


import numpy as np
from matplotlib import pyplot as plt


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[str]:
						return 1 / (1 + np.exp(-1 * x				))




class 					_UpperCamelCase :







							'''simple docstring'''







							def __init__(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Dict=None		,				SCREAMING_SNAKE_CASE_  :				Tuple=None		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=False		):
													_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  :				Dict		,				SCREAMING_SNAKE_CASE_  :				str		):
													_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  :				Optional[int]		):
													# activation function may be sigmoid or linear
													if self.activation == sigmoid:
																			_a     =			np.dot(self.output		,				(1 - self.output).T		)
																			_a     =			np.diag(np.diag(SCREAMING_SNAKE_CASE_		)		)
													else:
																			_a     =			1
													return gradient_activation







							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
													_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  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
													_a     =			self.cal_gradient()  # i * i 维
													_a     =			np.asmatrix(np.dot(gradient.T		,				SCREAMING_SNAKE_CASE_		)		)

													_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(SCREAMING_SNAKE_CASE_		,				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 					_UpperCamelCase :







							'''simple docstring'''







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







							def 		_UpperCAmelCase	(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				Tuple		):
													self.layers.append(SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
													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]		):
													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  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
													_a     =			train_round
													_a     =			accuracy

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

													_a     =			np.shape(SCREAMING_SNAKE_CASE_		)
													for _ in range(SCREAMING_SNAKE_CASE_		):
																			_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(SCREAMING_SNAKE_CASE_		)

																			_a     =			all_loss / x_shape[0]
																			self.train_mse.append(SCREAMING_SNAKE_CASE_		)

																			self.plot_loss()

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







							def 		_UpperCAmelCase	(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				List[Any]		):
													_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]		):
													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 							SCREAMING_SNAKE_CASE					(  )    ->  List[str]:
						_a     =			np.random.randn(10		,     10				)
						_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 (10, 20, 30, 2):
												model.add_layer(DenseLayer(_UpperCAmelCase				)				)
						model.build()
						model.summary()
						model.train(xdata=_UpperCAmelCase		,     ydata=_UpperCAmelCase		,     train_round=100		,     accuracy=0.01				)


if __name__ == "__main__":
		example()






from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims


