It generates samples from predefined shapes, set by dname parameter.
Also incorporated a functionality to add white noise with degree noise.
aux.gensamples(
n = 496,
noise = 0.01,
dname = c("swiss", "crown", "helix", "saddle", "ribbon", "bswiss", "cswiss",
"twinpeaks", "sinusoid", "mobius", "R12in72"),
...
)the number of points to be generated.
level of additive white noise.
name of a predefined shape. Should be one of
"swiss"swiss roll
"crown"crown
"helix"helix
"saddle"manifold near saddle point
"ribbon"ribbon
"bswiss"broken swiss
"cswiss"cut swiss
"twinpeaks"two peaks
"sinusoid"sinusoid on the circle
"mobius"mobius strip embedded in \(\mathbf{R}^3\)
"R12in72"12-dimensional manifold in \(\mathbf{R}^{12}\)
extra parameters for the followings #'
| parameter | dname | description |
ntwist | "mobius" | number of twists |
an \((n\times p)\) matrix of generated data by row. For all methods other than "R12in72", it returns a matrix with \(p=3\).
Hein M, Audibert J (2005). “Intrinsic Dimensionality Estimation of Submanifolds in $R^d$.” In Proceedings of the 22nd International Conference on Machine Learning, 289--296.
van der Maaten L (2009). “Learning a Parametric Embedding by Preserving Local Structure.” Proceedings of AI-STATS.
# \donttest{
## generating toy example datasets
set.seed(100)
dat.swiss = aux.gensamples(50, dname="swiss")
dat.crown = aux.gensamples(50, dname="crown")
dat.helix = aux.gensamples(50, dname="helix")
# }