
'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt





class 					A							:





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






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         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(SCREAMING_SNAKE_CASE		,     '''wb'''      ) as f:
             pickle.dump(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

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





     @classmethod
     def 							__lowerCAmelCase				(				cls		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






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

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         return round(SCREAMING_SNAKE_CASE		,     3      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Optional[Any]   =		convs[0]
         A			:  Any   =		convs[1]
         A			:  Optional[Any]   =		np.shape(SCREAMING_SNAKE_CASE      )[0]
         # get the data slice of original image data, data_focus
         A			:  Union[str, Any]   =		[]
         for i_focus in range(0		,     size_data - size_conv + 1		,     SCREAMING_SNAKE_CASE      ):
             for j_focus in range(0		,     size_data - size_conv + 1		,     SCREAMING_SNAKE_CASE      ):
                 A			:  Any   =		data[
                     i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                 ]
                 data_focus.append(SCREAMING_SNAKE_CASE      )
        # 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(SCREAMING_SNAKE_CASE      ):
             A			:  Tuple   =		[]
             for i_focus in range(len(SCREAMING_SNAKE_CASE      )      ):
                 A			:  int   =		(
                     np.sum(np.multiply(data_focus[i_focus]		,     w_convs[i_map]      )      )
                     - thre_convs[i_map]
                 )
                 featuremap.append(self.sig(SCREAMING_SNAKE_CASE      )      )
             A			:  Optional[Any]   =		np.asmatrix(SCREAMING_SNAKE_CASE      ).reshape(
                 SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             data_featuremap.append(SCREAMING_SNAKE_CASE      )

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE="average_pool"      )					->							Dict:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         A			:  int   =		[]
         for i in range(len(SCREAMING_SNAKE_CASE      )      ):
             A			:  List[Any]   =		np.shape(data[i]      )
             A			:  List[Any]   =		data[i].reshape(1		,     shapes[0] * shapes[1]      )
             A			:  Optional[Any]   =		data_listed.getA().tolist()[0]
             data_expanded.extend(SCREAMING_SNAKE_CASE      )
         A			:  Tuple   =		np.asarray(SCREAMING_SNAKE_CASE      )
         return data_expanded





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         A			:  Optional[Any]   =		np.asarray(SCREAMING_SNAKE_CASE      )
         A			:  Any   =		np.shape(SCREAMING_SNAKE_CASE      )
         A			:  Optional[int]   =		data_mat.reshape(1		,     shapes[0] * shapes[1]      )
         return data_expanded





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=bool      )					->							List[Any]:






         """simple docstring"""






         print('''----------------------Start Training-------------------------'''      )
         print((''' - - Shape: Train_Data  ''', np.shape(SCREAMING_SNAKE_CASE      ))      )
         print((''' - - Shape: Teach_Data  ''', np.shape(SCREAMING_SNAKE_CASE      ))      )
         A			:  int   =		0
         A			:  Union[str, Any]   =		[]
         A			:  Dict   =		10000
         while rp < n_repeat and mse >= error_accuracy:
             A			:  Tuple   =		0
             print(F'-------------Learning Time {rp}--------------'      )
             for p in range(len(SCREAMING_SNAKE_CASE      )      ):
                 # print('------------Learning Image: %d--------------'%p)
                 A			:  str   =		np.asmatrix(datas_train[p]      )
                 A			:  Any   =		np.asarray(datas_teach[p]      )
                 A,    A			:  Union[str, Any]   =		self.convolute(
                     SCREAMING_SNAKE_CASE		,     self.conva		,     self.w_conva		,     self.thre_conva		,     conv_step=self.step_conva		,     )
                 A			:  int   =		self.pooling(SCREAMING_SNAKE_CASE		,     self.size_poolinga      )
                 A			:  int   =		np.shape(SCREAMING_SNAKE_CASE      )
                 A			:  str   =		self._expand(SCREAMING_SNAKE_CASE      )
                 A			:  Dict   =		data_bp_input

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

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

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

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

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

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

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  Optional[int]   =		[]
         print('''-------------------Start Testing-------------------------'''      )
         print((''' - - Shape: Test_Data  ''', np.shape(SCREAMING_SNAKE_CASE      ))      )
         for p in range(len(SCREAMING_SNAKE_CASE      )      ):
             A			:  Optional[Any]   =		np.asmatrix(datas_test[p]      )
             A,    A			:  Tuple   =		self.convolute(
                 SCREAMING_SNAKE_CASE		,     self.conva		,     self.w_conva		,     self.thre_conva		,     conv_step=self.step_conva		,     )
             A			:  List[str]   =		self.pooling(SCREAMING_SNAKE_CASE		,     self.size_poolinga      )
             A			:  str   =		self._expand(SCREAMING_SNAKE_CASE      )

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  List[Any]   =		np.asmatrix(SCREAMING_SNAKE_CASE      )
         A,    A			:  Union[str, Any]   =		self.convolute(
             SCREAMING_SNAKE_CASE		,     self.conva		,     self.w_conva		,     self.thre_conva		,     conv_step=self.step_conva		,     )
         A			:  Optional[Any]   =		self.pooling(SCREAMING_SNAKE_CASE		,     self.size_poolinga      )

         return data_conveda, data_pooleda


if __name__ == "__main__":
      pass






'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		a[left_index]
    A			:  List[str]   =		left_index + 1
    for j in range(left_index + 1						,							snake_case__		):
        if a[j] < pivot:
            A,    A			:  Optional[int]   =		a[i], a[j]
            i += 1
    A,    A			:  str   =		a[i - 1], a[left_index]
    return i - 1







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if left < right:
        A			:  Optional[Any]   =		random.randint(snake_case__						,							right - 1		)
        A,    A			:  List[str]   =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        A			:  Any   =		partition(snake_case__						,							snake_case__						,							snake_case__		)
        quick_sort_random(
            snake_case__						,							snake_case__						,							snake_case__		)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            snake_case__						,							pivot_index + 1						,							snake_case__		)  # recursive quicksort to the right of the pivot point







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Any   =		input('''Enter numbers separated by a comma:\n'''		).strip()
    A			:  List[str]   =		[int(snake_case__		) for item in user_input.split(''','''		)]

    quick_sort_random(snake_case__						,							0						,							len(snake_case__		)		)

    print(snake_case__		)


if __name__ == "__main__":
      main()




