pcomp¶

class pydl.pcomp(x, standardize=False, covariance=False)[source]¶

Bases: object

Replicates the IDL PCOMP() function.

The attributes of this class are all read-only properties, implemented with lazyproperty.

Parameters:

x : array-like

A 2-D array with \(N\) rows and \(M\) columns.

standardize : bool, optional

If set to True, the input data will have its mean subtracted off and will be scaled to unit variance.

covariance : bool, optional.

If set to True, the covariance matrix of the data will be used for the computation. Otherwise the correlation matrix will be used.

References

Attributes Summary

`coefficients`	(`ndarray`) The principal components.
`derived`	(`ndarray`) The derived variables.
`eigenvalues`	(`ndarray`) The eigenvalues.
`variance`	(`ndarray`) The variances of each derived variable.

Attributes Documentation

coefficients¶: (ndarray) The principal components. These are the coefficients of derived. Basically, they are a re-scaling of the eigenvectors.

eigenvalues¶: (ndarray) The eigenvalues. There is one eigenvalue for each principal component.