Elastic Functional Clustering

Elastic Functional Clustering

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

kmeans.kmeans_align(f, time, K, seeds=None, lam=0, showplot=True, smooth_data=False, parallel=False, alignment=True, omethod='DP2', MaxItr=50, thresh=0.01)

This function clusters functions and aligns using the elastic square-root slope (srsf) framework.

Parameters:

• f – numpy ndarray of shape (M,N) of N functions with M samples
• time – vector of size M describing the sample points

:param K number of clusters :param seeds indexes of cluster center functions (default = None) :param lam controls the elasticity (default = 0) :param showplot shows plots of functions (default = T) :param smooth_data smooth data using box filter (default = F) :param parallel enable parallel mode using code{link{joblib}} and

code{doParallel} package (default=F)

:param alignment whether to perform alignment (default = T) :param omethod optimization method (DP,DP2,RBFGS) :param MaxItr maximum number of iterations :param thresh cost function threshold :type f: np.ndarray :type time: np.ndarray

Return type:dictionary Return fn:aligned functions - matrix (N x M) of M functions with N samples which is a list for each cluster Return qn:aligned SRSFs - similar structure to fn Return q0:original SRSFs Return labels:cluster labels Return templates:  cluster center functions Return templates_q:  cluster center SRSFs Return gam:warping functions - similar structure to fn Return qun:Cost Function