



__SCREAMING_SNAKE_CASE :      List[Any]             ='\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

__SCREAMING_SNAKE_CASE :      Optional[int]             =[{'type': 'code', 'content': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE :      List[Any]             ={
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}










'''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()





