'''simple docstring'''



import pickle

import numpy as np
from matplotlib import pyplot as plt


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=0.2						,   lowerCamelCase=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 	UpperCamelCase(					self						,   lowerCamelCase	):
       # 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 	UpperCamelCase(					cls						,   lowerCamelCase	):
       # 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 	UpperCamelCase(					self						,   lowerCamelCase	):
       return 1 / (1 + np.exp(-1 * x	))

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return round(__lowercase						,   3	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       # 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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase="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 	UpperCamelCase(					self						,   lowerCamelCase	):
       # 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 	UpperCamelCase(					self						,   lowerCamelCase	):
       # 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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=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     		=				10_000
       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 	UpperCamelCase(					self						,   lowerCamelCase	):
       # 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 	UpperCamelCase(					self						,   lowerCamelCase	):
       # 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









'''simple docstring'''



def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[0 for i in range(len(SCREAMING_SNAKE_CASE__       )       )]

  # initialize interval's left pointer and right pointer
  _snake_case		,		_snake_case     		=				0, 0

  for i in range(1					,	len(SCREAMING_SNAKE_CASE__       )       ):
    # case when current index is inside the interval
    if i <= right_pointer:
      _snake_case     		=				min(right_pointer - i + 1					,	z_result[i - left_pointer]       )
      _snake_case     		=				min_edge

    while go_next(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      _snake_case		,		_snake_case     		=				i, i + z_result[i] - 1

  return z_result




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return i + z_result[i] < len(SCREAMING_SNAKE_CASE__       ) and s[z_result[i]] == s[i + z_result[i]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  _snake_case     		=				z_function(pattern + input_str       )

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(SCREAMING_SNAKE_CASE__       ):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


