diff --git a/HasGP.cabal b/HasGP.cabal
new file mode 100644
--- /dev/null
+++ b/HasGP.cabal
@@ -0,0 +1,102 @@
+-- HasGP.cabal auto-generated by cabal init. For additional options,
+-- see
+-- http://www.haskell.org/cabal/release/cabal-latest/doc/users-guide/authors.html#pkg-descr.
+-- The name of the package.
+Name:                HasGP
+
+-- The package version. See the Haskell package versioning policy
+-- (http://www.haskell.org/haskellwiki/Package_versioning_policy) for
+-- standards guiding when and how versions should be incremented.
+Version:             0.1
+
+-- A short (one-line) description of the package.
+Synopsis:            A Haskell library for inference using Gaussian processes
+
+-- A longer description of the package.
+Description:         A Haskell library implementing algorithms for supervised learning, roughly corresponding to chapters 1 to 5 of "Gaussian Processes for Machine Learning" by Carl Rasmussen and Christopher Williams, The MIT Press 2006. In particular, algorithms are provides for regression and for two-class classification using either the Laplace or EP approximation.    
+
+-- URL for the project homepage or repository.
+Homepage:            http://www.cl.cam.ac.uk/~sbh11/HasGP
+
+-- The license under which the package is released.
+License:             GPL-3
+
+-- The file containing the license text.
+License-file:        LICENSE
+
+-- The package author(s).
+Author:              Sean B. Holden
+
+-- An email address to which users can send suggestions, bug reports,
+-- and patches.
+Maintainer:          sbh11@cl.cam.ac.uk
+
+-- A copyright notice.
+Copyright:           Copyright (C) 2011 Sean Holden
+
+Category:            AI, Classification, Datamining, Statistics
+
+Build-type:          Simple
+
+-- Extra files to be distributed with the package, such as examples or
+-- a README.
+Extra-source-files: README
+
+-- Constraint on the version of Cabal needed to build this package.
+Cabal-version:       >=1.6
+
+Stability:           Experimental
+
+Bug-reports:         sbh11@cl.cam.ac.uk
+
+Package-url:         http://www.cl.cam.ac.uk/~sbh11/HasGP/HasGP-0.1.tar.gz
+
+Tested-with:         GHC ==7.0.3
+
+Data-files:          *.txt
+
+Data-dir:            src/HasGP/Data/Files
+
+Library
+  -- Modules exported by the library.
+  Exposed-modules:     HasGP.Classification.EP.ClassificationEP, 
+                       HasGP.Classification.Laplace.ClassificationLaplace, 
+                       HasGP.Covariance.Basic, 
+                       HasGP.Covariance.SquaredExp, 
+                       HasGP.Covariance.SquaredExpARD, 
+                       HasGP.Data.BishopData, 
+                       HasGP.Data.Normalise, 
+                       HasGP.Data.RWData1, 
+                       HasGP.Demos.ClassificationDemo1, 
+                       HasGP.Demos.ClassificationDemo2, 
+                       HasGP.Demos.RegressionDemo1, 
+                       HasGP.Likelihood.Basic, 
+                       HasGP.Likelihood.LogLogistic, 
+                       HasGP.Likelihood.LogPhi, 
+                       HasGP.Parsers.SvmLight, 
+                       HasGP.Regression.Regression, 
+                       HasGP.Support.Functions, 
+                       HasGP.Support.Iterate, 
+                       HasGP.Support.Linear, 
+                       HasGP.Support.MatrixFunction, 
+                       HasGP.Support.Random, 
+                       HasGP.Support.Solve, 
+                       HasGP.Types.MainTypes
+  
+  -- Packages needed in order to build this package.
+  Build-depends: base == 4.*,
+                 haskell98 == 1.*,
+                 parsec == 3.*,
+                 random == 1.*,
+                 mtl == 2.*,
+                 hmatrix == 0.12.*,
+                 hmatrix-special == 0.1.*
+  
+  hs-source-dirs: src
+
+  -- Modules not exported by this package.
+  -- Other-modules:       
+  
+  -- Extra tools (e.g. alex, hsc2hs, ...) needed to build the source.
+  -- Build-tools:         
+  
diff --git a/LICENSE b/LICENSE
new file mode 100644
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,675 @@
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+                Version 3, 29 June 2007
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+
diff --git a/README b/README
new file mode 100644
--- /dev/null
+++ b/README
@@ -0,0 +1,10 @@
+The HasGP package for Gaussian process inference in Haskell
+-----------------------------------------------------------
+
+Copyright (C) 2011 Sean Holden sbh11@cl.cam.ac.uk
+
+For a detailed description of how to install, use and modify this code 
+please download the User Manual from the project site at:
+
+http://www.cl.cam.ac.uk/~sbh11/HasGP/
+
diff --git a/Setup.hs b/Setup.hs
new file mode 100644
--- /dev/null
+++ b/Setup.hs
@@ -0,0 +1,2 @@
+import Distribution.Simple
+main = defaultMain
diff --git a/src/HasGP/Classification/EP/ClassificationEP.hs b/src/HasGP/Classification/EP/ClassificationEP.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Classification/EP/ClassificationEP.hs
@@ -0,0 +1,406 @@
+{- | ClassificationEP is a module in the HasGP Gaussian Process
+   library. It implements basic Gaussian Process Classification for two 
+   classes using the EP approximation. Targets should be +1/-1. 
+   
+   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.Classification.EP.ClassificationEP 
+   ( 
+     EPValue(eValue,siteState,count),
+     EPConvergenceTest,
+     EPSiteState,
+     EPState,
+     SiteOrder,
+     generateRandomSiteOrder,
+     generateFixedSiteOrder,
+     gpClassifierEPEvidence,
+     gpClassifierEPLearn,
+     gpClassifierEPPredict,
+     gpClassifierEPLogEvidence,
+     gpClassifierEPLogEvidenceList,
+     gpClassifierEPLogEvidenceVec
+   ) where 
+
+import Numeric.LinearAlgebra
+
+import Control.Monad.State    
+import System.Random
+
+import HasGP.Types.MainTypes
+import HasGP.Support.MatrixFunction
+import HasGP.Support.Linear
+import HasGP.Support.Functions
+import HasGP.Support.Solve
+import HasGP.Support.Iterate
+import HasGP.Support.Random
+import HasGP.Covariance.Basic
+import HasGP.Likelihood.Basic
+
+-- | A convergence test for EP usually depends on the evidence and 
+--   the number of iterations so far. This allows us to specify 
+--   completely arbitrary convergence tests.
+data EPValue = EPValue {
+      eValue::Double,
+      siteState::EPSiteState,
+      count::Int
+    }
+
+-- | By passing a function with this type we can specify arbitrary 
+--   convergence tests.   
+type EPConvergenceTest = (EPValue -> EPValue -> Bool)
+
+-- | When updating a single site at a time you keep track of var, 
+--   tauTilde, mu, nuTilde, TauMinus, and MuMinus.
+data EPSiteState = EPSiteState {
+     var::DMatrix,                  
+     tauTilde::DVector, 
+     mu::DVector, 
+     nuTilde::DVector,
+     tauMinus::DVector,
+     muMinus::DVector
+   }       
+
+-- | We hide the state used in performing EP using the state monad. 
+--   We need to include a random number generator and the number of 
+--   iterations.
+type EPState = (EPSiteState, StdGen, Int)
+
+-- | If we're updating sites in a random order then we need access to 
+--   the random number generator. 
+type SiteOrder = State EPState [Int]
+
+-- | Generates a basic start state for the sites, with var = covariance 
+--   matrix and all vectors = 0.
