


'''simple docstring'''



import pickle

import numpy as np
from matplotlib import pyplot as plt






class 	UpperCamelCase__:





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

										"""simple docstring"""




										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 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										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(a__				,   '''wb'''				) as f:
																pickle.dump(a__				,   a__				)

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





				@classmethod
				def 					a__(		cls      :			Any				,   lowerCAmelCase      :			Dict				)->					List[str]:

										"""simple docstring"""




										with open(a__				,   '''rb'''				) as f:
																UpperCAmelCase					    =     pickle.load(a__				)  # 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(a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				)
										# 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 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




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





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

										"""simple docstring"""




										return round(a__				,   3				)





				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




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

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





				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Dict="average_pool"				)->					Tuple:

										"""simple docstring"""




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





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




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





				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Tuple				)->					Any:

										"""simple docstring"""




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





				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Dict				)->					Optional[Any]:

										"""simple docstring"""




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





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

										"""simple docstring"""




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

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

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

																												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(a__				)

										def draw_error():
																UpperCAmelCase					    =     [error_accuracy for i in range(int(n_repeat * 1.2				)				)]
																plt.plot(a__				,   '''+-'''				)
																plt.plot(a__				,   '''r--'''				)
																plt.xlabel('''Learning Times'''				)
																plt.ylabel('''All_mse'''				)
																plt.grid(a__				,   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      :			int				,   lowerCAmelCase      :			Optional[Any]				)->					List[Any]:

										"""simple docstring"""




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

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





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

										"""simple docstring"""




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

										return data_conveda, data_pooleda


if __name__ == "__main__":
	pass









'''simple docstring'''



from __future__ import annotations

from typing import Any






class 	UpperCamelCase__:




				def __init__(		self      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			float = 0				)->					None:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     row, column
										UpperCAmelCase					    =     [[default_value for c in range(lowerCAmelCase				)] for r in range(lowerCAmelCase				)]




				def __str__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

										# Make string identifier
										UpperCAmelCase					    =     0
										for row_vector in self.array:
																for obj in row_vector:
																						UpperCAmelCase					    =     max(lowerCAmelCase				,   len(str(lowerCAmelCase				)				)				)
										UpperCAmelCase					    =     F"""%{max_element_length}s"""

										# Make string and return
										def single_line(lowerCAmelCase      :			list[float]				) -> str:
																nonlocal string_format_identifier
																UpperCAmelCase					    =     '''['''
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector				)
																line += "]"
																return line

										s += "\n".join(single_line(lowerCAmelCase				) for row_vector in self.array				)
										return s




				def __repr__(		self      :			Tuple				)->					str:

										"""simple docstring"""




										return str(self				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			tuple[int, int]				)->					bool:

										"""simple docstring"""




										if not (isinstance(lowerCAmelCase				,   (list, tuple)				) and len(lowerCAmelCase				) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True




				def __getitem__(		self      :			int				,   lowerCAmelCase      :			tuple[int, int]				)->					Any:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										return self.array[loc[0]][loc[1]]




				def __setitem__(		self      :			List[str]				,   lowerCAmelCase      :			tuple[int, int]				,   lowerCAmelCase      :			float				)->					None:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										UpperCAmelCase					    =     value




				def __add__(		self      :			int				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == another.row and self.column == another.column

										# Add
										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c] + another[r, c]
										return result




				def __neg__(		self      :			Dict				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     -self[r, c]
										return result




				def __sub__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										return self + (-another)




				def __mul__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			int | float | Matrix				)->					Matrix:

										"""simple docstring"""




										if isinstance(lowerCAmelCase				,   (int, float)				):  # Scalar multiplication
																UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
																for r in range(self.row				):
																						for c in range(self.column				):
																												UpperCAmelCase					    =     self[r, c] * another
																return result
										elif isinstance(lowerCAmelCase				,   lowerCAmelCase				):  # Matrix multiplication
																assert self.column == another.row
																UpperCAmelCase					    =     Matrix(self.row				,   another.column				)
																for r in range(self.row				):
																						for c in range(another.column				):
																												for i in range(self.column				):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																UpperCAmelCase					    =     F"""Unsupported type given for another ({type(lowerCAmelCase				)})"""
																raise TypeError(lowerCAmelCase				)




				def 					a__(		self      :			Optional[Any]				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.column				,   self.row				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c]
										return result





				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Matrix				,   lowerCAmelCase      :			Matrix				)->					Any:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				) and isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										UpperCAmelCase					    =     v.transpose()
										UpperCAmelCase					    =     (v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							UpperCAmelCase					    =     Matrix(3				,       3				,       0					)
							for i in range(3					):
													UpperCAmelCase					    =     1
							print(f"""a^(-1) is {ainv}"""					)
							# u, v
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     1, 2, -3
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     4, -2, 5
							print(f"""u is {u}"""					)
							print(f"""v is {v}"""					)
							print(f"""uv^T is {u * v.transpose()}"""					)
							# Sherman Morrison
							print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(A				,       A					)}"""					)


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							import doctest

							doctest.testmod()




	testa()





