https://github.com/mlampros/vmdecomp
R package for 'Variational Mode Decomposition' (1- and 2-dimensional)
https://github.com/mlampros/vmdecomp
r rcpparmadillo variational-mode-decomposition
Last synced: 4 months ago
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R package for 'Variational Mode Decomposition' (1- and 2-dimensional)
- Host: GitHub
- URL: https://github.com/mlampros/vmdecomp
- Owner: mlampros
- Created: 2022-06-08T09:11:07.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-05-27T04:33:51.000Z (over 2 years ago)
- Last Synced: 2025-06-15T21:55:50.378Z (4 months ago)
- Topics: r, rcpparmadillo, variational-mode-decomposition
- Language: MATLAB
- Homepage: https://mlampros.github.io/VMDecomp/
- Size: 13.1 MB
- Stars: 8
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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## VMDecomp
The **VMDecomp** R package is the *RcppArmadillo* implementation of the ["Variational Mode Decomposition" Matlab code](https://math.montana.edu/dzosso/code/) in R. More details on the functionality of VMDecomp can be found in the package [Documentation](https://mlampros.github.io/VMDecomp/reference/index.html), [Vignette](https://mlampros.github.io/VMDecomp/articles/variatonal_mode_decomposition.html) and [blog post](http://mlampros.github.io/2022/06/11/variatonal_mode_decomposition/).
### Examples:
**Variational Mode Decomposition** (including residuals)
```R
require(VMDecomp)
data(arrhythmia)
alpha = 2000 # moderate bandwidth constraint
tau = 0 # noise-tolerance (no strict fidelity enforcement)
K = 9 # 9 modes
DC = FALSE # no DC part imposed
init = 1 # initialize omegas uniformly
tol = 1e-6
vec_arrhythmia = arrhythmia[['MLII']]
set.seed(1)
arr_vmd = vmd(data = vec_arrhythmia,
alpha = alpha,
tau = tau,
K = K,
DC = DC,
init = init,
tol = tol,
verbose = TRUE)
imfs = data.table::data.table(arr_vmd$u)
colnames(imfs) = glue::glue("IMF_{1:ncol(imfs)}")
imfs$residual = rowSums(imfs) - vec_arrhythmia
round(imfs, digits = 5)
# IMF_1 IMF_2 IMF_3 IMF_4 IMF_5 IMF_6 IMF_7 IMF_8 IMF_9 residual
# 1: -0.06947 0.07831 0.13355 0.14031 0.10371 0.05622 -0.00143 -0.09686 -0.00629 -0.01194
# 2: -0.06971 0.07765 0.13199 0.13698 0.09920 0.05226 -0.00062 -0.07683 -0.01055 -0.00963
# 3: -0.07016 0.07639 0.12896 0.13047 0.09046 0.04475 0.00097 -0.04075 -0.01487 -0.00377
# 4: -0.07068 0.07468 0.12466 0.12114 0.07810 0.03447 0.00329 0.00427 -0.01331 0.00662
# 5: -0.07108 0.07273 0.11937 0.10947 0.06297 0.02246 0.00622 0.04929 -0.00211 0.01932
# ---
# 9996: -0.07001 -0.13154 -0.24738 0.18826 0.03381 -0.07354 0.00076 0.03773 0.01426 0.01234
# 9997: -0.06980 -0.13333 -0.25498 0.21256 0.03400 -0.11214 0.05833 -0.00481 0.01450 0.00432
# 9998: -0.06951 -0.13452 -0.26056 0.23154 0.03414 -0.14316 0.10925 -0.04580 0.00791 -0.00570
# 9999: -0.06934 -0.13534 -0.26432 0.24447 0.03413 -0.16496 0.14706 -0.07813 -0.00137 -0.00780
# 10000: -0.06932 -0.13581 -0.26626 0.25095 0.03402 -0.17629 0.16708 -0.09601 -0.00797 0.00040
```
**Estimation of the K-modes Parameter** (*correlation threshold* of 0.1 and a *minimum K* of 2)
```R
require(VMDecomp)
data(arrhythmia)
default_vmd_params = list(alpha = 2000,
tau = 0,
DC = FALSE,
init = 1,
tol = 1e-6)
res_k = estimate_k_modes(signal_1d = arrhythmia[['MLII']],
cor_thresh = 0.1,
default_vmd_params = default_vmd_params,
min_K = 2,
seed = 1,
verbose = TRUE)
# VMD based on a K of '2' will be computed ...
# VMD based on a K of '3' will be computed ...
# VMD based on a K of '4' will be computed ...
# VMD based on a K of '5' will be computed ...
# VMD based on a K of '6' will be computed ...
# VMD based on a K of '7' will be computed ...
# VMD based on a K of '8' will be computed ...
# VMD based on a K of '9' will be computed ...
# Optimal K parameter: '8' Pre-specified correlation coefficient threshold: '0.1'
# Elapsed time: 0 hours and 1 minutes and 19 seconds.
res_k
# [1] 8
```
### Installation:
To install the package from CRAN use,
```R
install.packages("VMDecomp")
```
and to download the latest version of the package from Github,
```R
remotes::install_github('mlampros/VMDecomp')
```
#### **Docker Image**
**Docker images** of the *VMDecomp* package are available to download from my [dockerhub](https://hub.docker.com/r/mlampros/vmdecomp) account. The images come with *Rstudio* and the *R-development* version (latest) installed. The whole process was tested on Ubuntu 18.04. To **pull** & **run** the image do the following,
```R
docker pull mlampros/vmdecomp:rstudiodev
docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/vmdecomp:rstudiodev
```
The user can also **bind** a home directory / folder to the image to use its files by specifying the **-v** command,
```R
docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 -v /home/YOUR_DIR:/home/rstudio/YOUR_DIR mlampros/vmdecomp:rstudiodev
```
The **USER** defaults to *rstudio* but you have to give your **PASSWORD** of preference (see [www.rocker-project.org](https://www.rocker-project.org/) for more information).
Open your web-browser and depending where the docker image was *build / run* give,
**1st. Option** on your personal computer,
```R
http://0.0.0.0:8787
```
**2nd. Option** on a cloud instance,
```R
http://Public DNS:8787
```
to access the Rstudio console in order to give your username and password.
### **Similar Projects:**
* https://github.com/vrcarva/vmdpy (*Variational Mode Decomposition in Python*)
* https://github.com/helske/Rlibeemd (*ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN)*)
### **Citation:**
If you use the **VMDecomp** R package in your paper or research please cite both **VMDecomp** and the **original articles / software** `https://CRAN.R-project.org/package=VMDecomp`:
```R
@Manual{,
title = {{VMDecomp}: Variational Mode Decomposition using R},
author = {Lampros Mouselimis},
year = {2022},
note = {R package version 1.0.1},
url = {https://CRAN.R-project.org/package=VMDecomp},
}
```