gradient.search | R Documentation |
Gradient based optimization for user defined central orientation of a rotation sample.
gradient.search(
sample,
error,
minerr = 1e-05,
start = mean(sample),
theta = NULL
)
sample |
sample of rotations. |
error |
user defined function to observed distance between sample and estimate, has to have parameters for the sample and the estimate. |
minerr |
minimal distance to consider for convergence. |
start |
starting value for the estimation. |
theta |
size of the grid considered. |
list of
Shat
estimate of the main direction
iter
number of iterations necessary for convergence
theta
final size of the grid
minerr
error used for convergence
error
numeric value of total sample's distance from main direction
# minimize L1 norm:
L1.error <- function(sample, Shat) {
sum(rot.dist(sample, Shat, method = "intrinsic", p = 1))
}
cayley.sample <- ruars(n = 10, rangle = rcayley, nu = 1, space = 'SO3')
SL1 <- gradient.search(cayley.sample, L1.error, start = id.SO3)
# visually no perceptible difference between median estimates from in-built function and
# gradient based search (for almost all starting values)
plot(cayley.sample, center=SL1$Shat, show_estimates="all")
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