






"""simple docstring"""





import numpy as np
from matplotlib import pyplot as plt







def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple			)				->    Any:
  return 1 / (1 + np.exp(-1 * x			))






class 					a						:






    def __init__(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							List[Any]=None	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=None	,						__SCREAMING_SNAKE_CASE					:							Optional[int]=False )					->		Optional[int]:
      lowerCamelCase_   					=						units
      lowerCamelCase_   					=						None
      lowerCamelCase_   					=						None
      lowerCamelCase_   					=						activation
      if learning_rate is None:
        lowerCamelCase_   					=						0.3
      lowerCamelCase_   					=						learning_rate
      lowerCamelCase_   					=						is_input_layer






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any] )					->		List[Any]:
      lowerCamelCase_   					=						np.asmatrix(np.random.normal(0	,						0.5	,						(self.units, back_units) ) )
      lowerCamelCase_   					=						np.asmatrix(np.random.normal(0	,						0.5	,						self.units ) ).T
      if self.activation is None:
        lowerCamelCase_   					=						sigmoid






    def        UpperCamelCase	(	self					:							List[str] )					->		int:
      # activation function may be sigmoid or linear
      if self.activation == sigmoid:
        lowerCamelCase_   					=						np.dot(self.output	,						(1 - self.output).T )
        lowerCamelCase_   					=						np.diag(np.diag(__SCREAMING_SNAKE_CASE ) )
      else:
        lowerCamelCase_   					=						1
      return gradient_activation






    def        UpperCamelCase	(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Any )					->		int:
      lowerCamelCase_   					=						xdata
      if self.is_input_layer:
        # input layer
        lowerCamelCase_   					=						xdata
        lowerCamelCase_   					=						xdata
        return xdata
      else:
        lowerCamelCase_   					=						np.dot(self.weight	,						self.xdata ) - self.bias
        lowerCamelCase_   					=						self.activation(self.wx_plus_b )
        return self.output






    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Any )					->		List[Any]:
      lowerCamelCase_   					=						self.cal_gradient()  # i * i 维
      lowerCamelCase_   					=						np.asmatrix(np.dot(gradient.T	,						__SCREAMING_SNAKE_CASE ) )

      lowerCamelCase_   					=						np.asmatrix(self.xdata )
      lowerCamelCase_   					=						-1
      lowerCamelCase_   					=						self.weight

      lowerCamelCase_   					=						np.dot(gradient.T	,						self._gradient_weight.T )
      lowerCamelCase_   					=						gradient * self._gradient_bias
      lowerCamelCase_   					=						np.dot(__SCREAMING_SNAKE_CASE	,						self._gradient_x ).T
      # upgrade: the Negative gradient direction
      lowerCamelCase_   					=						self.weight - self.learn_rate * self.gradient_weight
      lowerCamelCase_   					=						self.bias - self.learn_rate * self.gradient_bias.T
      # updates the weights and bias according to learning rate (0.3 if undefined)
      return self.gradient






class 					a						:






    def __init__(	self					:							Tuple )					->		Optional[Any]:
      lowerCamelCase_   					=						[]
      lowerCamelCase_   					=						[]
      lowerCamelCase_   					=						plt.figure()
      lowerCamelCase_   					=						self.fig_loss.add_subplot(1	,						1	,						1 )






    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Any )					->		Dict:
      self.layers.append(__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Tuple )					->		List[str]:
      for i, layer in enumerate(self.layers[:] ):
        if i < 1:
          lowerCamelCase_   					=						True
        else:
          layer.initializer(self.layers[i - 1].units )






    def        UpperCamelCase	(	self					:							int )					->		Union[str, Any]:
      for i, layer in enumerate(self.layers[:] ):
        print(F'''------- layer {i} -------''' )
        print('weight.shape '	,						np.shape(layer.weight ) )
        print('bias.shape '	,						np.shape(layer.bias ) )






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		str:
      lowerCamelCase_   					=						train_round
      lowerCamelCase_   					=						accuracy

      self.ax_loss.hlines(self.accuracy	,						0	,						self.train_round * 1.1 )

      lowerCamelCase_   					=						np.shape(__SCREAMING_SNAKE_CASE )
      for _ in range(__SCREAMING_SNAKE_CASE ):
        lowerCamelCase_   					=						0
        for row in range(x_shape[0] ):
          lowerCamelCase_   					=						np.asmatrix(xdata[row, :] ).T
          lowerCamelCase_   					=						np.asmatrix(ydata[row, :] ).T

          # forward propagation
          for layer in self.layers:
            lowerCamelCase_   					=						layer.forward_propagation(_xdata )

          lowerCamelCase_					,     lowerCamelCase_   					=						self.cal_loss(_ydata	,						_xdata )
          lowerCamelCase_   					=						all_loss + loss

          # back propagation: the input_layer does not upgrade
          for layer in self.layers[:0:-1]:
            lowerCamelCase_   					=						layer.back_propagation(__SCREAMING_SNAKE_CASE )

        lowerCamelCase_   					=						all_loss / x_shape[0]
        self.train_mse.append(__SCREAMING_SNAKE_CASE )

        self.plot_loss()

        if mse < self.accuracy:
          print('----达到精度----' )
          return mse
      return None






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		int:
      lowerCamelCase_   					=						np.sum(np.power((ydata - ydata_)	,						2 ) )
      lowerCamelCase_   					=						2 * (ydata_ - ydata)
      # vector (shape is the same as _ydata.shape)
      return self.loss, self.loss_gradient






    def        UpperCamelCase	(	self					:							Optional[Any] )					->		List[str]:
      if self.ax_loss.lines:
        self.ax_loss.lines.remove(self.ax_loss.lines[0] )
      self.ax_loss.plot(self.train_mse	,						'r-' )
      plt.ion()
      plt.xlabel('step' )
      plt.ylabel('loss' )
      plt.show()
      plt.pause(0.1 )







def 					lowerCamelCase__	(							)				->    Any:
  lowerCamelCase_   					=						np.random.randn(10		,    10			)
  lowerCamelCase_   					=						np.asarray(
      [
          [0.8, 0.4],
          [0.4, 0.3],
          [0.34, 0.45],
          [0.67, 0.32],
          [0.88, 0.67],
          [0.78, 0.77],
          [0.55, 0.66],
          [0.55, 0.43],
          [0.54, 0.1],
          [0.1, 0.5],
      ]			)
  lowerCamelCase_   					=						BPNN()
  for i in (10, 20, 30, 2):
    model.add_layer(DenseLayer(_lowerCamelCase			)			)
  model.build()
  model.summary()
  model.train(xdata=_lowerCamelCase		,    ydata=_lowerCamelCase		,    train_round=100		,    accuracy=0.01			)


if __name__ == "__main__":
  example()












"""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}''')




