Learn R Programming
GPTreeO (version 1.0.1)

WrappedDiceKrigingGP: R6 class WrappedDiceKrigingGP

Description

Contains the GP created by DiceKriging::km from the DiceKriging package

Arguments

Public fields

gp

The DiceKriging GP object (DiceKriging::km in the DiceKriging manual)

X_buffer

Buffer matrix to collect x points until first GP can be trained

y_buffer

Buffer vector to collect y points until first GP can be trained

y_var_buffer

Buffer vector to collect variance of y points until first GP can be trained

add_y_var

Small additional variance used to keep the covariance matrix condition number under control

n_points_train_limit

Number of points needed before we can create the GP

n_points

The number of collected points belonging to this GP

x_dim

Dimensionality of input points

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

The initial covariance parameters when training the DiceKriging GP object in self@gp

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

retrain_buffer_length

Only retrain after this many new points have been added to the buffer

retrain_buffer_counter

Counter for the number of new points added since last retraining

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

n_shared_points

The number of own points shared with the GP in the sibling node

Methods

Method new()

Create a new WrappedDiceKrigingGP object

Usage

WrappedDiceKrigingGP$new(
  X,
  y,
  y_var,
  gp_control,
  init_covpars,
  retrain_buffer_length,
  add_buffer_in_prediction,
  estimate_covpars = TRUE,
  X_shared = NULL,
  y_shared = NULL,
  y_var_shared = NULL
)

Arguments

X

Input data matrix with x_dim columns and at maximum Nbar rows. Is used to create the first iteration of the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

Initial covariance parameters of the local GP

retrain_buffer_length

Only retrain when the number of buffer points or collected points exceeds this value

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

Returns

A new WrappedDiceKrigingGP object. Besides the local GP, information on the shared points and those stored in the buffer are collected. For more information on the GP, consult the method DiceKriging::km in the DiceKriging package.

Method update_init_covpars()

Stores the initial covariance parameters (length-scales, standard deviation and trend coefficients) of the GP in the field init_covpars

Usage

WrappedDiceKrigingGP$update_init_covpars()

Method get_lengthscales()

Retrieves the length-scales of the kernel of the local GP

Usage

WrappedDiceKrigingGP$get_lengthscales()

Method get_X_data()

Retrieves the design matrix X

Usage

WrappedDiceKrigingGP$get_X_data(include_shared = FALSE)

Arguments

include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_data()

Retrieves the response

Usage

WrappedDiceKrigingGP$get_y_data(include_shared = FALSE)

Arguments

include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_var_data()

Retrieves the individual variances from the response

Usage

WrappedDiceKrigingGP$get_y_var_data(include_shared = FALSE)

Arguments

include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_cov_mat()

Retrieves the covariance matrix

Usage

WrappedDiceKrigingGP$get_cov_mat()

Returns

the covariance matrix

Method update_add_y_var()

Method for updating add_y_var based on a bound for the covariance matrix condition number, based on this paper, Section 5.4

Usage

WrappedDiceKrigingGP$update_add_y_var(max_cond_num)

Arguments

max_cond_num

Max allowed condition number

Method store_point()

Stores a new point into the respective buffer method

Usage

WrappedDiceKrigingGP$store_point(
  x,
  y,
  y_var,
  shared = FALSE,
  remove_shared = TRUE
)

Arguments

x

Single input data point from the data stream; has to be a vector or row matrix with length equal to x_dim

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

shared

If TRUE, this point is shared between this GP and its sibling GP

remove_shared

If TRUE, the last of the shared points is removed

Method delete_buffers()

Method for clearing the buffers

Usage

WrappedDiceKrigingGP$delete_buffers()

Method train()

Method for (re)creating / (re)training the GP

Usage

WrappedDiceKrigingGP$train(do_buffer_check = TRUE)

Arguments

do_buffer_check

If TRUE, only train the GP if the number of stored points is larger than retrain_buffer_length

Returns

TRUE if training was performed, otherwise FALSE

Method predict()

Method for prediction

Usage

WrappedDiceKrigingGP$predict(x, return_std = TRUE)

Arguments

x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector or row matrix with length equal to x_dim

return_std

If TRUE, the standard error is returned in addition to the prediction

Returns

Prediction for input point x

Method delete_gp()

Method to delete the GP object in self$gp

Usage

WrappedDiceKrigingGP$delete_gp()

Method create_DiceKriging_gp()

Method for calling the 'km' function in DiceKriging to create a GP object, stored in self$gp

Usage

WrappedDiceKrigingGP$create_DiceKriging_gp(X, y, y_var)

Arguments

X

Input data matrix with x_dim columns and at maximum Nbar rows for the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

Returns

TRUE

Method call_DiceKriging_predict()

Method for calling the 'predict' function in DiceKriging

Usage

WrappedDiceKrigingGP$call_DiceKriging_predict(x, use_gp = NULL)

Arguments

x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector with length equal to x_dim

use_gp

optional user-defined GP which is evaluated instead of the local GP

Returns

The predictions for x from the specified GP, by default the local GP

Method clone()

The objects of this class are cloneable with this method.

Usage

WrappedDiceKrigingGP$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.