The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

VMDecomp


The VMDecomp R package is the RcppArmadillo implementation of the “Variational Mode Decomposition” Matlab code in R. More details on the functionality of VMDecomp can be found in the package Documentation, Vignette and blog post.


Examples:


Variational Mode Decomposition (including residuals)



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)



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,

install.packages("VMDecomp")


and to download the latest version of the package from Github,

remotes::install_github('mlampros/VMDecomp')


Docker Image


Docker images of the VMDecomp package are available to download from my dockerhub 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,



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,



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 for more information).


Open your web-browser and depending where the docker image was build / run give,


1st. Option on your personal computer,


http://0.0.0.0:8787


2nd. Option on a cloud instance,


http://Public DNS:8787


to access the Rstudio console in order to give your username and password.


Similar Projects:


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:


@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},
}


These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.