Elastic GLM Regression

Warping Invariant GML Regression using SRSF

moduleauthor:: Derek Tucker <jdtuck@sandia.gov>

class elastic_glm_regression.elastic_glm_regression(f, y, time)

This class provides elastic glm regression for functional data using the SRVF framework accounting for warping

Usage: obj = elastic_glm_regression(f,y,time)

Parameters:

• f – (M,N) % matrix defining N functions of M samples

• y – response vector of length N

• time – time vector of length M

• alpha – intercept

• b – coefficient vector

• B – basis matrix

• lambda – regularization parameter

• SSE – sum of squared errors

Author : J. D. Tucker (JDT) <jdtuck AT sandia.gov> Date : 18-Mar-2018

calc_model(link='linear', B=None, lam=0, df=20, max_itr=20, smooth_data=False, sparam=25, parallel=False)

This function identifies a regression model with phase-variability using elastic pca

Parameters:

• link – string of link function (‘linear’, ‘quadratic’, ‘cubic’)

• B – optional matrix describing Basis elements

• lam – regularization parameter (default 0)

• df – number of degrees of freedom B-spline (default 20)

• max_itr – maximum number of iterations (default 20)

• smooth_data – smooth data using box filter (default = F)

• sparam – number of times to apply box filter (default = 25)

• parallel – run in parallel (default = F)

predict(newdata=None, parallel=True)

This function performs prediction on regression model on new data if available or current stored data in object Usage: obj.predict(newdata)

Parameters:

• newdata (dict) – dict containing new data for prediction (needs the keys below, if None predicts on training data)

• f – (M,N) matrix of functions

• time – vector of time points

• y – truth if available

• smooth – smooth data if needed

• sparam – number of times to run filter