






import numpy as np
import requests



def      UpperCAmelCase						( ):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							len_data

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							np.dot(lowerCAmelCase__					,     data_x.transpose()	)
		prod -= data_y.transpose()
		__A    =							np.sum(np.square(lowerCAmelCase__	)	)
		__A    =							sum_elem / (2 * len_data)
		return error



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							10_0000
		__A    =							0.0_001_550

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

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

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

		return theta



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							sum(abs(y - predicted_y[i]	) for i, y in enumerate(lowerCAmelCase__	)	)
		return total / len(lowerCAmelCase__	)



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							collect_dataset()

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

		__A    =							run_linear_regression(lowerCAmelCase__					,     lowerCAmelCase__	)
		__A    =							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'''

import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    CustomConfig




					def __init__(	self					:						int  ,	UpperCamelCase_					:						List[str]       )	->							int:



						'''simple docstring'''





						super().__init__(UpperCamelCase_       )
						_lowercase       :							List[Any]  	=		torch.nn.Linear(config.hidden_size  ,	config.hidden_size       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Any       )	->							Union[str, Any]:



						'''simple docstring'''





						return self.linear(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[Any]       )	->							str:



						'''simple docstring'''





						pass






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    NoSuperInitConfig




					def __init__(	self					:						Optional[int]  ,	UpperCamelCase_					:						Dict       )	->							Optional[Any]:



						'''simple docstring'''





						super().__init__(UpperCamelCase_       )
						_lowercase       :							List[str]  	=		torch.nn.Linear(config.attribute  ,	config.attribute       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





						return self.linear(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						int       )	->							List[str]:



						'''simple docstring'''





						pass
