






'''simple docstring'''






import pickle

import numpy as np
from matplotlib import pyplot as plt

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Tuple	,    __snake_case    :  Dict	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Dict	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  str	,    __snake_case    :  Optional[int]=0.2	,    __snake_case    :  List[str]=0.2	)    ->	Optional[Any]:
								UpperCAmelCase				: Tuple											=	bp_numa
								UpperCAmelCase				: Any											=	bp_numa
								UpperCAmelCase				: Optional[int]											=	bp_numa
								UpperCAmelCase				: Union[str, Any]											=	conva_get[:2]
								UpperCAmelCase				: List[Any]											=	conva_get[2]
								UpperCAmelCase				: Dict											=	size_pa
								UpperCAmelCase				: Dict											=	rate_w
								UpperCAmelCase				: Dict											=	rate_t
								UpperCAmelCase				: Dict											=	[
								    np.mat(-1 * np.random.rand(self.conva[0]	,    self.conva[0]	) + 0.5	)
								    for i in range(self.conva[1]	)
								]
								UpperCAmelCase				: int											=	np.mat(-1 * np.random.rand(self.num_bpa	,    self.num_bpa	) + 0.5	)
								UpperCAmelCase				: List[Any]											=	np.mat(-1 * np.random.rand(self.num_bpa	,    self.num_bpa	) + 0.5	)
								UpperCAmelCase				: Optional[Any]											=	-2 * np.random.rand(self.conva[1]	) + 1
								UpperCAmelCase				: List[str]											=	-2 * np.random.rand(self.num_bpa	) + 1
								UpperCAmelCase				: Union[str, Any]											=	-2 * np.random.rand(self.num_bpa	) + 1







	def   A			(		self    :  Dict	,    __snake_case    :  List[str]	)    ->	List[str]:
								# save model dict with pickle
								UpperCAmelCase				: Any											=	{
								    '''num_bp1''': self.num_bpa,
								    '''num_bp2''': self.num_bpa,
								    '''num_bp3''': self.num_bpa,
								    '''conv1''': self.conva,
								    '''step_conv1''': self.step_conva,
								    '''size_pooling1''': self.size_poolinga,
								    '''rate_weight''': self.rate_weight,
								    '''rate_thre''': self.rate_thre,
								    '''w_conv1''': self.w_conva,
								    '''wkj''': self.wkj,
								    '''vji''': self.vji,
								    '''thre_conv1''': self.thre_conva,
								    '''thre_bp2''': self.thre_bpa,
								    '''thre_bp3''': self.thre_bpa,
								}
								with open(__snake_case	,    '''wb'''	) as f:
															pickle.dump(__snake_case	,    __snake_case	)

								print(F"""Model saved： {save_path}"""	)







	@classmethod
	def   A			(		cls    :  List[str]	,    __snake_case    :  Dict	)    ->	Union[str, Any]:
								# read saved model
								with open(__snake_case	,    '''rb'''	) as f:
															UpperCAmelCase				: Union[str, Any]											=	pickle.load(__snake_case	)  # noqa: S301

								UpperCAmelCase				: str											=	model_dic.get('''conv1'''	)
								conv_get.append(model_dic.get('''step_conv1'''	)	)
								UpperCAmelCase				: List[str]											=	model_dic.get('''size_pooling1'''	)
								UpperCAmelCase				: str											=	model_dic.get('''num_bp1'''	)
								UpperCAmelCase				: Optional[int]											=	model_dic.get('''num_bp2'''	)
								UpperCAmelCase				: Union[str, Any]											=	model_dic.get('''num_bp3'''	)
								UpperCAmelCase				: Tuple											=	model_dic.get('''rate_weight'''	)
								UpperCAmelCase				: Optional[int]											=	model_dic.get('''rate_thre'''	)
								# create model instance
								UpperCAmelCase				: Dict											=	CNN(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)
								# modify model parameter
								UpperCAmelCase				: List[str]											=	model_dic.get('''w_conv1'''	)
								UpperCAmelCase				: Union[str, Any]											=	model_dic.get('''wkj'''	)
								UpperCAmelCase				: Optional[Any]											=	model_dic.get('''vji'''	)
								UpperCAmelCase				: str											=	model_dic.get('''thre_conv1'''	)
								UpperCAmelCase				: List[str]											=	model_dic.get('''thre_bp2'''	)
								UpperCAmelCase				: Union[str, Any]											=	model_dic.get('''thre_bp3'''	)
								return conv_ins







