





import pickle

import numpy as np
from matplotlib import pyplot as plt



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=0.2 ,							__magic_name__=0.2   )					->       List[Any]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  {
							    '''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(__magic_name__ ,							'''wb'''   ) as f:
											pickle.dump(__magic_name__ ,							__magic_name__   )

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






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__   )					->       Optional[int]:


							'''simple docstring'''



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

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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Optional[int]:


							'''simple docstring'''



							return round(__magic_name__ ,							3   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



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

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__="average_pool"   )					->       Optional[int]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Dict           =  np.asarray(__magic_name__   )
							snake_case_						:   Union[str, Any]           =  np.shape(__magic_name__   )
							snake_case_						:   Dict           =  data_mat.reshape(1 ,							shapes[0] * shapes[1]   )
							return data_expanded






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Any:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=bool   )					->       Optional[Any]:


							'''simple docstring'''



							print('''----------------------Start Training-------------------------'''   )
							print((''' - - Shape: Train_Data  ''', np.shape(__magic_name__   ))   )
							print((''' - - Shape: Teach_Data  ''', np.shape(__magic_name__   ))   )
							snake_case_						:   Optional[Any]           =  0
							snake_case_						:   str           =  []
							snake_case_						:   Tuple           =  1_0000
							while rp < n_repeat and mse >= error_accuracy:
											snake_case_						:   int           =  0
											print(F'''-------------Learning Time {rp}--------------'''   )
											for p in range(len(__magic_name__   )   ):
															# print('------------Learning Image: %d--------------'%p)
															snake_case_						:   List[Any]           =  np.asmatrix(datas_train[p]   )
															snake_case_						:   Optional[Any]           =  np.asarray(datas_teach[p]   )
															snake_case_      ,							snake_case_						:   List[str]           =  self.convolute(
															    __magic_name__ ,							self.conva ,							self.w_conva ,							self.thre_conva ,							conv_step=self.step_conva ,							)
															snake_case_						:   Optional[int]           =  self.pooling(__magic_name__ ,							self.size_poolinga   )
															snake_case_						:   Optional[Any]           =  np.shape(__magic_name__   )
															snake_case_						:   Any           =  self._expand(__magic_name__   )
															snake_case_						:   int           =  data_bp_input

															snake_case_						:   str           =  np.dot(__magic_name__ ,							self.vji.T   ) - self.thre_bpa
															snake_case_						:   Optional[Any]           =  self.sig(__magic_name__   )
															snake_case_						:   Dict           =  np.dot(__magic_name__ ,							self.wkj.T   ) - self.thre_bpa
															snake_case_						:   Optional[Any]           =  self.sig(__magic_name__   )

															# --------------Model Leaning ------------------------
															# calculate error and gradient---------------
															snake_case_						:   Dict           =  np.multiply(
															    (data_teach - bp_outa) ,							np.multiply(__magic_name__ ,							(1 - bp_outa)   )   )
															snake_case_						:   Union[str, Any]           =  np.multiply(
															    np.dot(__magic_name__ ,							self.wkj   ) ,							np.multiply(__magic_name__ ,							(1 - bp_outa)   )   )
															snake_case_						:   Optional[int]           =  np.dot(__magic_name__ ,							self.vji   )

															snake_case_						:   str           =  pd_i_all / (self.size_poolinga * self.size_poolinga)
															snake_case_						:   str           =  pd_conva_pooled.T.getA().tolist()
															snake_case_						:   Any           =  self._calculate_gradient_from_pool(
															    __magic_name__ ,							__magic_name__ ,							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_						:   Any           =  self._expand_mat(pd_conva_all[k_conv]   )
																			snake_case_						:   Optional[int]           =  self.rate_weight * np.dot(__magic_name__ ,							__magic_name__   )

																			snake_case_						:   Optional[Any]           =  self.w_conva[k_conv] + delta_w.reshape(
																			    (self.conva[0], self.conva[0])   )

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

							def draw_error():
											snake_case_						:   Any           =  [error_accuracy for i in range(int(n_repeat * 1.2   )   )]
											plt.plot(__magic_name__ ,							'''+-'''   )
											plt.plot(__magic_name__ ,							'''r--'''   )
											plt.xlabel('''Learning Times'''   )
											plt.ylabel('''All_mse'''   )
											plt.grid(__magic_name__ ,							alpha=0.5   )
											plt.show()

							print('''------------------Training Complished---------------------'''   )
							print((''' - - Training epoch: ''', rp, F'''     - - Mse: {mse:.6f}''')   )
							if draw_e:
											draw_error()
							return mse






			def       lowerCamelCase  (self ,							__magic_name__   )					->       List[str]:


							'''simple docstring'''



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

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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   Dict           =  np.asmatrix(__magic_name__   )
							snake_case_      ,							snake_case_						:   List[str]           =  self.convolute(
							    __magic_name__ ,							self.conva ,							self.w_conva ,							self.thre_conva ,							conv_step=self.step_conva ,							)
							snake_case_						:   List[Any]           =  self.pooling(__magic_name__ ,							self.size_poolinga   )

							return data_conveda, data_pooleda


if __name__ == "__main__":
	pass







from math import isclose, sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						tuple[float, float, float]:






				"""simple docstring"""


				snake_case_						:   Dict           =  point_y / 4 / point_x
				snake_case_						:   List[str]           =  2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				snake_case_						:   Union[str, Any]           =  (1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				snake_case_						:   Tuple           =  (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				snake_case_						:   Union[str, Any]           =  outgoing_gradient**2 + 4
				snake_case_						:   Tuple           =  2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				snake_case_						:   Optional[Any]           =  (point_y - outgoing_gradient * point_x) ** 2 - 100

				snake_case_						:   Dict           =  (
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)
				snake_case_						:   Optional[int]           =  (
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				snake_case_						:   Any           =  x_minus if isclose(_UpperCamelCase			,    _UpperCamelCase			) else x_plus
				snake_case_						:   int           =  point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient






def 					lowerCamelCase_						(    _UpperCamelCase = 1.4			,    _UpperCamelCase = -9.6			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  0
				snake_case_						:   float           =  first_x_coord
				snake_case_						:   float           =  first_y_coord
				snake_case_						:   float           =  (10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  next_point(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								num_reflections += 1

				return num_reflections


if __name__ == "__main__":
	print(F'''{solution() = }''')
