## ----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}"))) #