| OrderKmeansCpp {offlineChange} | R Documentation |
Detect Location of Change Points of Independent Data using Rcpp
Description
Detect the location of change points based on minimizing within segment quadratic loss with fixed number of change points.
Usage
OrderKmeansCpp(x, K = 4, num_init = 10)
Arguments
x |
The data to find change points with dimension N x D, must be matrix |
K |
The number of change points. |
num_init |
The number of repetition times, in order to avoid local minimal. Default is 10. Must be integer. |
Details
The K change points form K+1 segments (1 2 ... change_point(1)) ... (change_point(K) ... N).
Value
wgss_sum |
total within segment sum of squared distances to the segment mean (wgss) of all segments. |
num_each |
number of observations of each segment |
wgss |
total wgss of each segment. |
change_point |
location of optimal change points. |
References
J. Ding, Y. Xiang, L. Shen, and V. Tarokh, Multiple Change Point Analysis: Fast Implementation and Strong Consistency. IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4495-4510, 2017.
Examples
a<-matrix(rnorm(40,mean=-1,sd=1),nrow=20,ncol=2)
b<-matrix(rnorm(120,mean=0,sd=1),nrow=60,ncol=2)
c<-matrix(rnorm(40,mean=1,sd=1),nrow=20,ncol=2)
x<-rbind(a,b,c)
OrderKmeansCpp(x,K=3)
OrderKmeansCpp(x,K=3,num_init=8)