principal_component_decomposition¶
-
menpo.math.
principal_component_decomposition
(X, whiten=False, centre=True, bias=False, inplace=False)[source]¶ Apply Principal Component Analysis (PCA) on the data matrix X. In the case where the data matrix is very large, it is advisable to set
inplace = True
. However, note this destructively edits the data matrix by subtracting the mean inplace.Parameters: - X (
(n_samples, n_features)
ndarray) – Training data. - whiten (bool, optional) – Normalise the eigenvectors to have unit magnitude.
- centre (bool, optional) – Whether to centre the data matrix. If
False
, zero will be subtracted. - bias (bool, optional) – Whether to use a biased estimate of the number of samples. If
False
, subtracts1
from the number of samples. - inplace (bool, optional) – Whether to do the mean subtracting inplace or not. This is crucial if the data matrix is greater than half the available memory size.
Returns: - eigenvectors (
(n_components, n_features)
ndarray) – The eigenvectors of the data matrix. - eigenvalues (
(n_components,)
ndarray) – The positive eigenvalues from the data matrix. - mean_vector (
(n_components,)
ndarray) – The mean that was subtracted from the dataset.
- X (