





'''simple docstring'''


import pickle

import numpy as np
from matplotlib import pyplot as plt

class  lowerCAmelCase_		:




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




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     # save model dict with pickle
     _lowerCAmelCase						      =   {
         "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 				_snake_case (						cls      ,  _lowerCAmelCase		)     ->				Any:
     # read saved model
     with open(_lowerCAmelCase      ,  "rb"		) as f:
         _lowerCAmelCase						      =   pickle.load(_lowerCAmelCase		)  # noqa: S301

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




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     return 1 / (1 + np.exp(-1 * x		))




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Tuple:
     return round(_lowerCAmelCase      ,  3		)




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

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




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




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




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Any:
     # expanding matrix to one dimension list
     _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
     _lowerCAmelCase						      =   np.shape(_lowerCAmelCase		)
     _lowerCAmelCase						      =   data_mat.reshape(1      ,  shapes[0] * shapes[1]		)
     return data_expanded




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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=bool		)     ->				Tuple:
     # model traning
     print("----------------------Start Training-------------------------"		)
     print((" - - Shape: Train_Data  ", np.shape(_lowerCAmelCase		))		)
     print((" - - Shape: Teach_Data  ", np.shape(_lowerCAmelCase		))		)
     _lowerCAmelCase						      =   0
     _lowerCAmelCase						      =   []
     _lowerCAmelCase						      =   10000
     while rp < n_repeat and mse >= error_accuracy:
         _lowerCAmelCase						      =   0
         print(f'''-------------Learning Time {rp}--------------'''		)
         for p in range(len(_lowerCAmelCase		)		):
             # print('------------Learning Image: %d--------------'%p)
             _lowerCAmelCase						      =   np.asmatrix(datas_train[p]		)
             _lowerCAmelCase						      =   np.asarray(datas_teach[p]		)
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.convolute(
                 _lowerCAmelCase      ,  self.conva      ,  self.w_conva      ,  self.thre_conva      ,  conv_step=self.step_conva      ,  )
             _lowerCAmelCase						      =   self.pooling(_lowerCAmelCase      ,  self.size_poolinga		)
             _lowerCAmelCase						      =   np.shape(_lowerCAmelCase		)
             _lowerCAmelCase						      =   self._expand(_lowerCAmelCase		)
             _lowerCAmelCase						      =   data_bp_input

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

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

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

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

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

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

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





 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Tuple:
     # return the data of image after convoluting process so we can check it out
     _lowerCAmelCase						      =   np.asmatrix(_lowerCAmelCase		)
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.convolute(
         _lowerCAmelCase      ,  self.conva      ,  self.w_conva      ,  self.thre_conva      ,  conv_step=self.step_conva      ,  )
     _lowerCAmelCase						      =   self.pooling(_lowerCAmelCase      ,  self.size_poolinga		)

     return data_conveda, data_pooleda


if __name__ == "__main__":
       pass






'''simple docstring'''
from __future__ import annotations

import math


def      a (     __a					,   __a					,   __a					,   __a					,   __a  )    ->      int:



					'''simple docstring'''






					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''  )

					if len(__a  ) == 0:
										raise ValueError('''Scores cannot be empty'''  )

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1					,   node_index * 2					,   __a					,   __a					,   __a  )					,   minimax(depth + 1					,   node_index * 2 + 1					,   __a					,   __a					,   __a  )					,   )

					return min(
					    minimax(depth + 1					,   node_index * 2					,   __a					,   __a					,   __a  )					,   minimax(depth + 1					,   node_index * 2 + 1					,   __a					,   __a					,   __a  )					,   )


def      a (     )    ->      None:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					[90, 23, 6, 33, 21, 65, 123, 34423]
					UpperCamelCase__  :List[Any]				   =					math.log(len(__a  )					,   2  )
					print('''Optimal value : '''					,   end=''''''  )
					print(minimax(0					,   0					,   __a					,   __a					,   __a  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()