+generateInitialSiteState :: CovarianceMatrix  
+                         -> Int     -- ^ Number of sites
+                         -> EPSiteState
+generateInitialSiteState k n = EPSiteState k z1 z2 z3 z4 z5
+    where
+      z1 = constant 0.0 n
+      z2 = constant 0.0 n
+      z3 = constant 0.0 n
+      z4 = constant 0.0 n
+      z5 = constant 0.0 n
+
+-- | Updates for the EP version of Gaussian Process Classifiers.
+--   cavityParameters, marginalMoments and siteParameters 
+--   are successive parts of the update for a single site.
+cavityParameters :: Double -- ^ varI
+                 -> Double -- ^ tauTildeI
+                 -> Double -- ^ muI
+                 -> Double -- ^ nuTildeI
+                 -> (Double,Double)
+cavityParameters varI tauTildeI muI nuTildeI = (tauMinusI, nuMinusI)
+    where
+      varIInv = 1 / varI
+      tauMinusI = varIInv - tauTildeI 
+      nuMinusI = (varIInv * muI) - nuTildeI
+
+marginalMoments :: Double -- ^ muMinusI
+                -> Double -- ^ tI
+                -> Double -- ^ varMinusI
+                -> (Double,Double)
+marginalMoments muMinusI tI varMinusI    = (muHatI, varHatI)
+    where 
+      zI = (tI * muMinusI) / (sqrt (1 + varMinusI))
+      nopz = nOverPhi zI
+      muHatI = muMinusI + (((tI * varMinusI) / (sqrt (1 + varMinusI))) * nopz)
+      varHatI = varMinusI - 
+                ((((square varMinusI) / (1 + varMinusI)) * nopz) * (zI + nopz))
+
+siteParameters :: Double -- ^ tauTildeI
+               -> Double -- ^ varHatI
+               -> Double -- ^ tauMinusI
+               -> Double -- ^ muHatI
+               -> Double -- ^ nuMinusI
+               -> (Double,Double,Double)
+siteParameters tauTildeI varHatI tauMinusI muHatI nuMinusI = 
+    (deltaTauTilde, tauTildeINew, nuTildeINew)
+        where
+          deltaTauTilde = (1 / varHatI) - tauMinusI - tauTildeI
+          tauTildeINew = tauTildeI + deltaTauTilde
+          nuTildeINew = ((1 / varHatI) * muHatI) - nuMinusI
+
+-- | Do a complete update for site i.
+updateOneSite :: Targets -- ^ Labels
+              -> Int     -- ^ Number of sites
+              -> Int     -- ^ Site to update
+              -> EPSiteState
+              -> EPSiteState
+updateOneSite t n i (EPSiteState var tauTilde mu nuTilde oldTauMinus 
+                     oldMuMinus) = 
+    -- oldTauMinusI, oldMuMinus not used but need to be updated.
+    EPSiteState newVar newTauTilde newMu newNuTilde newTauMinus newMuMinus 
+        where      
+          -- hmatrix counts from 0 whereas I'm using 1.
+          i2 = i-1 
+          tauTildeI = (tauTilde @> i2)
+          varII = var @@> (i2,i2)
+          (tauMinusI, nuMinusI) = 
+              cavityParameters varII tauTildeI (mu @> i2) (nuTilde @> i2)
+          varMinusI = (1 / tauMinusI)
+          muMinusI = (nuMinusI / tauMinusI)
+          (muHatI, varHatI) = marginalMoments muMinusI (t @> i2) varMinusI
+          (deltaTauTilde, tauTildeINew, nuTildeINew) = 
+              siteParameters tauTildeI varHatI tauMinusI muHatI nuMinusI
+          newTauTilde = replaceInVector tauTilde (i2+1) tauTildeINew 
+          newNuTilde = replaceInVector nuTilde (i2+1) nuTildeINew
+          newTauMinus = replaceInVector oldTauMinus (i2+1) tauMinusI 
+          newMuMinus = replaceInVector oldMuMinus (i2+1) muMinusI
+          sI = subMatrix (0,i2) (n,1) var
+          newVar = var - (scale (deltaTauTilde / (1 + (deltaTauTilde * varII)))
+                          (sI <> (trans sI)))
+          newMu = flatten $ (newVar <> (asColumn newNuTilde))
+
+-- | Generate a random permutation. This is wrapped up in the state 
+--   transformer generateRandomSiteOrder.
+randomPermutation :: StdGen           -- ^ Random number generator
+                  -> Int              -- ^ Size of list required
+                  -> (StdGen, [Int])  -- ^ New generator and result.
+randomPermutation g n = rP g (n-1) [1..n] []
+    where
+      rP g' n' [] result = (g', result)
+      rP g' n' [x] result = (g', x:result)
+      rP g' n' xs result = rP newG (n' - 1) newXs (m:result)
+          where
+            (r, newG) = randomR (0, n') g'
+            m = xs !! r
+            newXs = filter (\x -> x /= m) xs 
+
+-- | We're often going to want to update sites in a random order. 
+--   So we need a state transformer that takes the current state (which 
+--   includes a random number generator) and produces a random permutation.
+generateRandomSiteOrder :: SiteOrder
+generateRandomSiteOrder = do 
+  (state, g, n) <- get
+  let (newG, p) = randomPermutation g (dim $ tauTilde state)
+  put (state, newG, n)
+  return p
+
+-- | For completeness: just in case you want to update sites in a 
+--   non-random manner, this state transformer does exactly that.
+generateFixedSiteOrder :: SiteOrder
+generateFixedSiteOrder = do
+  (state, g, n) <- get
+  return [1..(dim $ tauTilde state)]
+
+-- | Update all the sites in the order specified by a list of Ints
+updateAllSites :: Targets 
+               -> Int     -- ^ Number of sites
+               -> [Int]   -- ^ Sites to update
+               -> EPSiteState
+               -> EPSiteState
+updateAllSites t n [] state = state
+updateAllSites t n (s:ss) state = updateAllSites t n ss newState
+    where
+      newState = updateOneSite t n s state
+
+-- | Re-compute the approximation after updating all the sites.
+--   Outputs \Sigma and \mu.
+recomputeApproximation :: CovarianceMatrix
+                       -> Int     -- ^ Number of sites
+                       -> DVector -- ^ tauTilde
+                       -> DVector -- ^ nuTilde
+                       -> (DMatrix, DMatrix, DVector)
+recomputeApproximation k n tauTilde nuTilde = 
+  (l, finalVar, finalMu)
+    where
+      rootSTilde = mapVector sqrt tauTilde
+      l = trans $ chol ((ident n) + (abaDiagDiag rootSTilde k))
+      v = generalSolve lowerSolve l (preMultiply rootSTilde k) 
+      finalVar = k - ((trans v) <> v) 
+      finalMu = flatten $ finalVar <> (asColumn nuTilde) 
+
+-- | Compute the approximation to the log marginal likelihood.
+gpClassifierEPEvidence :: CovarianceMatrix 
+                       -> Targets 
+                       -> DMatrix -- ^ L matrix
+                       -> EPSiteState
+                       -> Double  -- ^ log marginal likelihood.
