
'''simple docstring'''







import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")





def 					UpperCAmelCase_		(__a      :    np.ndarray ):






   """simple docstring"""

   return input_array.reshape((input_array.size, 1) )





def 					UpperCAmelCase_		(__a      :    np.ndarray ,	__a      :    np.ndarray ,	__a      :    int ):






   """simple docstring"""

   _a				:	List[str]			=   np.nan
   for i in range(__a ):
      _a				:	Any			=   features[:, labels == i]
      _a				:	Optional[int]			=   data.mean(1 )
      # Centralize the data of class i
      _a				:	Tuple			=   data - column_reshape(__a )
      if i > 0:
         # If covariance_sum is not None
         covariance_sum += np.dot(__a ,	centered_data.T )
      else:
         # If covariance_sum is np.nan (i.e. first loop)
         _a				:	Optional[int]			=   np.dot(__a ,	centered_data.T )

   return covariance_sum / features.shape[1]





def 					UpperCAmelCase_		(__a      :    np.ndarray ,	__a      :    np.ndarray ,	__a      :    int ):






   """simple docstring"""

   _a				:	List[str]			=   features.mean(1 )
   _a				:	Optional[Any]			=   np.nan
   for i in range(__a ):
      _a				:	Optional[int]			=   features[:, labels == i]
      _a				:	Tuple			=   data.shape[1]
      _a				:	Dict			=   data.mean(1 )
      if i > 0:
         # If covariance_sum is not None
         covariance_sum += device_data * np.dot(
             column_reshape(__a ) - column_reshape(__a ) ,	(column_reshape(__a ) - column_reshape(__a )).T ,	)
      else:
         # If covariance_sum is np.nan (i.e. first loop)
         _a				:	Any			=   device_data * np.dot(
             column_reshape(__a ) - column_reshape(__a ) ,	(column_reshape(__a ) - column_reshape(__a )).T ,	)

   return covariance_sum / features.shape[1]





def 					UpperCAmelCase_		(__a      :    np.ndarray ,	__a      :    int ):






   """simple docstring"""

   if features.any():
      _a				:	Any			=   features.mean(1 )
      # Center the dataset
      _a				:	str			=   features - np.reshape(__a ,	(data_mean.size, 1) )
      _a				:	Optional[int]			=   np.dot(__a ,	centered_data.T ) / features.shape[1]
      _a,  _a				:	Optional[int]			=   np.linalg.eigh(__a )
      # Take all the columns in the reverse order (-1), and then takes only the first
      _a				:	Optional[Any]			=   eigenvectors[:, ::-1][:, 0:dimensions]
      # Project the database on the new space
      _a				:	int			=   np.dot(filtered_eigenvectors.T ,	__a )
      logging.info('Principal Component Analysis computed' )

      return projected_data
   else:
      logging.basicConfig(level=logging.ERROR ,	format='%(message)s' ,	force=__a )
      logging.error('Dataset empty' )
      raise AssertionError





def 					UpperCAmelCase_		(__a      :    np.ndarray ,	__a      :    np.ndarray ,	__a      :    int ,	__a      :    int ):






   """simple docstring"""

   assert classes > dimensions

   # Check if features have been already loaded
   if features.any:
      _a,  _a				:	Optional[int]			=   eigh(
          covariance_between_classes(__a ,	__a ,	__a ) ,	covariance_within_classes(__a ,	__a ,	__a ) ,	)
      _a				:	List[str]			=   eigenvectors[:, ::-1][:, :dimensions]
      _a,  _a,  _a				:	str			=   np.linalg.svd(__a )
      _a				:	Union[str, Any]			=   svd_matrix[:, 0:dimensions]
      _a				:	Any			=   np.dot(filtered_svd_matrix.T ,	__a )
      logging.info('Linear Discriminant Analysis computed' )

      return projected_data
   else:
      logging.basicConfig(level=logging.ERROR ,	format='%(message)s' ,	force=__a )
      logging.error('Dataset empty' )
      raise AssertionError





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	int			=   np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]] )
   _a				:	Optional[int]			=   np.array([0, 0, 0, 1, 1] )
   _a				:	Optional[int]			=   2
   _a				:	Any			=   2

   # Assert that the function raises an AssertionError if dimensions > classes
   with pytest.raises(__a ) as error_info:
      _a				:	List[str]			=   linear_discriminant_analysis(
          __a ,	__a ,	__a ,	__a )
      if isinstance(__a ,	np.ndarray ):
         raise AssertionError(
             'Did not raise AssertionError for dimensions > classes' )
      assert error_info.type is AssertionError





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	str			=   np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]] )
   _a				:	Union[str, Any]			=   2
   _a				:	Union[str, Any]			=   np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]] )

   with pytest.raises(__a ) as error_info:
      _a				:	str			=   principal_component_analysis(__a ,	__a )
      if not np.allclose(__a ,	__a ):
         raise AssertionError
      assert error_info.type is AssertionError


if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     int							,*_a      :     Optional[int]							,**_a      :     str       ):


     '''simple docstring'''






     warnings.warn(
         'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
         ' use BeitImageProcessor instead.'							,_a							,)
     super().__init__(*_a							,**_a       )