	def   A			(		self    :  List[Any]	,    __snake_case    :  List[str]	)    ->	Optional[int]:
								return 1 / (1 + np.exp(-1 * x	))







	def   A			(		self    :  Dict	,    __snake_case    :  Any	)    ->	Optional[Any]:
								return round(__snake_case	,    3	)







	def   A			(		self    :  Tuple	,    __snake_case    :  Optional[int]	,    __snake_case    :  Any	,    __snake_case    :  Tuple	,    __snake_case    :  Optional[int]	,    __snake_case    :  Tuple	)    ->	int:
								# convolution process
								UpperCAmelCase				: Optional[Any]											=	convs[0]
								UpperCAmelCase				: str											=	convs[1]
								UpperCAmelCase				: Optional[Any]											=	np.shape(__snake_case	)[0]
								# get the data slice of original image data, data_focus
								UpperCAmelCase				: Any											=	[]
								for i_focus in range(0	,    size_data - size_conv + 1	,    __snake_case	):
															for j_focus in range(0	,    size_data - size_conv + 1	,    __snake_case	):
																						UpperCAmelCase				: Tuple											=	data[
																						    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																						]
																						data_focus.append(__snake_case	)
        # calculate the feature map of every single kernel, and saved as list of matrix
								UpperCAmelCase				: Optional[Any]											=	[]
								UpperCAmelCase				: int											=	int((size_data - size_conv) / conv_step + 1	)
								for i_map in range(__snake_case	):
															UpperCAmelCase				: str											=	[]
															for i_focus in range(len(__snake_case	)	):
																						UpperCAmelCase				: List[Any]											=	(
																						    np.sum(np.multiply(data_focus[i_focus]	,    w_convs[i_map]	)	)
																						    - thre_convs[i_map]
																						)
																						featuremap.append(self.sig(__snake_case	)	)
															UpperCAmelCase				: List[Any]											=	np.asmatrix(__snake_case	).reshape(
															    __snake_case	,    __snake_case	)
															data_featuremap.append(__snake_case	)

								# expanding the data slice to One dimenssion
								UpperCAmelCase				: Optional[Any]											=	[]
								for each_focus in data_focus:
															focusa_list.extend(self.Expand_Mat(__snake_case	)	)
								UpperCAmelCase				: List[Any]											=	np.asarray(__snake_case	)
								return focus_list, data_featuremap







	def   A			(		self    :  List[str]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[int]	,    __snake_case    :  List[Any]="average_pool"	)    ->	str:
								# pooling process
								UpperCAmelCase				: Dict											=	len(featuremaps[0]	)
								UpperCAmelCase				: List[str]											=	int(size_map / size_pooling	)
								UpperCAmelCase				: Any											=	[]
								for i_map in range(len(__snake_case	)	):
															UpperCAmelCase				: List[Any]											=	featuremaps[i_map]
															UpperCAmelCase				: str											=	[]
															for i_focus in range(0	,    __snake_case	,    __snake_case	):
																						for j_focus in range(0	,    __snake_case	,    __snake_case	):
																													UpperCAmelCase				: Dict											=	feature_map[
																													    i_focus : i_focus + size_pooling,
																													    j_focus : j_focus + size_pooling,
																													]
																													if pooling_type == "average_pool":
																																				# average pooling
																																				map_pooled.append(np.average(__snake_case	)	)
																													elif pooling_type == "max_pooling":
																																				# max pooling
																																				map_pooled.append(np.max(__snake_case	)	)
															UpperCAmelCase				: Any											=	np.asmatrix(__snake_case	).reshape(__snake_case	,    __snake_case	)
															featuremap_pooled.append(__snake_case	)
								return featuremap_pooled







	def   A			(		self    :  Tuple	,    __snake_case    :  Optional[int]	)    ->	Any:
								# expanding three dimension data to one dimension list
								UpperCAmelCase				: Optional[int]											=	[]
								for i in range(len(__snake_case	)	):
															UpperCAmelCase				: Optional[int]											=	np.shape(data[i]	)
															UpperCAmelCase				: Optional[int]											=	data[i].reshape(1	,    shapes[0] * shapes[1]	)
															UpperCAmelCase				: List[Any]											=	data_listed.getA().tolist()[0]
															data_expanded.extend(__snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	np.asarray(__snake_case	)
								return data_expanded







