


'''simple docstring'''






import numpy as np
from matplotlib import pyplot as plt


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




class 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=None					,    lowerCamelCase_=None					,    lowerCamelCase_=False   )       ->							Optional[Any]:
														lowerCAmelCase__          =	units
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	activation
														if learning_rate is None:
																					lowerCAmelCase__          =	0.3
														lowerCAmelCase__          =	learning_rate
														lowerCAmelCase__          =	is_input_layer







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Optional[Any]:
														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   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														# 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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Any:
														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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							str:
														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 		a__				:





							'''simple docstring'''






							def __init__(		self   )       ->							Optional[int]:
														lowerCAmelCase__          =	[]
														lowerCAmelCase__          =	[]
														lowerCAmelCase__          =	plt.figure()
														lowerCAmelCase__          =	self.fig_loss.add_subplot(1					,    1					,    1   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Dict:
														self.layers.append(lowerCamelCase_   )







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







							def   __SCREAMING_SNAKE_CASE							(		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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Any:
														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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Tuple:
														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   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														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   (     )						->    Optional[int]:
							lowerCAmelCase__          =	np.random.randn(10 ,    10       )
							lowerCAmelCase__          =	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__          =	BPNN()
							for i in (10, 20, 30, 2):
														model.add_layer(DenseLayer(A       )       )
							model.build()
							model.summary()
							model.train(xdata=A ,    ydata=A ,    train_round=100 ,    accuracy=0.01       )


if __name__ == "__main__":
		example()



'''simple docstring'''






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__UpperCAmelCase    				=							TypeVar('''T''')
__UpperCAmelCase    				=							TypeVar('''U''')




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	key
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None







							def __repr__(		self   )       ->							str:
														return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
														    self.key					,    self.val					,    self.freq					,    self.next is not None					,    self.prev is not None   )




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self   )       ->							None:
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.rear, self.head







							def __repr__(		self   )       ->							str:
														lowerCAmelCase__          =	['''DoubleLinkedList''']
														lowerCAmelCase__          =	self.head
														while node.next is not None:
																					rep.append(str(lowerCamelCase_   )   )
																					lowerCAmelCase__          =	node.next
														rep.append(str(self.rear   )   )
														return ",\n    ".join(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__          =	self.rear.prev

														# All nodes other than self.head are guaranteed to have non-None previous
														assert previous is not None

														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	previous
														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	self.rear
														node.freq += 1
														self._position_node(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:

														while node.prev is not None and node.prev.freq > node.freq:
																					# swap node with previous node
																					lowerCAmelCase__          =	node.prev

																					lowerCAmelCase__          =	previous_node.prev
																					lowerCAmelCase__          =	node.prev
																					lowerCAmelCase__          =	previous_node
																					lowerCAmelCase__          =	node







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							DoubleLinkedListNode[T, U] | None:

														if node.prev is None or node.next is None:
																					return None

														lowerCAmelCase__          =	node.next
														lowerCAmelCase__          =	node.prev
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														return node




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							lowercase__			:   dict[Callable[[T], U], LFUCache[T, U]]							=      {}







							def __init__(		self					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	DoubleLinkedList()
														lowerCAmelCase__          =	capacity
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	{}







							def __repr__(		self   )       ->							str:
														return (
														    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
														    F"""capacity={self.capacity}, current_size={self.num_keys})"""
														)







							def __contains__(		self					,    lowerCamelCase_   )       ->							bool:
														return key in self.cache







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							U | None:

														if key in self.cache:
																					self.hits += 1
																					lowerCAmelCase__          =	self.cache[key]
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node == value_node

																					# node is guaranteed not None because it is in self.cache
																					assert node is not None
																					self.list.add(lowerCamelCase_   )
																					return node.val
														self.miss += 1
														return None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:

														if key not in self.cache:
																					if self.num_keys >= self.capacity:
																												# delete first node when over capacity
																												lowerCAmelCase__          =	self.list.head.next

																												# guaranteed to have a non-None first node when num_keys > 0
																												# explain to type checker via assertions
																												assert first_node is not None
																												assert first_node.key is not None
																												assert self.list.remove(lowerCamelCase_   ) is not None
																												# first_node guaranteed to be in list

																												del self.cache[first_node.key]
																												self.num_keys -= 1
																					lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
																					self.list.add(self.cache[key]   )
																					self.num_keys += 1

														else:
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node is not None  # node guaranteed to be in list
																					lowerCAmelCase__          =	value
																					self.list.add(lowerCamelCase_   )







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_ = 1_28   )       ->							Callable[[Callable[[T], U]], Callable[..., U]]:

														def cache_decorator_inner(lowerCamelCase_   ) -> Callable[..., U]:
																					def cache_decorator_wrapper(*lowerCamelCase_   ) -> U:
																												if func not in cls.decorator_function_to_instance_map:
																																			lowerCAmelCase__          =	LFUCache(lowerCamelCase_   )

																												lowerCAmelCase__          =	cls.decorator_function_to_instance_map[func].get(args[0]   )
																												if result is None:
																																			lowerCAmelCase__          =	func(*lowerCamelCase_   )
																																			cls.decorator_function_to_instance_map[func].put(args[0]					,    lowerCamelCase_   )
																												return result

																					def cache_info() -> LFUCache[T, U]:
																												return cls.decorator_function_to_instance_map[func]

																					setattr(lowerCamelCase_					,    '''cache_info'''					,    lowerCamelCase_   )  # noqa: B010

																					return cache_decorator_wrapper

														return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()