"""simple docstring"""







import logging

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

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.ndarray   ):
       return input_array.reshape((input_array.size, 1)   )



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

       return covariance_sum / features.shape[1]



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

       return covariance_sum / features.shape[1]



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.ndarray  ,    __SCREAMING_SNAKE_CASE							:  int   ):

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

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.ndarray  ,    __SCREAMING_SNAKE_CASE							:  np.ndarray  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int   ):
       assert classes > dimensions

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

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



def     lowercase__( ):
       # Create dummy dataset with 2 classes and 3 features
       lowercase_			:       str									=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]   )
       lowercase_			:       List[str]									=			np.array([0, 0, 0, 1, 1]   )
       lowercase_			:       Any									=			2
       lowercase_			:       Any									=			2

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



def     lowercase__( ):
       lowercase_			:       List[str]									=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]   )
       lowercase_			:       List[str]									=			2
       lowercase_			:       Any									=			np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]   )

       with pytest.raises(__SCREAMING_SNAKE_CASE   ) as error_info:
              lowercase_			:       Union[str, Any]									=			principal_component_analysis(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
              if not np.allclose(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
                     raise AssertionError
              assert error_info.type is AssertionError


if __name__ == "__main__":
       import doctest

       doctest.testmod()


"""simple docstring"""







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__SCREAMING_SNAKE_CASE								="2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
       raise ImportWarning(
           "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
       )

if version.parse(pyarrow.__version__).major < 8:
       raise ImportWarning(
           "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
           "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
       )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__SCREAMING_SNAKE_CASE								=concatenate_datasets
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadManager
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager

