




import logging

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

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray				)  ->							np.ndarray:
    return input_array.reshape((input_array.size, 1)				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						int				)  ->							np.ndarray:
    UpperCAmelCase_										=					np.nan
    for i in range(__UpperCamelCase				):
        UpperCAmelCase_										=					features[:, labels == i]
        UpperCAmelCase_										=					data.mean(1				)
        # Centralize the data of class i
        UpperCAmelCase_										=					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)
            UpperCAmelCase_										=					np.dot(__UpperCamelCase	,					centered_data.T				)

    return covariance_sum / features.shape[1]




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						int				)  ->							np.ndarray:
    UpperCAmelCase_										=					features.mean(1				)
    UpperCAmelCase_										=					np.nan
    for i in range(__UpperCamelCase				):
        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(__UpperCamelCase				) - column_reshape(__UpperCamelCase				)	,					(column_reshape(__UpperCamelCase				) - column_reshape(__UpperCamelCase				)).T	,					)
        else:
            # If covariance_sum is np.nan (i.e. first loop)
            UpperCAmelCase_										=					device_data * np.dot(
                column_reshape(__UpperCamelCase				) - column_reshape(__UpperCamelCase				)	,					(column_reshape(__UpperCamelCase				) - column_reshape(__UpperCamelCase				)).T	,					)

    return covariance_sum / features.shape[1]




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						int				)  ->							np.ndarray:

    # Check if the features have been loaded
    if features.any():
        UpperCAmelCase_										=					features.mean(1				)
        # Center the dataset
        UpperCAmelCase_										=					features - np.reshape(__UpperCamelCase	,					(data_mean.size, 1)				)
        UpperCAmelCase_										=					np.dot(__UpperCamelCase	,					centered_data.T				) / features.shape[1]
        UpperCAmelCase_			,							UpperCAmelCase_										=					np.linalg.eigh(__UpperCamelCase				)
        # 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	,					__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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							np.ndarray:
    assert classes > dimensions

    # Check if features have been already loaded
    if features.any:
        UpperCAmelCase_			,							UpperCAmelCase_										=					eigh(
            covariance_between_classes(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)	,					covariance_within_classes(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)	,					)
        UpperCAmelCase_										=					eigenvectors[:, ::-1][:, :dimensions]
        UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					np.linalg.svd(__UpperCamelCase				)
        UpperCAmelCase_										=					svd_matrix[:, 0:dimensions]
        UpperCAmelCase_										=					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  SCREAMING_SNAKE_CASE     (	)  ->							None:
    # 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(__UpperCamelCase				) as error_info:
        UpperCAmelCase_										=					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  SCREAMING_SNAKE_CASE     (	)  ->							None:
    UpperCAmelCase_										=					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]				)
    UpperCAmelCase_										=					2
    UpperCAmelCase_										=					np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]]				)

    with pytest.raises(__UpperCamelCase				) as error_info:
        UpperCAmelCase_										=					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()












def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    while cur > 1:
        # Find the maximum number in arr
        UpperCAmelCase_										=					arr.index(max(arr[0:cur]				)				)
        # Reverse from 0 to mi
        UpperCAmelCase_										=					arr[mi::-1] + arr[mi + 1 : len(__UpperCamelCase				)]
        # Reverse whole list
        UpperCAmelCase_										=					arr[cur - 1 :: -1] + arr[cur : len(__UpperCamelCase				)]
        cur -= 1
    return arr


if __name__ == "__main__":
 _lowerCamelCase      			=						input('Enter numbers separated by a comma:\n').strip()
 _lowerCamelCase      			=						[int(item) for item in user_input.split(',')]
 print(pancake_sort(unsorted))





