principal_component_decomposition

menpo.math.decomposition.principal_component_decomposition(X, whiten=False, center=True, bias=False, inplace=False)

Apply 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 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

Default: False

center : bool, optional

Whether to center the data matrix. If False, zero will be subtracted.

Default: True

bias : bool, optional

Whether to use a biased estimate of the number of samples. If False, subtracts 1 from the number of samples.

Default: False

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.

Default: False

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