# Tapkee wrapper R wrapper for the 'tapkee' dimension reduction library ```r Tapkee(data, method="pca", td=2, verbose=FALSE, add="", prefix="Dim", rm=TRUE) ``` ## Arguments - `data`: R numerical matrix or data frame (will be converted into matrix) - `method`: 'tapkee' method, run "system('tapkee -h')" for the list, default is "pca" - `td`: Number of dimensions to output, default is 2 - `verbose`: If TRUE, 'tapkee' is verbose, defalut is FALSE - `add`: 'tapkee' additional arguments as character string: see "system('tapkee -h')" - `prefix`: Variable name prefix in the resulted data frame, default is "Dim" - `rm`: Remove temp files (but temp folder will be removed anyway in the end of R session), default is TRUE ## Details Interface (wrapper) for the 'tapkee', flexible and efficient C++ template library for dimension reduction. 'tapkee' is extremely fast comparing with other DR tools. For methods used in 'tapkee', run 'vignette(tapkee_methods)'. Users should install 'tapkee' independently from author Web site (https://github.com/lisitsyn/tapkee) or associated GitHub (https://github.com/lisitsyn/tapkee). Run 'package?tapkee' or help("tapkee-package") for details related with your operation system. If 'tapkee' is not installed, Tapkee() will fail gracefully and output the input data with warning. Please note that "[warning] The neighborhood graph is not connected" message in most cases means that 'tapkee' run was unsuccessful. As a result, Tapkee() might return the matrix of NaN's. One of possible workarounds is to specify the higher number of neigbors ('-k' option, default is 10). See below for the example. Note that the wrapper catches only one (main) type of 'tapkee' utility outputs. For other possible output types (see 'tapkee -h' for explanation), run 'tapkee' without wrapper. ## Returns Data frame with number of columns equal to number of dimensions given and "prefix" column names prefixes. ## References Sergey Lisitsyn and Christian Widmer and Fernando J. Iglesias Garcia. Tapkee: An Efficient Dimension Reduction Library. Journal of Machine Learning Research, 14: 2355-2359, 2013. ## Author(s) Alexey Shipunov ## See Also `tapkee-package` ## Examples ```r ## 'tapkee' vs. R base functions system.time(Tapkee(iris[, -5], method="mds")) system.time(cmdscale(dist(iris[, -5]))) ## How to use 'add' option plot(Tapkee(iris[, -5], "isomap", add="-k 47"), col=iris[, 5]) ## 'tapkee' methods as of March 2019: TM <- c( "lle", # 1) locally_linear_embedding (lle), "npe", # 2) neighborhood_preserving_embedding (npe), "ltsa", # 3) local_tangent_space_alignment (ltsa), "lltsa", # 4) linear_local_tangent_space_alignment (lltsa), "hlle", # 5) hessian_locally_linear_embedding (hlle), "la", # 6) laplacian_eigenmaps (la), "lpp", # 7) locality_preserving_projections (lpp), "dm", # 8) diffusion_map (dm), "isomap", # 9) isomap (isomap), "l-isomap", # 10) landmark_isomap (l-isomap), "mds", # 11) multidimensional_scaling (mds), "l-mds", # 12) landmark_multidimensional_scaling (l-mds), "spe", # 13) stochastic_proximity_embedding (spe), "kpca", # 14) kernel_pca (kpca), "pca", # 15) pca (pca), "ra", # 16) random_projection (ra), "fa", # 17) factor_analysis (fa), "t-sne", # 18) t-stochastic_neighborhood_embedding (t-sne), "ms") # 19) manifold_sculpting (ms) ## Iris example oldpar <- par(mfrow=c(4, 5), mar=c(1, 1, 3, 1), xaxt="n", yaxt="n") for (n in c(1:18)) { plot(Tapkee(iris[, -5], method=TM[n], add="-k 50"), col=iris[, 5], pch=20, main=TM[n], xlab="", ylab="") } plot(iris[, 1:2], col=iris[, 5], pch=20, main="iris[, 1:2]", xlab="", ylab="") par(oldpar) ## Generate typical 3D data SR <- Gen.dr.data("swissroll") SC <- Gen.dr.data("scurve") HX <- Gen.dr.data("helix") SS <- Gen.dr.data("ssphere") ## This will separate colors better COL <- rainbow(1100)[1:1000] ## 3D plot (if no 'scatterplot3d' package, plots XY axes) T3D <- function(dat, title, col=COL) { if (requireNamespace("scatterplot3d", quietly = TRUE)) { scatterplot3d::scatterplot3d(dat, color=col, main=title, pch=20, xlab="", ylab="", zlab="", axis=FALSE, tick.marks=FALSE, label.tick.marks=FALSE, mar=c(1, 1, 3, 1)) } else { plot(dat[, 1:2], col=col, main=paste(title, "(2D projection)"), pch=20) warning("Please install 'scatterplot3d' package to see the 3D plot") }} ## Swiss Roll oldpar <- par(mfrow=c(4, 5)) T3D(SR, title="Swiss Roll") for (n in 1:18) plot(Tapkee(SR, method=TM[n]), col=COL, pch=20, main=TM[n], xlab="", ylab="", xaxt="n", yaxt="n") par(oldpar) ## S-Curve oldpar <- par(mfrow=c(4, 5)) T3D(SC, title="S-Curve") for (n in 1:18) plot(Tapkee(SC, method=TM[n]), col=COL, pch=20, main=TM[n], xlab="", ylab="", xaxt="n", yaxt="n") par(oldpar) ## Helix oldpar <- par(mfrow=c(4, 5)) T3D(HX, title="Helix") for (n in 1:18) plot(Tapkee(HX, method=TM[n]), col=COL, pch=20, main=TM[n], xlab="", ylab="", xaxt="n", yaxt="n") par(oldpar) ## Severed Sphere oldpar <- par(mfrow=c(4, 5)) T3D(SS, title="Severed Sphere", col=rainbow(nrow(SS))) for (n in 1:18) plot(Tapkee(SS, method=TM[n]), col=rainbow(nrow(SS)), pch=20, main=TM[n], xlab="", ylab="", xaxt="n", yaxt="n") par(oldpar) ```

Tapkee function