
import pickle

import numpy as np
from matplotlib import pyplot as plt







class 					lowerCamelCase		:





     def __init__(self				:   str    ,   _A				:   Optional[Any]    ,   _A				:   Tuple    ,   _A				:   Tuple    ,   _A				:   List[str]    ,   _A				:   Tuple    ,   _A				:   Union[str, Any]=0.2    ,   _A				:   Tuple=0.2       )						->						str:
         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 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Optional[int]:
         # 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(lowerCamelCase_    ,   "wb"       ) as f:
             pickle.dump(lowerCamelCase_    ,   lowerCamelCase_       )

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





     @classmethod
     def 		UpperCAmelCase(cls				:   Dict    ,   _A				:   Any       )						->						Dict:
         # read saved model
         with open(lowerCamelCase_    ,   "rb"       ) as f:
             snake_case								= pickle.load(lowerCamelCase_       )  # 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(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_       )
         # 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 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   Dict       )						->						Any:
         return 1 / (1 + np.exp(-1 * x       ))





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[Any]       )						->						Optional[Any]:
         return round(lowerCamelCase_    ,   3       )





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

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





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





     def 		UpperCAmelCase(self				:   Optional[int]    ,   _A				:   Any       )						->						List[str]:
         # expanding three dimension data to one dimension list
         snake_case								= []
         for i in range(len(lowerCamelCase_       )       ):
             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(lowerCamelCase_       )
         snake_case								= np.asarray(lowerCamelCase_       )
         return data_expanded





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Tuple       )						->						Dict:
         # expanding matrix to one dimension list
         snake_case								= np.asarray(lowerCamelCase_       )
         snake_case								= np.shape(lowerCamelCase_       )
         snake_case								= data_mat.reshape(1    ,   shapes[0] * shapes[1]       )
         return data_expanded





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





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]    ,   _A				:   Union[str, Any]    ,   _A				:   Optional[Any]    ,   _A				:   Any    ,   _A				:   Any    ,   _A				:   Optional[Any]=bool       )						->						List[str]:
         # model traning
         print("----------------------Start Training-------------------------"       )
         print((" - - Shape: Train_Data  ", np.shape(lowerCamelCase_       ))       )
         print((" - - Shape: Teach_Data  ", np.shape(lowerCamelCase_       ))       )
         snake_case								= 0
         snake_case								= []
         snake_case								= 1_0_0_0_0
         while rp < n_repeat and mse >= error_accuracy:
             snake_case								= 0
             print(f'-------------Learning Time {rp}--------------'       )
             for p in range(len(lowerCamelCase_       )       ):
                 # print('------------Learning Image: %d--------------'%p)
                 snake_case								= np.asmatrix(datas_train[p]       )
                 snake_case								= np.asarray(datas_teach[p]       )
                 snake_case								= self.convolute(
                     lowerCamelCase_    ,   self.conva    ,   self.w_conva    ,   self.thre_conva    ,   conv_step=self.step_conva    ,   )
                 snake_case								= self.pooling(lowerCamelCase_    ,   self.size_poolinga       )
                 snake_case								= np.shape(lowerCamelCase_       )
                 snake_case								= self._expand(lowerCamelCase_       )
                 snake_case								= data_bp_input

                 snake_case								= np.dot(lowerCamelCase_    ,   self.vji.T       ) - self.thre_bpa
                 snake_case								= self.sig(lowerCamelCase_       )
                 snake_case								= np.dot(lowerCamelCase_    ,   self.wkj.T       ) - self.thre_bpa
                 snake_case								= self.sig(lowerCamelCase_       )

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

                     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(lowerCamelCase_       )

         def draw_error():
             snake_case								= [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				:   List[str]    ,   _A				:   Dict       )						->						List[str]:
         # model predict
         snake_case								= []
         print("-------------------Start Testing-------------------------"       )
         print((" - - Shape: Test_Data  ", np.shape(lowerCamelCase_       ))       )
         for p in range(len(lowerCamelCase_       )       ):
             snake_case								= np.asmatrix(datas_test[p]       )
             snake_case								= self.convolute(
                 lowerCamelCase_    ,   self.conva    ,   self.w_conva    ,   self.thre_conva    ,   conv_step=self.step_conva    ,   )
             snake_case								= self.pooling(lowerCamelCase_    ,   self.size_poolinga       )
             snake_case								= self._expand(lowerCamelCase_       )

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





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Tuple       )						->						Optional[int]:
         # return the data of image after convoluting process so we can check it out
         snake_case								= np.asmatrix(lowerCamelCase_       )
         snake_case								= self.convolute(
             lowerCamelCase_    ,   self.conva    ,   self.w_conva    ,   self.thre_conva    ,   conv_step=self.step_conva    ,   )
         snake_case								= self.pooling(lowerCamelCase_    ,   self.size_poolinga       )

         return data_conveda, data_pooleda


if __name__ == "__main__":
   pass



from __future__ import annotations



def 						lowercase_      (       A__			,		A__			,		A__		)							->							int | float:
    """simple docstring"""

    if len(A__		) == 0:
        raise ValueError("find_max() arg is an empty sequence"		)
    if (
        left >= len(A__		)
        or left < -len(A__		)
        or right >= len(A__		)
        or right < -len(A__		)
    ):
        raise IndexError("list index out of range"		)
    if left == right:
        return nums[left]
    snake_case								= (left + right) >> 1  # the middle
    snake_case								= find_max(A__			,		A__			,		A__		)  # find max in range[left, mid]
    snake_case								= find_max(A__			,		mid + 1			,		A__		)  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

