knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE )
library(cardinalR) library(langevitour)
This illustrates a variety of example data structures that can be generated. These synthetic datasets represent common and challenging shapes found.
A simple spherical Gaussian cluster with very small variance in $4\text{-}D$ space.
gau_data <- gen_gaussian(n = 500, p = 4, s = diag(4) * 0.01) langevitour(gau_data, pointSize = 2)
Uniform points in a hypercube with a hollow center, useful for detecting voids or gaps.
cube_grd_data <- gen_unifcubehole(n = 5000, p = 6) langevitour(cube_grd_data, pointSize = 2)
A curvilinear loop structure that forms a closed cycle in $4\text{-}D$.
curvy_cyc_data <- gen_curvycycle(n = 500, p = 4) langevitour(curvy_cyc_data, pointSize = 2)
A tree-like curvilinear structure with six branches in $6\text{-}D$, ideal for studying bifurcation patterns.
tree_data <- gen_orgcurvybranches(n = 600, p = 5, k = 6) langevitour(tree_data, pointSize = 2)
A pointed cone shape in $4\text{-}D$, controlled by height and radius ratio.
cone_data <- gen_cone(n = 500, h = 5, ratio = 0.5) langevitour(cone_data, pointSize = 2)
A spiral winding around a conical surface in $5\text{-}D$ space.
spiral_data <- gen_conicspiral(n = 500, spins = 2) langevitour(spiral_data, pointSize = 2)
A regularly spaced sphere with high point density, useful for manifold learning.
sphere_data <- gen_gridedsphere(n = 500, p = 4) langevitour(sphere_data, pointSize = 2)
An S-shaped manifold in $8\text{-}D$ with a missing section, to evaluate resilience to structural gaps.
scurve_data <- gen_scurvehole(n = 600) langevitour(scurve_data, pointSize = 2)
A $4\text{-}D$ pyramid-shaped structure with a void at the center.
pyr_data <- gen_pyrfrac(n = 1000, p = 4) langevitour(pyr_data, pointSize = 2)
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