




'''simple docstring'''







import numpy as np
from matplotlib import pyplot as plt



def 					lowerCamelCase__							(			__lowercase							):
     return 1 / (1 + np.exp(-1 * x							))







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a       ,__a=None       ,__a=None       ,__a=False       )			->							List[str]:
          snake_case :			Any					   = units
          snake_case :			str					   = None
          snake_case :			List[Any]					   = None
          snake_case :			Optional[Any]					   = activation
          if learning_rate is None:
               snake_case :			str					   = 0.3
          snake_case :			str					   = learning_rate
          snake_case :			Optional[Any]					   = is_input_layer







     def  snake_case_			(							self       ,__a       )			->							int:
          snake_case :			Union[str, Any]					   = np.asmatrix(np.random.normal(0       ,0.5       ,(self.units, back_units)       )       )
          snake_case :			Optional[Any]					   = np.asmatrix(np.random.normal(0       ,0.5       ,self.units       )       ).T
          if self.activation is None:
               snake_case :			Union[str, Any]					   = sigmoid







     def  snake_case_			(							self       )			->							Union[str, Any]:
          # activation function may be sigmoid or linear
          if self.activation == sigmoid:
               snake_case :			Any					   = np.dot(self.output       ,(1 - self.output).T       )
               snake_case :			Union[str, Any]					   = np.diag(np.diag(__a       )       )
          else:
               snake_case :			List[Any]					   = 1
          return gradient_activation







     def  snake_case_			(							self       ,__a       )			->							Union[str, Any]:
          snake_case :			int					   = xdata
          if self.is_input_layer:
               # input layer
               snake_case :			int					   = xdata
               snake_case :			List[str]					   = xdata
               return xdata
          else:
               snake_case :			List[str]					   = np.dot(self.weight       ,self.xdata       ) - self.bias
               snake_case :			List[Any]					   = self.activation(self.wx_plus_b       )
               return self.output







     def  snake_case_			(							self       ,__a       )			->							List[Any]:
          snake_case :			Union[str, Any]					   = self.cal_gradient()  # i * i 维
          snake_case :			Tuple					   = np.asmatrix(np.dot(gradient.T       ,__a       )       )

          snake_case :			Tuple					   = np.asmatrix(self.xdata       )
          snake_case :			Any					   = -1
          snake_case :			Optional[int]					   = self.weight

          snake_case :			Union[str, Any]					   = np.dot(gradient.T       ,self._gradient_weight.T       )
          snake_case :			Optional[int]					   = gradient * self._gradient_bias
          snake_case :			Dict					   = np.dot(__a       ,self._gradient_x       ).T
          # upgrade: the Negative gradient direction
          snake_case :			List[Any]					   = self.weight - self.learn_rate * self.gradient_weight
          snake_case :			Optional[Any]					   = 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   :





     '''simple docstring'''



     def __init__(							self       )			->							Any:
          snake_case :			int					   = []
          snake_case :			str					   = []
          snake_case :			Any					   = plt.figure()
          snake_case :			str					   = self.fig_loss.add_subplot(1       ,1       ,1       )







     def  snake_case_			(							self       ,__a       )			->							int:
          self.layers.append(__a       )







     def  snake_case_			(							self       )			->							Dict:
          for i, layer in enumerate(self.layers[:]       ):
               if i < 1:
                    snake_case :			Optional[int]					   = True
               else:
                    layer.initializer(self.layers[i - 1].units       )







     def  snake_case_			(							self       )			->							int:
          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  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       )			->							List[Any]:
          snake_case :			Dict					   = train_round
          snake_case :			Dict					   = accuracy

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

          snake_case :			List[Any]					   = np.shape(__a       )
          for _ in range(__a       ):
               snake_case :			Dict					   = 0
               for row in range(x_shape[0]       ):
                    snake_case :			str					   = np.asmatrix(xdata[row, :]       ).T
                    snake_case :			Optional[int]					   = np.asmatrix(ydata[row, :]       ).T

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

                    snake_case   ,		snake_case :			Union[str, Any]					   = self.cal_loss(_ydata       ,_xdata       )
                    snake_case :			Dict					   = all_loss + loss

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

               snake_case :			List[str]					   = all_loss / x_shape[0]
               self.train_mse.append(__a       )

               self.plot_loss()

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







     def  snake_case_			(							self       ,__a       ,__a       )			->							Tuple:
          snake_case :			Dict					   = np.sum(np.power((ydata - ydata_)       ,2       )       )
          snake_case :			List[str]					   = 2 * (ydata_ - ydata)
          # vector (shape is the same as _ydata.shape)
          return self.loss, self.loss_gradient







     def  snake_case_			(							self       )			->							Any:
          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__							(			):
     snake_case :			Optional[int]					   = np.random.randn(10    ,      10							)
     snake_case :			Tuple					   = 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 :			Any					   = BPNN()
     for i in (10, 20, 30, 2):
          model.add_layer(DenseLayer(__lowercase							)							)
     model.build()
     model.summary()
     model.train(xdata=__lowercase    ,      ydata=__lowercase    ,      train_round=100    ,      accuracy=0.01							)


if __name__ == "__main__":
      example()









'''simple docstring'''







def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 9 / 5) + 32    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) + 273.15    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 9 / 5) + 491.67    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) - 32) * 5 / 9    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(((float(__lowercase							) - 32) * 5 / 9) + 273.15    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) + 459.67    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) - 273.15    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(((float(__lowercase							) - 273.15) * 9 / 5) + 32    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 9 / 5)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) - 491.67) * 5 / 9    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) - 459.67    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 5 / 9)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 1.25 + 273.15)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 2.25 + 32)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 1.25)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 2.25 + 32 + 459.67)    ,      __lowercase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



