requests.get(
     'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
     'master/Week1/ADRvsRating.csv'				)
 lowerCamelCase__     :			Any     				=						response.text.splitlines()
 lowerCamelCase__     :			Dict     				=						[]
 for item in lines:
  lowerCamelCase__     :			List[str]     				=						item.split(','				)
  data.append(_lowerCamelCase				)
 data.pop(0				)  # This is for removing the labels from the list
 lowerCamelCase__     :			Optional[Any]     				=						np.matrix(_lowerCamelCase				)
 return dataset






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			int     				=						len_data

 lowerCamelCase__     :			Dict     				=						np.dot(_lowerCamelCase			,   data_x.transpose()				)
 prod -= data_y.transpose()
 lowerCamelCase__     :			Optional[int]     				=						np.dot(_lowerCamelCase			,   _lowerCamelCase				)
 lowerCamelCase__     :			Optional[Any]     				=						theta - (alpha / n) * sum_grad
 return theta






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Dict     				=						np.dot(_lowerCamelCase			,   data_x.transpose()				)
 prod -= data_y.transpose()
 lowerCamelCase__     :			Optional[Any]     				=						np.sum(np.square(_lowerCamelCase				)				)
 lowerCamelCase__     :			str     				=						sum_elem / (2 * len_data)
 return error






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						10_0000
 lowerCamelCase__     :			Union[str, Any]     				=						0.0_001_550

 lowerCamelCase__     :			Optional[Any]     				=						data_x.shape[1]
 lowerCamelCase__     :			int     				=						data_x.shape[0] - 1

 lowerCamelCase__     :			Union[str, Any]     				=						np.zeros((1, no_features)				)

 for i in range(0			,   _lowerCamelCase				):
  lowerCamelCase__     :			List[str]     				=						run_steep_gradient_descent(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)
  lowerCamelCase__     :			Union[str, Any]     				=						sum_of_square_error(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)
  print(f'''At Iteration {i + 1} - Error is {error:.5f}'''				)

 return theta






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						sum(abs(y - predicted_y[i]				) for i, y in enumerate(_lowerCamelCase				)				)
 return total / len(_lowerCamelCase				)






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			int     				=						collect_dataset()

 lowerCamelCase__     :			int     				=						data.shape[0]
 lowerCamelCase__     :			Optional[int]     				=						np.c_[np.ones(_lowerCamelCase				), data[:, :-1]].astype(_lowerCamelCase				)
 lowerCamelCase__     :			List[Any]     				=						data[:, -1].astype(_lowerCamelCase				)

 lowerCamelCase__     :			int     				=						run_linear_regression(_lowerCamelCase			,   _lowerCamelCase				)
 lowerCamelCase__     :			List[str]     				=						theta.shape[1]
 print('Resultant Feature vector : '				)
 for i in range(0			,   _lowerCamelCase				):
  print(f'''{theta[0, i]:.5f}'''				)


if __name__ == "__main__":
      main()









"""simple docstring"""
import bsa
import requests






def        lowerCamelCase_  (						_lowerCamelCase				):
 return {
     "name": soup.ha.a.text,
     "genre": soup.find('span'			,   class_='genre'				).text.strip(),
     "rating": soup.strong.text,
     "page_link": f'''https://www.imdb.com{soup.a.get('href'				)}''',
 }






def        lowerCamelCase_  (						_lowerCamelCase = 5				):
 lowerCamelCase__     :			Optional[int]     				=						int(float(_lowerCamelCase				)				)
 if num_movies < 1:
  return ()
 lowerCamelCase__     :			Any     				=						(
     'https://www.imdb.com/search/title?title_type='
     f'''feature&sort=num_votes,desc&count={num_movies}'''
 )
 lowerCamelCase__     :			Union[str, Any]     				=						bsa.BeautifulSoup(requests.get(_lowerCamelCase				).content			,   'html.parser'				)
 return tuple(
     get_movie_data_from_soup(_lowerCamelCase				)
     for movie in source.find_all('div'			,   class_='lister-item mode-advanced'				)				)


if __name__ == "__main__":
      import json

      A_			: str				=			int(input("How many movies would you like to see? "))
      print(
          ", ".join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )

