## ------------------------------------------------------------------------ library(intrinsicDimension) ## ------------------------------------------------------------------------ local.data <- cutHyperPlane(Ns = 30, d = 50, n = 100, sd = 0.01) essLocalDimEst(local.data, ver = 'a') maxLikLocalDimEst(local.data) maxLikLocalDimEst(local.data, dnoise='dnoiseGaussH', sigma=0.01, n=100) pcaLocalDimEst(local.data, ver = 'FO') ## ------------------------------------------------------------------------ manifold.data <- swissRoll(500) maxLikGlobalDimEst(manifold.data, k=10, unbiased = TRUE) maxLikGlobalDimEst(manifold.data, k=10, unbiased = TRUE, neighborhood.aggregation = 'robust') dancoDimEst(manifold.data, k=10, D=10) N <- dim(manifold.data)[1] k <- 2 ps <- seq(max(k + 1, round(N/2)), N - 1, length.out = 5) knnDimEst(manifold.data, k, ps, M = 10, gamma = 2) maxLikPointwiseDimEst(manifold.data, k=10, unbiased = TRUE) pcaOtpmPointwiseDimEst(manifold.data, 10) ## ---- fig.show='hold', fig.width=6, fig.height=4------------------------- data <- swissRoll3Sph(300, 300) essPointwiseDimEst <- asPointwiseEstimator(essLocalDimEst, neighborhood.size=10, indices = c(1:10, 301:310)) ess.pw.res <- essPointwiseDimEst(data) palette <- c('#11FF1111', '#FF111111') hist(ess.pw.res$dim.est[1:10], breaks=seq(0, max(ess.pw.res$dim.est)+1, by=0.5), col=palette[1], main='ESS pointwise dimension estimation', xlab='') hist(ess.pw.res$dim.est[11:20], breaks=seq(0, max(ess.pw.res$dim.est)+1, by=0.5), add=TRUE, col=palette[2]) legend('topright', c('Swiss roll (2D)', '3-sphere (3D)'), fill=palette) max.lik.pw.res <- maxLikPointwiseDimEst(data, k=10, indices = c(1:10, 301:310)) hist(max.lik.pw.res$dim.est[1:10], breaks=seq(0, max(max.lik.pw.res$dim.est)+1, by=0.5), col=palette[1], main='ML pointwise dimension estimation', xlab='') hist(max.lik.pw.res$dim.est[11:20], breaks=seq(0, max(max.lik.pw.res$dim.est)+1, by=0.5), add=TRUE, col=palette[2]) legend('topright', c('Swiss roll (2D)', '3-sphere (3D)'), fill=palette)