



'''simple docstring'''
import numpy as np
import requests



def 		_SCREAMING_SNAKE_CASE							(				):


   '''simple docstring'''







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



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      int				):


   '''simple docstring'''







   A:      Union[str, Any]														=   len_data

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



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      int							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Union[str, Any]							,							lowerCamelCase__   :      Union[str, Any]				):


   '''simple docstring'''







   A:      str														=   np.dot(lowerCamelCase__							,							data_x.transpose()				)
   prod -= data_y.transpose()
   A:      Any														=   np.sum(np.square(lowerCamelCase__				)				)
   A:      List[Any]														=   sum_elem / (2 * len_data)
   return error



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Union[str, Any]				):


   '''simple docstring'''







   A:      Dict														=   100000
   A:      Dict														=   0.0_00_15_50

   A:      Union[str, Any]														=   data_x.shape[1]
   A:      Tuple														=   data_x.shape[0] - 1

   A:      str														=   np.zeros((1, no_features)				)

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

   return theta



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      List[str]				):


   '''simple docstring'''







   A:      List[Any]														=   sum(abs(y - predicted_y[i]				) for i, y in enumerate(lowerCamelCase__				)				)
   return total / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				):


   '''simple docstring'''







   A:      Optional[Any]														=   collect_dataset()

   A:      str														=   data.shape[0]
   A:      Union[str, Any]														=   np.c_[np.ones(lowerCamelCase__				), data[:, :-1]].astype(lowerCamelCase__				)
   A:      Any														=   data[:, -1].astype(lowerCamelCase__				)

   A:      Dict														=   run_linear_regression(lowerCamelCase__							,							lowerCamelCase__				)
   A:      Union[str, Any]														=   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'''
from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	def __init__(		self					,   *A					,   **A			) ->							Dict:
				super().__init__(*A					,   **A			)


	def      a__ (		self					,   A					,   A			) ->							Dict:
				A:      int														=   (
				    next_prime(value % self.size_table			)
				    if not is_prime(value % self.size_table			)
				    else value % self.size_table
				)  # gt = bigger than
				return next_prime_gt - (data % next_prime_gt)


	def      a__ (		self					,   A					,   A					,   A			) ->							int:
				return (increment * self.__hash_function_a(A					,   A			)) % self.size_table





	def      a__ (		self					,   A					,   A=None			) ->							str:
				A:      Tuple														=   1
				A:      str														=   self.hash_function(A			)

				while self.values[new_key] is not None and self.values[new_key] != key:
							A:      int														=   (
							    self.__hash_double_function(A					,   A					,   A			)
							    if self.balanced_factor() >= self.lim_charge
							    else None
							)
							if new_key is None:
										break
							else:
										i += 1

				return new_key





