



'''simple docstring'''

import numpy as np
from matplotlib import pyplot as plt



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Any:
		return 1 / (1 + np.exp(-1 * x						))

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : List[Any]			,_UpperCAmelCase      : Any=None			,_UpperCAmelCase      : Union[str, Any]=None			,_UpperCAmelCase      : List[str]=False	):
									_a  :   Dict				 =							units
									_a  :   List[Any]				 =							None
									_a  :   str				 =							None
									_a  :   List[str]				 =							activation
									if learning_rate is None:
											_a  :   str				 =							0.3
									_a  :   Any				 =							learning_rate
									_a  :   int				 =							is_input_layer







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : List[Any]	):
									_a  :   List[str]				 =							np.asmatrix(np.random.normal(0			,0.5			,(self.units, back_units)	)	)
									_a  :   int				 =							np.asmatrix(np.random.normal(0			,0.5			,self.units	)	).T
									if self.activation is None:
											_a  :   Tuple				 =							sigmoid







							def 							__lowercase    (							self      : Tuple	):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
											_a  :   Dict				 =							np.dot(self.output			,(1 - self.output).T	)
											_a  :   str				 =							np.diag(np.diag(_UpperCAmelCase	)	)
									else:
											_a  :   int				 =							1
									return gradient_activation







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : Union[str, Any]	):
									_a  :   Union[str, Any]				 =							xdata
									if self.is_input_layer:
											# input layer
											_a  :   Optional[int]				 =							xdata
											_a  :   str				 =							xdata
											return xdata
									else:
											_a  :   Tuple				 =							np.dot(self.weight			,self.xdata	) - self.bias
											_a  :   Any				 =							self.activation(self.wx_plus_b	)
											return self.output







							def 							__lowercase    (							self      : str			,_UpperCAmelCase      : List[str]	):
									_a  :   Any				 =							self.cal_gradient()  # i * i 维
									_a  :   Optional[Any]				 =							np.asmatrix(np.dot(gradient.T			,_UpperCAmelCase	)	)

									_a  :   List[str]				 =							np.asmatrix(self.xdata	)
									_a  :   str				 =							-1
									_a  :   str				 =							self.weight

									_a  :   Tuple				 =							np.dot(gradient.T			,self._gradient_weight.T	)
									_a  :   List[str]				 =							gradient * self._gradient_bias
									_a  :   List[Any]				 =							np.dot(_UpperCAmelCase			,self._gradient_x	).T
									# upgrade: the Negative gradient direction
									_a  :   List[Any]				 =							self.weight - self.learn_rate * self.gradient_weight
									_a  :   int				 =							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 			__magic_name__		:







							def __init__(							self      : str	):
									_a  :   Dict				 =							[]
									_a  :   List[Any]				 =							[]
									_a  :   Optional[Any]				 =							plt.figure()
									_a  :   List[Any]				 =							self.fig_loss.add_subplot(1			,1			,1	)







							def 							__lowercase    (							self      : Optional[int]			,_UpperCAmelCase      : str	):
									self.layers.append(_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[Any]	):
									for i, layer in enumerate(self.layers[:]	):
											if i < 1:
													_a  :   Union[str, Any]				 =							True
											else:
													layer.initializer(self.layers[i - 1].units	)







							def 							__lowercase    (							self      : 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 							__lowercase    (							self      : str			,_UpperCAmelCase      : str			,_UpperCAmelCase      : str			,_UpperCAmelCase      : List[Any]			,_UpperCAmelCase      : Optional[Any]	):
									_a  :   Union[str, Any]				 =							train_round
									_a  :   Optional[Any]				 =							accuracy

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

									_a  :   List[Any]				 =							np.shape(_UpperCAmelCase	)
									for _ in range(_UpperCAmelCase	):
											_a  :   str				 =							0
											for row in range(x_shape[0]	):
													_a  :   List[str]				 =							np.asmatrix(xdata[row, :]	).T
													_a  :   List[str]				 =							np.asmatrix(ydata[row, :]	).T

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

													_a ,      _a  :   Optional[int]				 =							self.cal_loss(_ydata			,_xdata	)
													_a  :   Optional[Any]				 =							all_loss + loss

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

											_a  :   str				 =							all_loss / x_shape[0]
											self.train_mse.append(_UpperCAmelCase	)

											self.plot_loss()

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







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : str			,_UpperCAmelCase      : List[str]	):
									_a  :   Union[str, Any]				 =							np.sum(np.power((ydata - ydata_)			,2	)	)
									_a  :   Optional[Any]				 =							2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient







							def 							__lowercase    (							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   __lowerCamelCase  (   )    ->   List[str]:
		_a  :   Union[str, Any]				 =							np.random.randn(10		,    10						)
		_a  :   str				 =							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],
		    ]						)
		_a  :   Tuple				 =							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'''

from __future__ import annotations

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : int	):
									_a  :   Tuple				 =							value
									_a  :   Node | None				 =							None
									_a  :   Node | None				 =							None

class 			__magic_name__		:
							lowerCAmelCase :					int







							def __init__(							self      : Union[str, Any]	):
									_a  :   Tuple				 =							0







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int	):
									if node is None:
											return

									if path_sum == self.target:
											self.paths += 1

									if node.left:
											self.depth_first_search(node.left			,path_sum + node.left.value	)
									if node.right:
											self.depth_first_search(node.right			,path_sum + node.right.value	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int | None = None	):
									if node is None:
											return 0
									if target is not None:
											_a  :   Any				 =							target

									self.depth_first_search(_UpperCAmelCase			,node.value	)
									self.path_sum(node.left	)
									self.path_sum(node.right	)

									return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()




