"""simple docstring"""

import numpy as np
from matplotlib import pyplot as plt


def    lowerCAmelCase			(__UpperCamelCase							:    int			):
							"""simple docstring"""


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






class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    Union[str, Any]     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    Union[str, Any]=None     , UpperCamelCase__							:    List[Any]=None     , UpperCamelCase__							:    List[Any]=False				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=units
													__UpperCamelCase							=None
													__UpperCamelCase							=None
													__UpperCamelCase							=activation
													if learning_rate is None:
																				__UpperCamelCase							=0.3
													__UpperCamelCase							=learning_rate
													__UpperCamelCase							=is_input_layer





						def   UpperCAmelCase_ (				self							:    Dict     , UpperCamelCase__							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=np.asmatrix(np.random.normal(0     , 0.5     , (self.units, back_units)				)				)
													__UpperCamelCase							=np.asmatrix(np.random.normal(0     , 0.5     , self.units				)				).T
													if self.activation is None:
																				__UpperCamelCase							=sigmoid





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[Any]:
													'''simple docstring'''



													if self.activation == sigmoid:
																				__UpperCamelCase							=np.dot(self.output     , (1 - self.output).T				)
																				__UpperCamelCase							=np.diag(np.diag(UpperCamelCase__				)				)
													else:
																				__UpperCamelCase							=1
													return gradient_activation





						def   UpperCAmelCase_ (				self							:    Any     , UpperCamelCase__							:    Union[str, Any]				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=xdata
													if self.is_input_layer:
																				# input layer
																				__UpperCamelCase							=xdata
																				__UpperCamelCase							=xdata
																				return xdata
													else:
																				__UpperCamelCase							=np.dot(self.weight     , self.xdata				) - self.bias
																				__UpperCamelCase							=self.activation(self.wx_plus_b				)
																				return self.output





						def   UpperCAmelCase_ (				self							:    Optional[Any]     , UpperCamelCase__							:    List[Any]				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.cal_gradient()  # i * i 维
													__UpperCamelCase							=np.asmatrix(np.dot(gradient.T     , UpperCamelCase__				)				)

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

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



						"""simple docstring"""




						def __init__(				self							:    int				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=[]
													__UpperCamelCase							=[]
													__UpperCamelCase							=plt.figure()
													__UpperCamelCase							=self.fig_loss.add_subplot(1     , 1     , 1				)





						def   UpperCAmelCase_ (				self							:    List[Any]     , UpperCamelCase__							:    List[Any]				)				->				Optional[Any]:
													'''simple docstring'''



													self.layers.append(UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				str:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				str:
													'''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							:    int     , UpperCamelCase__							:    List[str]     , UpperCamelCase__							:    Optional[int]     , UpperCamelCase__							:    Tuple     , UpperCamelCase__							:    Tuple				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=train_round
													__UpperCamelCase							=accuracy

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

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

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

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

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

																				__UpperCamelCase							=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   UpperCAmelCase_ (				self							:    List[str]     , UpperCamelCase__							:    Tuple     , UpperCamelCase__							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[str]:
													'''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    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=np.random.randn(1_0							,					1_0			)
							__UpperCamelCase							=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],
							    ]			)
							__UpperCamelCase							=BPNN()
							for i in (1_0, 2_0, 3_0, 2):
														model.add_layer(DenseLayer(__UpperCamelCase			)			)
							model.build()
							model.summary()
							model.train(xdata=__UpperCamelCase							,					ydata=__UpperCamelCase							,					train_round=1_0_0							,					accuracy=0.0_1			)


if __name__ == "__main__":
				example()




"""simple docstring"""

class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    List[str]     , UpperCamelCase__							:    int				)				->				None:
													'''simple docstring'''



													__UpperCamelCase							=data
													__UpperCamelCase							=None
													__UpperCamelCase							=None






def    lowerCAmelCase			(__UpperCamelCase							:    BinaryTreeNode | None							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							if node is None:
														__UpperCamelCase							=BinaryTreeNode(__UpperCamelCase			)
														return node

							# binary search tree is not empty,
							# so we will insert it into the tree
							# if new_value is less than value of data in node,
							#  add it to left subtree and proceed recursively
							if new_value < node.data:
														__UpperCamelCase							=insert(node.left_child							,					__UpperCamelCase			)
							else:
														# if new_value is greater than value of data in node,
														#  add it to right subtree and proceed recursively
														__UpperCamelCase							=insert(node.right_child							,					__UpperCamelCase			)
							return node


def    lowerCAmelCase			(__UpperCamelCase							:    None | BinaryTreeNode			):  # if node is None,return
							"""simple docstring"""


							if node:
														__UpperCamelCase							=inorder(node.left_child			)
														__UpperCamelCase							=[*inorder_array, node.data]
														__UpperCamelCase							=inorder_array + inorder(node.right_child			)
							else:
														__UpperCamelCase							=[]
							return inorder_array


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=insert(__UpperCamelCase							,					1_5			)
							insert(__UpperCamelCase							,					1_0			)
							insert(__UpperCamelCase							,					2_5			)
							insert(__UpperCamelCase							,					6			)
							insert(__UpperCamelCase							,					1_4			)
							insert(__UpperCamelCase							,					2_0			)
							insert(__UpperCamelCase							,					6_0			)
							return root


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=make_tree()
							print('''Printing values of binary search tree in Inorder Traversal.'''			)
							inorder(__UpperCamelCase			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()



