



import numpy as np
import requests





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





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Tuple            =len_data

   SCREAMING_SNAKE_CASE_:	Optional[Any]            =np.dot(lowercase_							,     data_x.transpose()	)
   prod -= data_y.transpose()
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =np.dot(lowercase_							,     lowercase_	)
   SCREAMING_SNAKE_CASE_:	Tuple            =theta - (alpha / n) * sum_grad
   return theta





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	str            =np.dot(lowercase_							,     data_x.transpose()	)
   prod -= data_y.transpose()
   SCREAMING_SNAKE_CASE_:	Dict            =np.sum(np.square(lowercase_	)	)
   SCREAMING_SNAKE_CASE_:	List[Any]            =sum_elem / (2 * len_data)
   return error





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =10_0000
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =0.0_001_550

   SCREAMING_SNAKE_CASE_:	Tuple            =data_x.shape[1]
   SCREAMING_SNAKE_CASE_:	int            =data_x.shape[0] - 1

   SCREAMING_SNAKE_CASE_:	Any            =np.zeros((1, no_features)	)

   for i in range(0							,     lowercase_	):
      SCREAMING_SNAKE_CASE_:	int            =run_steep_gradient_descent(lowercase_							,     lowercase_							,     lowercase_							,     lowercase_							,     lowercase_	)
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =sum_of_square_error(lowercase_							,     lowercase_							,     lowercase_							,     lowercase_	)
      print(f'''At Iteration {i + 1} - Error is {error:.5f}'''	)

   return theta





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =sum(abs(y - predicted_y[i]	) for i, y in enumerate(lowercase_	)	)
   return total / len(lowercase_	)





def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =collect_dataset()

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

   SCREAMING_SNAKE_CASE_:	Dict            =run_linear_regression(lowercase_							,     lowercase_	)
   SCREAMING_SNAKE_CASE_:	List[Any]            =theta.shape[1]
   print("""Resultant Feature vector : """	)
   for i in range(0							,     lowercase_	):
      print(f'''{theta[0, i]:.5f}'''	)


if __name__ == "__main__":
   main()








"""simple docstring"""
def        __magic_name__			(							lowercase = 1000	):
   SCREAMING_SNAKE_CASE_:	Dict            =0
   SCREAMING_SNAKE_CASE_:	Any            =0
   while 1:
      num += 3
      if num >= n:
         break
      total += num
      num += 2
      if num >= n:
         break
      total += num
      num += 1
      if num >= n:
         break
      total += num
      num += 3
      if num >= n:
         break
      total += num
      num += 1
      if num >= n:
         break
      total += num
      num += 2
      if num >= n:
         break
      total += num
      num += 3
      if num >= n:
         break
      total += num
   return total


if __name__ == "__main__":
   print(f"""{solution() = }""")



