
import numpy as np
from matplotlib import pyplot as plt







def 				A__    (  lowerCamelCase  )     ->		List[Any]:
				return 1 / (1 + np.exp(-1 * x  ))

class 				_UpperCamelCase :






			'''simple docstring'''

			def __init__(				self       :				List[Any]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				Union[str, Any]=None  ,		snake_case_       :				Tuple=None  ,		snake_case_       :				Union[str, Any]=False					):
							UpperCamelCase_:							Dict		=  units
							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Union[str, Any]		=  None
							UpperCamelCase_:							List[str]		=  activation
							if learning_rate is None:
											UpperCamelCase_:							Tuple		=  0.3
							UpperCamelCase_:							List[Any]		=  learning_rate
							UpperCamelCase_:							Tuple		=  is_input_layer

			def 					lowerCAmelCase__		(				self       :				Optional[int]  ,		snake_case_       :				Dict					):
							UpperCamelCase_:							Optional[int]		=  np.asmatrix(np.random.normal(0  ,		0.5  ,		(self.units, back_units)					)					)
							UpperCamelCase_:							Optional[int]		=  np.asmatrix(np.random.normal(0  ,		0.5  ,		self.units					)					).T
							if self.activation is None:
											UpperCamelCase_:							List[Any]		=  sigmoid

			def 					lowerCAmelCase__		(				self       :				List[Any]					):
							# activation function may be sigmoid or linear
							if self.activation == sigmoid:
											UpperCamelCase_:							Optional[Any]		=  np.dot(self.output  ,		(1 - self.output).T					)
											UpperCamelCase_:							List[str]		=  np.diag(np.diag(lowerCamelCase_					)					)
							else:
											UpperCamelCase_:							Any		=  1
							return gradient_activation

			def 					lowerCAmelCase__		(				self       :				str  ,		snake_case_       :				str					):
							UpperCamelCase_:							int		=  xdata
							if self.is_input_layer:
											# input layer
											UpperCamelCase_:							Tuple		=  xdata
											UpperCamelCase_:							Dict		=  xdata
											return xdata
							else:
											UpperCamelCase_:							List[Any]		=  np.dot(self.weight  ,		self.xdata					) - self.bias
											UpperCamelCase_:							List[Any]		=  self.activation(self.wx_plus_b					)
											return self.output




			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				Tuple					):
							UpperCamelCase_:							Any		=  self.cal_gradient()  # i * i 维
							UpperCamelCase_:							str		=  np.asmatrix(np.dot(gradient.T  ,		lowerCamelCase_					)					)

							UpperCamelCase_:							str		=  np.asmatrix(self.xdata					)
							UpperCamelCase_:							Any		=  -1
							UpperCamelCase_:							Tuple		=  self.weight

							UpperCamelCase_:							int		=  np.dot(gradient.T  ,		self._gradient_weight.T					)
							UpperCamelCase_:							Optional[Any]		=  gradient * self._gradient_bias
							UpperCamelCase_:							List[Any]		=  np.dot(lowerCamelCase_  ,		self._gradient_x					).T
							# upgrade: the Negative gradient direction
							UpperCamelCase_:							int		=  self.weight - self.learn_rate * self.gradient_weight
							UpperCamelCase_:							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 				_UpperCamelCase :






			'''simple docstring'''

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

			def 					lowerCAmelCase__		(				self       :				List[str]  ,		snake_case_       :				List[str]					):
							self.layers.append(lowerCamelCase_					)

			def 					lowerCAmelCase__		(				self       :				str					):
							for i, layer in enumerate(self.layers[:]					):
											if i < 1:
															UpperCamelCase_:							Dict		=  True
											else:
															layer.initializer(self.layers[i - 1].units					)

			def 					lowerCAmelCase__		(				self       :				List[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 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				List[str]  ,		snake_case_       :				Union[str, Any]  ,		snake_case_       :				Union[str, Any]  ,		snake_case_       :				Union[str, Any]					):
							UpperCamelCase_:							Tuple		=  train_round
							UpperCamelCase_:							List[Any]		=  accuracy

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

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

															# forward propagation
															for layer in self.layers:
																			UpperCamelCase_:							Optional[Any]		=  layer.forward_propagation(_xdata					)

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

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

											UpperCamelCase_:							Union[str, Any]		=  all_loss / x_shape[0]
											self.train_mse.append(lowerCamelCase_					)

											self.plot_loss()

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

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				Union[str, Any]  ,		snake_case_       :				Union[str, Any]					):
							UpperCamelCase_:							Any		=  np.sum(np.power((ydata - ydata_)  ,		2					)					)
							UpperCamelCase_:							Dict		=  2 * (ydata_ - ydata)
							# vector (shape is the same as _ydata.shape)
							return self.loss, self.loss_gradient




			def 					lowerCAmelCase__		(				self       :				str					):
							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 				A__    (  )     ->		Optional[int]:
				UpperCamelCase_:							Tuple		=  np.random.randn(10   ,			10  )
				UpperCamelCase_:							int		=  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],
				    ]  )
				UpperCamelCase_:							int		=  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=1_00   ,			accuracy=0.01  )


if __name__ == "__main__":
							example()










'''simple docstring'''






from __future__ import annotations


def 					_snake_case   (     A ,    A       )						->    list[int]:
							lowerCAmelCase__          =	{}
							for index, val in enumerate(A       ):
														lowerCAmelCase__          =	target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														lowerCAmelCase__          =	index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"""{two_sum([2, 7, 11, 15], 9) = }""")