




'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt






class 						a				:


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


				def   A_ (		self    :					List[str]       ,				lowercase_    :					Tuple					):
									# save model dict with pickle
									snake_case_									=				{
									    '''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   A_ (		cls    :					List[Any]       ,				lowercase_    :					Tuple					):
									# read saved model
									with open(lowercase_       ,				'''rb'''					) as f:
														snake_case_									=				pickle.load(lowercase_					)  # noqa: S301

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


				def   A_ (		self    :					Any       ,				lowercase_    :					Optional[Any]					):
									return 1 / (1 + np.exp(-1 * x					))


				def   A_ (		self    :					Any       ,				lowercase_    :					Dict					):
									return round(lowercase_       ,				3					)


				def   A_ (		self    :					int       ,				lowercase_    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Tuple       ,				lowercase_    :					List[str]					):
									# convolution process
									snake_case_									=				convs[0]
									snake_case_									=				convs[1]
									snake_case_									=				np.shape(lowercase_					)[0]
									# get the data slice of original image data, data_focus
									snake_case_									=				[]
									for i_focus in range(0       ,				size_data - size_conv + 1       ,				lowercase_					):
														for j_focus in range(0       ,				size_data - size_conv + 1       ,				lowercase_					):
																			snake_case_									=				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
									snake_case_									=				[]
									snake_case_									=				int((size_data - size_conv) / conv_step + 1					)
									for i_map in range(lowercase_					):
														snake_case_									=				[]
														for i_focus in range(len(lowercase_					)					):
																			snake_case_									=				(
																			    np.sum(np.multiply(data_focus[i_focus]       ,				w_convs[i_map]					)					)
																			    - thre_convs[i_map]
																			)
																			featuremap.append(self.sig(lowercase_					)					)
														snake_case_									=				np.asmatrix(lowercase_					).reshape(
														    lowercase_       ,				lowercase_					)
														data_featuremap.append(lowercase_					)

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


				def   A_ (		self    :					str       ,				lowercase_    :					Any       ,				lowercase_    :					Dict       ,				lowercase_    :					Tuple="average_pool"					):
									# pooling process
									snake_case_									=				len(featuremaps[0]					)
									snake_case_									=				int(size_map / size_pooling					)
									snake_case_									=				[]
									for i_map in range(len(lowercase_					)					):
														snake_case_									=				featuremaps[i_map]
														snake_case_									=				[]
														for i_focus in range(0       ,				lowercase_       ,				lowercase_					):
																			for j_focus in range(0       ,				lowercase_       ,				lowercase_					):
																								snake_case_									=				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_					)					)
														snake_case_									=				np.asmatrix(lowercase_					).reshape(lowercase_       ,				lowercase_					)
														featuremap_pooled.append(lowercase_					)
									return featuremap_pooled


				def   A_ (		self    :					Dict       ,				lowercase_    :					Any					):
									# expanding three dimension data to one dimension list
									snake_case_									=				[]
									for i in range(len(lowercase_					)					):
														snake_case_									=				np.shape(data[i]					)
														snake_case_									=				data[i].reshape(1       ,				shapes[0] * shapes[1]					)
														snake_case_									=				data_listed.getA().tolist()[0]
														data_expanded.extend(lowercase_					)
									snake_case_									=				np.asarray(lowercase_					)
									return data_expanded


				def   A_ (		self    :					Any       ,				lowercase_    :					Any					):
									# expanding matrix to one dimension list
									snake_case_									=				np.asarray(lowercase_					)
									snake_case_									=				np.shape(lowercase_					)
									snake_case_									=				data_mat.reshape(1       ,				shapes[0] * shapes[1]					)
									return data_expanded


				def   A_ (		self    :					str       ,				lowercase_    :					Tuple       ,				lowercase_    :					int       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[Any]					):
									snake_case_									=				[]
									snake_case_									=				0
									for i_map in range(lowercase_					):
														snake_case_									=				np.ones((size_map, size_map)					)
														for i in range(0       ,				lowercase_       ,				lowercase_					):
																			for j in range(0       ,				lowercase_       ,				lowercase_					):
																								snake_case_									=				pd_pool[
																								    i_pool
																								]
																								snake_case_									=				i_pool + 1
														snake_case_									=				np.multiply(
														    lowercase_       ,				np.multiply(out_map[i_map]       ,				(1 - out_map[i_map])					)					)
														pd_all.append(lowercase_					)
									return pd_all


				def   A_ (		self    :					Tuple       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Dict=bool					):
									# model traning
									print('''----------------------Start Training-------------------------'''					)
									print((''' - - Shape: Train_Data  ''', np.shape(lowercase_					))					)
									print((''' - - Shape: Teach_Data  ''', np.shape(lowercase_					))					)
									snake_case_									=				0
									snake_case_									=				[]
									snake_case_									=				1_0000
									while rp < n_repeat and mse >= error_accuracy:
														snake_case_									=				0
														print(F"-------------Learning Time {rp}--------------"					)
														for p in range(len(lowercase_					)					):
																			# print('------------Learning Image: %d--------------'%p)
																			snake_case_									=				np.asmatrix(datas_train[p]					)
																			snake_case_									=				np.asarray(datas_teach[p]					)
																			snake_case_			,snake_case_									=				self.convolute(
																			    lowercase_       ,				self.conva       ,				self.w_conva       ,				self.thre_conva       ,				conv_step=self.step_conva       ,				)
																			snake_case_									=				self.pooling(lowercase_       ,				self.size_poolinga					)
																			snake_case_									=				np.shape(lowercase_					)
																			snake_case_									=				self._expand(lowercase_					)
																			snake_case_									=				data_bp_input

																			snake_case_									=				np.dot(lowercase_       ,				self.vji.T					) - self.thre_bpa
																			snake_case_									=				self.sig(lowercase_					)
																			snake_case_									=				np.dot(lowercase_       ,				self.wkj.T					) - self.thre_bpa
																			snake_case_									=				self.sig(lowercase_					)

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

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

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

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

									def draw_error():
														snake_case_									=				[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   A_ (		self    :					str       ,				lowercase_    :					Union[str, Any]					):
									# model predict
									snake_case_									=				[]
									print('''-------------------Start Testing-------------------------'''					)
									print((''' - - Shape: Test_Data  ''', np.shape(lowercase_					))					)
									for p in range(len(lowercase_					)					):
														snake_case_									=				np.asmatrix(datas_test[p]					)
														snake_case_			,snake_case_									=				self.convolute(
														    lowercase_       ,				self.conva       ,				self.w_conva       ,				self.thre_conva       ,				conv_step=self.step_conva       ,				)
														snake_case_									=				self.pooling(lowercase_       ,				self.size_poolinga					)
														snake_case_									=				self._expand(lowercase_					)

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


				def   A_ (		self    :					List[str]       ,				lowercase_    :					List[str]					):
									# return the data of image after convoluting process so we can check it out
									snake_case_									=				np.asmatrix(lowercase_					)
									snake_case_			,snake_case_									=				self.convolute(
									    lowercase_       ,				self.conva       ,				self.w_conva       ,				self.thre_conva       ,				conv_step=self.step_conva       ,				)
									snake_case_									=				self.pooling(lowercase_       ,				self.size_poolinga					)

									return data_conveda, data_pooleda


if __name__ == "__main__":
							pass




import os
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


if __name__ == "__main__":
  print(f"{solution() = }")


