


'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt



class  lowerCAmelCase				:



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



	def 				lowercase     (					self ,					snake_case__    ):
		# save model dict with pickle
		lowerCAmelCase					:    Union[str, 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(snake_case__ ,					'wb'    ) as f:
			pickle.dump(snake_case__ ,					snake_case__    )

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



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

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



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



	def 				lowercase     (					self ,					snake_case__    ):
		return round(snake_case__ ,					3    )



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

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



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



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



	def 				lowercase     (					self ,					snake_case__    ):
		# expanding matrix to one dimension list
		lowerCAmelCase					:    List[str]      							= np.asarray(snake_case__    )
		lowerCAmelCase					:    int      							= np.shape(snake_case__    )
		lowerCAmelCase					:    str      							= data_mat.reshape(1 ,					shapes[0] * shapes[1]    )
		return data_expanded



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Tuple      							= []
		lowerCAmelCase					:    Tuple      							= 0
		for i_map in range(snake_case__    ):
			lowerCAmelCase					:    Dict      							= np.ones((size_map, size_map)    )
			for i in range(0 ,					snake_case__ ,					snake_case__    ):
				for j in range(0 ,					snake_case__ ,					snake_case__    ):
					lowerCAmelCase					:    List[str]      							= pd_pool[
					    i_pool
					]
					lowerCAmelCase					:    Union[str, Any]      							= i_pool + 1
			lowerCAmelCase					:    Tuple      							= np.multiply(
			    snake_case__ ,					np.multiply(out_map[i_map] ,					(1 - out_map[i_map])    )    )
			pd_all.append(snake_case__    )
		return pd_all



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

				lowerCAmelCase					:    Union[str, Any]      							= np.dot(snake_case__ ,					self.vji.T    ) - self.thre_bpa
				lowerCAmelCase					:    str      							= self.sig(snake_case__    )
				lowerCAmelCase					:    Union[str, Any]      							= np.dot(snake_case__ ,					self.wkj.T    ) - self.thre_bpa
				lowerCAmelCase					:    Union[str, Any]      							= self.sig(snake_case__    )

				# --------------Model Leaning ------------------------
				# calculate error and gradient---------------
				lowerCAmelCase					:    List[Any]      							= np.multiply(
				    (data_teach - bp_outa) ,					np.multiply(snake_case__ ,					(1 - bp_outa)    )    )
				lowerCAmelCase					:    str      							= np.multiply(
				    np.dot(snake_case__ ,					self.wkj    ) ,					np.multiply(snake_case__ ,					(1 - bp_outa)    )    )
				lowerCAmelCase					:    List[str]      							= np.dot(snake_case__ ,					self.vji    )

				lowerCAmelCase					:    Dict      							= pd_i_all / (self.size_poolinga * self.size_poolinga)
				lowerCAmelCase					:    Tuple      							= pd_conva_pooled.T.getA().tolist()
				lowerCAmelCase					:    List[Any]      							= 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]    ):
					lowerCAmelCase					:    Tuple      							= self._expand_mat(pd_conva_all[k_conv]    )
					lowerCAmelCase					:    int      							= self.rate_weight * np.dot(snake_case__ ,					snake_case__    )

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

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

		def draw_error():
			lowerCAmelCase					:    Union[str, Any]      							= [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 				lowercase     (					self ,					snake_case__    ):
		# model predict
		lowerCAmelCase					:    Union[str, Any]      							= []
		print('-------------------Start Testing-------------------------'    )
		print((' - - Shape: Test_Data  ', np.shape(snake_case__    ))    )
		for p in range(len(snake_case__    )    ):
			lowerCAmelCase					:    Dict      							= np.asmatrix(datas_test[p]    )
			lowerCAmelCase							,   lowerCAmelCase					:    str      							= self.convolute(
			    snake_case__ ,					self.conva ,					self.w_conva ,					self.thre_conva ,					conv_step=self.step_conva ,					)
			lowerCAmelCase					:    Any      							= self.pooling(snake_case__ ,					self.size_poolinga    )
			lowerCAmelCase					:    Union[str, Any]      							= self._expand(snake_case__    )

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



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

		return data_conveda, data_pooleda


if __name__ == "__main__":
				pass




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				List[str]				)    ->      Optional[Any]:




	"""simple docstring"""





	if not head:
		return True
	# split the list to two parts
	lowerCAmelCase							,   lowerCAmelCase					:    str      							= head.next, head
	while fast and fast.next:
		lowerCAmelCase					:    Optional[int]      							= fast.next.next
		lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    Optional[Any]      							= None  # Don't forget here! But forget still works!
	# reverse the second part
	lowerCAmelCase					:    List[Any]      							= None
	while second:
		lowerCAmelCase					:    List[Any]      							= second.next
		lowerCAmelCase					:    Union[str, Any]      							= node
		lowerCAmelCase					:    Optional[Any]      							= second
		lowerCAmelCase					:    Any      							= nxt
	# compare two parts
	# second part has the same or one less node
	while node:
		if node.val != head.val:
			return False
		lowerCAmelCase					:    Optional[Any]      							= node.next
		lowerCAmelCase					:    Tuple      							= head.next
	return True

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True

	# 1. Get the midpoint (slow)
	lowerCAmelCase					:    Optional[int]      							= head
	while fast and fast.next:
		lowerCAmelCase							,   lowerCAmelCase					:    Optional[Any]      							= fast.next.next, slow.next

	# 2. Push the second half into the stack
	lowerCAmelCase					:    Tuple      							= [slow.val]
	while slow.next:
		lowerCAmelCase					:    Tuple      							= slow.next
		stack.append(slow.val				)

	# 3. Comparison
	while stack:
		if stack.pop() != cur.val:
			return False
		lowerCAmelCase					:    Union[str, Any]      							= cur.next

	return True






def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True
	lowerCAmelCase					:    Optional[int]      							= {}
	lowerCAmelCase					:    int      							= 0
	while head:
		if head.val in d:
			d[head.val].append(_A				)
		else:
			lowerCAmelCase					:    Any      							= [pos]
		lowerCAmelCase					:    int      							= head.next
		pos += 1
	lowerCAmelCase					:    str      							= pos - 1
	lowerCAmelCase					:    Optional[Any]      							= 0
	for v in d.values():
		if len(_A				) % 2 != 0:
			middle += 1
		else:
			lowerCAmelCase					:    Any      							= 0
			for i in range(0					,   len(_A				)				):
				if v[i] + v[len(_A				) - 1 - step] != checksum:
					return False
				step += 1
		if middle > 1:
			return False
	return True
