



'''simple docstring'''





import logging

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

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




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  np.nan
     for i in range(__A):
          _a      =  features[:, labels == i]
          _a      =  data.mean(1)
          # Centralize the data of class i
          _a      =  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      =  np.dot(__A  ,	centered_data.T)

     return covariance_sum / features.shape[1]




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  features.mean(1)
     _a      =  np.nan
     for i in range(__A):
          _a      =  features[:, labels == i]
          _a      =  data.shape[1]
          _a      =  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      =  device_data * np.dot(
                   column_reshape(__A) - column_reshape(__A)  ,	(column_reshape(__A) - column_reshape(__A)).T  ,	)

     return covariance_sum / features.shape[1]




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     if features.any():
          _a      =  features.mean(1)
          # Center the dataset
          _a      =  features - np.reshape(__A  ,	(data_mean.size, 1))
          _a      =  np.dot(__A  ,	centered_data.T) / features.shape[1]
          _a  ,    _a      =  np.linalg.eigh(__A)
          # Take all the columns in the reverse order (-1), and then takes only the first
          _a      =  eigenvectors[:, ::-1][:, 0:dimensions]
          # Project the database on the new space
          _a      =  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 lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     assert classes > dimensions

     # Check if features have been already loaded
     if features.any:
          _a  ,    _a      =  eigh(
              covariance_between_classes(__A  ,	__A  ,	__A)  ,	covariance_within_classes(__A  ,	__A  ,	__A)  ,	)
          _a      =  eigenvectors[:, ::-1][:, :dimensions]
          _a  ,    _a  ,    _a      =  np.linalg.svd(__A)
          _a      =  svd_matrix[:, 0:dimensions]
          _a      =  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 lowerCAmelCase  ():



     """simple docstring"""



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

     # Assert that the function raises an AssertionError if dimensions > classes
     with pytest.raises(__A) as error_info:
          _a      =  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 lowerCAmelCase  ():



     """simple docstring"""



     _a      =  np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
     _a      =  2
     _a      =  np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]])

     with pytest.raises(__A) as error_info:
          _a      =  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'''





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_		       =  {"configuration_mmbt": ["MMBTConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_		       =  ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
  from .configuration_mmbt import MMBTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
  import sys

  lowercase_		       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



