





import pickle

import numpy as np
from matplotlib import pyplot as plt



class 					A__   :





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_=0.2       ,							A_=0.2   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   {
           "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(A_       ,							"wb"   ) as f:
        pickle.dump(A_       ,							A_   )

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





      @classmethod
      def        __UpperCamelCase( cls       ,							A_   ):
       '''simple docstring'''




       with open(A_       ,							"rb"   ) as f:
        UpperCamelCase	:					Optional[Any]       	=   pickle.load(A_   )  # noqa: S301

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return round(A_       ,							3   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_="average_pool"   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   np.asarray(A_   )
       UpperCamelCase	:					List[Any]       	=   np.shape(A_   )
       UpperCamelCase	:					Any       	=   data_mat.reshape(1       ,							shapes[0] * shapes[1]   )
       return data_expanded





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_=bool   ):
       '''simple docstring'''




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

         UpperCamelCase	:					str       	=   np.dot(A_       ,							self.vji.T   ) - self.thre_bpa
         UpperCamelCase	:					Optional[int]       	=   self.sig(A_   )
         UpperCamelCase	:					List[Any]       	=   np.dot(A_       ,							self.wkj.T   ) - self.thre_bpa
         UpperCamelCase	:					Dict       	=   self.sig(A_   )

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

         UpperCamelCase	:					Dict       	=   pd_i_all / (self.size_poolinga * self.size_poolinga)
         UpperCamelCase	:					List[Any]       	=   pd_conva_pooled.T.getA().tolist()
         UpperCamelCase	:					List[Any]       	=   self._calculate_gradient_from_pool(
             A_       ,							A_       ,							shape_featuremapa[0]       ,							shape_featuremapa[1]       ,							self.size_poolinga       ,							)
         # weight and threshold learning process---------
         # convolution layer
         for k_conv in range(self.conva[1]   ):
          UpperCamelCase	:					List[Any]       	=   self._expand_mat(pd_conva_all[k_conv]   )
          UpperCamelCase	:					List[Any]       	=   self.rate_weight * np.dot(A_       ,							A_   )

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

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

       def draw_error():
        UpperCamelCase	:					Tuple       	=   [error_accuracy for i in range(int(n_repeat * 1.2   )   )]
        plt.plot(A_       ,							"+-"   )
        plt.plot(A_       ,							"r--"   )
        plt.xlabel("Learning Times"   )
        plt.ylabel("All_mse"   )
        plt.grid(A_       ,							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       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass


"""simple docstring"""





from graphs.minimum_spanning_tree_kruskal import kruskal



def       lowerCamelCase__		(			)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  9
					_UpperCamelCase							 =  [
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_UpperCamelCase							 =  kruskal(__snake_case,     __snake_case	)

					_UpperCamelCase							 =  [
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(__snake_case	) == sorted(__snake_case	)






