



"""simple docstring"""






import numpy as np
from matplotlib import pyplot as plt






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]      ):
			"""simple docstring"""
			return 1 / (1 + np.exp(-1 * x      ))

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Tuple,    lowerCamelCase				:    int,    lowerCamelCase				:    Optional[Any]=None,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Union[str, Any]=False	)->   List[Any]:
								lowerCamelCase__						:						Union[str, Any]						      =units
								lowerCamelCase__						:						str						      =None
								lowerCamelCase__						:						int						      =None
								lowerCamelCase__						:						List[Any]						      =activation
								if learning_rate is None:
											lowerCamelCase__						:						Union[str, Any]						      =0.3
								lowerCamelCase__						:						List[str]						      =learning_rate
								lowerCamelCase__						:						List[Any]						      =is_input_layer



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Optional[int]	)->   Optional[Any]:
								lowerCamelCase__						:						List[Any]						      =np.asmatrix(np.random.normal(0,    0.5,    (self.units, back_units)	)	)
								lowerCamelCase__						:						Union[str, Any]						      =np.asmatrix(np.random.normal(0,    0.5,    self.units	)	).T
								if self.activation is None:
											lowerCamelCase__						:						Dict						      =sigmoid



					def 						snake_case			(			self				:    Tuple	)->   Dict:
								# activation function may be sigmoid or linear
								if self.activation == sigmoid:
											lowerCamelCase__						:						List[Any]						      =np.dot(self.output,    (1 - self.output).T	)
											lowerCamelCase__						:						Union[str, Any]						      =np.diag(np.diag(lowerCamelCase	)	)
								else:
											lowerCamelCase__						:						Union[str, Any]						      =1
								return gradient_activation



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    Dict	)->   Dict:
								lowerCamelCase__						:						Union[str, Any]						      =xdata
								if self.is_input_layer:
											# input layer
											lowerCamelCase__						:						Tuple						      =xdata
											lowerCamelCase__						:						Optional[Any]						      =xdata
											return xdata
								else:
											lowerCamelCase__						:						Union[str, Any]						      =np.dot(self.weight,    self.xdata	) - self.bias
											lowerCamelCase__						:						Any						      =self.activation(self.wx_plus_b	)
											return self.output




					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    Union[str, Any]	)->   int:
								lowerCamelCase__						:						int						      =self.cal_gradient()  # i * i 维
								lowerCamelCase__						:						Optional[Any]						      =np.asmatrix(np.dot(gradient.T,    lowerCamelCase	)	)

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

								lowerCamelCase__						:						int						      =np.dot(gradient.T,    self._gradient_weight.T	)
								lowerCamelCase__						:						Optional[int]						      =gradient * self._gradient_bias
								lowerCamelCase__						:						Tuple						      =np.dot(lowerCamelCase,    self._gradient_x	).T
								# upgrade: the Negative gradient direction
								lowerCamelCase__						:						Any						      =self.weight - self.learn_rate * self.gradient_weight
								lowerCamelCase__						:						Optional[Any]						      =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 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Any	)->   Dict:
								lowerCamelCase__						:						Union[str, Any]						      =[]
								lowerCamelCase__						:						str						      =[]
								lowerCamelCase__						:						int						      =plt.figure()
								lowerCamelCase__						:						Optional[Any]						      =self.fig_loss.add_subplot(1,    1,    1	)



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    str	)->   Tuple:
								self.layers.append(lowerCamelCase	)



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



					def 						snake_case			(			self				:    List[Any]	)->   Tuple:
								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 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    Dict,    lowerCamelCase				:    List[Any],    lowerCamelCase				:    Optional[Any]	)->   Optional[Any]:
								lowerCamelCase__						:						Dict						      =train_round
								lowerCamelCase__						:						List[Any]						      =accuracy

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

								lowerCamelCase__						:						Optional[int]						      =np.shape(lowerCamelCase	)
								for _ in range(lowerCamelCase	):
											lowerCamelCase__						:						List[str]						      =0
											for row in range(x_shape[0]	):
														lowerCamelCase__						:						str						      =np.asmatrix(xdata[row, :]	).T
														lowerCamelCase__						:						List[str]						      =np.asmatrix(ydata[row, :]	).T

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

														lowerCamelCase__      ,  lowerCamelCase__						:						Tuple						      =self.cal_loss(_ydata,    _xdata	)
														lowerCamelCase__						:						Dict						      =all_loss + loss

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

											lowerCamelCase__						:						List[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 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Any	)->   Tuple:
								lowerCamelCase__						:						Union[str, Any]						      =np.sum(np.power((ydata - ydata_),    2	)	)
								lowerCamelCase__						:						Union[str, Any]						      =2 * (ydata_ - ydata)
								# vector (shape is the same as _ydata.shape)
								return self.loss, self.loss_gradient




					def 						snake_case			(			self				:    Tuple	)->   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 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =np.random.randn(10				,						10      )
			lowerCamelCase__						:						List[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],
			    ]      )
			lowerCamelCase__						:						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"""






def 	snake_case__  (						__lowerCamelCase							:       int = 998001      ):
			"""simple docstring"""

			# fetches the next number
			for number in range(n - 1				,						9999				,						-1      ):
						lowerCamelCase__						:						Optional[int]						      =str(__lowerCamelCase      )

						# checks whether 'str_number' is a palindrome.
						if str_number == str_number[::-1]:
									lowerCamelCase__						:						Optional[int]						      =999

									# if 'number' is a product of two 3-digit numbers
									# then number is the answer otherwise fetch next number.
									while divisor != 99:
												if (number % divisor == 0) and (len(str(number // divisor      )      ) == 3.0):
															return number
												divisor -= 1
			raise ValueError('''That number is larger than our acceptable range.'''      )


if __name__ == "__main__":
		print(f'{solution() = }')




