predict {dgpsi} | R Documentation |
Prediction from GP, DGP, or linked (D)GP emulators
Description
This function implements prediction from GP, DGP, or linked (D)GP emulators.
Usage
## S3 method for class 'dgp'
predict(
object,
x,
method = NULL,
mode = "label",
full_layer = FALSE,
sample_size = 50,
M = 50,
cores = 1,
chunks = NULL,
...
)
## S3 method for class 'lgp'
predict(
object,
x,
method = NULL,
full_layer = FALSE,
sample_size = 50,
M = 50,
cores = 1,
chunks = NULL,
...
)
## S3 method for class 'gp'
predict(
object,
x,
method = NULL,
sample_size = 50,
M = 50,
cores = 1,
chunks = NULL,
...
)
Arguments
object |
an instance of the |
x |
the testing input data:
|
method |
|
mode |
|
full_layer |
a bool indicating whether to output the predictions of all layers. Defaults to |
sample_size |
the number of samples to draw for each given imputation if |
M |
|
cores |
the number of processes to be used for prediction. If set to |
chunks |
the number of chunks that the testing input matrix |
... |
N/A. |
Details
See further examples and tutorials at https://mingdeyu.github.io/dgpsi-R/.
Value
If
object
is an instance of thegp
class:if
method = "mean_var"
: an updatedobject
is returned with an additional slot calledresults
that contains two matrices namedmean
for the predictive means andvar
for the predictive variances. Each matrix has only one column with its rows corresponding to testing positions (i.e., rows ofx
).if
method = "sampling"
: an updatedobject
is returned with an additional slot calledresults
that contains a matrix whose rows correspond to testing positions and columns correspond tosample_size
number of samples drawn from the predictive distribution of GP.
If
object
is an instance of thedgp
class:if
method = "mean_var"
andfull_layer = FALSE
: an updatedobject
is returned with an additional slot calledresults
that contains two matrices namedmean
for the predictive means andvar
for the predictive variances respectively. Each matrix has its rows corresponding to testing positions and columns corresponding to DGP global output dimensions (i.e., the number of GP/likelihood nodes in the final layer).if
method = "mean_var"
andfull_layer = TRUE
: an updatedobject
is returned with an additional slot calledresults
that contains two sub-lists namedmean
for the predictive means andvar
for the predictive variances respectively. Each sub-list contains L (i.e., the number of layers) matrices namedlayer1, layer2,..., layerL
. Each matrix has its rows corresponding to testing positions and columns corresponding to output dimensions (i.e., the number of GP/likelihood nodes from the associated layer).if
method = "sampling"
andfull_layer = FALSE
: an updatedobject
is returned with an additional slot calledresults
that contains D (i.e., the number of GP/likelihood nodes in the final layer) matrices namedoutput1, output2,..., outputD
. Each matrix has its rows corresponding to testing positions and columns corresponding to samples of size:B * sample_size
, whereB
is the number of imputations specified indgp()
.if
method = "sampling"
andfull_layer = TRUE
: an updatedobject
is returned with an additional slot calledresults
that contains L (i.e., the number of layers) sub-lists namedlayer1, layer2,..., layerL
. Each sub-list represents samples drawn from the GP/likelihood nodes in the corresponding layer, and contains D (i.e., the number of GP/likelihood nodes in the corresponding layer) matrices namedoutput1, output2,..., outputD
. Each matrix gives samples of the output from one of D GP/likelihood nodes, and has its rows corresponding to testing positions and columns corresponding to samples of size:B * sample_size
, whereB
is the number of imputations specified indgp()
.
-
If
object
is an instance of thedgp
class with a categorical likelihood:if
full_layer = FALSE
andmode = "label"
: an updatedobject
is returned with an additional slot calledresults
that contains one matrix namedlabel
. The matrix has rows corresponding to testing positions and columns corresponding to sample labels of size:B * sample_size
, whereB
is the number of imputations specified indgp()
.if
full_layer = FALSE
andmode = "proba"
, an updatedobject
is returned with an additional slot calledresults
. This slot contains D matrices (where D is the number of classes in the training output), where each matrix gives probability samples for the corresponding class with its rows corresponding to testing positions and columns containing probabilities. The number of columns of each matrix isB * sample_size
, whereB
is the number of imputations specified in thedgp()
function.if
method = "mean_var"
andfull_layer = TRUE
: an updatedobject
is returned with an additional slot calledresults
that contains L (i.e., the number of layers) sub-lists namedlayer1, layer2,..., layerL
. Each of firstL-1
sub-lists contains two matrices namedmean
for the predictive means andvar
for the predictive variances of the GP nodes in the associated layer. Rows of each matrix correspond to testing positions.when
mode = "label"
, the sub-listLayerL
contains one matrix namedlabel
. The matrix has its rows corresponding to testing positions and columns corresponding to label samples of size:B * sample_size
.B
is the number of imputations specified indgp()
.when
mode = "proba"
, the sub-listLayerL
contains D matrices (where D is the number of classes in the training output), where each matrix gives probability samples for the corresponding class with its rows corresponding to testing positions and columns containing probabilities. The number of columns of each matrix isB * sample_size
.B
is the number of imputations specified indgp()
.
