train_mego: Trainer for a surmodel object based on the MEGO algorithm
In coldfir3/suropt: Surrogate-Based Optimization
Description
Usage
Arguments
Examples
Description
Trainer for a surmodel object based on the MEGO algorithm
Usage
1
train_mego(model, niter, optimizer = "gen")
Arguments
model
surmodel object to be trained
niter
integer indicating number of iterations
optimizer
string one of: "gen" (genetic, default),
"sa" (simulated annealing) or "ps" (particle swarm)
Examples
1
2
3
fn <- binh
model <- build_surmodel(fn, 5, 2) %>% train_mego(1, 'sa')
plot(model)
coldfir3/suropt documentation built on May 12, 2019, 5:44 a.m.
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Description Usage Arguments Examples
Description
Trainer for a surmodel object based on the MEGO algorithm
Usage
1 | train_mego(model, niter, optimizer = "gen")
|
Arguments
model |
surmodel object to be trained |
niter |
integer indicating number of iterations |
optimizer |
string one of: "gen" (genetic, default), "sa" (simulated annealing) or "ps" (particle swarm) |
Examples
1 2 3 | fn <- binh
model <- build_surmodel(fn, 5, 2) %>% train_mego(1, 'sa')
plot(model)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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