


'''simple docstring'''




import numpy as np
from matplotlib import pyplot as plt





def      _a		(lowercase__ :      Union[str, Any]	) -> Any:






     """simple docstring"""
     return 1 / (1 + np.exp(-1 * x	))




class  _lowercase   :
   def __init__(	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			int=None					,      SCREAMING_SNAKE_CASE_					:			List[Any]=None					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=False			)   ->       Optional[Any]:
        __snake_case    			=	units
        __snake_case    			=	None
        __snake_case    			=	None
        __snake_case    			=	activation
        if learning_rate is None:
             __snake_case    			=	0.3
        __snake_case    			=	learning_rate
        __snake_case    			=	is_input_layer
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       str:
        __snake_case    			=	np.asmatrix(np.random.normal(0					,      0.5					,      (self.units, back_units)			)			)
        __snake_case    			=	np.asmatrix(np.random.normal(0					,      0.5					,      self.units			)			).T
        if self.activation is None:
             __snake_case    			=	sigmoid
   def a     (	self					:			Any			)   ->       Union[str, Any]:
        # activation function may be sigmoid or linear
        if self.activation == sigmoid:
             __snake_case    			=	np.dot(self.output					,      (1 - self.output).T			)
             __snake_case    			=	np.diag(np.diag(SCREAMING_SNAKE_CASE_			)			)
        else:
             __snake_case    			=	1
        return gradient_activation
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]			)   ->       Tuple:
        __snake_case    			=	xdata
        if self.is_input_layer:
             # input layer
             __snake_case    			=	xdata
             __snake_case    			=	xdata
             return xdata
        else:
             __snake_case    			=	np.dot(self.weight					,      self.xdata			) - self.bias
             __snake_case    			=	self.activation(self.wx_plus_b			)
             return self.output





   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Tuple:
        __snake_case    			=	self.cal_gradient()  # i * i 维
        __snake_case    			=	np.asmatrix(np.dot(gradient.T					,      SCREAMING_SNAKE_CASE_			)			)

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

        __snake_case    			=	np.dot(gradient.T					,      self._gradient_weight.T			)
        __snake_case    			=	gradient * self._gradient_bias
        __snake_case    			=	np.dot(SCREAMING_SNAKE_CASE_					,      self._gradient_x			).T
        # upgrade: the Negative gradient direction
        __snake_case    			=	self.weight - self.learn_rate * self.gradient_weight
        __snake_case    			=	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  _lowercase   :
   def __init__(	self					:			Optional[Any]			)   ->       Union[str, Any]:
        __snake_case    			=	[]
        __snake_case    			=	[]
        __snake_case    			=	plt.figure()
        __snake_case    			=	self.fig_loss.add_subplot(1					,      1					,      1			)
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Optional[int]:
        self.layers.append(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       Dict:
        for i, layer in enumerate(self.layers[:]			):
             if i < 1:
                  __snake_case    			=	True
             else:
                  layer.initializer(self.layers[i - 1].units			)
   def a     (	self					:			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 a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Any			)   ->       List[Any]:
        __snake_case    			=	train_round
        __snake_case    			=	accuracy

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

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

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

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

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

             __snake_case    			=	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 a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			List[Any]			)   ->       Union[str, Any]:
        __snake_case    			=	np.sum(np.power((ydata - ydata_)					,      2			)			)
        __snake_case    			=	2 * (ydata_ - ydata)
        # vector (shape is the same as _ydata.shape)
        return self.loss, self.loss_gradient





   def a     (	self					:			Union[str, Any]			)   ->       int:
        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      _a		() -> Optional[int]:






     """simple docstring"""
     __snake_case    			=	np.random.randn(1_0       ,      1_0	)
     __snake_case    			=	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],
         ]	)
     __snake_case    			=	BPNN()
     for i in (1_0, 2_0, 3_0, 2):
          model.add_layer(DenseLayer(lowercase__	)	)
     model.build()
     model.summary()
     model.train(xdata=lowercase__       ,      ydata=lowercase__       ,      train_round=1_0_0       ,      accuracy=0.01	)


if __name__ == "__main__":
       example()








'''simple docstring'''




def      _a		(lowercase__ :      int	) -> int:






     """simple docstring"""
     return 1 if input_a == 0 else 0





def      _a		() -> None:






     """simple docstring"""
     assert not_gate(0	) == 1
     assert not_gate(1	) == 0


if __name__ == "__main__":
       print(not_gate(0))
       print(not_gate(1))