if
method = "sampling"
andfull_layer = TRUE
: an updatedobject
is returned with an additional slot calledresults
that contains L (i.e., the number of layers) sub-lists namedlayer1, layer2,..., layerL
. Each of firstL-1
sub-lists represents samples drawn from the GP nodes in the corresponding layer, and contains D (i.e., the number of GP nodes in the corresponding layer) matrices namedoutput1, output2,..., outputD
. Each matrix gives samples of the output from one of D GP nodes, and has its rows corresponding to testing positions and columns corresponding to samples of size:B * sample_size
.when
mode = "label"
, the sub-listLayerL
contains one matrix namedlabel
. The matrix has its rows corresponding to testing positions and columns corresponding to label samples of size:B * sample_size
.when
mode = "proba"
, the sub-listLayerL
contains D matrices (where D is the number of classes in the training output), where each matrix gives probability samples for the corresponding class with its rows corresponding to testing positions and columns containing probabilities. The number of columns of each matrix isB * sample_size
.
B
is the number of imputations specified indgp()
.
-
If
object
is an instance of thelgp
class:if
method = "mean_var"
andfull_layer = FALSE
: an updatedobject
is returned with an additional slot calledresults
that contains two sub-lists namedmean
for the predictive means andvar
for the predictive variances respectively. Each sub-list contains K (same number of emulators in the final layer of the system) matrices named using theID
s of the corresponding emulators in the final layer. Each matrix has rows corresponding to global testing positions and columns corresponding to output dimensions of the associated emulator in the final layer.if
method = "mean_var"
andfull_layer = TRUE
: an updatedobject
is returned with an additional slot calledresults
that contains two sub-lists namedmean
for the predictive means andvar
for the predictive variances respectively. Each sub-list contains L (i.e., the number of layers in the emulated system) components namedlayer1, layer2,..., layerL
. Each component represents a layer and contains K (same number of emulators in the corresponding layer of the system) matrices named using theID
s of the corresponding emulators in that layer. Each matrix has its rows corresponding to global testing positions and columns corresponding to output dimensions of the associated GP/DGP emulator in the corresponding layer.if
method = "sampling"
andfull_layer = FALSE
: an updatedobject
is returned with an additional slot calledresults
that contains K (same number of emulators in the final layer of the system) sub-lists named using theID
s of the corresponding emulators in the final layer. Each sub-list contains D matrices, namedoutput1, output2,..., outputD
, that correspond to the output dimensions of the GP/DGP emulator. Each matrix has rows corresponding to testing positions and columns corresponding to samples of size:B * sample_size
, whereB
is the number of imputations specified inlgp()
.if
method = "sampling"
andfull_layer = TRUE
: an updatedobject
is returned with an additional slot calledresults
that contains L (i.e., the number of layers of the emulated system) sub-lists namedlayer1, layer2,..., layerL
. Each sub-list represents a layer and contains K (same number of emulators in the corresponding layer of the system) components named using theID
s of the corresponding emulators in that layer. Each component contains D matrices, namedoutput1, output2,..., outputD
, that correspond to the output dimensions of the GP/DGP emulator. Each matrix has its rows corresponding to testing positions and columns corresponding to samples of size:B * sample_size
, whereB
is the number of imputations specified inlgp()
.
If
object
is an instance of thelgp
class created bylgp()
without specifying thestruc
argument in data frame form, theID
s, that are used as names of sub-lists or matrices withinresults
, will be replaced byemulator1
,emulator2
, and so on.
The results
slot will also include:
-
the value of
M
, which represents the size of the conditioning set for the Vecchia approximation, if used, in the emulator prediction. the value of
sample_size
ifmethod = "sampling"
.
Examples
## Not run:
# See gp(), dgp(), or lgp() for an example.
## End(Not run)