+gpClassifierEPEvidence k t l state = 
+    (terms1and4 + term3 + terms2and5)
+        where
+          tT = tauTilde state
+          nT = nuTilde state
+          tM = tauMinus state
+          mM = muMinus state
+          oneOverTauMinus = mapVector (1/) $ tM
+          sumLog = sum $ toList $ mapVector log $ takeDiag l
+          terms1and4 = 
+              (0.5 * (sum $ toList $ mapVector (log . (1+)) 
+                      (zipVectorWith (*) tT oneOverTauMinus))) - sumLog
+          term3 = 
+              sum $ toList $ 
+              mapVector logPhi (zipVectorWith (/) 
+                                (zipVectorWith (*) t mM) 
+                                (mapVector (sqrt . (1+)) oneOverTauMinus))
+          rootSTilde = diag $ mapVector sqrt tT
+          vM = generalSolve lowerSolve l 
+               (preMultiply (mapVector sqrt tT) k)
+          --      vM = generalSolve upperSolve (trans l) (rootSTilde <> k)    
+          invTPlusSTilde = 
+              diag $ mapVector (1/) $ tT + tM
+          part1 = 
+              0.5 * ((asRow nT) <> 
+                     (k - ((trans vM) <> vM) - invTPlusSTilde) <> 
+                     (asColumn nT))
+          part2 = 
+              0.5 * ((asRow mM) <> (diag tM) <> invTPlusSTilde <> 
+                     ((asColumn (zipVectorWith (*) tT mM)) - 
+                      (asColumn $ (scale 2 nT))))
+          terms2and5 = (part1 + part2) @@> (0,0)  
+
+-- | As we're hiding the state using the State monad, we make a state 
+--   transformer that uses updateAllSites and recomputeApproximation to 
+--   do a complete single update. This will make use of an arbitrary 
+--   state transformer to produce a list specifying the order to update 
+--   the sites in. The output is the l matrix produced when recomputing the 
+--   approximation.
+doOneUpdate :: CovarianceMatrix    
+            -> Targets            
+            -> SiteOrder           -- ^ Supplier of update order. 
+            -> State EPState EPValue
+doOneUpdate k t siteOrder = do
+  order <- siteOrder
+  (state,g,i) <- get
+  let sites = dim $ tauTilde state
+  let state' = updateAllSites t sites order state
+  let finalTT =  tauTilde state' 
+  let finalNT = nuTilde state'
+  let finalTM = tauMinus state'
+  let finalMM = muMinus state'
+  let (l, finalVar, finalMu) = recomputeApproximation k sites finalTT finalNT
+  let state'' = EPSiteState finalVar finalTT finalMu finalNT finalTM finalMM
+  put (state'', g, i+1) 
+  let logML = gpClassifierEPEvidence k t l state''
+  return $ EPValue logML state'' (i+1)
+  
+-- | The learning algorithm. Takes an arbitrary function for convergence 
+--   testing.
+gpClassifierEPLearn :: CovarianceMatrix 
+                    -> Targets         
+                    -> SiteOrder
+                    -> EPConvergenceTest
+                    -> (EPValue, EPState)
+gpClassifierEPLearn k t siteOrder converged = 
+    runState (iterateToConvergence'' doOnce converged) start
+        where
+          doOnce = doOneUpdate k t siteOrder 
+          start = ((generateInitialSiteState k (dim t)), mkStdGen 0, 0) 
+ 
+-- | Prediction with GP classifiers based on EP learning.
+--   Takes a matrix in which each row is an example to be 
+--   classified.
+gpClassifierEPPredict :: (CovarianceFunction c) => EPSiteState
+                      -> Inputs          
+                      -> Targets          -- ^ Inputs in training set
+                      -> CovarianceMatrix
+                      -> c                -- ^ Covariance Function
+                      -> Inputs           -- ^ New inputs
+                      -> DVector
+gpClassifierEPPredict state i t k c xStars 
+    = fromList $ map phiIntegral (zipWith (/) fStar (map (sqrt . (1+)) vfStar))
+      where
+        nT = nuTilde state 
+        tT = tauTilde state 
+        d = dim t
+        rootSTildeV = mapVector sqrt tT
+        rootSTilde = diag rootSTildeV
+        l = trans $ chol ((ident d) + (abaDiagDiag rootSTildeV k))
+        z = rootSTilde 
+            <> (asColumn 
+                (upperSolve (trans l) 
+                 (lowerSolve l (flatten $ 
+                                (rootSTilde <> k <> (asColumn nT))))))
+        xStarsRows = toRows xStars
+        covarianceWithTestInputs = 
+            fromRows [covarianceWithPoint c i xStar | xStar <- xStarsRows]
+        fStar = 
+            toList $ flatten $ 
+                       (covarianceWithTestInputs <> ((asColumn nT) - z))
+        v = [lowerSolve l (rootSTilde <> kxStar) | 
+             kxStar <- (toColumns $ trans $ covarianceWithTestInputs)]
+        vTv = zipWith (<.>) v v
+        kxStarxStar = zipWith (covariance c) xStarsRows xStarsRows
+        vfStar = zipWith (-) kxStarxStar vTv
+
+-- | Compute the log evidence and its first derivative for the EP approximation 
+--   for GP classification. Targets should be +1/-1. Outputs the -log 
+--   marginal likelihood and a vector of its derivatives.
+gpClassifierEPLogEvidence :: (CovarianceFunction c) => c -- ^ Covariance
+                          -> Inputs 
+                          -> Targets 
+                          -> SiteOrder
+                          -> EPConvergenceTest
+                          -> (Double, DVector)
+gpClassifierEPLogEvidence c i t siteOrder converged 
+  = (-logEvidence, -(zipVectorWith (*) (trueHyper c) (fromList dLogEvidence))) 
+    where
+      d = dim t
+      k = covarianceMatrix c i
+      (value, s) = gpClassifierEPLearn k t siteOrder converged
+      nT = nuTilde $ siteState value
+      tT =  tauTilde $ siteState value
+      logEvidence =  eValue value
+      rootSTildeV = mapVector sqrt tT
+      rootSTilde = diag rootSTildeV
+      l = trans $ chol ((ident d) + (abaDiagDiag rootSTildeV k))
+      b = (asColumn nT) - 
+           (rootSTilde <> 
+            (asColumn $ upperSolve (trans l) 
+                         (lowerSolve l (flatten $ 
+                                     rootSTilde <> k <> (asColumn nT)))))
+      r = (b <> (trans b)) - 
+             (rootSTilde <> 
+                 (generalSolve upperSolve (trans l) 
+                                       (generalSolve lowerSolve l rootSTilde)))
+      dLogEvidence = map ((0.5 *) . sum . toList . (abDiagOnly r)) 
+                           (makeMatricesFromPairs (dCovarianceDParameters c) i)
+
+-- | Essentially the same as gpClassifierEPLogEvidence, but makes a 
+--   covariance function using the hyperparameters supplied in a list 
+--   and passes it on. 
+gpClassifierEPLogEvidenceList :: (CovarianceFunction c) => Inputs 
+                          -> Targets
+                          -> c -- ^ Covariance 
+                          -> SiteOrder
+                          -> EPConvergenceTest
+                          -> [Double]
+                          -> (Double, DVector)
+gpClassifierEPLogEvidenceList i t cov siteOrder converged hyper = 
+    gpClassifierEPLogEvidence cov2 i t siteOrder converged
+        where 
+          cov2 = makeCovarianceFromList cov hyper
+
+-- | Essentially the same as gpClassifierEPLogEvidence, but makes a 
+--   covariance function using the hyperparameters supplied in a vector 
+--   and passes it on. 
