



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    )->       np.ndarray:





						"""simple docstring"""





						return input_array.reshape((input_array.size, 1)    )
def  UpperCamelCase						(       _A			:				np.ndarray			,							_A			:				np.ndarray			,							_A			:				int    )->       np.ndarray:





						"""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  UpperCamelCase						(       _A			:				np.ndarray			,							_A			:				np.ndarray			,							_A			:				int    )->       np.ndarray:





						"""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  UpperCamelCase						(       _A			:				np.ndarray			,							_A			:				int    )->       np.ndarray:





						"""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  UpperCamelCase						(       _A			:				np.ndarray			,							_A			:				np.ndarray			,							_A			:				int			,							_A			:				int    )->       np.ndarray:





						"""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  UpperCamelCase						(       )->       None:





						"""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  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]    )
						A__							=       2
						A__							=       np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [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()










import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[Any]:





						"""simple docstring"""





						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       0.01
						with locka.acquire():
												with pytest.raises(_A    ):
																		A__							=       time.time()
																		locka.acquire(_A    )
																		assert time.time() - _start > timeout
def  UpperCamelCase						(       _A			:				str    )->       List[Any]:





						"""simple docstring"""





						A__							=       "a" * 1000 + ".lock"
						A__							=       FileLock(str(tmpdir / filename    )    )
						assert locka._lock_file.endswith(".lock"    )
						assert not locka._lock_file.endswith(_A    )
						assert len(os.path.basename(locka._lock_file    )    ) <= 255
						A__							=       FileLock(tmpdir / filename    )
						with locka.acquire():
												with pytest.raises(_A    ):
																		locka.acquire(0    )





