"""simple docstring"""







import numpy as np
import requests



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



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Any      , lowercase_     :	str      , lowercase_     :	Optional[int]      , lowercase_     :	str						)  ->							Tuple:
   __lowerCamelCase    		=					len_data

   __lowerCamelCase    		=					np.dot(lowercase_      , data_x.transpose()						)
   prod -= data_y.transpose()
   __lowerCamelCase    		=					np.dot(lowercase_      , lowercase_						)
   __lowerCamelCase    		=					theta - (alpha / n) * sum_grad
   return theta



def       A__    (	lowercase_     :	str      , lowercase_     :	Tuple      , lowercase_     :	Optional[int]      , lowercase_     :	Optional[int]						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					np.dot(lowercase_      , data_x.transpose()						)
   prod -= data_y.transpose()
   __lowerCamelCase    		=					np.sum(np.square(lowercase_						)						)
   __lowerCamelCase    		=					sum_elem / (2 * len_data)
   return error



def       A__    (	lowercase_     :	List[Any]      , lowercase_     :	Any						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					100000
   __lowerCamelCase    		=					0.0_0_0_1_5_5_0

   __lowerCamelCase    		=					data_x.shape[1]
   __lowerCamelCase    		=					data_x.shape[0] - 1

   __lowerCamelCase    		=					np.zeros((1, no_features)						)

   for i in range(0      , lowercase_						):
      __lowerCamelCase    		=					run_steep_gradient_descent(lowercase_      , lowercase_      , lowercase_      , lowercase_      , lowercase_						)
      __lowerCamelCase    		=					sum_of_square_error(lowercase_      , lowercase_      , lowercase_      , lowercase_						)
      print(F'''At Iteration {i + 1} - Error is {error:.5f}'''						)

   return theta



def       A__    (	lowercase_     :	Optional[int]      , lowercase_     :	List[Any]						)  ->							Optional[Any]:
   __lowerCamelCase    		=					sum(abs(y - predicted_y[i]						) for i, y in enumerate(lowercase_						)						)
   return total / len(lowercase_						)



def       A__    (	)  ->							List[Any]:
   __lowerCamelCase    		=					collect_dataset()

   __lowerCamelCase    		=					data.shape[0]
   __lowerCamelCase    		=					np.c_[np.ones(lowercase_						), data[:, :-1]].astype(lowercase_						)
   __lowerCamelCase    		=					data[:, -1].astype(lowercase_						)

   __lowerCamelCase    		=					run_linear_regression(lowercase_      , lowercase_						)
   __lowerCamelCase    		=					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       A__    (	lowercase_     :	list						)  ->							list:

   for insert_index, insert_value in enumerate(collection[1:]						):
      __lowerCamelCase    		=					insert_index
      while insert_index >= 0 and insert_value < collection[insert_index]:
         __lowerCamelCase    		=					collection[insert_index]
         insert_index -= 1
      if insert_index != temp_index:
         __lowerCamelCase    		=					insert_value
   return collection


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _SCREAMING_SNAKE_CASE	:		List[str]   =   input('''Enter numbers separated by a comma:\n''').strip()
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   [int(item) for item in user_input.split(''',''')]
     print(F"{insertion_sort(unsorted) = }")






