
import numpy as np
import requests

def 					SCREAMING_SNAKE_CASE  (   )			->							Optional[Any]:

				"""simple docstring"""







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

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    List[str]				,    __UpperCamelCase			:    Any				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    Any							)			->							Any:

				"""simple docstring"""







				A__	:						Optional[int]								=				len_data

				A__	:						Tuple								=				np.dot(__UpperCamelCase				,    data_x.transpose()							)
				prod -= data_y.transpose()
				A__	:						Tuple								=				np.dot(__UpperCamelCase				,    __UpperCamelCase							)
				A__	:						Union[str, Any]								=				theta - (alpha / n) * sum_grad
				return theta

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[Any]				,    __UpperCamelCase			:    Any				,    __UpperCamelCase			:    Any				,    __UpperCamelCase			:    Union[str, Any]							)			->							List[str]:

				"""simple docstring"""







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

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    List[Any]							)			->							List[str]:

				"""simple docstring"""







				A__	:						Any								=				10_00_00
				A__	:						Dict								=				0.0_0_0_1_5_5_0

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

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

				for i in range(0				,    __UpperCamelCase							):
								A__	:						int								=				run_steep_gradient_descent(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)
								A__	:						Union[str, Any]								=				sum_of_square_error(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)
								print(F"At Iteration {i + 1} - Error is {error:.5f}"							)

				return theta

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    Union[str, Any]							)			->							Dict:

				"""simple docstring"""







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

def 					SCREAMING_SNAKE_CASE  (   )			->							Any:

				"""simple docstring"""







				A__	:						Optional[int]								=				collect_dataset()

				A__	:						Tuple								=				data.shape[0]
				A__	:						str								=				np.c_[np.ones(__UpperCamelCase							), data[:, :-1]].astype(__UpperCamelCase							)
				A__	:						Optional[Any]								=				data[:, -1].astype(__UpperCamelCase							)

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


if __name__ == "__main__":
					main()

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list[int]							)			->							int:

				"""simple docstring"""







				if not isinstance(__UpperCamelCase				,    __UpperCamelCase							) or not all(
				    isinstance(__UpperCamelCase				,    __UpperCamelCase							) and item >= 0 for item in citations							):
								raise ValueError('''The citations should be a list of non negative integers.'''							)

				citations.sort()
				A__	:						str								=				len(__UpperCamelCase							)

				for i in range(__UpperCamelCase							):
								if citations[len_citations - 1 - i] <= i:
												return i

				return len_citations


if __name__ == "__main__":
					import doctest

					doctest.testmod()