pca(X, centre=True, inplace=False, eps=1e-10)¶
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.
- X (
(n_samples, n_dims)ndarray) – Data matrix.
- centre (bool, optional) – Whether to centre the data matrix. If False, zero will be subtracted.
- 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.
- eps (float, optional) – Tolerance value for positive eigenvalue. Those eigenvalues smaller than the specified eps value, together with their corresponding eigenvectors, will be automatically discarded.
- U (eigenvectors) (
(``(n_components, n_dims))`` ndarray) – Eigenvectors of the data matrix.
- l (eigenvalues) (
(n_components,)ndarray) – Positive eigenvalues of the data matrix.
- m (mean vector) (
(n_dimensions,)ndarray) – Mean that was subtracted from the data matrix.
- X (