
"""simple docstring"""






import pickle

import numpy as np
from matplotlib import pyplot as plt
class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Dict		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Optional[Any]=0.2		, lowerCamelCase_    :int=0.2					)							->			Any:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Optional[int]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[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(lowerCamelCase_		, '''wb'''					) as f:
					pickle.dump(lowerCamelCase_		, lowerCamelCase_					)

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





	@classmethod
	def   __lowerCAmelCase (						cls    :List[Any]		, lowerCamelCase_    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			with open(lowerCamelCase_		, '''rb'''					) as f:
					SCREAMING_SNAKE_CASE       :     Any				= pickle.load(lowerCamelCase_					)  # noqa: S301

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





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]					)							->			Optional[Any]:







			'''simple docstring'''




			return 1 / (1 + np.exp(-1 * x					))





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			return round(lowerCamelCase_		, 3					)





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :List[str]					)							->			str:







			'''simple docstring'''




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

			# expanding the data slice to One dimenssion
			SCREAMING_SNAKE_CASE       :     Tuple				= []
			for each_focus in data_focus:
					focusa_list.extend(self.Expand_Mat(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Dict				= np.asarray(lowerCamelCase_					)
			return focus_list, data_featuremap





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Any		, lowerCamelCase_    :int		, lowerCamelCase_    :str="average_pool"					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= len(featuremaps[0]					)
			SCREAMING_SNAKE_CASE       :     List[str]				= int(size_map / size_pooling					)
			SCREAMING_SNAKE_CASE       :     List[str]				= []
			for i_map in range(len(lowerCamelCase_					)					):
					SCREAMING_SNAKE_CASE       :     Any				= featuremaps[i_map]
					SCREAMING_SNAKE_CASE       :     int				= []
					for i_focus in range(0		, lowerCamelCase_		, lowerCamelCase_					):
							for j_focus in range(0		, lowerCamelCase_		, lowerCamelCase_					):
									SCREAMING_SNAKE_CASE       :     str				= 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(lowerCamelCase_					)					)
									elif pooling_type == "max_pooling":
											# max pooling
											map_pooled.append(np.max(lowerCamelCase_					)					)
					SCREAMING_SNAKE_CASE       :     int				= np.asmatrix(lowerCamelCase_					).reshape(lowerCamelCase_		, lowerCamelCase_					)
					featuremap_pooled.append(lowerCamelCase_					)
			return featuremap_pooled





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :List[Any]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= []
			for i in range(len(lowerCamelCase_					)					):
					SCREAMING_SNAKE_CASE       :     Tuple				= np.shape(data[i]					)
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= data[i].reshape(1		, shapes[0] * shapes[1]					)
					SCREAMING_SNAKE_CASE       :     List[str]				= data_listed.getA().tolist()[0]
					data_expanded.extend(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= np.asarray(lowerCamelCase_					)
			return data_expanded





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= np.asarray(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= np.shape(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= data_mat.reshape(1		, shapes[0] * shapes[1]					)
			return data_expanded





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :Any		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= []
			SCREAMING_SNAKE_CASE       :     Tuple				= 0
			for i_map in range(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     List[str]				= np.ones((size_map, size_map)					)
					for i in range(0		, lowerCamelCase_		, lowerCamelCase_					):
							for j in range(0		, lowerCamelCase_		, lowerCamelCase_					):
									SCREAMING_SNAKE_CASE       :     str				= pd_pool[
									    i_pool
									]
									SCREAMING_SNAKE_CASE       :     str				= i_pool + 1
					SCREAMING_SNAKE_CASE       :     Any				= np.multiply(
					    lowerCamelCase_		, np.multiply(out_map[i_map]		, (1 - out_map[i_map])					)					)
					pd_all.append(lowerCamelCase_					)
			return pd_all





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :str		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Dict		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :str=bool					)							->			str:







			'''simple docstring'''




			print('''----------------------Start Training-------------------------'''					)
			print((''' - - Shape: Train_Data  ''', np.shape(lowerCamelCase_					))					)
			print((''' - - Shape: Teach_Data  ''', np.shape(lowerCamelCase_					))					)
			SCREAMING_SNAKE_CASE       :     Any				= 0
			SCREAMING_SNAKE_CASE       :     List[str]				= []
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1_00_00
			while rp < n_repeat and mse >= error_accuracy:
					SCREAMING_SNAKE_CASE       :     List[str]				= 0
					print(f"-------------Learning Time {rp}--------------"					)
					for p in range(len(lowerCamelCase_					)					):
							# print('------------Learning Image: %d--------------'%p)
							SCREAMING_SNAKE_CASE       :     Dict				= np.asmatrix(datas_train[p]					)
							SCREAMING_SNAKE_CASE       :     List[Any]				= np.asarray(datas_teach[p]					)
							SCREAMING_SNAKE_CASE       :     Optional[int]				= self.convolute(
							    lowerCamelCase_		, self.conva		, self.w_conva		, self.thre_conva		, conv_step=self.step_conva		, )
							SCREAMING_SNAKE_CASE       :     str				= self.pooling(lowerCamelCase_		, self.size_poolinga					)
							SCREAMING_SNAKE_CASE       :     str				= np.shape(lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self._expand(lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= data_bp_input

							SCREAMING_SNAKE_CASE       :     Any				= np.dot(lowerCamelCase_		, self.vji.T					) - self.thre_bpa
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.sig(lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     int				= np.dot(lowerCamelCase_		, self.wkj.T					) - self.thre_bpa
							SCREAMING_SNAKE_CASE       :     Any				= self.sig(lowerCamelCase_					)

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

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

									SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.w_conva[k_conv] + delta_w.reshape(
									    (self.conva[0], self.conva[0])					)

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

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

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





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :List[Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= []
			print('''-------------------Start Testing-------------------------'''					)
			print((''' - - Shape: Test_Data  ''', np.shape(lowerCamelCase_					))					)
			for p in range(len(lowerCamelCase_					)					):
					SCREAMING_SNAKE_CASE       :     List[str]				= np.asmatrix(datas_test[p]					)
					SCREAMING_SNAKE_CASE       :     List[str]				= self.convolute(
					    lowerCamelCase_		, self.conva		, self.w_conva		, self.thre_conva		, conv_step=self.step_conva		, )
					SCREAMING_SNAKE_CASE       :     Any				= self.pooling(lowerCamelCase_		, self.size_poolinga					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= self._expand(lowerCamelCase_					)

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







	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Optional[int]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= np.asmatrix(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.convolute(
			    lowerCamelCase_		, self.conva		, self.w_conva		, self.thre_conva		, conv_step=self.step_conva		, )
			SCREAMING_SNAKE_CASE       :     List[str]				= self.pooling(lowerCamelCase_		, self.size_poolinga					)

			return data_conveda, data_pooleda


if __name__ == "__main__":
					pass







"""simple docstring"""






def __A						(   a_ :							list		,		a_ :							int		,		a_ :							int = 0		,		a_ :							int = 0     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= right or len(a_     ) - 1
		if left > right:
				return -1
		elif list_data[left] == key:
				return left
		elif list_data[right] == key:
				return right
		else:
				return search(a_		,		a_		,		left + 1		,		right - 1     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()





