



"""simple docstring"""




import numpy as np
from matplotlib import pyplot as plt


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


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


class   a__		:


   def __init__(							self      :							str,     lowerCAmelCase      :							int,     lowerCAmelCase      :							Dict=None,     lowerCAmelCase      :							Dict=None,     lowerCAmelCase      :							Optional[Any]=False			)    ->       List[str]:
         lowercase  :      Tuple					=   units
         lowercase  :      Union[str, Any]					=   None
         lowercase  :      Tuple					=   None
         lowercase  :      Any					=   activation
         if learning_rate is None:
               lowercase  :      Optional[Any]					=   0.3
         lowercase  :      Any					=   learning_rate
         lowercase  :      Tuple					=   is_input_layer


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							int			)    ->       Tuple:
         lowercase  :      Union[str, Any]					=   np.asmatrix(np.random.normal(0,     0.5,     (self.units, back_units)			)			)
         lowercase  :      List[Any]					=   np.asmatrix(np.random.normal(0,     0.5,     self.units			)			).T
         if self.activation is None:
               lowercase  :      Union[str, Any]					=   sigmoid


   def        lowercase				(							self      :							Any			)    ->       Optional[int]:
         # activation function may be sigmoid or linear
         if self.activation == sigmoid:
               lowercase  :      Optional[int]					=   np.dot(self.output,     (1 - self.output).T			)
               lowercase  :      List[str]					=   np.diag(np.diag(lowerCAmelCase			)			)
         else:
               lowercase  :      Any					=   1
         return gradient_activation


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							Optional[int]			)    ->       Optional[int]:
         lowercase  :      Tuple					=   xdata
         if self.is_input_layer:
               # input layer
               lowercase  :      List[str]					=   xdata
               lowercase  :      Dict					=   xdata
               return xdata
         else:
               lowercase  :      str					=   np.dot(self.weight,     self.xdata			) - self.bias
               lowercase  :      Optional[int]					=   self.activation(self.wx_plus_b			)
               return self.output






   def        lowercase				(							self      :							int,     lowerCAmelCase      :							Optional[Any]			)    ->       Optional[int]:
         lowercase  :      Optional[Any]					=   self.cal_gradient()  # i * i 维
         lowercase  :      Optional[Any]					=   np.asmatrix(np.dot(gradient.T,     lowerCAmelCase			)			)

         lowercase  :      List[Any]					=   np.asmatrix(self.xdata			)
         lowercase  :      Dict					=   -1
         lowercase  :      Tuple					=   self.weight

         lowercase  :      Optional[Any]					=   np.dot(gradient.T,     self._gradient_weight.T			)
         lowercase  :      List[Any]					=   gradient * self._gradient_bias
         lowercase  :      Dict					=   np.dot(lowerCAmelCase,     self._gradient_x			).T
         # upgrade: the Negative gradient direction
         lowercase  :      Optional[Any]					=   self.weight - self.learn_rate * self.gradient_weight
         lowercase  :      Optional[int]					=   self.bias - self.learn_rate * self.gradient_bias.T
         # updates the weights and bias according to learning rate (0.3 if undefined)
         return self.gradient




class   a__		:


   def __init__(							self      :							Optional[int]			)    ->       Union[str, Any]:
         lowercase  :      List[Any]					=   []
         lowercase  :      Any					=   []
         lowercase  :      Union[str, Any]					=   plt.figure()
         lowercase  :      Tuple					=   self.fig_loss.add_subplot(1,     1,     1			)


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							int			)    ->       Optional[Any]:
         self.layers.append(lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple			)    ->       int:
         for i, layer in enumerate(self.layers[:]			):
               if i < 1:
                     lowercase  :      Dict					=   True
               else:
                     layer.initializer(self.layers[i - 1].units			)


   def        lowercase				(							self      :							int			)    ->       Tuple:
         for i, layer in enumerate(self.layers[:]			):
               print(f'''------- layer {i} -------'''			)
               print('weight.shape ',     np.shape(layer.weight			)			)
               print('bias.shape ',     np.shape(layer.bias			)			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							str,     lowerCAmelCase      :							int,     lowerCAmelCase      :							List[str]			)    ->       List[str]:
         lowercase  :      Dict					=   train_round
         lowercase  :      Tuple					=   accuracy

         self.ax_loss.hlines(self.accuracy,     0,     self.train_round * 1.1			)

         lowercase  :      Optional[Any]					=   np.shape(lowerCAmelCase			)
         for _ in range(lowerCAmelCase			):
               lowercase  :      Any					=   0
               for row in range(x_shape[0]			):
                     lowercase  :      str					=   np.asmatrix(xdata[row, :]			).T
                     lowercase  :      Optional[int]					=   np.asmatrix(ydata[row, :]			).T

                     # forward propagation
                     for layer in self.layers:
                           lowercase  :      Any					=   layer.forward_propagation(_xdata			)

                     lowercase	,    lowercase  :      Optional[int]					=   self.cal_loss(_ydata,     _xdata			)
                     lowercase  :      Optional[Any]					=   all_loss + loss

                     # back propagation: the input_layer does not upgrade
                     for layer in self.layers[:0:-1]:
                           lowercase  :      List[Any]					=   layer.back_propagation(lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   all_loss / x_shape[0]
               self.train_mse.append(lowerCAmelCase			)

               self.plot_loss()

               if mse < self.accuracy:
                     print('----达到精度----'			)
                     return mse
         return None


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Optional[int],     lowerCAmelCase      :							List[Any]			)    ->       Any:
         lowercase  :      List[Any]					=   np.sum(np.power((ydata - ydata_),     2			)			)
         lowercase  :      int					=   2 * (ydata_ - ydata)
         # vector (shape is the same as _ydata.shape)
         return self.loss, self.loss_gradient






   def        lowercase				(							self      :							Any			)    ->       Tuple:
         if self.ax_loss.lines:
               self.ax_loss.lines.remove(self.ax_loss.lines[0]			)
         self.ax_loss.plot(self.train_mse,     'r-'			)
         plt.ion()
         plt.xlabel('step'			)
         plt.ylabel('loss'			)
         plt.show()
         plt.pause(0.1			)




def   lowercase__     (			)     ->			Optional[Any]:






      '''simple docstring'''


      lowercase  :      Any					=   np.random.randn(10	,      10				)
      lowercase  :      Union[str, Any]					=   np.asarray(
          [
              [0.8, 0.4],
              [0.4, 0.3],
              [0.3_4, 0.4_5],
              [0.6_7, 0.3_2],
              [0.8_8, 0.6_7],
              [0.7_8, 0.7_7],
              [0.5_5, 0.6_6],
              [0.5_5, 0.4_3],
              [0.5_4, 0.1],
              [0.1, 0.5],
          ]				)
      lowercase  :      List[str]					=   BPNN()
      for i in (10, 20, 30, 2):
            model.add_layer(DenseLayer(_UpperCAmelCase				)				)
      model.build()
      model.summary()
      model.train(xdata=_UpperCAmelCase	,      ydata=_UpperCAmelCase	,      train_round=1_00	,      accuracy=0.0_1				)


if __name__ == "__main__":
     example()





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      Any					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '			)
         with self.assertRaises(lowerCAmelCase			):
               DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      int					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)