+gpClassifierEPLogEvidenceVec :: (CovarianceFunction c) => Inputs 
+                          -> Targets 
+                          -> c -- ^ Covariance
+                          -> SiteOrder
+                          -> EPConvergenceTest
+                          -> DVector
+                          -> (Double, DVector)
+gpClassifierEPLogEvidenceVec i t cov siteOrder converged hyper = 
+    gpClassifierEPLogEvidence cov2 i t siteOrder converged
+        where 
+          cov2 = makeCovarianceFromList cov (toList hyper)
+
+
+
diff --git a/src/HasGP/Classification/Laplace/ClassificationLaplace.hs b/src/HasGP/Classification/Laplace/ClassificationLaplace.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Classification/Laplace/ClassificationLaplace.hs
@@ -0,0 +1,280 @@
+{- | ClassificationLaplace is a module in the HasGP Gaussian Process
+   library. It implements basic Gaussian Process Classification for two 
+   classes using the Laplace approximation. For details see 
+   www.gaussianprocesses.org.
+
+   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.Classification.Laplace.ClassificationLaplace 
+   (
+     LaplaceValue(..),
+     LaplaceConvergenceTest,
+     gpCLaplaceLearn,
+     convertToP_CG,
+     gpCLaplacePredict,
+     gpCLaplacePredict',
+     gpCLaplaceLogEvidence,
+     gpCLaplaceLogEvidenceList,
+     gpCLaplaceLogEvidenceVec
+   ) where 
+
+import Numeric.LinearAlgebra
+
+import Control.Monad.State
+
+import HasGP.Types.MainTypes
+import HasGP.Support.MatrixFunction
+import HasGP.Support.Linear
+import HasGP.Support.Functions
+import HasGP.Support.Solve
+import HasGP.Support.Iterate
+import HasGP.Covariance.Basic
+import HasGP.Likelihood.Basic
+
+-- | Computing the Laplace approximation requires us to deal with 
+--   quite a lot of information. To keep things straightforward we 
+--   wrap this up in a type.
+--
+--   The value associated with a state includes f, evidence, objective, 
+--   derivative of the objective, the vector a needed to compute the 
+--   derivative of the evidence, and the number of iterations.
+data LaplaceValue = LaplaceValue {
+      fValue::DVector,
+      eValue::Double,
+      psiValue::Double,
+      dPsiValue::DVector,
+      aValue::DVector,
+      count::Int
+    }
+
+-- | The state is the vector f and the number of iterations.
+type LaplaceState = (DVector,Int)
+
+-- | A convergence test is a function that takes two consecutive values 
+--   during iteration and works out whether you've converged or not.
+type LaplaceConvergenceTest = (LaplaceValue -> LaplaceValue -> Bool)
+
+-- | Compute the Laplace update for the latent variables f.
+--
+--   Produces new f, log marginal likelihood, objective, derivative 
+--   of objective, and the vector a which is needed to compute the derivative 
+--   of the log marginal likelihood.
+gpCLaplaceUpdate :: LogLikelihood l => CovarianceMatrix 
+                 -> Targets                  
+                 -> l               -- ^ log likelihood
+                 -> LaplaceState    -- ^ Current f and n.
+                 -> LaplaceValue
+gpCLaplaceUpdate c t like (f,n) = 
+    LaplaceValue newF e psi dPsi aV (n+1)
+        where
+          d = dim t
+          diagW = -(zipVectorWith (ddLikelihood like) t f)
+          w = diag diagW
+          diagRootW = mapVector sqrt diagW
+          rootW = diag diagRootW
+          -- MUST use abaDiagDiag, or errors accumulate and symmetry is lost.
+          ll = trans $ chol ((ident d) + (abaDiagDiag diagRootW c))
+          dL = zipVectorWith (dLikelihood like) t f                   
+          b = (w <> (asColumn f)) + (asColumn dL)    
+          a = b - 
+              (rootW <> 
+               asColumn (upperSolve (trans ll) 
+                         (lowerSolve ll (head $ toColumns (rootW <> c <> b)))))
+          aV = head $ toColumns a
+          newF = head $ toColumns (c <> a)
+          psi = (-(1/2) * (aV <.> newF)) + 
+                (sum $ toList $ zipVectorWith (likelihood like) t newF)
+          -- Not in the book but easily proved using the fact that f = Ka and 
+          -- dPsi = d log likelihood - (inverse of K) f
+          dPsi = dL - aV 
+          -- log marginal likelihood.
+          e = (psi - (sum $ map log $ toList $ takeDiag ll)) 
+                        
+-- | Iteration to convergence is much nicer if the state is hidden using 
+--   the State monad.
+--
+--   This uses the pure gpCLaplaceUpdate function, and wraps it up in a 
+--   state transformer that's usable by the general functions in 
+--   HasGP.Support.Iterate.
+singleIteration::LogLikelihood l => CovarianceMatrix 
+               -> Targets                 
+               -> l                        -- ^ log likelihood
+               -> State LaplaceState LaplaceValue
+singleIteration c t like = state sI 
+    where
+      sI (f, n) = (newValue, ((fValue newValue), (n+1)))
+          where
+            newValue = gpCLaplaceUpdate c t like (f,n)
+
+-- | Iteration to convergence is much nicer if the state is hidden using 
+--   the State monad.
+--
+--   This uses a general function from HasGP.Support.Iterate to implement 
+--   the learning algorithm. Convergence testing is done using a user 
+--   supplied function.
+gpCLaplaceLearn::LogLikelihood l => CovarianceMatrix 
+               -> Targets   
+               -> l          -- ^ log likelihood
+               -> LaplaceConvergenceTest
+               -> LaplaceValue
+gpCLaplaceLearn c t like converged = 
+    evalState (iterateToConvergence'' doOnce converged) (constant 0.0 (dim t),1)
+        where
+          doOnce = singleIteration c t like
+
+-- | Converts pairs of fStar and V produced by the prediction functions 
+--   to actual probabilities, assuming the cumulative Gaussian likelihood 
+--   was used.
+convertToP_CG :: (Double,Double) -> Double
+convertToP_CG (fStar,v) = phiIntegral (fStar / (sqrt (1 + v)))
+
+-- | Predict using a GP classifier based on the Laplace approximation.
+--
+--   Produces fStar and V rather than the actual probability as 
+--   further approximations are then required to compute this.
+gpCLaplacePredict :: (CovarianceFunction cF, LogLikelihood l) => DVector -- ^ f
+                  -> Inputs           
+                  -> Targets          
+                  -> CovarianceMatrix -- ^ Covariance matrix
+                  -> cF               -- ^ Covariance function
+                  -> l                -- ^ log likelihood
+                  -> Input            -- ^ Input to classify
+                  -> (Double,Double)
+gpCLaplacePredict f inputs t c cov like x = 
+    (fStar, (((covariance cov) x x) - (v <.> v)))
+        where
+          d = dim t
+          diagW = -(zipVectorWith (ddLikelihood like) t f)
+          w = diag diagW
+          diagRootW = mapVector sqrt diagW
+          rootW = diag diagRootW
+          ll = trans $ chol ((ident d) + (abaDiagDiag diagRootW c))   
+          kxxStar = covarianceWithPoint cov inputs x 
+          fStar = kxxStar <.> (zipVectorWith (dLikelihood like) t f) 
+          v = lowerSolve ll (zipVectorWith (*) diagRootW kxxStar)
+
+-- | Predict using a GP classifier based on the Laplace approximation.
+--
+--   The same as gpLaplacePredict but applies to a collection of new 
+--   inputs supplied as the rows of a matrix.
+--
+--   Produces a list of pairs of fStar and V rather than the actual 
+--   probabilities as further approximations are then required to compute 
+--   these.
