| sim_circle {bark} | R Documentation |
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-package-deprecated,
bark-package,
bark(),
sim_Friedman1(),
sim_Friedman2(),
sim_Friedman3()
Examples
sim_circle(n=100, dim=5)