



"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

__SCREAMING_SNAKE_CASE     :  Optional[int]										=					TypeVar('''T''')


class 							lowerCamelCase_(      Generic[T]			):





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = data  # Assign data
									_lowerCamelCase					     = None  # Initialize next as null
									_lowerCamelCase					     = None  # Initialize prev as null


class 							lowerCamelCase_(      Generic[T]			):





					'''simple docstring'''



					def __init__(			self      ):
									_lowerCamelCase					     = None

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									if self.head is None:
													_lowerCamelCase					     = Node(lowerCamelCase__      )
									else:
													_lowerCamelCase					     = Node(lowerCamelCase__      )
													_lowerCamelCase					     = new_node
													_lowerCamelCase					     = self.head
													_lowerCamelCase					     = None
													_lowerCamelCase					     = new_node

					def 	snake_case__					(			self      ):
									if self.head is None:
													return None
									else:
													assert self.head is not None
													_lowerCamelCase					     = self.head.data
													_lowerCamelCase					     = self.head.next
													if self.head is not None:
																	_lowerCamelCase					     = None
													return temp

					def 	snake_case__					(			self      ):
									return self.head.data if self.head is not None else None

					def __len__(			self      ):
									_lowerCamelCase					     = self.head
									_lowerCamelCase					     = 0
									while temp is not None:
													count += 1
													_lowerCamelCase					     = temp.next
									return count

					def 	snake_case__					(			self      ):
									return self.head is None





					def 	snake_case__					(			self      ):
									print('''stack elements are:'''      )
									_lowerCamelCase					     = self.head
									while temp is not None:
													print(temp.data ,				end='''->'''      )
													_lowerCamelCase					     = temp.next


# Code execution starts here
if __name__ == "__main__":
			# Start with the empty stack
			__SCREAMING_SNAKE_CASE     :  Stack[int]										=					Stack()

			# Insert 4 at the beginning. So stack becomes 4->None
			print('''Stack operations using Doubly LinkedList''')
			stack.push(4)

			# Insert 5 at the beginning. So stack becomes 4->5->None
			stack.push(5)

			# Insert 6 at the beginning. So stack becomes 4->5->6->None
			stack.push(6)

			# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
			stack.push(7)

			# Print the stack
			stack.print_stack()

			# Print the top element
			print('''\nTop element is ''', stack.top())

			# Print the stack size
			print('''Size of the stack is ''', len(stack))

			# pop the top element
			stack.pop()

			# pop the top element
			stack.pop()

			# two elements have now been popped off
			stack.print_stack()

			# Print True if the stack is empty else False
			print('''\nstack is empty:''', stack.is_empty())





"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				return 1 / (1 + np.exp(-1 * x							))


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=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 ,				lowerCamelCase__      ):
									_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      ):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
													_lowerCamelCase					     = np.dot(self.output ,				(1 - self.output).T      )
													_lowerCamelCase					     = np.diag(np.diag(lowerCamelCase__      )      )
									else:
													_lowerCamelCase					     = 1
									return gradient_activation

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_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 ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.cal_gradient()  # i * i 维
									_lowerCamelCase					     = np.asmatrix(np.dot(gradient.T ,				lowerCamelCase__      )      )

									_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(lowerCamelCase__ ,				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 							lowerCamelCase_:





					'''simple docstring'''



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

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									self.layers.append(lowerCamelCase__      )

					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 ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = train_round
									_lowerCamelCase					     = accuracy

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

									_lowerCamelCase					     = np.shape(lowerCamelCase__      )
									for _ in range(lowerCamelCase__      ):
													_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(lowerCamelCase__      )

													_lowerCamelCase					     = 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 ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_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 				lowerCAmelCase_(      )		->				Optional[Any]:
				_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(lowercase_							)							)
				model.build()
				model.summary()
				model.train(xdata=lowercase_  ,      ydata=lowercase_  ,      train_round=1_00  ,      accuracy=0.0_1							)


if __name__ == "__main__":
			example()
