


import logging

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

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




def 	__lowercase		(  _UpperCamelCase   )			->List[str]:







      """simple docstring"""

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




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->str:







      """simple docstring"""

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

      return covariance_sum / features.shape[1]




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->int:







      """simple docstring"""

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

      return covariance_sum / features.shape[1]




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->List[Any]:







      """simple docstring"""

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

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




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->List[Any]:







      """simple docstring"""

      assert classes > dimensions

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

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




def 	__lowercase		(  )			->Union[str, Any]:







      """simple docstring"""

      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	:      List[Any]          =						2
      lowercase	:      Union[str, Any]          =						2

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




def 	__lowercase		(  )			->List[str]:







      """simple docstring"""

      lowercase	:      str          =						np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]   )
      lowercase	:      Union[str, Any]          =						2
      lowercase	:      List[Any]          =						np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]   )

      with pytest.raises(_UpperCamelCase   ) as error_info:
            lowercase	:      int          =						principal_component_analysis(_UpperCamelCase,			_UpperCamelCase   )
            if not np.allclose(_UpperCamelCase,			_UpperCamelCase   ):
                  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_tokenizers_available, is_torch_available


lowerCAmelCase__  :  str						     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						pass

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[str]						     =	[
						    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RoCBertForCausalLM',
						    'RoCBertForMaskedLM',
						    'RoCBertForMultipleChoice',
						    'RoCBertForPreTraining',
						    'RoCBertForQuestionAnswering',
						    'RoCBertForSequenceClassification',
						    'RoCBertForTokenClassification',
						    'RoCBertLayer',
						    'RoCBertModel',
						    'RoCBertPreTrainedModel',
						    'load_tf_weights_in_roc_bert',
						]

if TYPE_CHECKING:
						from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
						from .tokenization_roc_bert import RoCBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												raise OptionalDependencyNotAvailable()

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roc_bert import (
												    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoCBertForCausalLM,
												    RoCBertForMaskedLM,
												    RoCBertForMultipleChoice,
												    RoCBertForPreTraining,
												    RoCBertForQuestionAnswering,
												    RoCBertForSequenceClassification,
												    RoCBertForTokenClassification,
												    RoCBertLayer,
												    RoCBertModel,
												    RoCBertPreTrainedModel,
												    load_tf_weights_in_roc_bert,
												)


else:
						import sys

						lowerCAmelCase__  :  str						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



