




import numpy as np
from matplotlib import pyplot as plt



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str] )     -> Optional[int]:




				'''simple docstring'''





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

class    a__  :
			"""simple docstring"""







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






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


			def        UpperCamelCase						(      self , lowercase    )					->  List[str]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase    )					->  Optional[Any]:
							'''simple docstring'''






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

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

							A__            =     np.dot(gradient.T , self._gradient_weight.T    )
							A__            =     gradient * self._gradient_bias
							A__            =     np.dot(lowercase , self._gradient_x    ).T
							# upgrade: the Negative gradient direction
							A__            =     self.weight - self.learn_rate * self.gradient_weight
							A__            =     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    a__  :
			"""simple docstring"""







			def __init__(      self    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase    )					->  Optional[int]:
							'''simple docstring'''






							self.layers.append(lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


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






							A__            =     train_round
							A__            =     accuracy

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

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

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

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

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

											A__            =     all_loss / x_shape[0]
											self.train_mse.append(lowercase    )

											self.plot_loss()

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


			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''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__							(		)     -> List[Any]:




				'''simple docstring'''





				A__            =     np.random.randn(1_0		,    1_0 )
				A__            =     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__            =     BPNN()
				for i in (1_0, 2_0, 3_0, 2):
								model.add_layer(DenseLayer(SCREAMING_SNAKE_CASE_ ) )
				model.build()
				model.summary()
				model.train(xdata=SCREAMING_SNAKE_CASE_		,    ydata=SCREAMING_SNAKE_CASE_		,    train_round=1_0_0		,    accuracy=0.01 )


if __name__ == "__main__":
	example()




def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int )     -> int:




				'''simple docstring'''





				A__            =     int((lower_bound + upper_bound) // 2 )
				if array[r] == value:
								return r
				if lower_bound >= upper_bound:
								return -1
				if array[r] < value:
								return binary_search(SCREAMING_SNAKE_CASE_		,    r + 1		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )
				else:
								return binary_search(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    r - 1		,    SCREAMING_SNAKE_CASE_ )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							list )     -> list:




				'''simple docstring'''





				A__            =     0
				if matrix[index][0] == value:
								return [index, 0]
				while index < len(SCREAMING_SNAKE_CASE_ ) and matrix[index][0] < value:
								A__            =     binary_search(matrix[index]		,    0		,    len(matrix[index] ) - 1		,    SCREAMING_SNAKE_CASE_ )
								if r != -1:
												return [index, r]
								index += 1
				return [-1, -1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

