





from __future__ import annotations

from random import choice






def    _A	(   lowerCamelCase ):
   return choice(lowerCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    List[str]   		=	random_pivot(lowerCamelCase )

   # partition based on pivot
   # linear time
   a__						:    List[str]   		=	[e for e in lst if e < pivot]
   a__						:    int   		=	[e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(lowerCamelCase ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(lowerCamelCase ) < k - 1:
      return kth_number(lowerCamelCase				,			k - len(lowerCamelCase ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(lowerCamelCase				,			lowerCamelCase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()








import pickle

import numpy as np
from matplotlib import pyplot as plt







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=0.2       ,      snake_case=0.2   )					->	Any:
      """simple docstring"""





      a__						:    List[Any]   		=	bp_numa
      a__						:    str   		=	bp_numa
      a__						:    Any   		=	bp_numa
      a__						:    Tuple   		=	conva_get[:2]
      a__						:    Optional[int]   		=	conva_get[2]
      a__						:    Any   		=	size_pa
      a__						:    Dict   		=	rate_w
      a__						:    Tuple   		=	rate_t
      a__						:    int   		=	[
          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__						:    Union[str, Any]   		=	-2 * np.random.rand(self.conva[1]   ) + 1
      a__						:    Optional[int]   		=	-2 * np.random.rand(self.num_bpa   ) + 1
      a__						:    int   		=	-2 * np.random.rand(self.num_bpa   ) + 1





   def 						_snake_case			(			self       ,      snake_case   )					->	int:
      """simple docstring"""





      a__						:    str   		=	{
          "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(snake_case       ,      "wb"   ) as f:
         pickle.dump(snake_case       ,      snake_case   )

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





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      with open(snake_case       ,      "rb"   ) as f:
         a__						:    List[str]   		=	pickle.load(snake_case   )  # noqa: S301

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      return round(snake_case       ,      3   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





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

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case="average_pool"   )					->	Tuple:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      a__						:    Dict   		=	np.asarray(snake_case   )
      a__						:    List[Any]   		=	np.shape(snake_case   )
      a__						:    Optional[Any]   		=	data_mat.reshape(1       ,      shapes[0] * shapes[1]   )
      return data_expanded





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Any:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=bool   )					->	Optional[int]:
      """simple docstring"""





      print("----------------------Start Training-------------------------"   )
      print((" - - Shape: Train_Data  ", np.shape(snake_case   ))   )
      print((" - - Shape: Teach_Data  ", np.shape(snake_case   ))   )
      a__						:    List[Any]   		=	0
      a__						:    Any   		=	[]
      a__						:    Dict   		=	10_000
      while rp < n_repeat and mse >= error_accuracy:
         a__						:    Optional[Any]   		=	0
         print(F"""-------------Learning Time {rp}--------------"""   )
         for p in range(len(snake_case   )   ):
            # print('------------Learning Image: %d--------------'%p)
            a__						:    Optional[int]   		=	np.asmatrix(datas_train[p]   )
            a__						:    Optional[Any]   		=	np.asarray(datas_teach[p]   )
            a__		,  a__						:    Tuple   		=	self.convolute(
                snake_case       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
            a__						:    List[Any]   		=	self.pooling(snake_case       ,      self.size_poolinga   )
            a__						:    str   		=	np.shape(snake_case   )
            a__						:    List[Any]   		=	self._expand(snake_case   )
            a__						:    Union[str, Any]   		=	data_bp_input

            a__						:    Optional[Any]   		=	np.dot(snake_case       ,      self.vji.T   ) - self.thre_bpa
            a__						:    Optional[int]   		=	self.sig(snake_case   )
            a__						:    Union[str, Any]   		=	np.dot(snake_case       ,      self.wkj.T   ) - self.thre_bpa
            a__						:    Dict   		=	self.sig(snake_case   )

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

            a__						:    List[str]   		=	pd_i_all / (self.size_poolinga * self.size_poolinga)
            a__						:    Tuple   		=	pd_conva_pooled.T.getA().tolist()
            a__						:    List[str]   		=	self._calculate_gradient_from_pool(
                snake_case       ,      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__						:    Dict   		=	self._expand_mat(pd_conva_all[k_conv]   )
               a__						:    Optional[int]   		=	self.rate_weight * np.dot(snake_case       ,      snake_case   )

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

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

      def draw_error():
         a__						:    List[Any]   		=	[error_accuracy for i in range(int(n_repeat * 1.2   )   )]
         plt.plot(snake_case       ,      "+-"   )
         plt.plot(snake_case       ,      "r--"   )
         plt.xlabel("Learning Times"   )
         plt.ylabel("All_mse"   )
         plt.grid(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 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





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

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Dict   		=	np.asmatrix(snake_case   )
      a__		,  a__						:    Optional[int]   		=	self.convolute(
          snake_case       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
      a__						:    Any   		=	self.pooling(snake_case       ,      self.size_poolinga   )

      return data_conveda, data_pooleda


if __name__ == "__main__":
  pass

