






"""simple docstring"""





import numpy as np
import requests







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







def 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		int			)				->    Tuple:
  lowerCamelCase_   					=						len_data

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







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Dict			)				->    Optional[int]:
  lowerCamelCase_   					=						np.dot(_lowerCamelCase		,    data_x.transpose()			)
  prod -= data_y.transpose()
  lowerCamelCase_   					=						np.sum(np.square(_lowerCamelCase			)			)
  lowerCamelCase_   					=						sum_elem / (2 * len_data)
  return error







def 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Optional[Any]			)				->    List[Any]:
  lowerCamelCase_   					=						100000
  lowerCamelCase_   					=						0.0_00_15_50

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

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

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

  return theta







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any			)				->    str:
  lowerCamelCase_   					=						sum(abs(y - predicted_y[i]			) for i, y in enumerate(_lowerCamelCase			)			)
  return total / len(_lowerCamelCase			)







def 					lowerCamelCase__	(							)				->    int:
  lowerCamelCase_   					=						collect_dataset()

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

  lowerCamelCase_   					=						run_linear_regression(_lowerCamelCase		,    _lowerCamelCase			)
  lowerCamelCase_   					=						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"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int			)				->    int:
  if not root or root not in binary_tree_mirror_dictionary:
    return
  lowerCamelCase_					,     lowerCamelCase_   					=						binary_tree_mirror_dictionary[root][:2]
  lowerCamelCase_   					=						[right_child, left_child]
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int = 1			)				->    dict:
  if not binary_tree:
    raise ValueError('binary tree cannot be empty'			)
  if root not in binary_tree:
    lowerCamelCase_   					=						F'''root {root} is not present in the binary_tree'''
    raise ValueError(_lowerCamelCase			)
  lowerCamelCase_   					=						dict(_lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Tuple    =						{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'''Binary tree: {binary_tree}''')
  _SCREAMING_SNAKE_CASE :    Dict    =						binary_tree_mirror(binary_tree, 5)
  print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')




