


"""simple docstring"""



import pickle

import numpy as np
from matplotlib import pyplot as plt







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=0.2		,			UpperCamelCase__=0.2		)			->	Any:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       snake_case					:   List[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(lowerCamelCase_		,			"wb"		) as f:
           pickle.dump(lowerCamelCase_		,			lowerCamelCase_		)

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







   @classmethod
   def 				lowerCamelCase	(			cls		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       with open(lowerCamelCase_		,			"rb"		) as f:
           snake_case					:   Any         =					pickle.load(lowerCamelCase_		)  # noqa: S301

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       return round(lowerCamelCase_		,			3		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	str:

       '''simple docstring'''




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

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__="average_pool"		)			->	str:

       '''simple docstring'''




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







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

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   List[str]         =					np.asarray(lowerCamelCase_		)
       snake_case					:   int         =					np.shape(lowerCamelCase_		)
       snake_case					:   Union[str, Any]         =					data_mat.reshape(1		,			shapes[0] * shapes[1]		)
       return data_expanded







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

       '''simple docstring'''




       snake_case					:   int         =					[]
       snake_case					:   Tuple         =					0
       for i_map in range(lowerCamelCase_		):
           snake_case					:   List[str]         =					np.ones((size_map, size_map)		)
           for i in range(0		,			lowerCamelCase_		,			lowerCamelCase_		):
               for j in range(0		,			lowerCamelCase_		,			lowerCamelCase_		):
                   snake_case					:   str         =					pd_pool[
                       i_pool
                   ]
                   snake_case					:   str         =					i_pool + 1
           snake_case					:   Any         =					np.multiply(
               lowerCamelCase_		,			np.multiply(out_map[i_map]		,			(1 - out_map[i_map])		)		)
           pd_all.append(lowerCamelCase_		)
       return pd_all







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=bool		)			->	str:

       '''simple docstring'''




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

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

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

               snake_case					:   Any         =					pd_i_all / (self.size_poolinga * self.size_poolinga)
               snake_case					:   Any         =					pd_conva_pooled.T.getA().tolist()
               snake_case					:   int         =					self._calculate_gradient_from_pool(
                   lowerCamelCase_		,			lowerCamelCase_		,			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					:   Union[str, Any]         =					self._expand_mat(pd_conva_all[k_conv]		)
                   snake_case					:   Union[str, Any]         =					self.rate_weight * np.dot(lowerCamelCase_		,			lowerCamelCase_		)

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

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

       def draw_error():
           snake_case					:   List[str]         =					[error_accuracy for i in range(int(n_repeat * 1.2		)		)]
           plt.plot(lowerCamelCase_		,			"+-"		)
           plt.plot(lowerCamelCase_		,			"r--"		)
           plt.xlabel("Learning Times"		)
           plt.ylabel("All_mse"		)
           plt.grid(lowerCamelCase_		,			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		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




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

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







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

       '''simple docstring'''




       snake_case					:   Optional[int]         =					np.asmatrix(lowerCamelCase_		)
       snake_case					:   Optional[Any]         =					self.convolute(
           lowerCamelCase_		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
       snake_case					:   List[str]         =					self.pooling(lowerCamelCase_		,			self.size_poolinga		)

       return data_conveda, data_pooleda


if __name__ == "__main__":
  pass









"""simple docstring"""



from __future__ import annotations

__snake_case         =					list[tuple[int, int]]

__snake_case         =					[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__snake_case         =					([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			)			->	str:

       '''simple docstring'''




       snake_case					:   int         =					pos_x
       snake_case					:   List[str]         =					pos_y
       snake_case					:   List[Any]         =					(pos_y, pos_x)
       snake_case					:   Optional[int]         =					goal_x
       snake_case					:   Dict         =					goal_y
       snake_case					:   Any         =					g_cost
       snake_case					:   List[Any]         =					parent
       snake_case					:   Union[str, Any]         =					self.calculate_heuristic()







   def 				lowerCamelCase	(			self		)			->	float:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					abs(self.pos_x - self.goal_x		)
       snake_case					:   Dict         =					abs(self.pos_y - self.goal_y		)
       return dx + dy







   def __lt__(			self		,			UpperCamelCase__		)			->	bool:

       '''simple docstring'''




       return self.f_cost < other.f_cost







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   int         =					Node(start[1]		,			start[0]		,			goal[1]		,			goal[0]		,			0		,			UpperCamelCase__		)
       snake_case					:   List[Any]         =					Node(goal[1]		,			goal[0]		,			goal[1]		,			goal[0]		,			9_9999		,			UpperCamelCase__		)

       snake_case					:   Tuple         =					[self.start]
       snake_case					:   list[Node]         =					[]

       snake_case					:   Dict         =					False







   def 				lowerCamelCase	(			self		)			->	Path | None:

       '''simple docstring'''




       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           snake_case					:   str         =					self.open_nodes.pop(0		)

           if current_node.pos == self.target.pos:
               snake_case					:   Tuple         =					True
               return self.retrace_path(UpperCamelCase__		)

           self.closed_nodes.append(UpperCamelCase__		)
           snake_case					:   Optional[Any]         =					self.get_successors(UpperCamelCase__		)

           for child_node in successors:
               if child_node in self.closed_nodes:
                   continue

               if child_node not in self.open_nodes:
                   self.open_nodes.append(UpperCamelCase__		)
               else:
                   # retrieve the best current path
                   snake_case					:   Dict         =					self.open_nodes.pop(self.open_nodes.index(UpperCamelCase__		)		)

                   if child_node.g_cost < better_node.g_cost:
                       self.open_nodes.append(UpperCamelCase__		)
                   else:
                       self.open_nodes.append(UpperCamelCase__		)

       if not self.reached:
           return [self.start.pos]
       return None







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	list[Node]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]
       for action in delta:
           snake_case					:   Union[str, Any]         =					parent.pos_x + action[1]
           snake_case					:   str         =					parent.pos_y + action[0]

           if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(UpperCamelCase__		) - 1):
               continue

           if grid[pos_y][pos_x] != 0:
               continue

           successors.append(
               Node(
                   UpperCamelCase__		,			UpperCamelCase__		,			self.target.pos_y		,			self.target.pos_x		,			parent.g_cost + 1		,			UpperCamelCase__		,			)		)
       return successors







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Path:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					node
       snake_case					:   int         =					[]
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)		)
           snake_case					:   Any         =					current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
  __snake_case         =					(0, 0)
  __snake_case         =					(len(grid) - 1, len(grid[0]) - 1)
  for elem in grid:
    print(elem)

  print("""------""")

  __snake_case         =					GreedyBestFirst(init, goal)
  __snake_case         =					greedy_bf.search()
  if path:
    for pos_x, pos_y in path:
      __snake_case         =					2

    for elem in grid:
      print(elem)