+gpCLaplacePredict' :: (CovarianceFunction cF, LogLikelihood l) => DVector -- ^ f
+                   -> Inputs          
+                   -> Targets         
+                   -> CovarianceMatrix 
+                   -> cF               -- ^ Covariance function
+                   -> l                -- ^ log likelihood
+                   -> Inputs           -- ^ Inputs to classify
+                   -> [(Double,Double)]
+gpCLaplacePredict' f inputs t c cov like x = 
+    map predict $ toRows x
+        where
+          predict = gpCLaplacePredict f inputs t c cov like
+
+-- | Compute the log marginal likelihood and its first derivative for the 
+--   Laplace approximation for GP classification.
+--
+--   The convergence test input tests for convergence when 
+--   using gpClassificationLaplaceLearn. Note that a covariance function 
+--   contains its own parameters and can compute its own derivative so 
+--   theta does not need to be passed seperately.
+--
+--   Outputs the NEGATIVE log marginal likelihood and a vector of its 
+--   derivatives. The derivatives are with respect to the actual, NOT log 
+--   parameters.
+gpCLaplaceLogEvidence :: (CovarianceFunction cF, LogLikelihood l) => Inputs 
+                      -> Targets 
+                      -> cF                  -- ^ Covariance function
+                      -> l                   -- ^ log likelihood
+                      -> LaplaceConvergenceTest 
+                      -> (Double, DVector)
+gpCLaplaceLogEvidence i t cov like converged = 
+    (-z, -dZ)
+        where
+          d = dim t
+          cM = covarianceMatrix cov i
+          LaplaceValue f z psi dPsi aV n = 
+              gpCLaplaceLearn cM t like converged
+          diagW = -(zipVectorWith (ddLikelihood like) t f)
+          w = diag diagW
+          diagRootW = mapVector sqrt diagW
+          rootW = diag diagRootW
+          ll = trans $ chol ((ident d) + (abaDiagDiag diagRootW cM))
+          r = rootW <> (generalSolve upperSolve (trans ll) 
+                        (generalSolve lowerSolve ll rootW))
+          c = generalSolve lowerSolve ll (rootW <> cM)
+          s2 = flatten $ 
+               (-0.5) * (diag ((takeDiag cM) - 
+                               (abDiagOnly (trans c) c))) <> 
+                          (asColumn (zipVectorWith (dddLikelihood like) t f))
+          cList = makeMatricesFromPairs (dCovarianceDParameters cov) i
+          s1a = fromList $ map (abaVV aV) cList
+          s1b = fromList $ map (sum . toList . (abDiagOnly r)) cList
+          s1 = 0.5 * (s1a - s1b)
+          b = map (<> (asColumn $ zipVectorWith (dLikelihood like) t f)) cList
+          s3 = map (\x -> x - (cM <> r <> x)) b 
+          dZ = s1 + (fromList $ map (s2 <.>) (map flatten s3)) 
+
+-- | A version of gpClassificationLaplaceEvidence that's usable by the
+--   conjugate gradient function included in the hmatrix library. Computes 
+--   the log evidence and its first derivative for the Laplace approximation 
+--   for GP classification. The issue is that while it makes sense for a 
+--   covariance function to be implemented as a class so that any can easily 
+--   be used, we need to supply evidence and its derivatives directly as 
+--   functions of the hyperparameters, and these have to be supplied as 
+--   vectors of Doubles. The solution is to include a function in the 
+--   CovarianceFunction class that takes a list and returns a new covariance 
+--   function of the required type having the specified hyperparameters.
+--
+--   Parameters: The same parameters as gpClassifierLaplaceEvidence, plus 
+--   the list of hyperparameters. Outputs: negative log marginal likelihood 
+--   and a vector of its first derivatives. 
+--   
+--   In addition to the above, this assumes that we want derivatives with 
+--   respect to log parameters and so converts using df/d log p = 
+--   p df/dp.
+gpCLaplaceLogEvidenceList :: (CovarianceFunction cF, LogLikelihood l) => Inputs 
+                          -> Targets 
+                          -> cF 
+                          -> l 
+                          -> LaplaceConvergenceTest
+                          -> [Double] -- ^ log hyperparameters
+                          -> (Double, DVector)
+gpCLaplaceLogEvidenceList i t cov like converged hyper = 
+    (negZ, zipVectorWith (*) (fromList $ map exp hyper) negDZ)
+        where 
+          cov2 = makeCovarianceFromList cov hyper
+          (negZ, negDZ) = gpCLaplaceLogEvidence i t cov2 like converged
+
+-- | This is the same as gpCLaplaceLogEvidenceList but takes a vector 
+--   instead of a list.
+gpCLaplaceLogEvidenceVec :: (CovarianceFunction cF, LogLikelihood l) => Inputs 
+                         -> Targets 
+                         -> cF 
+                         -> l 
+                         -> LaplaceConvergenceTest
+                         -> DVector 
+                         -> (Double, DVector)
+gpCLaplaceLogEvidenceVec i t cov like converged 
+    = (gpCLaplaceLogEvidenceList i t cov like converged) . toList
+
+
+
diff --git a/src/HasGP/Covariance/Basic.hs b/src/HasGP/Covariance/Basic.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Covariance/Basic.hs
@@ -0,0 +1,80 @@
+{- | Gaussian Process Library. This module contains assorted functions that 
+   support the computation of covariance, constructing covariance matrices 
+   etc.
+
+   Covariance functions store log parameters. Functions are needed to return 
+   the covariance and its derivative. Derivatives are with respect to the 
+   actual parameters, NOT their logs.
+
+   Copyright (C) 2011 Sean Holden. sbh11\@cl.cam.ac.uk.
+-}
+{- 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.Covariance.Basic 
+   (
+     CovarianceFunction,
+     trueHyper,
+     covariance,
+     dCovarianceDParameters,
+     makeCovarianceFromList,
+     makeListFromCovariance,
+     covarianceMatrix,
+     covarianceWithPoint,
+     covarianceWithPoints
+   ) where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+
+class CovarianceFunction a where
+    -- ^ The actual hyperparameter values.
+    trueHyper :: a -> DVector           
+    -- ^ The covariance
+    covariance :: a -> DVector -> DVector -> Double 
+    -- ^ Derivative of covariance with respect to parameters 
+    dCovarianceDParameters :: a -> DVector -> DVector -> DVector 
+    -- ^ Construct using log parameters.
+    makeCovarianceFromList :: a -> [Double] -> a 
+    -- ^ Get log parameters.
+    makeListFromCovariance :: a -> [Double]  
+
+-- | Construct a matrix of covariances from a covariance and a design matrix. 
+covarianceMatrix :: (CovarianceFunction c) => c -> Inputs 
+              -> CovarianceMatrix
+covarianceMatrix c d = (r><r) [(covariance c x y) | x <- dList, y <- dList]
+    where
+      r = rows d
+      dList = toRows d
+
+-- | Constructs the column vector required when a new input is included. 
+--   Constructed as a matrix to avoid further work elsewhere. 
+covarianceWithPoint :: (CovarianceFunction c) => c 
+                    -> Inputs
+                    -> Input
+                    -> DVector
+covarianceWithPoint c d xStar = fromList [((covariance c) x xStar) | x <- dList]
+    where
+      r = rows d
+      dList = toRows d
+
+-- | covarianceWithPoint applied to a list of points to produce 
+--   a list of vectors.
