





'''simple docstring'''


import pickle

import numpy as np
from matplotlib import pyplot as plt






class       UpperCamelCase__  :







	def __init__(				self			:   Optional[Any] , lowerCamelCase			:   str , lowerCamelCase			:   Tuple , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   Dict , lowerCamelCase			:   Dict=0.2 , lowerCamelCase			:   Any=0.2		):




				'''simple docstring'''
				a__     = bp_numa
				a__     = bp_numa
				a__     = bp_numa
				a__     = conva_get[:2]
				a__     = conva_get[2]
				a__     = size_pa
				a__     = rate_w
				a__     = rate_t
				a__     = [
				    np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0]		) + 0.5		)
				    for i in range(self.conva[1]		)
				]
				a__     = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa		) + 0.5		)
				a__     = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa		) + 0.5		)
				a__     = -2 * np.random.rand(self.conva[1]		) + 1
				a__     = -2 * np.random.rand(self.num_bpa		) + 1
				a__     = -2 * np.random.rand(self.num_bpa		) + 1







	def 						__a   (				self			:   int , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				# save model dict with pickle
				a__     = {
				    "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 						__a   (				cls			:   Tuple , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				# read saved model
				with open(lowerCamelCase , "rb"		) as f:
							a__     = pickle.load(lowerCamelCase		)  # noqa: S301

				a__     = model_dic.get("conv1"		)
				conv_get.append(model_dic.get("step_conv1"		)		)
				a__     = model_dic.get("size_pooling1"		)
				a__     = model_dic.get("num_bp1"		)
				a__     = model_dic.get("num_bp2"		)
				a__     = model_dic.get("num_bp3"		)
				a__     = model_dic.get("rate_weight"		)
				a__     = model_dic.get("rate_thre"		)
				# create model instance
				a__     = CNN(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				# modify model parameter
				a__     = model_dic.get("w_conv1"		)
				a__     = model_dic.get("wkj"		)
				a__     = model_dic.get("vji"		)
				a__     = model_dic.get("thre_conv1"		)
				a__     = model_dic.get("thre_bp2"		)
				a__     = model_dic.get("thre_bp3"		)
				return conv_ins







	def 						__a   (				self			:   Any , lowerCamelCase			:   List[str]		):




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







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				return round(lowerCamelCase , 3		)







	def 						__a   (				self			:   str , lowerCamelCase			:   List[str] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   str , lowerCamelCase			:   List[Any] , lowerCamelCase			:   str		):




				'''simple docstring'''
				# convolution process
				a__     = convs[0]
				a__     = convs[1]
				a__     = np.shape(lowerCamelCase		)[0]
				# get the data slice of original image data, data_focus
				a__     = []
				for i_focus in range(0 , size_data - size_conv + 1 , lowerCamelCase		):
							for j_focus in range(0 , size_data - size_conv + 1 , lowerCamelCase		):
										a__     = 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
				a__     = []
				a__     = int((size_data - size_conv) / conv_step + 1		)
				for i_map in range(lowerCamelCase		):
							a__     = []
							for i_focus in range(len(lowerCamelCase		)		):
										a__     = (
										    np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map]		)		)
										    - thre_convs[i_map]
										)
										featuremap.append(self.sig(lowerCamelCase		)		)
							a__     = np.asmatrix(lowerCamelCase		).reshape(
							    lowerCamelCase , lowerCamelCase		)
							data_featuremap.append(lowerCamelCase		)

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







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   int , lowerCamelCase			:   Optional[int]="average_pool"		):




				'''simple docstring'''
				# pooling process
				a__     = len(featuremaps[0]		)
				a__     = int(size_map / size_pooling		)
				a__     = []
				for i_map in range(len(lowerCamelCase		)		):
							a__     = featuremaps[i_map]
							a__     = []
							for i_focus in range(0 , lowerCamelCase , lowerCamelCase		):
										for j_focus in range(0 , lowerCamelCase , lowerCamelCase		):
													a__     = 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		)		)
							a__     = np.asmatrix(lowerCamelCase		).reshape(lowerCamelCase , lowerCamelCase		)
							featuremap_pooled.append(lowerCamelCase		)
				return featuremap_pooled