	def   A			(		self    :  Tuple	,    __snake_case    :  Any	)    ->	List[Any]:
								# expanding matrix to one dimension list
								UpperCAmelCase				: Any											=	np.asarray(__snake_case	)
								UpperCAmelCase				: List[Any]											=	np.shape(__snake_case	)
								UpperCAmelCase				: int											=	data_mat.reshape(1	,    shapes[0] * shapes[1]	)
								return data_expanded







	def   A			(		self    :  Tuple	,    __snake_case    :  Tuple	,    __snake_case    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  List[Any]	,    __snake_case    :  Tuple	)    ->	Optional[int]:
								UpperCAmelCase				: Any											=	[]
								UpperCAmelCase				: str											=	0
								for i_map in range(__snake_case	):
															UpperCAmelCase				: Tuple											=	np.ones((size_map, size_map)	)
															for i in range(0	,    __snake_case	,    __snake_case	):
																						for j in range(0	,    __snake_case	,    __snake_case	):
																													UpperCAmelCase				: int											=	pd_pool[
																													    i_pool
																													]
																													UpperCAmelCase				: List[str]											=	i_pool + 1
															UpperCAmelCase				: List[Any]											=	np.multiply(
															    __snake_case	,    np.multiply(out_map[i_map]	,    (1 - out_map[i_map])	)	)
															pd_all.append(__snake_case	)
								return pd_all







	def   A			(		self    :  str	,    __snake_case    :  int	,    __snake_case    :  Dict	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  List[Any]=bool	)    ->	Optional[Any]:
								# model traning
								print('''----------------------Start Training-------------------------'''	)
								print((''' - - Shape: Train_Data  ''', np.shape(__snake_case	))	)
								print((''' - - Shape: Teach_Data  ''', np.shape(__snake_case	))	)
								UpperCAmelCase				: Dict											=	0
								UpperCAmelCase				: Union[str, Any]											=	[]
								UpperCAmelCase				: Dict											=	10000
								while rp < n_repeat and mse >= error_accuracy:
															UpperCAmelCase				: Optional[Any]											=	0
															print(F"""-------------Learning Time {rp}--------------"""	)
															for p in range(len(__snake_case	)	):
																						# print('------------Learning Image: %d--------------'%p)
																						UpperCAmelCase				: List[str]											=	np.asmatrix(datas_train[p]	)
																						UpperCAmelCase				: Optional[int]											=	np.asarray(datas_teach[p]	)
																						UpperCAmelCase				: str											=	self.convolute(
																						    __snake_case	,    self.conva	,    self.w_conva	,    self.thre_conva	,    conv_step=self.step_conva	,    )
																						UpperCAmelCase				: Dict											=	self.pooling(__snake_case	,    self.size_poolinga	)
																						UpperCAmelCase				: str											=	np.shape(__snake_case	)
																						UpperCAmelCase				: int											=	self._expand(__snake_case	)
																						UpperCAmelCase				: List[Any]											=	data_bp_input

																						UpperCAmelCase				: List[str]											=	np.dot(__snake_case	,    self.vji.T	) - self.thre_bpa
																						UpperCAmelCase				: int											=	self.sig(__snake_case	)
																						UpperCAmelCase				: Dict											=	np.dot(__snake_case	,    self.wkj.T	) - self.thre_bpa
																						UpperCAmelCase				: Optional[Any]											=	self.sig(__snake_case	)

																						# --------------Model Leaning ------------------------
																						# calculate error and gradient---------------
																						UpperCAmelCase				: Optional[int]											=	np.multiply(
																						    (data_teach - bp_outa)	,    np.multiply(__snake_case	,    (1 - bp_outa)	)	)
																						UpperCAmelCase				: Any											=	np.multiply(
																						    np.dot(__snake_case	,    self.wkj	)	,    np.multiply(__snake_case	,    (1 - bp_outa)	)	)
																						UpperCAmelCase				: Dict											=	np.dot(__snake_case	,    self.vji	)

																						UpperCAmelCase				: Any											=	pd_i_all / (self.size_poolinga * self.size_poolinga)
																						UpperCAmelCase				: int											=	pd_conva_pooled.T.getA().tolist()
																						UpperCAmelCase				: str											=	self._calculate_gradient_from_pool(
																						    __snake_case	,    __snake_case	,    shape_featuremapa[0]	,    shape_featuremapa[1]	,    self.size_poolinga	,    )
																						# weight and threshold learning process---------
																						# convolution layer
																						for k_conv in range(self.conva[1]	):
																													UpperCAmelCase				: Optional[int]											=	self._expand_mat(pd_conva_all[k_conv]	)
																													UpperCAmelCase				: str											=	self.rate_weight * np.dot(__snake_case	,    __snake_case	)

