





import numpy as np
from matplotlib import pyplot as plt





def   _lowercase       (    _UpperCAmelCase					)    ->	Dict:
				return 1 / (1 + np.exp(-1 * x					))

class    __A  :


				def __init__(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=False				):
								lowerCamelCase									=units
								lowerCamelCase									=None
								lowerCamelCase									=None
								lowerCamelCase									=activation
								if learning_rate is None:
												lowerCamelCase									=0.3
								lowerCamelCase									=learning_rate
								lowerCamelCase									=is_input_layer


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


				def _snake_case							(		self				):



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


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


				def _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=self.cal_gradient()  # i * i 维
								lowerCamelCase									=np.asmatrix(np.dot(gradient.T ,			_A				)				)

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

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


				def __init__(		self				):
								lowerCamelCase									=[]
								lowerCamelCase									=[]
								lowerCamelCase									=plt.figure()
								lowerCamelCase									=self.fig_loss.add_subplot(1 ,			1 ,			1				)


				def _snake_case							(		self ,			UpperCAmelCase_				):
								self.layers.append(_A				)


				def _snake_case							(		self				):



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


				def _snake_case							(		self				):



								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 ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									=train_round
								lowerCamelCase									=accuracy

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

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

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

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

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

												lowerCamelCase									=all_loss / x_shape[0]
												self.train_mse.append(_A				)

												self.plot_loss()

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


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


				def _snake_case							(		self				):



								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   _lowercase       (    )    ->	List[str]:
				lowerCamelCase									=np.random.randn(10      ,   10					)
				lowerCamelCase									=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],
				    ]					)
				lowerCamelCase									=BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(_UpperCAmelCase					)					)
				model.build()
				model.summary()
				model.train(xdata=_UpperCAmelCase      ,   ydata=_UpperCAmelCase      ,   train_round=1_00      ,   accuracy=0.0_1					)


if __name__ == "__main__":
							example()







'''simple docstring'''





def 							snake_case				(		UpperCAmelCase						)->      list:







      """simple docstring"""
      __A													=      1.3
      __A													=      len(UpperCAmelCase						)
      __A													=      False

      while not completed:
            # Update the gap value for a next comb
            __A													=      int(gap / shrink_factor						)
            if gap <= 1:
                  __A													=      True

            __A													=      0
            while index + gap < len(UpperCAmelCase						):
                  if data[index] > data[index + gap]:
                        # Swap values
                        __A						,   __A													=      data[index + gap], data[index]
                        __A													=      False
                  index += 1

      return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a__		: Optional[Any]  							=							input("Enter numbers separated by a comma:\n").strip()
 a__		: Union[str, Any]  							=							[int(item) for item in user_input.split(",")]
 print(comb_sort(unsorted))