+covarianceWithPoints :: (CovarianceFunction c) => c 
+                    -> Inputs 
+                    -> [Input]
+                    -> [DVector]
+covarianceWithPoints c d xStars = map (covarianceWithPoint c d) xStars
+
+
diff --git a/src/HasGP/Covariance/SquaredExp.hs b/src/HasGP/Covariance/SquaredExp.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Covariance/SquaredExp.hs
@@ -0,0 +1,61 @@
+{- | Gaussian Process Library. This module contains the definition 
+     of the standard squared exponential covariance function.
+
+     Copyright (C) 2008-11 Sean Holden. sbh11\@cl.cam.ac.uk.
+-}
+{-   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.Covariance.SquaredExp 
+   (
+     SquaredExponential(..)
+   ) where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Covariance.Basic
+
+data SquaredExponential = SquaredExponential
+    {
+      f :: Double,         -- ^ log \sigma_f^2
+      l :: Double          -- ^ log l
+    }                      
+
+instance CovarianceFunction SquaredExponential where
+
+    trueHyper se = mapVector exp $ fromList [(f se), (l se)]
+
+    covariance se in1 in2 = 
+        f2 * exp (-(1/(2 * (l2^2))) * (diff <.> diff))
+            where 
+              diff = in1 - in2
+              f2 = exp (f se)
+              l2 = exp (l se)
+
+    dCovarianceDParameters se in1 in2 = 
+        fromList [dLogByDF, dLogByDL]
+            where 
+              diff = in1 - in2
+              d = diff <.> diff
+              f2 = exp (f se)
+              l2 = exp (l se)
+              dLogByDF = exp (-(1/(2 * (l2^2))) * d) 
+              dLogByDL = f2 * dLogByDF * (d * (l2^^(-3)))
+
+    makeCovarianceFromList se [f, l] = SquaredExponential f l 
+    makeCovarianceFromList se _ = 
+        error "SquaredExp requires exactly 2 hyperparameters"
+
+    makeListFromCovariance se = [f se, l se]
+
diff --git a/src/HasGP/Covariance/SquaredExpARD.hs b/src/HasGP/Covariance/SquaredExpARD.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Covariance/SquaredExpARD.hs
@@ -0,0 +1,73 @@
+{- | Gaussian Process Library. This module contains the definition 
+   of the standard squared exponential covariance function, extended 
+   for use with Automatic Relevance Determination.
+
+   s_f^2 exp (-1\/2 (x_1 - x_2)^T M (x_1 - x_2)) 
+
+   Parameters: s_f^2 and vector containing the diagonal of M. 
+   M is diag (1\/l_1^2,...,1\/l_?^2)
+
+   Copyright (C) 2011 Sean Holden. sbh11\@cl.cam.ac.uk.
+-}
+{- 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.Covariance.SquaredExpARD 
+   ( 
+     SquaredExponentialARD(..)
+   ) where
+
+import Numeric.LinearAlgebra
+import HasGP.Types.MainTypes
+import HasGP.Support.Linear
+import HasGP.Support.Functions
+import HasGP.Covariance.Basic
+
+data SquaredExponentialARD = SquaredExponentialARD 
+    {
+      fARD            :: Double,
+      m               :: DVector
+    }
+
+instance CovarianceFunction SquaredExponentialARD where
+
+    trueHyper se = mapVector exp $ join [fromList [fARD se], m se]
+      
+    covariance se x1 x2 = f2 * (exp ((-(1/2)) * (xAxDiag diff newM2)))
+        where
+          diff = x1 - x2
+          f2 = exp (fARD se)
+          newM2 = mapVector ((^^(-2)) . exp) (m se) 
+
+    dCovarianceDParameters se x1 x2 = 
+        join [(fromList [dKDLogF]), dKDLogM] -- You need to compute dK/dtheta, 
+                                             -- NOT dK/dlogtheta
+            where
+               diff = x1 - x2
+               d = mapVector square diff
+               f2 = exp (fARD se)
+               m2 = mapVector exp (m se)
+               newM2 = mapVector (^^(-2)) m2
+               dKDLogF = exp ((-(1/2)) * (xAxDiag diff newM2))
+               dKDLogM = scale (f2 * dKDLogF) 
+                            (zipVectorWith (*) d (mapVector (^^(-3)) m2)) 
+
+    makeCovarianceFromList se (f:rest) =  
+        if (length rest) == (dim $ (m se))
+        then SquaredExponentialARD f (fromList rest) 
+        else error "SquaredExpARD needs the correct number of hyperparameters"
+    makeCovarianceFromList se _ = 
+        error "SquaredExpARD needs the correct number of hyperparameters"
+
+    makeListFromCovariance se = (fARD se):(toList $ m se) 
+
diff --git a/src/HasGP/Data/BishopData.hs b/src/HasGP/Data/BishopData.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Data/BishopData.hs
@@ -0,0 +1,44 @@
+{- | BishopData is a module in the HasGP Gaussian Process library. 
+     It contains functions to generate toy data as used in "Neural 
+     Networks for Pattern Recognition," by Chris Bishop.
+
+     There is one difference between this data and that in the book. 
+     Namely: this data is adjusted to have zero mean, making it easier 
+     to use in the demonstrations.
+
+     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.BishopData where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Random
+
+h :: Double -> Double
+h x = (0.4 * (sin (2 * pi * x))) -- to get the original data add 0.5 to this
+
+bishopData :: (Inputs, Targets)
+bishopData = (asColumn inputs, (mapVector h inputs) + n)
+    where
+      xVar = 0.05
+      random = normalVectorSimple 1 1 60
+      n = scale (0.05) $ subVector 0 30 random
+      i1 = (constant 0.25 15) + (scale (xVar) $ subVector 30 15 random)
+      i2 = (constant 0.75 15) + (scale (xVar) $ subVector 45 15 random)
+      inputs = join [i1, i2]
diff --git a/src/HasGP/Data/Files/gpml-classifier-test.txt b/src/HasGP/Data/Files/gpml-classifier-test.txt
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Data/Files/gpml-classifier-test.txt
@@ -0,0 +1,6561 @@
+-4.000000 -4.000000
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diff --git a/src/HasGP/Data/Files/gpml-classifier-x.txt b/src/HasGP/Data/Files/gpml-classifier-x.txt
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diff --git a/src/HasGP/Data/Files/gpml-classifier-y.txt b/src/HasGP/Data/Files/gpml-classifier-y.txt
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Data/Files/gpml-classifier-y.txt
@@ -0,0 +1,120 @@
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diff --git a/src/HasGP/Data/Normalise.hs b/src/HasGP/Data/Normalise.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Data/Normalise.hs
@@ -0,0 +1,203 @@
+{- | Normalise is a module in the HasGP Gaussian process library. 
+     It contains functions for performing basic normalisation 
+     tasks on training examples, and for computing assorted 
+     standard statistics.
+
+     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.Normalise where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Linear as L
+import HasGP.Support.Functions as F
+
+-- | Compute the mean for each attribute in a set of examples. 
+exampleMean :: Inputs  -- ^ Matrix - one row per example
+            -> DVector -- ^ Vector of means for each attribute.
+exampleMean examples = 
+    fromList $ map (L.sumVectorDiv r) (toColumns examples)
+        where
+          r = rows examples
+
+-- | Compute the variance for each attribute in a set of examples.
+exampleVariance :: Inputs  -- ^ Matrix - one row per example
+                -> DVector -- ^ Vector of variances for each attribute.
+exampleVariance examples = 
+    fromList $ map (L.sumVectorDiv r) 
+    (toColumns $ fromRows $ map ((mapVector (^2)) . (\x -> x-m)) 
+     (toRows examples))
+        where
+          r = rows examples
+          m = exampleMean examples
+
+-- | Compute the mean and variance for each attribute in a set of examples.
