



import pickle

import numpy as np
from matplotlib import pyplot as plt



class 						UpperCAmelCase_					:







       def __init__(     self,    __a,    __a,    __a,    __a,    __a,    __a=0.2,    __a=0.2):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[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(__a,    "wb") as f:
                     pickle.dump(__a,    __a)

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







       @classmethod
       def      snake_case__   (     cls,    __a):

              '''simple docstring'''

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

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







       def      snake_case__   (     self,    __a):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a):

              '''simple docstring'''

              return round(__a,    3)







       def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a):

              '''simple docstring'''

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

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







       def      snake_case__   (     self,    __a,    __a,    __a="average_pool"):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    __a=bool):

              '''simple docstring'''

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

                            _lowerCAmelCase  :							str				     =					np.dot(__a,    self.vji.T) - self.thre_bpa
                            _lowerCAmelCase  :							List[Any]				     =					self.sig(__a)
                            _lowerCAmelCase  :							List[str]				     =					np.dot(__a,    self.wkj.T) - self.thre_bpa
                            _lowerCAmelCase  :							Any				     =					self.sig(__a)

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

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

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

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

              def draw_error():
                     _lowerCAmelCase  :							Any				     =					[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      snake_case__   (     self,    __a):

              '''simple docstring'''

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

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







       def      snake_case__   (     self,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Union[str, Any]				     =					np.asmatrix(__a)
              _lowerCAmelCase  :							int				     =					self.convolute(
                  __a,    self.conva,    self.w_conva,    self.thre_conva,    conv_step=self.step_conva,    )
              _lowerCAmelCase  :							List[str]				     =					self.pooling(__a,    self.size_poolinga)

              return data_conveda, data_pooleda


if __name__ == "__main__":
      pass





import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase = "https://www.worldometers.info/coronavirus"						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					BeautifulSoup(requests.get(_lowerCamelCase						).text       ,				"html.parser"						)
							_lowerCAmelCase  :							str				     =					soup.findAll("h1"						)
							_lowerCAmelCase  :							Optional[int]				     =					soup.findAll("div"       ,				{"class": "maincounter-number"}						)
							keys += soup.findAll("span"       ,				{"class": "panel-title"}						)
							values += soup.findAll("div"       ,				{"class": "number-table-main"}						)
							return {key.text.strip(): value.text.strip() for key, value in zip(_lowerCamelCase       ,				_lowerCamelCase						)}


if __name__ == "__main__":
						print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
						for key, value in world_covidaa_stats().items():
												print(f'''{key}\n{value}\n''')
