sim_circle: Simulate Data from Hyper-Sphere for Classification Problems

In bark: Bayesian Additive Regression Kernels

Simulate Data from Hyper-Sphere for Classification Problems

Description

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 simulation functions: sim_Friedman1(), sim_Friedman2(), sim_Friedman3()

Other bark functions: bark(), bark-package, bark-package-deprecated, sim_Friedman1(), sim_Friedman2(), sim_Friedman3()

Examples

sim_circle(n=100, dim=5)

bark documentation built on Oct. 6, 2024, 1:08 a.m.