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				# expanding three dimension data to one dimension list
				a__     = []
				for i in range(len(lowerCamelCase		)		):
							a__     = np.shape(data[i]		)
							a__     = data[i].reshape(1 , shapes[0] * shapes[1]		)
							a__     = data_listed.getA().tolist()[0]
							data_expanded.extend(lowerCamelCase		)
				a__     = np.asarray(lowerCamelCase		)
				return data_expanded







	def 						__a   (				self			:   Dict , lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				# expanding matrix to one dimension list
				a__     = np.asarray(lowerCamelCase		)
				a__     = np.shape(lowerCamelCase		)
				a__     = data_mat.reshape(1 , shapes[0] * shapes[1]		)
				return data_expanded







	def 						__a   (				self			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   str , lowerCamelCase			:   List[Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = []
				a__     = 0
				for i_map in range(lowerCamelCase		):
							a__     = np.ones((size_map, size_map)		)
							for i in range(0 , lowerCamelCase , lowerCamelCase		):
										for j in range(0 , lowerCamelCase , lowerCamelCase		):
													a__     = pd_pool[
													    i_pool
													]
													a__     = i_pool + 1
							a__     = np.multiply(
							    lowerCamelCase , np.multiply(out_map[i_map] , (1 - out_map[i_map])		)		)
							pd_all.append(lowerCamelCase		)
				return pd_all







	def 						__a   (				self			:   Dict , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Any , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Dict , lowerCamelCase			:   List[Any]=bool		):




				'''simple docstring'''
				# model traning
				print("----------------------Start Training-------------------------"		)
				print((" - - Shape: Train_Data  ", np.shape(lowerCamelCase		))		)
				print((" - - Shape: Teach_Data  ", np.shape(lowerCamelCase		))		)
				a__     = 0
				a__     = []
				a__     = 1_0_0_0_0
				while rp < n_repeat and mse >= error_accuracy:
							a__     = 0
							print(F'''-------------Learning Time {rp}--------------'''		)
							for p in range(len(lowerCamelCase		)		):
										# print('------------Learning Image: %d--------------'%p)
										a__     = np.asmatrix(datas_train[p]		)
										a__     = np.asarray(datas_teach[p]		)
										a__   ,				a__     = self.convolute(
										    lowerCamelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
										a__     = self.pooling(lowerCamelCase , self.size_poolinga		)
										a__     = np.shape(lowerCamelCase		)
										a__     = self._expand(lowerCamelCase		)
										a__     = data_bp_input

										a__     = np.dot(lowerCamelCase , self.vji.T		) - self.thre_bpa
										a__     = self.sig(lowerCamelCase		)
										a__     = np.dot(lowerCamelCase , self.wkj.T		) - self.thre_bpa
										a__     = self.sig(lowerCamelCase		)

										# --------------Model Leaning ------------------------
										# calculate error and gradient---------------
										a__     = np.multiply(
										    (data_teach - bp_outa) , np.multiply(lowerCamelCase , (1 - bp_outa)		)		)
										a__     = np.multiply(
										    np.dot(lowerCamelCase , self.wkj		) , np.multiply(lowerCamelCase , (1 - bp_outa)		)		)
										a__     = np.dot(lowerCamelCase , self.vji		)

										a__     = pd_i_all / (self.size_poolinga * self.size_poolinga)
										a__     = pd_conva_pooled.T.getA().tolist()
										a__     = 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]		):
													a__     = self._expand_mat(pd_conva_all[k_conv]		)
													a__     = self.rate_weight * np.dot(lowerCamelCase , lowerCamelCase		)

													a__     = self.w_conva[k_conv] + delta_w.reshape(
													    (self.conva[0], self.conva[0])		)

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

				def draw_error():
							a__     = [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 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				# model predict
				a__     = []
				print("-------------------Start Testing-------------------------"		)
				print((" - - Shape: Test_Data  ", np.shape(lowerCamelCase		))		)
				for p in range(len(lowerCamelCase		)		):
							a__     = np.asmatrix(datas_test[p]		)
							a__   ,				a__     = self.convolute(
							    lowerCamelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
							a__     = self.pooling(lowerCamelCase , self.size_poolinga		)
							a__     = self._expand(lowerCamelCase		)

							a__     = data_bp_input
							a__     = bp_outa * self.vji.T - self.thre_bpa
							a__     = self.sig(lowerCamelCase		)
							a__     = bp_outa * self.wkj.T - self.thre_bpa
							a__     = self.sig(lowerCamelCase		)
							produce_out.extend(bp_outa.getA().tolist()		)
				a__     = [list(map(self.do_round , lowerCamelCase		)		) for each in produce_out]
				return np.asarray(lowerCamelCase		)







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				# return the data of image after convoluting process so we can check it out
				a__     = np.asmatrix(lowerCamelCase		)
				a__   ,				a__     = self.convolute(
				    lowerCamelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
				a__     = self.pooling(lowerCamelCase , self.size_poolinga		)

				return data_conveda, data_pooleda


if __name__ == "__main__":
		pass










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    bool:
			a__     = 0
			for ch in input_str:
						a__     = ord(__lowerCamelCase     )
						a__     = pow(2					,  __lowerCamelCase     )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
		import doctest

		doctest.testmod()



