Functional Principal Nested Spheres Analysis#
Horizontal Functional Principal Nested Spheres Analysis using SRSF
moduleauthor:: J. Derek Tucker <jdtuck@sandia.gov>
- class fPNS.fdahpns(fdawarp)[source]#
This class provides horizontal fPNS using the SRVF framework
Usage: obj = fdapns(warp_data)
- Parameters:
warp_data – fdawarp class with alignment data
gam_pca – warping functions principal directions
psi_pca – srvf principal directions
latent – latent values
U – eigenvectors
coef – coefficients
vec – shooting vectors
mu – Karcher Mean
tau – principal directions
Author : J. D. Tucker (JDT) <jdtuck AT sandia.gov> Date : 03-Apr-2025
- calc_pns(var_exp=0.99, stds=array([-1, 0, 1]))[source]#
This function calculates horizontal functional principal nested spheres on aligned data
- Parameters:
var_exp – compute no based on value percent variance explained (example: 0.95)
stds – number of standard deviations along geodesic to compute (default = -1,0,1)
- Return type:
fdapns object of numpy ndarray
- Return gam_pca:
srsf principal directions
- Return psi_pca:
functional principal directions
- Return latent:
latent values
- Return coef:
coefficients
- Return U:
eigenvectors