PriorRangeOrderKmeans {offlineChange} | R Documentation |
Detect the number and locations of change points based on minimizing within segment quadratic loss with restriction of prior ranges that contaion change points.
PriorRangeOrderKmeans(x, prior_range_x, num_init = NULL)
x |
The data to find change points. |
prior_range_x |
The prior ranges that contain change points. |
num_init |
The number of repetition times, in order to avoid local minimal. Default is squared root of number of observations. Must be integer. |
The K prior ranges contain K change points, each prior range contaions one change point.
num_change_point |
optimal number of change points. |
change_point |
location of change points. |
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.
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) l1<-c(15,25) l2<-c(75,100) prior_range_x<-list(l1,l2) PriorRangeOrderKmeans(x,prior_range_x=list(l1,l2))