+exampleMeanVariance :: Inputs               -- ^ Matrix - one row per example
+                    -> (DVector, DVector)   -- ^ Means and variances
+exampleMeanVariance examples = (exampleMean examples, exampleVariance examples) 
+      
+-- | Normalise a set of examples to have specified mean and variance.
+normaliseMeanVariance :: DVector -- ^ Vector of new means required
+                      -> DVector -- ^ Vector of new variances required
+                      -> Inputs  -- ^ Matrix - one row per example
+                      -> Inputs  -- ^ Normalised matrix
+normaliseMeanVariance newMean newVariance examples = 
+    fromRows $ map (\x -> x+newMean) varianceAdjusted
+    where
+      (m, v) = exampleMeanVariance examples
+      zeroMean = map (\x -> x-m) (toRows examples)
+      varianceAdjustment = zipVectorWith (\x y -> (sqrt x)/(sqrt y)) 
+                                         newVariance v
+      varianceAdjusted = map (zipVectorWith (*) varianceAdjustment) zeroMean
+
+-- | The same as normaliseMeanVariance but every column (attribute) is 
+--   normalised in the same way.
+normaliseMeanVarianceSimple :: Double  -- ^ New mean required 
+                            -> Double  -- ^ New variance required
+                            -> Inputs  -- ^ Matrix - one row per example
+                            -> Inputs  -- ^ Normalised matrix
+normaliseMeanVarianceSimple newMean newVariance examples = 
+    normaliseMeanVariance (constant newMean c) (constant newVariance c) examples
+        where
+          c = cols examples
+
+-- | Normalise a set of examples to have specified maximum and minimum.
+normaliseBetweenLimits :: Double -- ^ New min required 
+                       -> Double -- ^ New max required 
+                       -> Inputs -- ^ Matrix - one row per example
+                       -> Inputs -- ^ Normalised matrix
+normaliseBetweenLimits min max examples = 
+    fromColumns $ zipWith (\x y -> mapVector (x+) y) 
+                cV (zipWith scale mV columns)
+        where
+          columns = toColumns examples
+          minV = map minElement columns
+          maxV = map maxElement columns
+          mV = zipWith (\x y -> ((max - min) / (y - x))) minV maxV
+          cV = zipWith (\x y -> (min - (y * x))) minV mV
+
+-- | Find the columns of a matrix in which all values are equal.      
+findRedundantAttributes :: Inputs  -- ^ Matrix - one row per example
+                        -> [Bool]  -- ^ List - True elements mark redundancy
+findRedundantAttributes examples = map allSame columns
+    where
+      columns = map toList (toColumns examples)
+      allSame []        = True
+      allSame [h]       = True
+      allSame [h1,h2]   = (h1 == h2)
+      allSame (h1:h2:t) = (h1 == h2) && (allSame (h2:t)) 
+
+-- | List column numbers for redundant attributes.
+listRedundantAttributes :: Inputs -- ^ Matrix - one row per example
+                        -> [Int]  -- ^ List - positions of redundant attributes
+listRedundantAttributes examples = findColumns boolean 1 []
+    where
+      boolean = findRedundantAttributes examples
+      findColumns [] n result = reverse result
+      findColumns (h:t) n result
+          | h = findColumns t (n+1) (n:result)
+          | otherwise = findColumns t (n+1) result
+
+-- | Remove any redundant columns from a matrix.
+removeRedundantAttributes :: Inputs -- ^ Matrix - one row per example
+                          -> Inputs -- ^ Modified matrix - one row per example
+removeRedundantAttributes examples = 
+    fromColumns $ removeTrueColumns [] r (toColumns examples)
+        where
+          r = findRedundantAttributes examples
+          removeTrueColumns result [] []
+              = reverse result
+          removeTrueColumns result (True:t1) (c:t2)  
+              = removeTrueColumns result t1 t2
+          removeTrueColumns result (False:t1) (c:t2) 
+              = removeTrueColumns (c:result) t1 t2
+
+-- | Specify a list of columns (matrix numbered from 1).
+--   Produce a matrix with ONLY those columns in the 
+--   order specified in the list.
+retainAttributes :: [Int]   -- ^ List of columns to keep.
+                 -> Inputs  -- ^ Matrix - one row per example
+                 -> Inputs  -- ^ Modified matrix - one row per example
+retainAttributes l m = trans $ extractRows l2 $ trans m
+    where
+      l2 = map (\x -> x-1) l
+
+-- | Compute the numbers for the confusion matrix.
+--   It is assumed that classes are +1 (positive) and -1 (negative).
+--   Result is (a,b,c,d):
+--   a - correct negatives
+--   b - predict positive when correct is negative
+--   c - predict negative when correct is positive
+--   d - correct positives
+confusionMatrix :: Targets    
+                -> Outputs    
+                -> (Double,Double,Double,Double)
+confusionMatrix correct predicted = 
+    cm (toList correct) (toList predicted) (0,0,0,0)
+        where
+          cm [] [] result = result
+          cm (h1:t1) (h2:t2) (a,b,c,d) = case (h1, h2) of
+                                           (1.0, 1.0) -> cm t1 t2 (a,b,c,d+1)
+                                           (1.0,-1.0) -> cm t1 t2 (a,b,c+1,d)
+                                           (-1.0, 1.0) -> cm t1 t2 (a,b+1,c,d)
+                                           (-1.0,-1.0) -> cm t1 t2 (a+1,b,c,d)
+          cm _ _ result 
+              = error "Correct and predicted vectors must have the same length"
+
+-- | Print the confusion matrix and some other statistics
+printConfusionMatrix :: Targets -- ^ Vector of targets 
+                     -> Outputs -- ^ Vector of actual outputs
+                     -> IO ()
+printConfusionMatrix correct predicted = do
+  let (a,b,c,d) = confusionMatrix correct predicted
+  let n = a+b+c+d
+  let trueP = d/(d+c)
+  let precision = d/(d+b)
+  putStrLn ("------------------------------------------------")
+  putStrLn ("Correct -1, Predicted -1: a = " ++ (show a))
+  putStrLn ("Correct -1, Predicted +1: b = " ++ (show b))
+  putStrLn ("Correct +1, Predicted -1: c = " ++ (show c))
+  putStrLn ("Correct +1, Predicted +1: d = " ++ (show d))
+  putStrLn ("------------------------------------------------")
+  putStrLn ("Number of examples: n = a+b+c+d = " ++ (show n))
+  putStrLn ("Accuracy:               a+d/n   = " ++ (show ((a+d)/n)))
+  putStrLn ("Recall/True Positive:   d/d+c   = " ++ (show trueP))
+  putStrLn ("False Positive:         b/b+a   = " ++ (show (b/(b+a))))
+  putStrLn ("True Negative:          a/b+a   = " ++ (show (a/(b+a))))
+  putStrLn ("False Negative:         c/d+c   = " ++ (show (c/(d+c))))
+  putStrLn ("Precision:              d/d+b   = " ++ (show precision))
+  putStrLn ("F Measure (beta = 1)            = " ++ 
+            (show ((2 * trueP * precision)/(trueP + precision))))
+  putStrLn ("------------------------------------------------")
+  return ()
+
+-- | Assuming the labels are +1 or -1, count how many there are of each.
