FWHVA-package {FWHVA} | R Documentation |
Implementation horizontal visiblity graph needs to iterate three variables (i, j and k), which means that the worst computing time to construct a HVG from a time series with n data points is $O(n^3)$.
This algorithm excutes the HVG with $O(n)$ and also include the weighted information.
The limitation, the input length of FWHVA is less than 12193, if the input length is large than it, the stack may be overflow.
FWHVA(data)
data |
input data, one dimension |
Package: | FWHVA |
Type: | Package |
Version: | 1.3 |
Date: | 2014-4-30 |
License: | GPL (version 2 or later) |
mean degree |
|
mean strength |
|
Number degree 2 |
|
repeat time |
Guohun Zhu
Maintainer: Guohun Zhu <zhuguohun@163.com> or <Guohun.Zhu@usq.edu.au>
Zhu, Guohun, Li, Yan, & Wen, Peng(Paul). (2012). An Efficient Visibility Graph Similarity Algorithm and Its Application on Sleep Stages Classification. In FabioMassimo Zanzotto, Shusaku Tsumoto, Niels Taatgen & Yiyu Yao (Eds.), Brain Informatics (Vol. 7670, pp. 185-195): Springer Berlin Heidelberg.
and
Guohun Zhu, Yan Li, Peng (Paul) Wen, Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm, Computer Methods and Programs in Biomedicine, Available online 15 April 2014, ISSN 0169-2607, http://dx.doi.org/10.1016/j.cmpb.2014.04.001. (http://www.sciencedirect.com/science/article/pii/S0169260714001266)
HVG
~~ <pkg>
~~
~~ <FastVG>
~~
~~ <GraphEntropy>
~~
a=c(10,8,6,4,2,1,3,5,7,9,11) FWHVA(a)