



import pickle

import numpy as np
from matplotlib import pyplot as plt


class 							__a							:





		'''simple docstring'''



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


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# save model dict with pickle
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {
						    '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(UpperCamelCase__ ,    'wb'       ) as f:
										pickle.dump(UpperCamelCase__ ,    UpperCamelCase__       )

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


		@classmethod
		def   __snake_case			(						cls ,    UpperCamelCase__       ):
						# read saved model
						with open(UpperCamelCase__ ,    'rb'       ) as f:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   pickle.load(UpperCamelCase__       )  # noqa: S301

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


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return 1 / (1 + np.exp(-1 * x       ))


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return round(UpperCamelCase__ ,    3       )


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

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


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__="average_pool"       ):
						# pooling process
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   len(featuremaps[0]       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   int(size_map / size_pooling       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   []
						for i_map in range(len(UpperCamelCase__       )       ):
										SCREAMING_SNAKE_CASE_   :	int													=   featuremaps[i_map]
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   []
										for i_focus in range(0 ,    UpperCamelCase__ ,    UpperCamelCase__       ):
														for j_focus in range(0 ,    UpperCamelCase__ ,    UpperCamelCase__       ):
																		SCREAMING_SNAKE_CASE_   :	Dict													=   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(UpperCamelCase__       )       )
																		elif pooling_type == "max_pooling":
																						# max pooling
																						map_pooled.append(np.max(UpperCamelCase__       )       )
										SCREAMING_SNAKE_CASE_   :	Tuple													=   np.asmatrix(UpperCamelCase__       ).reshape(UpperCamelCase__ ,    UpperCamelCase__       )
										featuremap_pooled.append(UpperCamelCase__       )
						return featuremap_pooled


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# expanding three dimension data to one dimension list
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []
						for i in range(len(UpperCamelCase__       )       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   np.shape(data[i]       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   data[i].reshape(1 ,    shapes[0] * shapes[1]       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   data_listed.getA().tolist()[0]
										data_expanded.extend(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   np.asarray(UpperCamelCase__       )
						return data_expanded


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# expanding matrix to one dimension list
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   np.asarray(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   np.shape(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   data_mat.reshape(1 ,    shapes[0] * shapes[1]       )
						return data_expanded


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   []
						SCREAMING_SNAKE_CASE_   :	Tuple													=   0
						for i_map in range(UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	str													=   np.ones((size_map, size_map)       )
										for i in range(0 ,    UpperCamelCase__ ,    UpperCamelCase__       ):
														for j in range(0 ,    UpperCamelCase__ ,    UpperCamelCase__       ):
																		SCREAMING_SNAKE_CASE_   :	Optional[int]													=   pd_pool[
																		    i_pool
																		]
																		SCREAMING_SNAKE_CASE_   :	List[str]													=   i_pool + 1
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   np.multiply(
										    UpperCamelCase__ ,    np.multiply(out_map[i_map] ,    (1 - out_map[i_map])       )       )
										pd_all.append(UpperCamelCase__       )
						return pd_all


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

														SCREAMING_SNAKE_CASE_   :	Dict													=   np.dot(UpperCamelCase__ ,    self.vji.T       ) - self.thre_bpa
														SCREAMING_SNAKE_CASE_   :	str													=   self.sig(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   np.dot(UpperCamelCase__ ,    self.wkj.T       ) - self.thre_bpa
														SCREAMING_SNAKE_CASE_   :	Dict													=   self.sig(UpperCamelCase__       )

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

														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   pd_i_all / (self.size_poolinga * self.size_poolinga)
														SCREAMING_SNAKE_CASE_   :	Tuple													=   pd_conva_pooled.T.getA().tolist()
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self._calculate_gradient_from_pool(
														    UpperCamelCase__ ,    UpperCamelCase__ ,    shape_featuremapa[0] ,    shape_featuremapa[1] ,    self.size_poolinga ,    )
														# weight and threshold learning process---------
														# convolution layer
														for k_conv in range(self.conva[1]       ):
																		SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self._expand_mat(pd_conva_all[k_conv]       )
																		SCREAMING_SNAKE_CASE_   :	Any													=   self.rate_weight * np.dot(UpperCamelCase__ ,    UpperCamelCase__       )

