"""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					(			lowercase		):







  """simple docstring"""
  return input_array.reshape((input_array.size, 1)		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      np.nan
  for i in range(lowercase		):
    _UpperCAmelCase						 =      features[:, labels == i]
    _UpperCAmelCase						 =      data.mean(1		)
    # Centralize the data of class i
    _UpperCAmelCase						 =      data - column_reshape(lowercase		)
    if i > 0:
      # If covariance_sum is not None
      covariance_sum += np.dot(lowercase   ,centered_data.T		)
    else:
      # If covariance_sum is np.nan (i.e. first loop)
      _UpperCAmelCase						 =      np.dot(lowercase   ,centered_data.T		)

  return covariance_sum / features.shape[1]






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      features.mean(1		)
  _UpperCAmelCase						 =      np.nan
  for i in range(lowercase		):
    _UpperCAmelCase						 =      features[:, labels == i]
    _UpperCAmelCase						 =      data.shape[1]
    _UpperCAmelCase						 =      data.mean(1		)
    if i > 0:
      # If covariance_sum is not None
      covariance_sum += device_data * np.dot(
          column_reshape(lowercase		) - column_reshape(lowercase		)   ,(column_reshape(lowercase		) - column_reshape(lowercase		)).T   ,)
    else:
      # If covariance_sum is np.nan (i.e. first loop)
      _UpperCAmelCase						 =      device_data * np.dot(
          column_reshape(lowercase		) - column_reshape(lowercase		)   ,(column_reshape(lowercase		) - column_reshape(lowercase		)).T   ,)

  return covariance_sum / features.shape[1]






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""

  # Check if the features have been loaded
  if features.any():
    _UpperCAmelCase						 =      features.mean(1		)
    # Center the dataset
    _UpperCAmelCase						 =      features - np.reshape(lowercase   ,(data_mean.size, 1)		)
    _UpperCAmelCase						 =      np.dot(lowercase   ,centered_data.T		) / features.shape[1]
    _UpperCAmelCase ,				_UpperCAmelCase						 =      np.linalg.eigh(lowercase		)
    # Take all the columns in the reverse order (-1), and then takes only the first
    _UpperCAmelCase						 =      eigenvectors[:, ::-1][:, 0:dimensions]
    # Project the database on the new space
    _UpperCAmelCase						 =      np.dot(filtered_eigenvectors.T   ,lowercase		)
    logging.info("""Principal Component Analysis computed"""		)

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






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  assert classes > dimensions

  # Check if features have been already loaded
  if features.any:
    _UpperCAmelCase ,				_UpperCAmelCase						 =      eigh(
        covariance_between_classes(lowercase   ,lowercase   ,lowercase		)   ,covariance_within_classes(lowercase   ,lowercase   ,lowercase		)   ,)
    _UpperCAmelCase						 =      eigenvectors[:, ::-1][:, :dimensions]
    _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      np.linalg.svd(lowercase		)
    _UpperCAmelCase						 =      svd_matrix[:, 0:dimensions]
    _UpperCAmelCase						 =      np.dot(filtered_svd_matrix.T   ,lowercase		)
    logging.info("""Linear Discriminant Analysis computed"""		)

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






def     __UpperCAmelCase					(			):







  """simple docstring"""
  # Create dummy dataset with 2 classes and 3 features
  _UpperCAmelCase						 =      np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
  _UpperCAmelCase						 =      np.array([0, 0, 0, 1, 1]		)
  _UpperCAmelCase						 =      2
  _UpperCAmelCase						 =      2

  # Assert that the function raises an AssertionError if dimensions > classes
  with pytest.raises(lowercase		) as error_info:
    _UpperCAmelCase						 =      linear_discriminant_analysis(
        lowercase   ,lowercase   ,lowercase   ,lowercase		)
    if isinstance(lowercase   ,np.ndarray		):
      raise AssertionError(
          """Did not raise AssertionError for dimensions > classes"""		)
    assert error_info.type is AssertionError






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
  _UpperCAmelCase						 =      2
  _UpperCAmelCase						 =      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(lowercase		) as error_info:
    _UpperCAmelCase						 =      principal_component_analysis(lowercase   ,lowercase		)
    if not np.allclose(lowercase   ,lowercase		):
      raise AssertionError
    assert error_info.type is AssertionError


if __name__ == "__main__":
    import doctest

    doctest.testmod()

"""simple docstring"""


# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase__  		=						abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  from diffusers.utils.testing_utils import pytest_addoption_shared

  pytest_addoption_shared(lowercase		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  from diffusers.utils.testing_utils import pytest_terminal_summary_main

  _UpperCAmelCase						 =      terminalreporter.config.getoption("""--make-reports"""		)
  if make_reports:
    pytest_terminal_summary_main(lowercase   ,id=lowercase		)
