PriorRangeOrderKmeansCpp: Detect Location of Change Points of Independent Data with...

Description Usage Arguments Details Value References Examples

View source: R/RcppExports.R

Description

Detect the location of change points based on minimizing within segment quadratic loss with restriction of prior ranges that contaion change points.

Usage

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PriorRangeOrderKmeansCpp(x, prior_range_x, num_init = 10)

Arguments

x

The data to find change points with dimension N x D, must be matrix

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 10. Must be integer.

Details

The K change points form K+1 segments (1 2 ... change_point(1)) ... (change_point(K) ... N).

Value

num_change_point

optimal number of change points.

change_point

location of 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

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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)
PriorRangeOrderKmeansCpp(x,prior_range_x=list(l1,l2))

offlineChange documentation built on April 20, 2020, 9:10 a.m.