#******************************************************************************* 
#
# Bayesian Regression and Adaptive Sampling with Gaussian Process Trees
# Copyright (C) 2005, University of California
# 
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
# 
# This library 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
# Lesser General Public License for more details.
# 
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
#
# Questions? Contact Robert B. Gramacy (rbgramacy@ams.ucsc.edu)
#
#*******************************************************************************


"undo.mean0.range1" <- 
function(X.m, undo)
{
	if(is.null(X.m)) return(NULL)
	else if(is.null(dim(X.m))) X <- matrix(X.m, ncol=1)
	else X <- X.m

	for(i in 1:(dim(X)[2])) {
		X[,i] <- X[,i] + undo$amean[i]
		X[,i] <- X[,i]*(undo$max[i] - undo$min[i])
	}
	if(is.null(dim(X.m))) X.m <- as.vector(X)
	else X.m <- X

	return(X.m)
}