



'''simple docstring'''

import numpy as np



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Optional[Any]:
		_a  :   Dict				 =							n_draws * class_counts / class_counts.sum()
		# floored means we don't overshoot n_samples, but probably undershoot
		_a  :   List[str]				 =							np.floor(lowerCAmelCase_						)
		# we add samples according to how much "left over" probability
		# they had, until we arrive at n_samples
		_a  :   List[Any]				 =							int(n_draws - floored.sum()						)
		if need_to_add > 0:
				_a  :   Optional[int]				 =							continuous - floored
				_a  :   Dict				 =							np.sort(np.unique(lowerCAmelCase_						)						)[::-1]
				# add according to remainder, but break ties
				# randomly to avoid biases
				for value in values:
						(_a      )  :   List[Any]				 =							np.where(remainder == value						)
						# if we need_to_add less than what's in inds
						# we draw randomly from them.
						# if we need to add more, we add them all and
						# go to the next value
						_a  :   str				 =							min(len(lowerCAmelCase_						)		,    lowerCAmelCase_						)
						_a  :   Tuple				 =							rng.choice(lowerCAmelCase_		,    size=lowerCAmelCase_		,    replace=lowerCAmelCase_						)
						floored[inds] += 1
						need_to_add -= add_now
						if need_to_add == 0:
								break
		return floored.astype(np.intaa						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_=10						)    ->   Union[str, Any]:
		_a  :   Union[str, Any]				 =							np.unique(lowerCAmelCase_		,    return_inverse=lowerCAmelCase_						)
		_a  :   Optional[int]				 =							classes.shape[0]
		_a  :   List[Any]				 =							np.bincount(lowerCAmelCase_						)
		if np.min(lowerCAmelCase_						) < 2:
				raise ValueError('Minimum class count error'						)
		if n_train < n_classes:
				raise ValueError(
				    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)						)
		if n_test < n_classes:
				raise ValueError(
				    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)						)
		_a  :   Any				 =							np.split(np.argsort(lowerCAmelCase_		,    kind='mergesort'						)		,    np.cumsum(lowerCAmelCase_						)[:-1]						)
		for _ in range(lowerCAmelCase_						):
				_a  :   Dict				 =							approximate_mode(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)
				_a  :   List[Any]				 =							class_counts - n_i
				_a  :   List[Any]				 =							approximate_mode(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)

				_a  :   Dict				 =							[]
				_a  :   Optional[int]				 =							[]

				for i in range(lowerCAmelCase_						):
						_a  :   str				 =							rng.permutation(class_counts[i]						)
						_a  :   Optional[int]				 =							class_indices[i].take(lowerCAmelCase_		,    mode='clip'						)
						train.extend(perm_indices_class_i[: n_i[i]]						)
						test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]						)
				_a  :   int				 =							rng.permutation(lowerCAmelCase_						)
				_a  :   int				 =							rng.permutation(lowerCAmelCase_						)

				yield train, test









'''simple docstring'''

import numpy as np
from matplotlib import pyplot as plt



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Any:
		return 1 / (1 + np.exp(-1 * x						))

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : List[Any]			,_UpperCAmelCase      : Any=None			,_UpperCAmelCase      : Union[str, Any]=None			,_UpperCAmelCase      : List[str]=False	):
									_a  :   Dict				 =							units
									_a  :   List[Any]				 =							None
									_a  :   str				 =							None
									_a  :   List[str]				 =							activation
									if learning_rate is None:
											_a  :   str				 =							0.3
									_a  :   Any				 =							learning_rate
									_a  :   int				 =							is_input_layer







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : List[Any]	):
									_a  :   List[str]				 =							np.asmatrix(np.random.normal(0			,0.5			,(self.units, back_units)	)	)
									_a  :   int				 =							np.asmatrix(np.random.normal(0			,0.5			,self.units	)	).T
									if self.activation is None:
											_a  :   Tuple				 =							sigmoid







							def 							__lowercase    (							self      : Tuple	):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
											_a  :   Dict				 =							np.dot(self.output			,(1 - self.output).T	)
											_a  :   str				 =							np.diag(np.diag(_UpperCAmelCase	)	)
									else:
											_a  :   int				 =							1
									return gradient_activation







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : Union[str, Any]	):
									_a  :   Union[str, Any]				 =							xdata
									if self.is_input_layer:
											# input layer
											_a  :   Optional[int]				 =							xdata
											_a  :   str				 =							xdata
											return xdata
									else:
											_a  :   Tuple				 =							np.dot(self.weight			,self.xdata	) - self.bias
											_a  :   Any				 =							self.activation(self.wx_plus_b	)
											return self.output







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

									_a  :   List[str]				 =							np.asmatrix(self.xdata	)
									_a  :   str				 =							-1
									_a  :   str				 =							self.weight

									_a  :   Tuple				 =							np.dot(gradient.T			,self._gradient_weight.T	)
									_a  :   List[str]				 =							gradient * self._gradient_bias
									_a  :   List[Any]				 =							np.dot(_UpperCAmelCase			,self._gradient_x	).T
									# upgrade: the Negative gradient direction
									_a  :   List[Any]				 =							self.weight - self.learn_rate * self.gradient_weight
									_a  :   int				 =							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 			__magic_name__		:







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







							def 							__lowercase    (							self      : Optional[int]			,_UpperCAmelCase      : str	):
									self.layers.append(_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[Any]	):
									for i, layer in enumerate(self.layers[:]	):
											if i < 1:
													_a  :   Union[str, Any]				 =							True
											else:
													layer.initializer(self.layers[i - 1].units	)







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

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

									_a  :   List[Any]				 =							np.shape(_UpperCAmelCase	)
									for _ in range(_UpperCAmelCase	):
											_a  :   str				 =							0
											for row in range(x_shape[0]	):
													_a  :   List[str]				 =							np.asmatrix(xdata[row, :]	).T
													_a  :   List[str]				 =							np.asmatrix(ydata[row, :]	).T

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

													_a ,      _a  :   Optional[int]				 =							self.cal_loss(_ydata			,_xdata	)
													_a  :   Optional[Any]				 =							all_loss + loss

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

											_a  :   str				 =							all_loss / x_shape[0]
											self.train_mse.append(_UpperCAmelCase	)

											self.plot_loss()

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







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : str			,_UpperCAmelCase      : List[str]	):
									_a  :   Union[str, Any]				 =							np.sum(np.power((ydata - ydata_)			,2	)	)
									_a  :   Optional[Any]				 =							2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient







							def 							__lowercase    (							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   __lowerCamelCase  (   )    ->   List[str]:
		_a  :   Union[str, Any]				 =							np.random.randn(10		,    10						)
		_a  :   str				 =							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  :   Tuple				 =							BPNN()
		for i in (10, 20, 30, 2):
				model.add_layer(DenseLayer(lowerCAmelCase_						)						)
		model.build()
		model.summary()
		model.train(xdata=lowerCAmelCase_		,    ydata=lowerCAmelCase_		,    train_round=100		,    accuracy=0.01						)


if __name__ == "__main__":
							example()




