## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = FALSE,
echo = TRUE)
## -----------------------------------------------------------------------------
#
# require(VMDecomp)
# require(glue)
#
# 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)
#
# # 1-dimensional VMD starts ...
# # --------------------------------
# # The 1-dimensional VMD starts ...
# # --------------------------------
# # Iteration: 10 uDiff: 2.28024
# # Iteration: 20 uDiff: 1.05777
# # Iteration: 30 uDiff: 0.515047
# # Iteration: 40 uDiff: 0.346114
# # Iteration: 50 uDiff: 0.277273
# # Iteration: 60 uDiff: 0.240478
# # Iteration: 70 uDiff: 0.212126
# # Iteration: 80 uDiff: 0.20821
# # Iteration: 90 uDiff: 0.196179
# # ...............
# # Iteration: 470 uDiff: 0.00728391
# # Iteration: 480 uDiff: 0.00698528
# # Iteration: 490 uDiff: 0.00663912
# # Iteration: 500 uDiff: 0.00629761
# # -----------------------------------------
# # The algorithm converged in iteration: 500
# # -----------------------------------------
# # Elapsed time: 0 hours and 0 minutes and 19 seconds.
#
#
# str(arr_vmd)
# # List of 3
# # $ u : num [1:10000, 1:9] -0.0695 -0.0697 -0.0702 -0.0707 -0.0711 ...
# # $ u_hat: cplx [1:10000, 1:9] 6.54e-04+0.0i -3.83e-03+5.1e-04i -1.99e-03+8.5e-04i ...
# # $ omega: num [1:500, 1:9] 0 0.00561 0.00595 0.00596 0.00586 ...
#
#
# op <- par(mfrow = c(3, K/3))
#
# for (item in K:1) {
# item_mode = glue::glue("IMF {K-item+1}")
# plot(x = arr_vmd$u[, item], type = 'l', main = item_mode, xlab = 'Time', ylab = '')
# }
#
## -----------------------------------------------------------------------------
#
# imfs_noise_free = rowSums(arr_vmd$u[, -c(1,K)])
#
# op <- par(mfrow = c(1,1))
#
# plot(x = vec_arrhythmia, type = 'l', col = "blue", xlab = 'Time', ylab = 'Signal')
# lines(x = imfs_noise_free, col = "orange")
# legend("topright",
# legend = c("Original ECG", "Clean ECG"),
# col = c("blue", "orange"),
# lty = 1:2,
# cex = 0.8)
#
## -----------------------------------------------------------------------------
#
# require(R.matlab)
# require(OpenImageR)
#
# pth_texture = system.file('Matlab', 'VMD_2D', 'texture.mat', package = 'VMDecomp')
# data = R.matlab::readMat(pth_texture)
# data = data$f
# dim(data)
# # [1] 256 256
#
# alpha = 1000 # bandwidth constraint
# tau = 0.25 # Lagrangian multipliers dual ascent time step
# K = 5 # number of modes
# DC = TRUE # includes DC part (first mode at DC)
# init = 1 # initialize omegas randomly, may need multiple runs!
# tol = 1e-7 # tolerance (for convergence)
#
# set.seed(2)
# res_2d = vmd(data = data,
# alpha = alpha,
# tau = tau,
# K = K,
# DC = DC,
# init = init,
# tol = tol,
# verbose = TRUE)
#
# # --------------------------------
# # The 2-dimensional VMD starts ...
# # --------------------------------
# # Iteration: 10 uDiff: 42.7451 omegaDiff: 0.00012395
# # Iteration: 20 uDiff: 1.05264 omegaDiff: 1.15039e-09
# # Iteration: 30 uDiff: 0.31892 omegaDiff: 4.14469e-10
# # Iteration: 40 uDiff: 0.117648 omegaDiff: 1.45234e-10
# # Iteration: 50 uDiff: 0.0491776 omegaDiff: 6.399e-11
# # Iteration: 60 uDiff: 0.0222704 omegaDiff: 3.33701e-11
# # Iteration: 70 uDiff: 0.0106591 omegaDiff: 1.87647e-11
# # Iteration: 80 uDiff: 0.0053165 omegaDiff: 1.08343e-11
# # Iteration: 90 uDiff: 0.00273909 omegaDiff: 6.30121e-12
# # ............
# # Iteration: 250 uDiff: 6.31694e-07 omegaDiff: 1.22605e-15
# # Iteration: 260 uDiff: 4.33964e-07 omegaDiff: 8.05652e-16
# # Iteration: 270 uDiff: 3.08055e-07 omegaDiff: 5.66624e-16
# # Iteration: 280 uDiff: 2.26559e-07 omegaDiff: 4.31241e-16
# # Iteration: 290 uDiff: 1.728e-07 omegaDiff: 3.54727e-16
# # Iteration: 300 uDiff: 1.36554e-07 omegaDiff: 3.11406e-16
# # Iteration: 310 uDiff: 1.11508e-07 omegaDiff: 2.86634e-16
# # -----------------------------------------
# # The algorithm converged in iteration: 317
# # -----------------------------------------
# # Elapsed time: 0 hours and 0 minutes and 41 seconds.
#
# end_dims = dim(res_2d$u)[3]
# str(res_2d)
# # List of 3
# # $ u : num [1:256, 1:256, 1:5] 3.16e-05 1.80e-05 -3.32e-06 -3.17e-05 -6.32e-05 ...
# # $ u_hat: cplx [1:256, 1:256, 1:5] 7.65e-04+0.00i -6.63e-04-3.28e-04i 7.74e-04-3.15e-04i ...
# # $ omega: num [1:317, 1:2, 1:5] 0 0 0 0 0 0 0 0 0 0 ...
#
## -----------------------------------------------------------------------------
#
# res_2d_lst = lapply(1:end_dims, function(x) res_2d$u[,, x])
# res_2d_lst = append(res_2d_lst, list(data), after = 0)
# str(res_2d_lst)
# # List of 6
# # $ : num [1:256, 1:256] 0 0 0 0 0 0 0 0 0 0 ...
# # $ : num [1:256, 1:256] 3.16e-05 1.80e-05 -3.32e-06 -3.17e-05 -6.32e-05 ...
# # $ : num [1:256, 1:256] -4.36e-05 -3.08e-05 -1.19e-05 2.28e-05 6.01e-05 ...
# # $ : num [1:256, 1:256] -2.67e-07 2.53e-06 1.03e-06 1.94e-06 2.44e-07 ...
# # $ : num [1:256, 1:256] 3.31e-06 1.21e-06 2.15e-06 1.66e-07 4.97e-07 ...
# # $ : num [1:256, 1:256] 6.72e-06 9.27e-06 1.14e-05 8.79e-06 1.45e-06 ...
#
#
# init_plt = OpenImageR::GaborFeatureExtract$new()
# mlt_plt = init_plt$plot_multi_images(list_images = res_2d_lst,
# par_ROWS = 3,
# par_COLS = 2,
# axes = TRUE,
# titles = c("Input Image", glue::glue("Imf_{1:end_dims}")))
#