alm {dgpsi} | R Documentation |
Locate the next design point(s) for a (D)GP emulator or a bundle of (D)GP emulators using Active Learning MacKay (ALM)
Description
This function searches from a candidate set to locate the next design point(s) to be added to a (D)GP emulator or a bundle of (D)GP emulators using the Active Learning MacKay (ALM) criterion (see the reference below).
Usage
alm(object, ...)
## S3 method for class 'gp'
alm(
object,
x_cand = NULL,
n_start = 20,
batch_size = 1,
M = 50,
workers = 1,
limits = NULL,
int = FALSE,
...
)
## S3 method for class 'dgp'
alm(
object,
x_cand = NULL,
n_start = 20,
batch_size = 1,
M = 50,
workers = 1,
limits = NULL,
int = FALSE,
aggregate = NULL,
...
)
## S3 method for class 'bundle'
alm(
object,
x_cand = NULL,
n_start = 20,
batch_size = 1,
M = 50,
workers = 1,
limits = NULL,
int = FALSE,
aggregate = NULL,
...
)
Arguments
object |
can be one of the following:
|
... |
any arguments (with names different from those of arguments used in |
x_cand |
a matrix (with each row being a design point and column being an input dimension) that gives a candidate set
from which the next design point(s) are determined. If |
n_start |
|
batch_size |
an integer that gives the number of design points to be chosen. Defaults to |
M |
|
workers |
the number of processes to be used for design point selection. If set to |
limits |
a two-column matrix that gives the ranges of each input dimension, or a vector of length two if there is only one input dimension.
If a vector is provided, it will be converted to a two-column row matrix. The rows of the matrix correspond to input dimensions, and its
first and second columns correspond to the minimum and maximum values of the input dimensions. This
argument is only used when |
int |
|
aggregate |
an R function that aggregates scores of the ALM across different output dimensions (if
Set to |
Details
See further examples and tutorials at https://mingdeyu.github.io/dgpsi-R/.
Value
If
x_cand
is notNULL
:When
object
is an instance of thegp
class, a vector of lengthbatch_size
is returned, containing the positions (row numbers) of the next design points fromx_cand
.When
object
is an instance of thedgp
class, a vector of lengthbatch_size * D
is returned, containing the positions (row numbers) of the next design points fromx_cand
to be added to the DGP emulator.-
D
is the number of output dimensions of the DGP emulator if no likelihood layer is included. For a DGP emulator with a
Hetero
orNegBin
likelihood layer,D = 2
.For a DGP emulator with a
Categorical
likelihood layer,D = 1
for binary output orD = K
for multi-class output withK
classes.
-
When
object
is an instance of thebundle
class, a matrix is returned withbatch_size
rows and a column for each emulator in the bundle, containing the positions (row numbers) of the next design points fromx_cand
for individual emulators.
If
x_cand
isNULL
:When
object
is an instance of thegp
class, a matrix withbatch_size
rows is returned, giving the next design points to be evaluated.When
object
is an instance of thedgp
class, a matrix withbatch_size * D
rows is returned, where:-
D
is the number of output dimensions of the DGP emulator if no likelihood layer is included. For a DGP emulator with a
Hetero
orNegBin
likelihood layer,D = 2
.For a DGP emulator with a
Categorical
likelihood layer,D = 1
for binary output orD = K
for multi-class output withK
classes.
-
When
object
is an instance of thebundle
class, a list is returned with a length equal to the number of emulators in the bundle. Each element of the list is a matrix withbatch_size
rows, where each row represents a design point to be added to the corresponding emulator.
Note
The first column of the matrix supplied to the first argument of aggregate
must correspond to the first output dimension of the DGP emulator
if object
is an instance of the dgp
class, and so on for subsequent columns and dimensions. If object
is an instance of the bundle
class,
the first column must correspond to the first emulator in the bundle, and so on for subsequent columns and emulators.
References
MacKay, D. J. (1992). Information-based objective functions for active data selection. Neural Computation, 4(4), 590-604.
Examples
## Not run:
# load packages and the Python env
library(lhs)
library(dgpsi)
# construct a 1D non-stationary function
f <- function(x) {
sin(30*((2*x-1)/2-0.4)^5)*cos(20*((2*x-1)/2-0.4))
}
# generate the initial design
X <- maximinLHS(10,1)
Y <- f(X)
# training a 2-layered DGP emulator with the global connection off
m <- dgp(X, Y, connect = F)
# specify the input range
lim <- c(0,1)
# locate the next design point using ALM
X_new <- alm(m, limits = lim)
# obtain the corresponding output at the located design point
Y_new <- f(X_new)
# combine the new input-output pair to the existing data
X <- rbind(X, X_new)
Y <- rbind(Y, Y_new)
# update the DGP emulator with the new input and output data and refit
m <- update(m, X, Y, refit = TRUE)
# plot the LOO validation
plot(m)
## End(Not run)