

'''simple docstring'''

import numpy as np
from matplotlib import pyplot as plt

def     __magic_name__       ( A							)			->		int:
	return 1 / (1 + np.exp(-1 * x							))







class lowerCamelCase      :

				def __init__(					self,	lowercase_,	lowercase_=None,	lowercase_=None,	lowercase_=False   )						->       Optional[int]:
					snake_case							 =				units
					snake_case							 =				None
					snake_case							 =				None
					snake_case							 =				activation
					if learning_rate is None:
						snake_case							 =				0.3
					snake_case							 =				learning_rate
					snake_case							 =				is_input_layer

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       List[Any]:
					snake_case							 =				np.asmatrix(np.random.normal(0,	0.5,	(self.units, back_units)   )   )
					snake_case							 =				np.asmatrix(np.random.normal(0,	0.5,	self.units   )   ).T
					if self.activation is None:
						snake_case							 =				sigmoid

				def 				_lowerCamelCase						(					self   )						->       Any:







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

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Any:
					snake_case							 =				xdata
					if self.is_input_layer:
						# input layer
						snake_case							 =				xdata
						snake_case							 =				xdata
						return xdata
					else:
						snake_case							 =				np.dot(self.weight,	self.xdata   ) - self.bias
						snake_case							 =				self.activation(self.wx_plus_b   )
						return self.output






				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Tuple:
					snake_case							 =				self.cal_gradient()  # i * i 维
					snake_case							 =				np.asmatrix(np.dot(gradient.T,	lowercase_   )   )

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

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

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

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[int]:
					self.layers.append(lowercase_   )

				def 				_lowerCamelCase						(					self   )						->       Dict:







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

				def 				_lowerCamelCase						(					self   )						->       List[str]:







					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 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_   )						->       Any:
					snake_case							 =				train_round
					snake_case							 =				accuracy

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

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

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

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

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

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






				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:







					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     __magic_name__       ( )			->		List[Any]:
	snake_case							 =				np.random.randn(1_0					,   1_0							)
	snake_case							 =				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],
	    ]							)
	snake_case							 =				BPNN()
	for i in (1_0, 2_0, 3_0, 2):
		model.add_layer(DenseLayer(__snake_case							)							)
	model.build()
	model.summary()
	model.train(xdata=__snake_case					,   ydata=__snake_case					,   train_round=1_0_0					,   accuracy=0.01							)


if __name__ == "__main__":
						example()








'''simple docstring'''

from itertools import compress, repeat
from math import ceil, sqrt

def     __magic_name__       ( A							)			->		list[int]:

	if num <= 2:
		return []
	if num == 3:
		return [2]

	# Odd sieve for numbers in range [3, num - 1]
	snake_case							 =				bytearray(b'\x01'							) * ((num >> 1) - 1)

	for i in range(3					,   int(sqrt(A							)							) + 1					,   2							):
		if sieve[(i >> 1) - 1]:
			snake_case							 =				i**2
			snake_case							 =				repeat(
			    0					,   ceil((num - i_squared) / (i << 1)							)							)

	return [2] + list(compress(range(3					,   A					,   2							)					,   A							)							)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





