
import pickle

import numpy as np
from matplotlib import pyplot as plt
class 	A__   :





							def __init__( self  :   Optional[int]     , _UpperCAmelCase  :   int     , _UpperCAmelCase  :   List[str]     , _UpperCAmelCase  :   List[str]     , _UpperCAmelCase  :   List[str]     , _UpperCAmelCase  :   Tuple     , _UpperCAmelCase  :   Tuple=0.2     , _UpperCAmelCase  :   Optional[Any]=0.2				)						->		Optional[int]:



											"""simple docstring"""


											__lowercase								=					bp_numa
											__lowercase								=					bp_numa
											__lowercase								=					bp_numa
											__lowercase								=					conva_get[:2]
											__lowercase								=					conva_get[2]
											__lowercase								=					size_pa
											__lowercase								=					rate_w
											__lowercase								=					rate_t
											__lowercase								=					[
											    np.mat(-1 * np.random.rand(self.conva[0]     , self.conva[0]				) + 0.5				)
											    for i in range(self.conva[1]				)
											]
											__lowercase								=					np.mat(-1 * np.random.rand(self.num_bpa     , self.num_bpa				) + 0.5				)
											__lowercase								=					np.mat(-1 * np.random.rand(self.num_bpa     , self.num_bpa				) + 0.5				)
											__lowercase								=					-2 * np.random.rand(self.conva[1]				) + 1
											__lowercase								=					-2 * np.random.rand(self.num_bpa				) + 1
											__lowercase								=					-2 * np.random.rand(self.num_bpa				) + 1





							def 			a__ ( self  :   List[str]     , _UpperCAmelCase  :   str				)						->		List[Any]:



											"""simple docstring"""


											__lowercase								=					{
											    '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 			a__ ( cls  :   Optional[Any]     , _UpperCAmelCase  :   Optional[Any]				)						->		Optional[Any]:



											"""simple docstring"""


											with open(snake_case_     , 'rb'				) as f:
															__lowercase								=					pickle.load(snake_case_				)  # noqa: S301

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





							def 			a__ ( self  :   Optional[Any]     , _UpperCAmelCase  :   Any				)						->		Tuple:



											"""simple docstring"""


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





							def 			a__ ( self  :   Optional[int]     , _UpperCAmelCase  :   Tuple				)						->		List[Any]:



											"""simple docstring"""


											return round(snake_case_     , 3				)





							def 			a__ ( self  :   Tuple     , _UpperCAmelCase  :   Union[str, Any]     , _UpperCAmelCase  :   int     , _UpperCAmelCase  :   Union[str, Any]     , _UpperCAmelCase  :   Optional[Any]     , _UpperCAmelCase  :   int				)						->		List[str]:



											"""simple docstring"""


											__lowercase								=					convs[0]
											__lowercase								=					convs[1]
											__lowercase								=					np.shape(snake_case_				)[0]
											# get the data slice of original image data, data_focus
											__lowercase								=					[]
											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_				):
																			__lowercase								=					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
											__lowercase								=					[]
											__lowercase								=					int((size_data - size_conv) / conv_step + 1				)
											for i_map in range(snake_case_				):
															__lowercase								=					[]
															for i_focus in range(len(snake_case_				)				):
																			__lowercase								=					(
																			    np.sum(np.multiply(data_focus[i_focus]     , w_convs[i_map]				)				)
																			    - thre_convs[i_map]
																			)
																			featuremap.append(self.sig(snake_case_				)				)
															__lowercase								=					np.asmatrix(snake_case_				).reshape(
															    snake_case_     , snake_case_				)
															data_featuremap.append(snake_case_				)

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





							def 			a__ ( self  :   Dict     , _UpperCAmelCase  :   str     , _UpperCAmelCase  :   Optional[Any]     , _UpperCAmelCase  :   Optional[Any]="average_pool"				)						->		Optional[int]:



											"""simple docstring"""


											__lowercase								=					len(featuremaps[0]				)
											__lowercase								=					int(size_map / size_pooling				)
											__lowercase								=					[]
											for i_map in range(len(snake_case_				)				):
															__lowercase								=					featuremaps[i_map]
															__lowercase								=					[]
															for i_focus in range(0     , snake_case_     , snake_case_				):
																			for j_focus in range(0     , snake_case_     , snake_case_				):
																							__lowercase								=					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_				)				)
															__lowercase								=					np.asmatrix(snake_case_				).reshape(snake_case_     , snake_case_				)
															featuremap_pooled.append(snake_case_				)
											return featuremap_pooled





							def 			a__ ( self  :   Dict     , _UpperCAmelCase  :   Union[str, Any]				)						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								=					[]
											for i in range(len(snake_case_				)				):
															__lowercase								=					np.shape(data[i]				)
															__lowercase								=					data[i].reshape(1     , shapes[0] * shapes[1]				)
															__lowercase								=					data_listed.getA().tolist()[0]
															data_expanded.extend(snake_case_				)
											__lowercase								=					np.asarray(snake_case_				)
											return data_expanded





							def 			a__ ( self  :   Union[str, Any]     , _UpperCAmelCase  :   Optional[Any]				)						->		Tuple:



