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)

[Package offlineChange version 0.0.4 Index]