



"""simple docstring"""




import numpy as np


class   a__		:


   def __init__(							self      :							int,     lowerCAmelCase      :							Optional[int]=5,     lowerCAmelCase      :							Dict=5			)    ->       Optional[int]:
         lowercase  :      Optional[int]					=   depth
         lowercase  :      Optional[Any]					=   0
         lowercase  :      Any					=   None
         lowercase  :      Any					=   None
         lowercase  :      Dict					=   min_leaf_size
         lowercase  :      List[str]					=   None


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							Optional[int],     lowerCAmelCase      :							Optional[int]			)    ->       int:
         if labels.ndim != 1:
               print('Error: Input labels must be one dimensional'			)

         return np.mean((labels - prediction) ** 2			)


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Any,     lowerCAmelCase      :							int			)    ->       int:
         if x.ndim != 1:
               print('Error: Input data set must be one dimensional'			)
               return
         if len(lowerCAmelCase			) != len(lowerCAmelCase			):
               print('Error: X and y have different lengths'			)
               return
         if y.ndim != 1:
               print('Error: Data set labels must be one dimensional'			)
               return

         if len(lowerCAmelCase			) < 2 * self.min_leaf_size:
               lowercase  :      Any					=   np.mean(lowerCAmelCase			)
               return

         if self.depth == 1:
               lowercase  :      int					=   np.mean(lowerCAmelCase			)
               return

         lowercase  :      int					=   0
         lowercase  :      int					=   self.mean_squared_error(lowerCAmelCase,     np.mean(lowerCAmelCase			)			) * 2
         for i in range(len(lowerCAmelCase			)			):
               if len(x[:i]			) < self.min_leaf_size:
                     continue
               elif len(x[i:]			) < self.min_leaf_size:
                     continue
               else:
                     lowercase  :      Union[str, Any]					=   self.mean_squared_error(x[:i],     np.mean(y[:i]			)			)
                     lowercase  :      Union[str, Any]					=   self.mean_squared_error(x[i:],     np.mean(y[i:]			)			)
                     lowercase  :      Optional[int]					=   error_left + error_right
                     if error < min_error:
                           lowercase  :      int					=   i
                           lowercase  :      List[str]					=   error

         if best_split != 0:
               lowercase  :      str					=   x[:best_split]
               lowercase  :      int					=   y[:best_split]
               lowercase  :      List[str]					=   x[best_split:]
               lowercase  :      List[Any]					=   y[best_split:]

               lowercase  :      Optional[Any]					=   x[best_split]
               lowercase  :      str					=   DecisionTree(
                   depth=self.depth - 1,     min_leaf_size=self.min_leaf_size			)
               lowercase  :      Tuple					=   DecisionTree(
                   depth=self.depth - 1,     min_leaf_size=self.min_leaf_size			)
               self.left.train(lowerCAmelCase,     lowerCAmelCase			)
               self.right.train(lowerCAmelCase,     lowerCAmelCase			)
         else:
               lowercase  :      List[str]					=   np.mean(lowerCAmelCase			)

         return






   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							List[str]			)    ->       Dict:
         if self.prediction is not None:
               return self.prediction
         elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                     return self.right.predict(lowerCAmelCase			)
               else:
                     return self.left.predict(lowerCAmelCase			)
         else:
               print('Error: Decision tree not yet trained'			)
               return None




class   a__		:


   @staticmethod
   def        lowercase				(							lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Dict			)    ->       Union[str, Any]:
         lowercase  :      Dict					=   float(0			)
         for label in labels:
               squared_error_sum += (label - prediction) ** 2

         return float(squared_error_sum / labels.size			)




def   lowercase__     (			)     ->			str:






      '''simple docstring'''


      lowercase  :      Dict					=   np.arange(-1.0	,      1.0	,      0.0_0_5				)
      lowercase  :      Any					=   np.sin(_UpperCAmelCase				)

      lowercase  :      Any					=   DecisionTree(depth=10	,      min_leaf_size=10				)
      tree.train(_UpperCAmelCase	,      _UpperCAmelCase				)

      lowercase  :      Tuple					=   (np.random.rand(10				) * 2) - 1
      lowercase  :      Any					=   np.array([tree.predict(_UpperCAmelCase				) for x in test_cases]				)
      lowercase  :      str					=   np.mean((predictions - test_cases) ** 2				)

      print('Test values: ' + str(_UpperCAmelCase				)				)
      print('Predictions: ' + str(_UpperCAmelCase				)				)
      print('Average error: ' + str(_UpperCAmelCase				)				)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name='mean_squarred_error', verbose=True)





"""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			)
