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## Copyright(c) 2021 Yoann Robin
##
## This file is part of SBCK.
##
## SBCK is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## SBCK is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with SBCK. If not, see <https://www.gnu.org/licenses/>.
#' R2D2 (Rank Resampling for Distributions and Dependences) method
#'
#' @description
#' Perform a multivariate (non stationary) bias correction.
#'
#' @details
#' Use rankshuffle in calibration and projection period with CDFt
#'
#' @references Vrac, M.: Multivariate bias adjustment of high-dimensional
#' climate simulations: the Rank Resampling for Distributions and
#' Dependences (R2 D2 ) bias correction, Hydrol. Earth Syst. Sci.,
#' 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018.
#'
#' @examples
#' ## Three bivariate random variables (rnorm and rexp are inverted between ref
#' ## and bias)
#' XY = SBCK::dataset_gaussian_exp_2d(2000)
#' X0 = XY$X0 ## Biased in calibration period
#' Y0 = XY$Y0 ## Reference in calibration period
#' X1 = XY$X1 ## Biased in projection period
#'
#' ## Bias correction
#' ## Step 1 : construction of the class R2D2
#' r2d2 = SBCK::R2D2$new()
#' ## Step 2 : Fit the bias correction model
#' r2d2$fit( Y0 , X0 , X1 )
#' ## Step 3 : perform the bias correction
#' Z = r2d2$predict(X1,X0)
#'
#' @export
R2D2 = R6::R6Class( "R2D2" ,
inherit = SBCK::CDFt,
public = list(
###############
## Arguments ##
###############
#' @field irefs [vector of int] Indexes for shuffle. Defaults is base::c(1)
irefs = NULL,
#################
## Constructor ##
#################
## initialize ##{{{
#' @description
#' Create a new R2D2 object.
#' @param irefs [vector of int] Indexes for shuffle. Defaults is base::c(1)
#' model
#' @param ... [] all others arguments are passed to CDFt class.
#'
#' @return A new `R2D2` object.
initialize = function( irefs = base::c(1) , ... )
{
kwargs = list(...)
base::do.call( super$initialize , kwargs )
self$irefs = irefs
private$ssr = SBCK::SchaakeShuffleRef$new( 1 )
},
##}}}
## fit ##{{{
#' @description
#' Fit the bias correction method
#' @param Y0 [matrix: n_samples * n_features] Observations in calibration
#' @param X0 [matrix: n_samples * n_features] Model in calibration
#' @param X1 [matrix: n_samples * n_features] Model in projection
#' @return NULL
fit = function( Y0 , X0 , X1 )
{
super$fit( Y0 , X0 , X1 )
private$ssr$fit(Y0)
},
##}}}
## predict ##{{{
#' @description
#' Predict the correction
#' @param X0 [matrix: n_samples * n_features or NULL] Model in calibration
#' @param X1 [matrix: n_samples * n_features] Model in projection
#' @return [matrix or list] Return the matrix of correction of X1 if X0 is
#' NULL, else return a list containing Z1 and Z0,
#' the corrections of X1 and X0
predict = function( X1 , X0 = NULL )
{
Z = super$predict(X1,X0)
if( is.null(X0) )
{
Z1 = base::array( NA , dim = base::c( base::dim(Z) , length(self$irefs) ) )
for( i in 1:length(self$irefs) )
{
private$ssr$ref = self$irefs[i]
Z1[,,i] = private$ssr$predict(Z)
}
if( length(self$irefs) == 1 )
return(Z1[,,1])
return(Z1)
}
Z0 = base::array( NA , dim = base::c( base::dim(Z$Z0) , length(self$irefs) ) )
Z1 = base::array( NA , dim = base::c( base::dim(Z$Z1) , length(self$irefs) ) )
for( i in 1:length(self$irefs) )
{
private$ssr$ref = self$irefs[i]
Z0[,,i] = private$ssr$predict(Z$Z0)
Z1[,,i] = private$ssr$predict(Z$Z1)
}
if( length(self$irefs) == 1 )
{
Z0 = Z0[,,1]
Z1 = Z1[,,1]
}
return( list( Z1 = Z1 , Z0 = Z0 ) )
}
##}}}
),
######################
## Private elements ##
######################
private = list(
###############
## Arguments ##
###############
ssr = NULL
)
)
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