																													UpperCAmelCase				: str											=	self.w_conva[k_conv] + delta_w.reshape(
																													    (self.conva[0], self.conva[0])	)

																													UpperCAmelCase				: Union[str, Any]											=	(
																													    self.thre_conva[k_conv]
																													    - np.sum(pd_conva_all[k_conv]	) * self.rate_thre
																													)
																						# all connected layer
																						UpperCAmelCase				: Any											=	self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																						UpperCAmelCase				: List[str]											=	self.vji + pd_j_all.T * bp_outa * self.rate_weight
																						UpperCAmelCase				: Union[str, Any]											=	self.thre_bpa - pd_k_all * self.rate_thre
																						UpperCAmelCase				: List[str]											=	self.thre_bpa - pd_j_all * self.rate_thre
																						# calculate the sum error of all single image
																						UpperCAmelCase				: List[Any]											=	np.sum(abs(data_teach - bp_outa	)	)
																						error_count += errors
																						# print('   ----Teach      ',data_teach)
																						# print('   ----BP_output  ',bp_out3)
															UpperCAmelCase				: int											=	rp + 1
															UpperCAmelCase				: Tuple											=	error_count / patterns
															all_mse.append(__snake_case	)

								def draw_error():
															UpperCAmelCase				: Union[str, Any]											=	[error_accuracy for i in range(int(n_repeat * 1.2	)	)]
															plt.plot(__snake_case	,    '''+-'''	)
															plt.plot(__snake_case	,    '''r--'''	)
															plt.xlabel('''Learning Times'''	)
															plt.ylabel('''All_mse'''	)
															plt.grid(__snake_case	,    alpha=0.5	)
															plt.show()

								print('''------------------Training Complished---------------------'''	)
								print((''' - - Training epoch: ''', rp, F"""     - - Mse: {mse:.6f}""")	)
								if draw_e:
															draw_error()
								return mse







	def   A			(		self    :  str	,    __snake_case    :  str	)    ->	List[str]:
								# model predict
								UpperCAmelCase				: Union[str, Any]											=	[]
								print('''-------------------Start Testing-------------------------'''	)
								print((''' - - Shape: Test_Data  ''', np.shape(__snake_case	))	)
								for p in range(len(__snake_case	)	):
															UpperCAmelCase				: Tuple											=	np.asmatrix(datas_test[p]	)
															UpperCAmelCase				: Any											=	self.convolute(
															    __snake_case	,    self.conva	,    self.w_conva	,    self.thre_conva	,    conv_step=self.step_conva	,    )
															UpperCAmelCase				: Optional[int]											=	self.pooling(__snake_case	,    self.size_poolinga	)
															UpperCAmelCase				: Optional[Any]											=	self._expand(__snake_case	)

															UpperCAmelCase				: str											=	data_bp_input
															UpperCAmelCase				: int											=	bp_outa * self.vji.T - self.thre_bpa
															UpperCAmelCase				: List[Any]											=	self.sig(__snake_case	)
															UpperCAmelCase				: Optional[int]											=	bp_outa * self.wkj.T - self.thre_bpa
															UpperCAmelCase				: Any											=	self.sig(__snake_case	)
															produce_out.extend(bp_outa.getA().tolist()	)
								UpperCAmelCase				: Optional[Any]											=	[list(map(self.do_round	,    __snake_case	)	) for each in produce_out]
								return np.asarray(__snake_case	)







	def   A			(		self    :  Any	,    __snake_case    :  List[str]	)    ->	Union[str, Any]:
								# return the data of image after convoluting process so we can check it out
								UpperCAmelCase				: Dict											=	np.asmatrix(__snake_case	)
								UpperCAmelCase				: Any											=	self.convolute(
								    __snake_case	,    self.conva	,    self.w_conva	,    self.thre_conva	,    conv_step=self.step_conva	,    )
								UpperCAmelCase				: Optional[int]											=	self.pooling(__snake_case	,    self.size_poolinga	)

								return data_conveda, data_pooleda


if __name__ == "__main__":
						pass










'''simple docstring'''






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=A__    )
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= field(default="""language-modeling"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}    )

	lowerCamelCase__    				= Features({"""text""": Value("""string"""    )}    )
	lowerCamelCase__    				= Features({}    )
	lowerCamelCase__    				= "text"







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Dict[str, str]:
								return {self.text_column: "text"}





