


import pickle

import numpy as np
from matplotlib import pyplot as plt
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_=0.2			,							snake_case_=0.2    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = bp_numa
								__lowercase			       = bp_numa
								__lowercase			       = bp_numa
								__lowercase			       = conva_get[:2]
								__lowercase			       = conva_get[2]
								__lowercase			       = size_pa
								__lowercase			       = rate_w
								__lowercase			       = rate_t
								__lowercase			       = [
								    np.mat(-1 * np.random.rand(self.conva[0]			,							self.conva[0]    ) + 0.5    )
								    for i in range(self.conva[1]    )
								]
								__lowercase			       = np.mat(-1 * np.random.rand(self.num_bpa			,							self.num_bpa    ) + 0.5    )
								__lowercase			       = np.mat(-1 * np.random.rand(self.num_bpa			,							self.num_bpa    ) + 0.5    )
								__lowercase			       = -2 * np.random.rand(self.conva[1]    ) + 1
								__lowercase			       = -2 * np.random.rand(self.num_bpa    ) + 1
								__lowercase			       = -2 * np.random.rand(self.num_bpa    ) + 1






					def A   (					self			,							snake_case_    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = {
								    '''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 A   (					cls			,							snake_case_    ) ->      List[Any]:







								'''simple docstring'''

								with open(snake_case_			,							'''rb'''    ) as f:
											__lowercase			       = pickle.load(snake_case_    )  # noqa: S301

								__lowercase			       = model_dic.get('''conv1'''    )
								conv_get.append(model_dic.get('''step_conv1'''    )    )
								__lowercase			       = model_dic.get('''size_pooling1'''    )
								__lowercase			       = model_dic.get('''num_bp1'''    )
								__lowercase			       = model_dic.get('''num_bp2'''    )
								__lowercase			       = model_dic.get('''num_bp3'''    )
								__lowercase			       = model_dic.get('''rate_weight'''    )
								__lowercase			       = model_dic.get('''rate_thre'''    )
								# create model instance
								__lowercase			       = CNN(snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_    )
								# modify model parameter
								__lowercase			       = model_dic.get('''w_conv1'''    )
								__lowercase			       = model_dic.get('''wkj'''    )
								__lowercase			       = model_dic.get('''vji'''    )
								__lowercase			       = model_dic.get('''thre_conv1'''    )
								__lowercase			       = model_dic.get('''thre_bp2'''    )
								__lowercase			       = model_dic.get('''thre_bp3'''    )
								return conv_ins






					def A   (					self			,							snake_case_    ) ->      Tuple:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_    ) ->      int:







								'''simple docstring'''

								return round(snake_case_			,							3    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      int:







								'''simple docstring'''

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

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_="average_pool"    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = len(featuremaps[0]    )
								__lowercase			       = int(size_map / size_pooling    )
								__lowercase			       = []
								for i_map in range(len(snake_case_    )    ):
											__lowercase			       = featuremaps[i_map]
											__lowercase			       = []
											for i_focus in range(0			,							snake_case_			,							snake_case_    ):
														for j_focus in range(0			,							snake_case_			,							snake_case_    ):
																	__lowercase			       = 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_    )    )
											__lowercase			       = np.asmatrix(snake_case_    ).reshape(snake_case_			,							snake_case_    )
											featuremap_pooled.append(snake_case_    )
								return featuremap_pooled






					def A   (					self			,							snake_case_    ) ->      Any:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_    ) ->      str:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      Union[str, Any]:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_=bool    ) ->      List[Any]:







								'''simple docstring'''

								print('''----------------------Start Training-------------------------'''    )
								print((''' - - Shape: Train_Data  ''', np.shape(snake_case_    ))    )
								print((''' - - Shape: Teach_Data  ''', np.shape(snake_case_    ))    )
								__lowercase			       = 0
								__lowercase			       = []
								__lowercase			       = 1_0_0_0_0
								while rp < n_repeat and mse >= error_accuracy:
											__lowercase			       = 0
											print(F'-------------Learning Time {rp}--------------'    )
											for p in range(len(snake_case_    )    ):
														# print('------------Learning Image: %d--------------'%p)
														__lowercase			       = np.asmatrix(datas_train[p]    )
														__lowercase			       = np.asarray(datas_teach[p]    )
														__lowercase       ,			__lowercase			       = self.convolute(
														    snake_case_			,							self.conva			,							self.w_conva			,							self.thre_conva			,							conv_step=self.step_conva			,							)
														__lowercase			       = self.pooling(snake_case_			,							self.size_poolinga    )
														__lowercase			       = np.shape(snake_case_    )
														__lowercase			       = self._expand(snake_case_    )
														__lowercase			       = data_bp_input

														__lowercase			       = np.dot(snake_case_			,							self.vji.T    ) - self.thre_bpa
														__lowercase			       = self.sig(snake_case_    )
														__lowercase			       = np.dot(snake_case_			,							self.wkj.T    ) - self.thre_bpa
														__lowercase			       = self.sig(snake_case_    )

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

														__lowercase			       = pd_i_all / (self.size_poolinga * self.size_poolinga)
														__lowercase			       = pd_conva_pooled.T.getA().tolist()
														__lowercase			       = 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]    ):
																	__lowercase			       = self._expand_mat(pd_conva_all[k_conv]    )
																	__lowercase			       = self.rate_weight * np.dot(snake_case_			,							snake_case_    )

																	__lowercase			       = self.w_conva[k_conv] + delta_w.reshape(
																	    (self.conva[0], self.conva[0])    )

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

								def draw_error():
											__lowercase			       = [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 A   (					self			,							snake_case_    ) ->      str:







								'''simple docstring'''

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

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






					def A   (					self			,							snake_case_    ) ->      Union[str, Any]:







								'''simple docstring'''

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

								return data_conveda, data_pooleda


if __name__ == "__main__":
	pass






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							split=snake_case_			,							features=snake_case_			,							cache_dir=snake_case_			,							keep_in_memory=snake_case_			,							streaming=snake_case_			,							num_proc=snake_case_			,							**snake_case_			,							)
								__lowercase			       = path_or_paths if isinstance(snake_case_			,							snake_case_    ) else {self.split: path_or_paths}
								__lowercase			       = Text(
								    cache_dir=snake_case_			,							data_files=snake_case_			,							features=snake_case_			,							**snake_case_			,							)






					def A   (					self    ) ->      int:







								'''simple docstring'''

								if self.streaming:
											__lowercase			       = self.builder.as_streaming_dataset(split=self.split    )
								# Build regular (map-style) dataset
								else:
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None

											self.builder.download_and_prepare(
											    download_config=snake_case_			,							download_mode=snake_case_			,							verification_mode=snake_case_			,							base_path=snake_case_			,							num_proc=self.num_proc			,							)
											__lowercase			       = self.builder.as_dataset(
											    split=self.split			,							verification_mode=snake_case_			,							in_memory=self.keep_in_memory    )
								return dataset


