

import logging

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

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





def       _lowercase     (   __UpperCamelCase  :		Any			):
      return input_array.reshape((input_array.size, 1)			)





def       _lowercase     (   __UpperCamelCase  :		Tuple ,							__UpperCamelCase  :		Tuple ,							__UpperCamelCase  :		Dict			):
      snake_case__											= np.nan
      for i in range(__UpperCamelCase			):
            snake_case__											= features[:, labels == i]
            snake_case__											= data.mean(1			)
            # Centralize the data of class i
            snake_case__											= 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)
                  snake_case__											= np.dot(__UpperCamelCase ,							centered_data.T			)

      return covariance_sum / features.shape[1]





def       _lowercase     (   __UpperCamelCase  :		Tuple ,							__UpperCamelCase  :		Union[str, Any] ,							__UpperCamelCase  :		Dict			):
      snake_case__											= features.mean(1			)
      snake_case__											= np.nan
      for i in range(__UpperCamelCase			):
            snake_case__											= features[:, labels == i]
            snake_case__											= data.shape[1]
            snake_case__											= 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)
                  snake_case__											= 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  :		str ,							__UpperCamelCase  :		Optional[Any]			):

      # Check if the features have been loaded
      if features.any():
            snake_case__											= features.mean(1			)
            # Center the dataset
            snake_case__											= features - np.reshape(__UpperCamelCase ,							(data_mean.size, 1)			)
            snake_case__											= np.dot(__UpperCamelCase ,							centered_data.T			) / features.shape[1]
            snake_case__											= np.linalg.eigh(__UpperCamelCase			)
            # Take all the columns in the reverse order (-1), and then takes only the first
            snake_case__											= eigenvectors[:, ::-1][:, 0:dimensions]
            # Project the database on the new space
            snake_case__											= 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  :		Optional[Any] ,							__UpperCamelCase  :		Dict ,							__UpperCamelCase  :		Tuple ,							__UpperCamelCase  :		Dict			):
      assert classes > dimensions

      # Check if features have been already loaded
      if features.any:
            snake_case__											= eigh(
                covariance_between_classes(__UpperCamelCase ,							__UpperCamelCase ,							__UpperCamelCase			) ,							covariance_within_classes(__UpperCamelCase ,							__UpperCamelCase ,							__UpperCamelCase			) ,							)
            snake_case__											= eigenvectors[:, ::-1][:, :dimensions]
            snake_case__											= np.linalg.svd(__UpperCamelCase			)
            snake_case__											= svd_matrix[:, 0:dimensions]
            snake_case__											= 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     (   ):
      # Create dummy dataset with 2 classes and 3 features
      snake_case__											= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]			)
      snake_case__											= np.array([0, 0, 0, 1, 1]			)
      snake_case__											= 2
      snake_case__											= 2

      # Assert that the function raises an AssertionError if dimensions > classes
      with pytest.raises(__UpperCamelCase			) as error_info:
            snake_case__											= 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     (   ):
      snake_case__											= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]			)
      snake_case__											= 2
      snake_case__											= 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:
            snake_case__											= 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()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)