"""simple docstring"""






import logging

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

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



def   a_						(					lowerCamelCase       ):
				return input_array.reshape((input_array.size, 1)       )



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

				return covariance_sum / features.shape[1]



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							features.mean(1       )
				UpperCAmelCase__             =							np.nan
				for i in range(lowerCamelCase       ):
								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(lowerCamelCase       ) - column_reshape(lowerCamelCase       )					,			(column_reshape(lowerCamelCase       ) - column_reshape(lowerCamelCase       )).T					,			)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												UpperCAmelCase__             =							device_data * np.dot(
												    column_reshape(lowerCamelCase       ) - column_reshape(lowerCamelCase       )					,			(column_reshape(lowerCamelCase       ) - column_reshape(lowerCamelCase       )).T					,			)

				return covariance_sum / features.shape[1]



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):

				# Check if the features have been loaded
				if features.any():
								UpperCAmelCase__             =							features.mean(1       )
								# Center the dataset
								UpperCAmelCase__             =							features - np.reshape(lowerCamelCase					,			(data_mean.size, 1)       )
								UpperCAmelCase__             =							np.dot(lowerCamelCase					,			centered_data.T       ) / features.shape[1]
								UpperCAmelCase__      ,	UpperCAmelCase__             =							np.linalg.eigh(lowerCamelCase       )
								# 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					,			lowerCamelCase       )
								logging.info('Principal Component Analysis computed'       )

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



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				assert classes > dimensions

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

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



def   a_						(					):
				# 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(lowerCamelCase       ) as error_info:
								UpperCAmelCase__             =							linear_discriminant_analysis(
								    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )
								if isinstance(lowerCamelCase					,			np.ndarray       ):
												raise AssertionError(
												    'Did not raise AssertionError for dimensions > classes'       )
								assert error_info.type is AssertionError



def   a_						(					):
				UpperCAmelCase__             =							np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       )
				UpperCAmelCase__             =							2
				UpperCAmelCase__             =							np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]       )

				with pytest.raises(lowerCamelCase       ) as error_info:
								UpperCAmelCase__             =							principal_component_analysis(lowerCamelCase					,			lowerCamelCase       )
								if not np.allclose(lowerCamelCase					,			lowerCamelCase       ):
												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__)



