

import pickle

import numpy as np
from matplotlib import pyplot as plt






class 						__magic_name__    :


 def __init__(						self		: List[str]			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Tuple=0.2			,						lowerCAmelCase__		: Union[str, Any]=0.2    )    ->      Tuple:
   UpperCAmelCase					      =  bp_numa
   UpperCAmelCase					      =  bp_numa
   UpperCAmelCase					      =  bp_numa
   UpperCAmelCase					      =  conva_get[:2]
   UpperCAmelCase					      =  conva_get[2]
   UpperCAmelCase					      =  size_pa
   UpperCAmelCase					      =  rate_w
   UpperCAmelCase					      =  rate_t
   UpperCAmelCase					      =  [
       np.mat(-1 * np.random.rand(self.conva[0]			,						self.conva[0]    ) + 0.5    )
       for i in range(self.conva[1]    )
   ]
   UpperCAmelCase					      =  np.mat(-1 * np.random.rand(self.num_bpa			,						self.num_bpa    ) + 0.5    )
   UpperCAmelCase					      =  np.mat(-1 * np.random.rand(self.num_bpa			,						self.num_bpa    ) + 0.5    )
   UpperCAmelCase					      =  -2 * np.random.rand(self.conva[1]    ) + 1
   UpperCAmelCase					      =  -2 * np.random.rand(self.num_bpa    ) + 1
   UpperCAmelCase					      =  -2 * np.random.rand(self.num_bpa    ) + 1


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: str    )    ->      Tuple:
   # save model dict with pickle
   UpperCAmelCase					      =  {
       "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		: int			,						lowerCAmelCase__		: Dict    )    ->      Optional[Any]:
   # read saved model
   with open(lowerCAmelCase__			,						"rb"    ) as f:
     UpperCAmelCase					      =  pickle.load(lowerCAmelCase__    )  # noqa: S301

   UpperCAmelCase					      =  model_dic.get("conv1"    )
   conv_get.append(model_dic.get("step_conv1"    )    )
   UpperCAmelCase					      =  model_dic.get("size_pooling1"    )
   UpperCAmelCase					      =  model_dic.get("num_bp1"    )
   UpperCAmelCase					      =  model_dic.get("num_bp2"    )
   UpperCAmelCase					      =  model_dic.get("num_bp3"    )
   UpperCAmelCase					      =  model_dic.get("rate_weight"    )
   UpperCAmelCase					      =  model_dic.get("rate_thre"    )
   # create model instance
   UpperCAmelCase					      =  CNN(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
   # modify model parameter
   UpperCAmelCase					      =  model_dic.get("w_conv1"    )
   UpperCAmelCase					      =  model_dic.get("wkj"    )
   UpperCAmelCase					      =  model_dic.get("vji"    )
   UpperCAmelCase					      =  model_dic.get("thre_conv1"    )
   UpperCAmelCase					      =  model_dic.get("thre_bp2"    )
   UpperCAmelCase					      =  model_dic.get("thre_bp3"    )
   return conv_ins


 def _UpperCamelCase	(						self		: Optional[int]			,						lowerCAmelCase__		: str    )    ->      Tuple:
   return 1 / (1 + np.exp(-1 * x    ))


 def _UpperCamelCase	(						self		: Optional[int]			,						lowerCAmelCase__		: int    )    ->      Optional[int]:
   return round(lowerCAmelCase__			,						3    )


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

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


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: Optional[int]="average_pool"    )    ->      List[Any]:
   # pooling process
   UpperCAmelCase					      =  len(featuremaps[0]    )
   UpperCAmelCase					      =  int(size_map / size_pooling    )
   UpperCAmelCase					      =  []
   for i_map in range(len(lowerCAmelCase__    )    ):
     UpperCAmelCase					      =  featuremaps[i_map]
     UpperCAmelCase					      =  []
     for i_focus in range(0			,						lowerCAmelCase__			,						lowerCAmelCase__    ):
       for j_focus in range(0			,						lowerCAmelCase__			,						lowerCAmelCase__    ):
         UpperCAmelCase					      =  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__    )    )
     UpperCAmelCase					      =  np.asmatrix(lowerCAmelCase__    ).reshape(lowerCAmelCase__			,						lowerCAmelCase__    )
     featuremap_pooled.append(lowerCAmelCase__    )
   return featuremap_pooled


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: List[str]    )    ->      Union[str, Any]:
   # expanding three dimension data to one dimension list
   UpperCAmelCase					      =  []
   for i in range(len(lowerCAmelCase__    )    ):
     UpperCAmelCase					      =  np.shape(data[i]    )
     UpperCAmelCase					      =  data[i].reshape(1			,						shapes[0] * shapes[1]    )
     UpperCAmelCase					      =  data_listed.getA().tolist()[0]
     data_expanded.extend(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.asarray(lowerCAmelCase__    )
   return data_expanded


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: Dict    )    ->      str:
   # expanding matrix to one dimension list
   UpperCAmelCase					      =  np.asarray(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.shape(lowerCAmelCase__    )
   UpperCAmelCase					      =  data_mat.reshape(1			,						shapes[0] * shapes[1]    )
   return data_expanded


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


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

       UpperCAmelCase					      =  np.dot(lowerCAmelCase__			,						self.vji.T    ) - self.thre_bpa
       UpperCAmelCase					      =  self.sig(lowerCAmelCase__    )
       UpperCAmelCase					      =  np.dot(lowerCAmelCase__			,						self.wkj.T    ) - self.thre_bpa
       UpperCAmelCase					      =  self.sig(lowerCAmelCase__    )

       # --------------Model Leaning ------------------------
       # calculate error and gradient---------------
       UpperCAmelCase					      =  np.multiply(
           (data_teach - bp_outa)			,						np.multiply(lowerCAmelCase__			,						(1 - bp_outa)    )    )
       UpperCAmelCase					      =  np.multiply(
           np.dot(lowerCAmelCase__			,						self.wkj    )			,						np.multiply(lowerCAmelCase__			,						(1 - bp_outa)    )    )
       UpperCAmelCase					      =  np.dot(lowerCAmelCase__			,						self.vji    )

       UpperCAmelCase					      =  pd_i_all / (self.size_poolinga * self.size_poolinga)
       UpperCAmelCase					      =  pd_conva_pooled.T.getA().tolist()
       UpperCAmelCase					      =  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]    ):
         UpperCAmelCase					      =  self._expand_mat(pd_conva_all[k_conv]    )
         UpperCAmelCase					      =  self.rate_weight * np.dot(lowerCAmelCase__			,						lowerCAmelCase__    )

         UpperCAmelCase					      =  self.w_conva[k_conv] + delta_w.reshape(
             (self.conva[0], self.conva[0])    )

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

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

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


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

   return data_conveda, data_pooleda


if __name__ == "__main__":
      pass






lowerCAmelCase__       = {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowerCAmelCase__       = {value: key for key, value in encode_dict.items()}
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  ""
  for letter in word.lower():
    if letter.isalpha() or letter == " ":
      encoded += encode_dict[letter]
    else:
      raise Exception("encode() accepts only letters of the alphabet and spaces"			)
  return encoded
def      _lowerCAmelCase(	__A			):
  if set(__A			) - {"A", "B", " "} != set():
    raise Exception("decode() accepts only 'A', 'B' and spaces"			)
  UpperCAmelCase					      =  ""
  for word in coded.split():
    while len(__A			) != 0:
      decoded += decode_dict[word[:5]]
      UpperCAmelCase					      =  word[5:]
    decoded += " "
  return decoded.strip()


if __name__ == "__main__":
      from doctest import testmod

      testmod()



