




import pickle

import numpy as np
from matplotlib import pyplot as plt




class     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     str=0.2					,      __lowerCamelCase				:     Union[str, Any]=0.2							)					->     Union[str, Any]:
							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 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     List[Any]							)					->     Union[str, Any]:
							# 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 							__UpperCAmelCase							(      cls				:     int					,      __lowerCamelCase				:     Optional[int]							)					->     Union[str, Any]:
							# 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 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     List[str]							)					->     str:
							return 1 / (1 + np.exp(-1 * x							))





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Dict							)					->     Optional[int]:
							return round(__lowerCamelCase					,      3							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     int							)					->     Any:
							# 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 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     List[str]="average_pool"							)					->     Tuple:
							# 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 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Any							)					->     Any:
							# 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 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     Tuple:
							# 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 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     int							)					->     str:
							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 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Dict=bool							)					->     str:
							# model traning
							print("----------------------Start Training-------------------------"							)
							print((" - - Shape: Train_Data  ", np.shape(__lowerCamelCase							))							)
							print((" - - Shape: Teach_Data  ", np.shape(__lowerCamelCase							))							)
							a    =			0
							a    =			[]
							a    =			1_00_00
							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 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     str							)					->     Dict:
							# 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 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     List[str]							)					->     Union[str, Any]:
							# 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














def 						_a    (      a :int )							->			bool:
 a		  =      n ** (1 / 3)
 return (val * val * val) == n


if __name__ == "__main__":
     print(perfect_cube(27))
     print(perfect_cube(4))


