tgp.get.partitions {tgp} | R Documentation |
Partition data according to the maximum a' posteriori (MAP)
tree contained in a "tgp"
-class object.
tgp.get.partitions(X, out)
X |
data.frame , matrix , or vector of inputs X
with the same dimension of out$X , i.e., dim(X)[2] == dim(out$X)[2] |
out |
"tgp" -class object which is the output of one
the model functions with tree support (e.g. btgpllm ,
btgp , btlm , or tgp ) |
Output is a list of data.frame
s populated with the inputs
X
contained in each region of the partition of the MAP tree
in the "tgp"
-class object out
Robert B. Gramacy rbgramacy@ams.ucsc.edu
Gramacy, R. B., Lee, H. K. H., & Macready, W. (2004). Parameter space exploration with Gaussian process trees. ICML (pp. 353–360). Omnipress & ACM Digital Library.
Gramacy, R. B., Lee, H. K. H., & Macready, W. (2005). Adaptive Exploration of Computer Experiment Parameter Spaces. submitted to JCGS, available as UCSC Technical Report ams2005-16
http://www.ams.ucsc.edu/~rbgramacy/tgp.php
# # 2-d Exponential data # (This example is based on random data. # It might be fun to run it a few times) # # get the data exp2d.data <- exp2d.rand() X <- exp2d.data$X; Z <- exp2d.data$Z Xcand <- exp2d.data$XX # fit treed GP LLM model to data w/o prediction # basically just to get MAP tree (and plot it) out <- btgpllm(X=X, Z=Z, pred.n=FALSE, BTE=c(2000,3000,2)) tgp.trees(out) # find a treed sequential D-Optimal design # with 10 more points Xcand.parts <- tgp.get.partitions(Xcand, out)