

import pickle

import numpy as np
from matplotlib import pyplot as plt







class 					__lowercase  :





							"""simple docstring"""

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




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       int:
														A      :					Dict      		=					{
														    '''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(UpperCAmelCase__    ,						'''wb'''						) as f:
																					pickle.dump(UpperCAmelCase__    ,						UpperCAmelCase__						)

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




							@classmethod
							def       snake_case			(					cls    ,						__UpperCAmelCase						)		->       Optional[Any]:

														with open(UpperCAmelCase__    ,						'''rb'''						) as f:
																					A      :					Tuple      		=					pickle.load(UpperCAmelCase__						)  # noqa: S301

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




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[str]:
														return 1 / (1 + np.exp(-1 * x						))




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[Any]:
														return round(UpperCAmelCase__    ,						3						)




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

														# expanding the data slice to One dimenssion
														A      :					Tuple      		=					[]
														for each_focus in data_focus:
																					focusa_list.extend(self.Expand_Mat(UpperCAmelCase__						)						)
														A      :					Union[str, Any]      		=					np.asarray(UpperCAmelCase__						)
														return focus_list, data_featuremap




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase="average_pool"						)		->       int:
														A      :					int      		=					len(featuremaps[0]						)
														A      :					Optional[int]      		=					int(size_map / size_pooling						)
														A      :					Union[str, Any]      		=					[]
														for i_map in range(len(UpperCAmelCase__						)						):
																					A      :					int      		=					featuremaps[i_map]
																					A      :					Union[str, Any]      		=					[]
																					for i_focus in range(0    ,						UpperCAmelCase__    ,						UpperCAmelCase__						):
																												for j_focus in range(0    ,						UpperCAmelCase__    ,						UpperCAmelCase__						):
																																			A      :					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(UpperCAmelCase__						)						)
																																			elif pooling_type == "max_pooling":
																																										# max pooling
																																										map_pooled.append(np.max(UpperCAmelCase__						)						)
																					A      :					Tuple      		=					np.asmatrix(UpperCAmelCase__						).reshape(UpperCAmelCase__    ,						UpperCAmelCase__						)
																					featuremap_pooled.append(UpperCAmelCase__						)
														return featuremap_pooled




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[Any]:
														A      :					Union[str, Any]      		=					[]
														for i in range(len(UpperCAmelCase__						)						):
																					A      :					List[Any]      		=					np.shape(data[i]						)
																					A      :					Optional[int]      		=					data[i].reshape(1    ,						shapes[0] * shapes[1]						)
																					A      :					Union[str, Any]      		=					data_listed.getA().tolist()[0]
																					data_expanded.extend(UpperCAmelCase__						)
														A      :					str      		=					np.asarray(UpperCAmelCase__						)
														return data_expanded




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Dict:
														A      :					Dict      		=					np.asarray(UpperCAmelCase__						)
														A      :					Optional[int]      		=					np.shape(UpperCAmelCase__						)
														A      :					int      		=					data_mat.reshape(1    ,						shapes[0] * shapes[1]						)
														return data_expanded




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




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

																												A      :					Optional[int]      		=					np.dot(UpperCAmelCase__    ,						self.vji.T						) - self.thre_bpa
																												A      :					Tuple      		=					self.sig(UpperCAmelCase__						)
																												A      :					List[Any]      		=					np.dot(UpperCAmelCase__    ,						self.wkj.T						) - self.thre_bpa
																												A      :					int      		=					self.sig(UpperCAmelCase__						)

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

																												A      :					int      		=					pd_i_all / (self.size_poolinga * self.size_poolinga)
																												A      :					Union[str, Any]      		=					pd_conva_pooled.T.getA().tolist()
																												A      :					Dict      		=					self._calculate_gradient_from_pool(
																												    UpperCAmelCase__    ,						UpperCAmelCase__    ,						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      :					Union[str, Any]      		=					self._expand_mat(pd_conva_all[k_conv]						)
																																			A      :					List[Any]      		=					self.rate_weight * np.dot(UpperCAmelCase__    ,						UpperCAmelCase__						)

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

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

														def draw_error():
																					A      :					List[Any]      		=					[error_accuracy for i in range(int(n_repeat * 1.2						)						)]
																					plt.plot(UpperCAmelCase__    ,						'''+-'''						)
																					plt.plot(UpperCAmelCase__    ,						'''r--'''						)
																					plt.xlabel('''Learning Times'''						)
																					plt.ylabel('''All_mse'''						)
																					plt.grid(UpperCAmelCase__    ,						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    ,						__UpperCAmelCase						)		->       str:
														A      :					Optional[Any]      		=					[]
														print('''-------------------Start Testing-------------------------'''						)
														print((''' - - Shape: Test_Data  ''', np.shape(UpperCAmelCase__						))						)
														for p in range(len(UpperCAmelCase__						)						):
																					A      :					Any      		=					np.asmatrix(datas_test[p]						)
																					A		,		A      :					Optional[int]      		=					self.convolute(
																					    UpperCAmelCase__    ,						self.conva    ,						self.w_conva    ,						self.thre_conva    ,						conv_step=self.step_conva    ,						)
																					A      :					str      		=					self.pooling(UpperCAmelCase__    ,						self.size_poolinga						)
																					A      :					int      		=					self._expand(UpperCAmelCase__						)

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






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       int:
														A      :					Dict      		=					np.asmatrix(UpperCAmelCase__						)
														A		,		A      :					List[Any]      		=					self.convolute(
														    UpperCAmelCase__    ,						self.conva    ,						self.w_conva    ,						self.thre_conva    ,						conv_step=self.step_conva    ,						)
														A      :					str      		=					self.pooling(UpperCAmelCase__    ,						self.size_poolinga						)

														return data_conveda, data_pooleda


if __name__ == "__main__":
		pass












class 							UpperCamelCase_       :  # Public class to implement a graph

    '''simple docstring'''






    def __init__(     self :      str   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       row
           A__ 					=       col
           A__ 					=       graph


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->bool:





           '''simple docstring'''

           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           A__ 					=       [-1, 0, 1, -1, 1, -1, 0, 1]
           A__ 					=       True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__):
                         self.diffs(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->int:  # And finally, count all islands.





           '''simple docstring'''

           A__ 					=       [[False for j in range(self.COL)] for i in range(self.ROW)]
           A__ 					=       0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)
                                count += 1
           return count