																		SCREAMING_SNAKE_CASE_   :	Dict													=   self.w_conva[k_conv] + delta_w.reshape(
																		    (self.conva[0], self.conva[0])       )

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

						def draw_error():
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [error_accuracy for i in range(int(n_repeat * 1.2       )       )]
										plt.plot(UpperCamelCase__ ,    '+-'       )
										plt.plot(UpperCamelCase__ ,    'r--'       )
										plt.xlabel('Learning Times'       )
										plt.ylabel('All_mse'       )
										plt.grid(UpperCamelCase__ ,    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 ,    UpperCamelCase__       ):
						# model predict
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   []
						print('-------------------Start Testing-------------------------'       )
						print((' - - Shape: Test_Data  ', np.shape(UpperCamelCase__       ))       )
						for p in range(len(UpperCamelCase__       )       ):
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   np.asmatrix(datas_test[p]       )
										SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   self.convolute(
										    UpperCamelCase__ ,    self.conva ,    self.w_conva ,    self.thre_conva ,    conv_step=self.step_conva ,    )
										SCREAMING_SNAKE_CASE_   :	str													=   self.pooling(UpperCamelCase__ ,    self.size_poolinga       )
										SCREAMING_SNAKE_CASE_   :	str													=   self._expand(UpperCamelCase__       )

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






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# return the data of image after convoluting process so we can check it out
						SCREAMING_SNAKE_CASE_   :	int													=   np.asmatrix(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.convolute(
						    UpperCamelCase__ ,    self.conva ,    self.w_conva ,    self.thre_conva ,    conv_step=self.step_conva ,    )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.pooling(UpperCamelCase__ ,    self.size_poolinga       )

						return data_conveda, data_pooleda


if __name__ == "__main__":
				pass







import colorsys

from PIL import Image  # type: ignore



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   x
				SCREAMING_SNAKE_CASE_   :	Tuple													=   y
				for step in range(lowerCAmelCase__      ):  # noqa: B007
								SCREAMING_SNAKE_CASE_   :	str													=   a * a - b * b + x
								SCREAMING_SNAKE_CASE_   :	Any													=   2 * a * b + y
								SCREAMING_SNAKE_CASE_   :	int													=   a_new

								# divergence happens for all complex number with an absolute value
								# greater than 4
								if a * a + b * b > 4:
												break
				return step / (max_step - 1)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return (255, 255, 255)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__    ,    1    ,    1      )      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 800    ,    lowerCAmelCase__				:		int = 600    ,    lowerCAmelCase__				:		float = -0.6    ,    lowerCAmelCase__				:		float = 0    ,    lowerCAmelCase__				:		float = 3.2    ,    lowerCAmelCase__				:		int = 50    ,    lowerCAmelCase__				:		bool = True    ,    ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   Image.new('RGB'    ,    (image_width, image_height)      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   img.load()

				# loop through the image-coordinates
				for image_x in range(lowerCAmelCase__      ):
								for image_y in range(lowerCAmelCase__      ):
												# determine the figure-coordinates based on the image-coordinates
												SCREAMING_SNAKE_CASE_   :	List[str]													=   figure_width / image_width * image_height
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   figure_center_x + (image_x / image_width - 0.5) * figure_width
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   figure_center_y + (image_y / image_height - 0.5) * figure_height

												SCREAMING_SNAKE_CASE_   :	str													=   get_distance(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

												# color the corresponding pixel based on the selected coloring-function
												if use_distance_color_coding:
																SCREAMING_SNAKE_CASE_   :	int													=   get_color_coded_rgb(lowerCAmelCase__      )
												else:
																SCREAMING_SNAKE_CASE_   :	Dict													=   get_black_and_white_rgb(lowerCAmelCase__      )

				return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				A        =			get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()