



import pickle

import numpy as np
from matplotlib import pyplot as plt
class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       Any			,     lowercase	:       Dict			,     lowercase	:       List[str]			,     lowercase	:       List[Any]			,     lowercase	:       str			,     lowercase	:       Optional[Any]			,     lowercase	:       Optional[int]=0.2			,     lowercase	:       Union[str, Any]=0.2   )						->					Any:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       int   )						->					int:
									"""simple docstring"""







									__lowercase					=				{
									    """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(lowercase			,     """wb"""   ) as f:
															pickle.dump(lowercase			,     lowercase   )

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





			@classmethod
			def 		snake_case__			(				cls	:       Any			,     lowercase	:       Optional[Any]   )						->					Dict:
									"""simple docstring"""







									with open(lowercase			,     """rb"""   ) as f:
															__lowercase					=				pickle.load(lowercase   )  # noqa: S301

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





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       List[str]   )						->					List[str]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       int			,     lowercase	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									return round(lowercase			,     3   )





			def 		snake_case__			(				self	:       Dict			,     lowercase	:       List[str]			,     lowercase	:       Dict			,     lowercase	:       str			,     lowercase	:       Any			,     lowercase	:       int   )						->					Union[str, Any]:
									"""simple docstring"""







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

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





			def 		snake_case__			(				self	:       Optional[Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Dict			,     lowercase	:       Optional[int]="average_pool"   )						->					Tuple:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				[]
									for i in range(len(lowercase   )   ):
															__lowercase					=				np.shape(data[i]   )
															__lowercase					=				data[i].reshape(1			,     shapes[0] * shapes[1]   )
															__lowercase					=				data_listed.getA().tolist()[0]
															data_expanded.extend(lowercase   )
									__lowercase					=				np.asarray(lowercase   )
									return data_expanded





			def 		snake_case__			(				self	:       Optional[Any]			,     lowercase	:       Union[str, Any]   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				np.asarray(lowercase   )
									__lowercase					=				np.shape(lowercase   )
									__lowercase					=				data_mat.reshape(1			,     shapes[0] * shapes[1]   )
									return data_expanded





			def 		snake_case__			(				self	:       Optional[Any]			,     lowercase	:       int			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[int]			,     lowercase	:       List[Any]   )						->					Tuple:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Any			,     lowercase	:       List[Any]			,     lowercase	:       Optional[int]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       str=bool   )						->					List[str]:
									"""simple docstring"""







									print("""----------------------Start Training-------------------------"""   )
									print((""" - - Shape: Train_Data  """, np.shape(lowercase   ))   )
									print((""" - - Shape: Teach_Data  """, np.shape(lowercase   ))   )
									__lowercase					=				0
									__lowercase					=				[]
									__lowercase					=				10_000
									while rp < n_repeat and mse >= error_accuracy:
															__lowercase					=				0
															print(F"-------------Learning Time {rp}--------------"   )
															for p in range(len(lowercase   )   ):
																					# print('------------Learning Image: %d--------------'%p)
																					__lowercase					=				np.asmatrix(datas_train[p]   )
																					__lowercase					=				np.asarray(datas_teach[p]   )
																					__lowercase	,			__lowercase					=				self.convolute(
																					    lowercase			,     self.conva			,     self.w_conva			,     self.thre_conva			,     conv_step=self.step_conva			,     )
																					__lowercase					=				self.pooling(lowercase			,     self.size_poolinga   )
																					__lowercase					=				np.shape(lowercase   )
																					__lowercase					=				self._expand(lowercase   )
																					__lowercase					=				data_bp_input

																					__lowercase					=				np.dot(lowercase			,     self.vji.T   ) - self.thre_bpa
																					__lowercase					=				self.sig(lowercase   )
																					__lowercase					=				np.dot(lowercase			,     self.wkj.T   ) - self.thre_bpa
																					__lowercase					=				self.sig(lowercase   )

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

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

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

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

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

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





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Optional[Any]   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				[]
									print("""-------------------Start Testing-------------------------"""   )
									print((""" - - Shape: Test_Data  """, np.shape(lowercase   ))   )
									for p in range(len(lowercase   )   ):
															__lowercase					=				np.asmatrix(datas_test[p]   )
															__lowercase	,			__lowercase					=				self.convolute(
															    lowercase			,     self.conva			,     self.w_conva			,     self.thre_conva			,     conv_step=self.step_conva			,     )
															__lowercase					=				self.pooling(lowercase			,     self.size_poolinga   )
															__lowercase					=				self._expand(lowercase   )

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







			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Any   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				np.asmatrix(lowercase   )
									__lowercase	,			__lowercase					=				self.convolute(
									    lowercase			,     self.conva			,     self.w_conva			,     self.thre_conva			,     conv_step=self.step_conva			,     )
									__lowercase					=				self.pooling(lowercase			,     self.size_poolinga   )

									return data_conveda, data_pooleda


if __name__ == "__main__":
			pass










def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				sum(lowercase__   )

						__lowercase					=				[[False for x in range(s + 1   )] for y in range(n + 1   )]

						for i in range(1						,       n + 1   ):
												__lowercase					=				True

						for i in range(1						,       s + 1   ):
												__lowercase					=				False

						for i in range(1						,       n + 1   ):
												for j in range(1						,       s + 1   ):
																		__lowercase					=				dp[i][j - 1]

																		if arr[i - 1] <= j:
																								__lowercase					=				dp[i][j] or dp[i - 1][j - arr[i - 1]]

						for j in range(int(s / 2   )						,       -1						,       -1   ):
												if dp[n][j] is True:
																		__lowercase					=				s - 2 * j
																		break

						return diff





