


import numpy as np
import requests







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







def   lowerCamelCase(				a__		,a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					=len_data

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







def   lowerCamelCase(				a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					=np.dot(a__		,data_x.transpose())
				prod -= data_y.transpose()
				_SCREAMING_SNAKE_CASE     					=np.sum(np.square(a__))
				_SCREAMING_SNAKE_CASE     					=sum_elem / (2 * len_data)
				return error







def   lowerCamelCase(				a__		,a__):
				_SCREAMING_SNAKE_CASE     					=10_0000
				_SCREAMING_SNAKE_CASE     					=0.000_1550

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

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

				for i in range(0		,a__):
								_SCREAMING_SNAKE_CASE     					=run_steep_gradient_descent(a__		,a__		,a__		,a__		,a__)
								_SCREAMING_SNAKE_CASE     					=sum_of_square_error(a__		,a__		,a__		,a__)
								print(f"At Iteration {i + 1} - Error is {error:.5f}")

				return theta







def   lowerCamelCase(				a__		,a__):
				_SCREAMING_SNAKE_CASE     					=sum(abs(y - predicted_y[i]) for i, y in enumerate(a__))
				return total / len(a__)







def   lowerCamelCase(				):
				_SCREAMING_SNAKE_CASE     					=collect_dataset()

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

				_SCREAMING_SNAKE_CASE     					=run_linear_regression(a__		,a__)
				_SCREAMING_SNAKE_CASE     					=theta.shape[1]
				print('''Resultant Feature vector : ''')
				for i in range(0		,a__):
								print(f"{theta[0, i]:.5f}")


if __name__ == "__main__":
							main()



import math

class   A__							:




						def __init__(    self   : Optional[int]			,      _a   : int			,      _a   : List[Any]			,      _a   : int					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=x
										_SCREAMING_SNAKE_CASE     					=y
										_SCREAMING_SNAKE_CASE     					=z




						def __repr__(    self   : List[str]					)							->       str:


										"""simple docstring"""





										return f"Point({self.x}, {self.y}, {self.z})"







def   lowerCamelCase(				a__		,a__):
				return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))


if __name__ == "__main__":
							import doctest

							doctest.testmod()