											"""simple docstring"""


											__lowercase								=					np.asarray(snake_case_				)
											__lowercase								=					np.shape(snake_case_				)
											__lowercase								=					data_mat.reshape(1     , shapes[0] * shapes[1]				)
											return data_expanded





							def 			a__ ( self  :   List[str]     , _UpperCAmelCase  :   Optional[Any]     , _UpperCAmelCase  :   Tuple     , _UpperCAmelCase  :   Optional[Any]     , _UpperCAmelCase  :   List[str]     , _UpperCAmelCase  :   int				)						->		str:



											"""simple docstring"""


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





							def 			a__ ( self  :   Any     , _UpperCAmelCase  :   Any     , _UpperCAmelCase  :   int     , _UpperCAmelCase  :   Any     , _UpperCAmelCase  :   Any     , _UpperCAmelCase  :   Optional[int]     , _UpperCAmelCase  :   Optional[int]=bool				)						->		Tuple:



											"""simple docstring"""


											print('----------------------Start Training-------------------------'				)
											print((' - - Shape: Train_Data  ', np.shape(snake_case_				))				)
											print((' - - Shape: Teach_Data  ', np.shape(snake_case_				))				)
											__lowercase								=					0
											__lowercase								=					[]
											__lowercase								=					1_00_00
											while rp < n_repeat and mse >= error_accuracy:
															__lowercase								=					0
															print(f"""-------------Learning Time {rp}--------------"""				)
															for p in range(len(snake_case_				)				):
																			# print('------------Learning Image: %d--------------'%p)
																			__lowercase								=					np.asmatrix(datas_train[p]				)
																			__lowercase								=					np.asarray(datas_teach[p]				)
																			__lowercase								=					self.convolute(
																			    snake_case_     , self.conva     , self.w_conva     , self.thre_conva     , conv_step=self.step_conva     , )
																			__lowercase								=					self.pooling(snake_case_     , self.size_poolinga				)
																			__lowercase								=					np.shape(snake_case_				)
																			__lowercase								=					self._expand(snake_case_				)
																			__lowercase								=					data_bp_input

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

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

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

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

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

											def draw_error():
															__lowercase								=					[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 			a__ ( self  :   Tuple     , _UpperCAmelCase  :   Union[str, Any]				)						->		str:



											"""simple docstring"""


											__lowercase								=					[]
											print('-------------------Start Testing-------------------------'				)
											print((' - - Shape: Test_Data  ', np.shape(snake_case_				))				)
											for p in range(len(snake_case_				)				):
															__lowercase								=					np.asmatrix(datas_test[p]				)
															__lowercase								=					self.convolute(
															    snake_case_     , self.conva     , self.w_conva     , self.thre_conva     , conv_step=self.step_conva     , )
															__lowercase								=					self.pooling(snake_case_     , self.size_poolinga				)
															__lowercase								=					self._expand(snake_case_				)

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





							def 			a__ ( self  :   List[str]     , _UpperCAmelCase  :   List[str]				)						->		List[str]:



											"""simple docstring"""


											__lowercase								=					np.asmatrix(snake_case_				)
											__lowercase								=					self.convolute(
											    snake_case_     , self.conva     , self.w_conva     , self.thre_conva     , conv_step=self.step_conva     , )
											__lowercase								=					self.pooling(snake_case_     , self.size_poolinga				)

											return data_conveda, data_pooleda


if __name__ == "__main__":
							pass












"""simple docstring"""




def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'float\' object cannot be interpreted as an integer'					)
  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'str\' object cannot be interpreted as an integer'					)

  if num == 0:
    return "0b0"

  A_  :       str										=  False

  if num < 0:
    A_  :       Dict										=  True
    A_  :       Union[str, Any]										=  -num

  A_  :       list[int]										=  []
  while num > 0:
    binary.insert(0 ,					num % 2					)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(_UpperCAmelCase					) for e in binary					)

  return "0b" + "".join(str(_UpperCAmelCase					) for e in binary					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()