Simulate Data from Hyper-Sphere for Classification Problems

The classification problem Circle is described in the BARK paper (2008). Inputs are dim independent variables uniformly distributed on the interval \([-1,1]\), only the first 2 out of these dim are actually signals. Outputs are created according to the formula $$y = 1(x1^2+x2^2 \le 2/\pi)$$

Usage

sim.Circle(n, dim=5)

Arguments

n

number of data points to generate

dim

number of dimension of the problem, no less than 2

Value

Returns a list with components

x

input values (independent variables)

y

0/1 output values (dependent variable)

References

Ouyang, Zhi (2008) Bayesian Additive Regression Kernels. Duke University. PhD dissertation, Chapter 3.

See also

Other bark deprecated functions: bark-deprecated, bark-package-deprecated, sim.Friedman1-deprecated, sim.Friedman2-deprecated, sim.Friedman3-deprecated

Examples

if (FALSE) { # \dontrun{
  sim.Circle(n=100, dim = 5)
} # }