Description Usage Arguments Value Author(s) Examples
Estimates a single change-point using the simulated annealing method.
1 2 3 4 5 | simulated_annealing(object, niter = 500, min_beta = 1e-04, buff = 100)
## S4 method for signature 'changepointsMod'
simulated_annealing(object, niter = 500,
min_beta = 1e-04, buff = 100)
|
object |
Corresponding |
niter |
Number of simulated annealing iterations. |
min_beta |
Lowest temperature. |
buff |
Distance from edge of sample to be maintained during search. |
An updated version of the change-point model. The update will effect:
1) the part_values
and/or whole_values
(depending on the initial
values provided). 2) An estimate for the current change-point. 3) The trace
for the search.
Leland Bybee <lelandb@umich.edu>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | set.seed(334)
scp_data = read.table(system.file("extdata", "scp.txt", package="changepointsHD"))
scp_data = as.matrix(scp_data)
# prox gradient black-box method
cov_est = cov(scp_data)
init = solve(cov_est)
res_map = prox_gradient_mapping(scp_data, init, 0.1, 0.99, 0.1, 100, 1e-20)
# prox gradient black-box ll
res_ll = prox_gradient_ll(scp_data, res_map, 0.1)
prox_gradient_params=list()
prox_gradient_params$update_w = 0.1
prox_gradient_params$update_change = 0.99
prox_gradient_params$regularizer = 0.1
prox_gradient_params$max_iter = 1
prox_gradient_params$tol = 1e-5
prox_gradient_ll_params=list()
prox_gradient_ll_params$regularizer = 0.1
changepoints_mod = changepointsMod(bbmod=prox_gradient_mapping,
log_likelihood=prox_gradient_ll,
bbmod_params=prox_gradient_params,
ll_params=prox_gradient_ll_params,
part_values=list(init, init),
data=list(scp_data))
changepoints_mod = simulated_annealing(changepoints_mod, buff=10)
|
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