import numpy as np
from matplotlib import pyplot as plt
def    __magic_name__(	__UpperCAmelCase		)    ->     Optional[int]:



      '''simple docstring'''

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



class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_=None					,					A_=None					,					A_=False    )			->					int:






           """simple docstring"""
           _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					,					A_    )			->					str:






           """simple docstring"""
           _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    )			->					Dict:






           """simple docstring"""
           # 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(A_    )    )
           else:
                 _lowerCamelCase           =					1
           return gradient_activation



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[int]:






           """simple docstring"""
           _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					,					A_    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.cal_gradient()  # i * i 维
           _lowerCamelCase           =					np.asmatrix(np.dot(gradient.T					,					A_    )    )

           _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(A_					,					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 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					[]
           _lowerCamelCase           =					[]
           _lowerCamelCase           =					plt.figure()
           _lowerCamelCase           =					self.fig_loss.add_subplot(1					,					1					,					1    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					int:






           """simple docstring"""
           self.layers.append(A_    )



     def 		UpperCamelCase_    (       self    )			->					List[str]:






           """simple docstring"""
           for i, layer in enumerate(self.layers[:]    ):
                 if i < 1:
                       _lowerCamelCase           =					True
                 else:
                       layer.initializer(self.layers[i - 1].units    )



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           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					,					A_					,					A_					,					A_					,					A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					train_round
           _lowerCamelCase           =					accuracy

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

           _lowerCamelCase           =					np.shape(A_    )
           for _ in range(A_    ):
                 _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(A_    )

                 _lowerCamelCase           =					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 		UpperCamelCase_    (       self					,					A_					,					A_    )			->					Any:






           """simple docstring"""
           _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    )			->					List[Any]:






           """simple docstring"""
           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    __magic_name__(	)    ->     int:



      '''simple docstring'''

      _lowerCamelCase           =					np.random.randn(10			,    10		)
      _lowerCamelCase           =					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],
          ]		)
      _lowerCamelCase           =					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=100			,    accuracy=0.0_1		)


if __name__ == "__main__":
     example()
from hashlib import mda, shaaaa

snake_case__						      =			(shaaaa, mda)



class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_ = 8    )			->					None:






           """simple docstring"""
           _lowerCamelCase           =					0b0
           _lowerCamelCase           =					size



     def 		UpperCamelCase_    (       self					,					A_    )			->					None:






           """simple docstring"""
           _lowerCamelCase           =					self.hash_(A_    )
           self.bitarray |= h



     def 		UpperCamelCase_    (       self					,					A_    )			->					bool:






           """simple docstring"""
           _lowerCamelCase           =					self.hash_(A_    )
           return (h & self.bitarray) == h



     def __contains__(       self					,					A_    )			->					bool:






           """simple docstring"""
           return self.exists(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					bin(A_    )[2:]
           return res.zfill(self.size    )



     @property
     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           return self.format_bin(self.bitarray    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					0b0
           for func in HASH_FUNCTIONS:
                 _lowerCamelCase           =					(
                     int.from_bytes(func(value.encode()    ).digest()					,					'''little'''    ) % self.size
                 )
                 res |= 2**position
           return res



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           return self.format_bin(self.hash_(A_    )    )





     @property
     def 		UpperCamelCase_    (       self    )			->					float:






           """simple docstring"""
           _lowerCamelCase           =					bin(self.bitarray    ).count('''1'''    )
           return (n_ones / self.size) ** len(A_    )