'''simple docstring'''




import numpy as np
from matplotlib import pyplot as plt





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any   )	->      str:
    return 1 / (1 + np.exp(-1 * x   ))




class 	__UpperCAmelCase				:





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=False )		->     List[str]:
           A_     =   units
           A_     =   None
           A_     =   None
           A_     =   activation
           if learning_rate is None:
               A_     =   0.3
           A_     =   learning_rate
           A_     =   is_input_layer

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     int:
           A_     =   np.asmatrix(np.random.normal(0    ,     0.5    ,     (self.units, back_units) ) )
           A_     =   np.asmatrix(np.random.normal(0    ,     0.5    ,     self.units ) ).T
           if self.activation is None:
               A_     =   sigmoid

       def 					__A       (      self )		->     Dict:
           # activation function may be sigmoid or linear
           if self.activation == sigmoid:
               A_     =   np.dot(self.output    ,     (1 - self.output).T )
               A_     =   np.diag(np.diag(_SCREAMING_SNAKE_CASE ) )
           else:
               A_     =   1
           return gradient_activation

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     List[str]:
           A_     =   xdata
           if self.is_input_layer:
               # input layer
               A_     =   xdata
               A_     =   xdata
               return xdata
           else:
               A_     =   np.dot(self.weight    ,     self.xdata ) - self.bias
               A_     =   self.activation(self.wx_plus_b )
               return self.output

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Union[str, Any]:
           A_     =   self.cal_gradient()  # i * i 维
           A_     =   np.asmatrix(np.dot(gradient.T    ,     _SCREAMING_SNAKE_CASE ) )

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

           A_     =   np.dot(gradient.T    ,     self._gradient_weight.T )
           A_     =   gradient * self._gradient_bias
           A_     =   np.dot(_SCREAMING_SNAKE_CASE    ,     self._gradient_x ).T
           # upgrade: the Negative gradient direction
           A_     =   self.weight - self.learn_rate * self.gradient_weight
           A_     =   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 	__UpperCAmelCase				:





       '''simple docstring'''



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

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[Any]:
           self.layers.append(_SCREAMING_SNAKE_CASE )

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

       def 					__A       (      self )		->     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 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Any:
           A_     =   train_round
           A_     =   accuracy

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

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

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

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

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

               A_     =   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    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           A_     =   np.sum(np.power((ydata - ydata_)    ,     2 ) )
           A_     =   2 * (ydata_ - ydata)
           # vector (shape is the same as _ydata.shape)
           return self.loss, self.loss_gradient

       def 					__A       (      self )		->     Optional[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      _UpperCAmelCase    (							)	->      List[Any]:
    A_     =   np.random.randn(10,   10   )
    A_     =   np.asarray(
        [
            [0.8, 0.4],
            [0.4, 0.3],
            [0.3_4, 0.4_5],
            [0.6_7, 0.3_2],
            [0.8_8, 0.6_7],
            [0.7_8, 0.7_7],
            [0.5_5, 0.6_6],
            [0.5_5, 0.4_3],
            [0.5_4, 0.1],
            [0.1, 0.5],
        ]   )
    A_     =   BPNN()
    for i in (10, 20, 30, 2):
        model.add_layer(DenseLayer(_UpperCamelCase   )   )
    model.build()
    model.summary()
    model.train(xdata=_UpperCamelCase,   ydata=_UpperCamelCase,   train_round=1_00,   accuracy=0.0_1   )


if __name__ == "__main__":
  example()













"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		list:
       _lowerCamelCase    	=					[]
       _lowerCamelCase    	=					1

       for i in range(len(snake_case		)		):
              if i + 1 < len(snake_case		) and text[i] == text[i + 1]:
                     count += 1
              else:
                     encoded.append((text[i], count)		)
                     _lowerCamelCase    	=					1

       return encoded





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list		)->		str:
       return "".join(char * length for char, length in encoded		)


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""run_length_encode""", verbose=True)
      testmod(name="""run_length_decode""", verbose=True)






