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")
# }