"""Function to reshape a row Numpy array into a column Numpy array

features: np.ndarray, labels: np.ndarray, classes: int

) -> np.ndarray:

"""Function to compute the covariance matrix inside each class.

covariance_sum = np.nan

for i in range(classes):

data = features[:, labels == i]

data_mean = data.mean(1)

# Centralize the data of class i

centered_data = data - column_reshape(data_mean)

if i > 0:

# If covariance_sum is not None

covariance_sum += np.dot(centered_data, centered_data.T)

else:

# If covariance_sum is np.nan (i.e. first loop)

covariance_sum = np.dot(centered_data, centered_data.T)

features: np.ndarray, labels: np.ndarray, classes: int

) -> np.ndarray:

"""Function to compute the covariance matrix between multiple classes

general_data_mean = features.mean(1)

covariance_sum = np.nan

for i in range(classes):

data = features[:, labels == i]

device_data = data.shape[1]

data_mean = data.mean(1)

if i > 0:

# If covariance_sum is not None

covariance_sum += device_data * np.dot(

column_reshape(data_mean) - column_reshape(general_data_mean),

(column_reshape(data_mean) - column_reshape(general_data_mean)).T,

)

else:

# If covariance_sum is np.nan (i.e. first loop)

covariance_sum = device_data * np.dot(

column_reshape(data_mean) - column_reshape(general_data_mean),

(column_reshape(data_mean) - column_reshape(general_data_mean)).T,

)

"""

# Check if the features have been loaded

if features.any():

data_mean = features.mean(1)

# Center the dataset

centered_data = features - np.reshape(data_mean, (data_mean.size, 1))

covariance_matrix = np.dot(centered_data, centered_data.T) / features.shape[1]

_, eigenvectors = np.linalg.eigh(covariance_matrix)

# Take all the columns in the reverse order (-1), and then takes only the first

filtered_eigenvectors = eigenvectors[:, ::-1][:, 0:dimensions]

# Project the database on the new space

projected_data = np.dot(filtered_eigenvectors.T, features)

logging.info("Principal Component Analysis computed")

return projected_data

else:

logging.basicConfig(level=logging.ERROR, format="%(message)s", force=True)

logging.error("Dataset empty")

features: np.ndarray, labels: np.ndarray, classes: int, dimensions: int

) -> np.ndarray:

"""

# Check if the dimension desired is less than the number of classes

assert classes > dimensions

# Check if features have been already loaded

if features.any:

_, eigenvectors = eigh(

covariance_between_classes(features, labels, classes),

covariance_within_classes(features, labels, classes),

)

filtered_eigenvectors = eigenvectors[:, ::-1][:, :dimensions]

svd_matrix, _, _ = np.linalg.svd(filtered_eigenvectors)

filtered_svd_matrix = svd_matrix[:, 0:dimensions]

projected_data = np.dot(filtered_svd_matrix.T, features)

logging.info("Linear Discriminant Analysis computed")

return projected_data

else:

logging.basicConfig(level=logging.ERROR, format="%(message)s", force=True)

logging.error("Dataset empty")

# Create dummy dataset with 2 classes and 3 features

features = np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]])

labels = np.array([0, 0, 0, 1, 1])

classes = 2

dimensions = 2

# Assert that the function raises an AssertionError if dimensions > classes

with pytest.raises(AssertionError) as error_info: # noqa: PT012

projected_data = linear_discriminant_analysis(

features, labels, classes, dimensions

)

if isinstance(projected_data, np.ndarray):

raise AssertionError(

"Did not raise AssertionError for dimensions > classes"

)

features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

dimensions = 2

expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])

with pytest.raises(AssertionError) as error_info: # noqa: PT012

output = principal_component_analysis(features, dimensions)

if not np.allclose(expected_output, output):

raise AssertionError