{- | Gaussian Process Library - functions for producing data sets From Rasmussen and Williams, "Gaussian Processes for Machine Learning." Copyright (C) 2011 Sean Holden. sbh11\@cl.cam.ac.uk. -} {- This file is part of HasGP. HasGP 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. HasGP 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 HasGP. If not, see <http://www.gnu.org/licenses/>. -} module HasGP.Data.RWData1 where import Numeric.LinearAlgebra import HasGP.Types.MainTypes import HasGP.Support.Random -- | Generate training data for a simple classification problem as in -- Rasmussen/Williams, page 62. simpleClassificationData :: Int -- ^ Seed for random number generator. -> (DMatrix, DVector) simpleClassificationData seed = ((asColumn $ join [(-6)+x1, x2, 2+x3]), join [constant 1 20, constant 0 30, constant 1 10]) where v = (0.8)^2 x1 = normalVectorSimple seed v 20 x2 = normalVectorSimple (seed+1) v 30 x3 = normalVectorSimple (seed+2) v 10