

'''simple docstring'''







import pickle

import numpy as np
from matplotlib import pyplot as plt


class 					A__      :



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=0.2		,				UpperCamelCase__=0.2       )						->      Dict:





           '''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 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[Any]:





           '''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(UpperCamelCase__		,				"""wb"""       ) as f:
                  pickle.dump(UpperCamelCase__		,				UpperCamelCase__       )

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



    @classmethod
    def 					snake_case_			(					cls		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           # read saved model
           with open(UpperCamelCase__		,				"""rb"""       ) as f:
                  A_						=      pickle.load(UpperCamelCase__       )  # 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(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )
           # 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 					snake_case_			(					self		,				UpperCamelCase__       )						->      int:





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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      int:





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



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      List[str]:





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

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



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__="average_pool"       )						->      List[Any]:





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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      int:





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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Tuple:





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



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      []
           A_						=      0
           for i_map in range(UpperCamelCase__       ):
                  A_						=      np.ones((size_map, size_map)       )
                  for i in range(0		,				UpperCamelCase__		,				UpperCamelCase__       ):
                         for j in range(0		,				UpperCamelCase__		,				UpperCamelCase__       ):
                                A_						=      pd_pool[
                                    i_pool
                                ]
                                A_						=      i_pool + 1
                  A_						=      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       )						->      List[Any]:





           '''simple docstring'''
           # model traning
           print("""----------------------Start Training-------------------------"""       )
           print((""" - - Shape: Train_Data  """, np.shape(UpperCamelCase__       ))       )
           print((""" - - Shape: Teach_Data  """, np.shape(UpperCamelCase__       ))       )
           A_						=      0
           A_						=      []
           A_						=      10000
           while rp < n_repeat and mse >= error_accuracy:
                  A_						=      0
                  print(f'''-------------Learning Time {rp}--------------'''       )
                  for p in range(len(UpperCamelCase__       )       ):
                         # print('------------Learning Image: %d--------------'%p)
                         A_						=      np.asmatrix(datas_train[p]       )
                         A_						=      np.asarray(datas_teach[p]       )
                         A_					,			A_						=      self.convolute(
                             UpperCamelCase__		,				self.conva		,				self.w_conva		,				self.thre_conva		,				conv_step=self.step_conva		,				)
                         A_						=      self.pooling(UpperCamelCase__		,				self.size_poolinga       )
                         A_						=      np.shape(UpperCamelCase__       )
                         A_						=      self._expand(UpperCamelCase__       )
                         A_						=      data_bp_input

                         A_						=      np.dot(UpperCamelCase__		,				self.vji.T       ) - self.thre_bpa
                         A_						=      self.sig(UpperCamelCase__       )
                         A_						=      np.dot(UpperCamelCase__		,				self.wkj.T       ) - self.thre_bpa
                         A_						=      self.sig(UpperCamelCase__       )

                         # --------------Model Leaning ------------------------
                         # calculate error and gradient---------------
                         A_						=      np.multiply(
                             (data_teach - bp_outa)		,				np.multiply(UpperCamelCase__		,				(1 - bp_outa)       )       )
                         A_						=      np.multiply(
                             np.dot(UpperCamelCase__		,				self.wkj       )		,				np.multiply(UpperCamelCase__		,				(1 - bp_outa)       )       )
                         A_						=      np.dot(UpperCamelCase__		,				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(
                             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_						=      self._expand_mat(pd_conva_all[k_conv]       )
                                A_						=      self.rate_weight * np.dot(UpperCamelCase__		,				UpperCamelCase__       )

                                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(UpperCamelCase__       )

           def draw_error():
                  A_						=      [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__       )						->      Tuple:





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

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







    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Any:





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

           return data_conveda, data_pooleda


if __name__ == "__main__":
      pass



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(a - b				) for a, b in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			None:
       if point:
              if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
                     for item in point:
                            if not isinstance(UpperCAmelCase__,       (int, float)				):
                                   A_						=      (
                                       """Expected a list of numbers as input, found """
                                       F'''{type(UpperCAmelCase__				).__name__}'''
                                   )
                                   raise TypeError(UpperCAmelCase__				)
              else:
                     A_						=      F'''Expected a list of numbers as input, found {type(UpperCAmelCase__				).__name__}'''
                     raise TypeError(UpperCAmelCase__				)
       else:
              raise ValueError("""Missing an input"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(x - y				) for x, y in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
