
"""simple docstring"""

import numpy as np
import requests






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






def 				A		(							snake_case :Any							,   snake_case :Optional[int]							,   snake_case :List[str]							,   snake_case :List[Any]							,   snake_case :Dict						) ->      Tuple:
 __UpperCamelCase							    =			len_data

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






def 				A		(							snake_case :Optional[Any]							,   snake_case :Tuple							,   snake_case :int							,   snake_case :Any						) ->      Dict:
 __UpperCamelCase							    =			np.dot(snake_case							,   data_x.transpose()						)
 prod -= data_y.transpose()
 __UpperCamelCase							    =			np.sum(np.square(snake_case						)						)
 __UpperCamelCase							    =			sum_elem / (2 * len_data)
 return error






def 				A		(							snake_case :Dict							,   snake_case :Dict						) ->      str:
 __UpperCamelCase							    =			1_0_0_0_0_0
 __UpperCamelCase							    =			0.0_001_550

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

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

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

 return theta






def 				A		(							snake_case :Any							,   snake_case :str						) ->      List[Any]:
 __UpperCamelCase							    =			sum(abs(y - predicted_y[i]						) for i, y in enumerate(snake_case						)						)
 return total / len(snake_case						)






def 				A		(							) ->      int:
 __UpperCamelCase							    =			collect_dataset()

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

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


if __name__ == "__main__":
      main()





"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