+countLabels :: Targets -> IO ()
+countLabels v = do
+  let d = dim v
+  let plus = length $ filter (==(1.0)) $ toList v
+  putStrLn ("Total number of labels: " ++ (show d))
+  putStrLn ("Number of +1 labels:    " ++ (show plus))
+  putStrLn ("Number of -1 labels:    " ++ (show (d - plus)))
+  return ()
+
+
+
+
+
diff --git a/src/HasGP/Data/RWData1.hs b/src/HasGP/Data/RWData1.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Data/RWData1.hs
@@ -0,0 +1,47 @@
+{- | 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
+ 
+
+
+                         
+                         
+
+
+
diff --git a/src/HasGP/Demos/ClassificationDemo1.hs b/src/HasGP/Demos/ClassificationDemo1.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Demos/ClassificationDemo1.hs
@@ -0,0 +1,120 @@
+{- | Demonstration of Gaussian process classification using the  
+     1-dimensional problem from Rasmussen and Williams' book.
+
+     This demo compares the Laplace and EP approximation approaches.
+
+     For details of the algorithms involved see www.gaussianprocesses.org.
+
+     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.Demos.ClassificationDemo1 where 
+
+import Numeric.LinearAlgebra
+import Numeric.GSL.Minimization
+
+import HasGP.Data.RWData1
+import HasGP.Types.MainTypes
+import HasGP.Support.Linear
+import HasGP.Classification.Laplace.ClassificationLaplace as L
+import HasGP.Classification.EP.ClassificationEP as EP
+import HasGP.Covariance.SquaredExp
+import HasGP.Covariance.Basic
+import HasGP.Likelihood.LogPhi
+import HasGP.Likelihood.Basic 
+
+-- | This function defines when iteration stops for the Laplace version.
+stopLaplace::LaplaceConvergenceTest
+stopLaplace s1 s2 = ((L.count s2) == 100) || 
+             (lengthV ((fValue s1) - (fValue s2)) < 0.001)
+
+-- | This function defines when iteration stops for the EP version.
+stopEP::EPConvergenceTest
+stopEP s1 s2 = ((EP.count s2) == 100) || 
+             ((EP.eValue s1) > (EP.eValue s2)) ||
+             (abs ((EP.eValue s1) - (EP.eValue s2)) < 0.001)
+
+demo = do 
+  putStrLn "Generating the training and testing data..."
+
+  let (inputs, t) = simpleClassificationData 0
+  let targets = mapVector (\x -> if (x==1) then 1 else -1) t
+  saveMatrix "training-inputs.txt" "%g" inputs
+  fprintfVector "training-targets.txt" "%g" targets
+
+  let points = asColumn $ linspace 50 (-9.0,5.0) 
+  saveMatrix "test-inputs.txt" "%g" points
+
+  putStrLn "Learning and predicting: Laplace + hyperparameter optimization..."
+
+  let cov1 = SquaredExponential (log 1.0) (log 1.0)
+  let c1 = covarianceMatrix cov1 inputs
+  
+  let f1 = (\v -> gpCLaplaceLogEvidenceVec inputs targets cov1 LogPhi 
+                 stopLaplace v)
+  let ev1 = fst . f1
+  let gev1 = snd . f1
+  let (solution1, path1) = 
+       minimizeVD ConjugatePR 0.0001 50 0.01 0.1 ev1 gev1 
+                      (constant (log 1) 2)
+                      
+  putStrLn $ "Solution: " ++ (show $ mapVector exp solution1)
+  putStrLn $ "Path: "
+  putStrLn $ show path1
+
+  let cov1' = SquaredExponential (solution1 @> 0) (solution1 @> 1)
+  let c1' = covarianceMatrix cov1' inputs
+  let result1 = gpCLaplaceLearn c1' targets LogPhi stopLaplace
+  let classify1 = 
+          gpCLaplacePredict' (fValue result1) inputs targets c1' cov1' LogPhi 
+  let newOuts1 = fromList $ map convertToP_CG $ classify1 $ points
+  
+  fprintfVector "test-outputs-laplace.txt" "%g" newOuts1
+
+  putStrLn $ "Done"
+
+  putStrLn "Learning and predicting: EP + hyperparameter optimization..."
+
+  let cov2 = SquaredExponential (log 1.0) (log 1.0)
+  let c2 = covarianceMatrix cov2 inputs
+  
+  let f2 = (\v -> gpClassifierEPLogEvidenceVec inputs targets cov2  
+                 generateRandomSiteOrder stopEP v)
+  let ev2 = fst . f2
+  let gev2 = snd . f2
+  let (solution2, path2) = 
+       minimizeVD ConjugatePR 0.0001 50 0.01 0.1 ev2 gev2 
+                      (constant (log 1) 2)
+                      
+  putStrLn $ "Solution: " ++ (show $ mapVector exp solution2)
+  putStrLn $ "Path: "
+  putStrLn $ show path2
+
+  let cov2' = SquaredExponential (solution2 @> 0) (solution2 @> 1)
+  let c2' = covarianceMatrix cov2' inputs
+  let (epValue, epState) = 
+         gpClassifierEPLearn c2' targets generateRandomSiteOrder stopEP
+  let classify2 = 
+          gpClassifierEPPredict (siteState epValue) inputs targets c2' cov2'  
+  let newOuts2 = classify2 points
+  
+  fprintfVector "test-outputs-ep.txt" "%g" newOuts2
+
+  putStrLn $ "Done"
+
+  return ()
+   
diff --git a/src/HasGP/Demos/ClassificationDemo2.hs b/src/HasGP/Demos/ClassificationDemo2.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Demos/ClassificationDemo2.hs
@@ -0,0 +1,85 @@
+{- | Demonstration of Gaussian process classification using the  
+     demonstration problem from
+
+     www.gaussianprocess.org/gpml/code/matlab/doc/
+
+     This demo uses the EP approximation approach.
+
+     For details of the algorithms involved see www.gaussianprocesses.org. 
+     For a detailed explanation of the following code see the HasGP user 
+     manual.
+
+     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.Demos.ClassificationDemo2 where
+
+import Numeric.LinearAlgebra
+import Numeric.GSL.Minimization
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Linear
+import HasGP.Classification.EP.ClassificationEP
+import HasGP.Covariance.SquaredExpARD
+import HasGP.Covariance.Basic
+
+-- | This function defines when iteration stops.
+stopEP::EPConvergenceTest
+stopEP s1 s2 = ((count s2) == 100) || 
+             ((eValue s1) > (eValue s2)) ||
+             (abs ((eValue s1) - (eValue s2)) < 0.001)
+
+demo = do 
+  putStrLn "Loading the training data..."
+  
+  inputs <- loadMatrix "gpml-classifier-x.txt"
+  targets <- fscanfVector "gpml-classifier-y.txt" 120
+  points <- loadMatrix "gpml-classifier-test.txt"
+
+  putStrLn "Learning and predicting: EP + hyperparameter optimization..."
+
+  let cov = SquaredExponentialARD (log 1.0) (constant (log 1.0) 2)
+  let c = covarianceMatrix cov inputs
+  
+  let f = (\v -> gpClassifierEPLogEvidenceVec inputs targets cov  
+                 generateRandomSiteOrder stopEP v)
+  let ev = fst . f
+  let gev = snd . f
+  let (solution, path) = 
+       minimizeVD ConjugatePR 0.0001 50 1 0.0001 ev gev 
+                      (constant (log 1) 3)
+                      
+  putStrLn $ "Solution: " ++ (show $ mapVector exp solution)
+  putStrLn $ "Path: "
+  putStrLn $ show path
+
+  let cov' = SquaredExponentialARD (solution @> 0) 
+                             (fromList [(solution @> 1), (solution @> 2)])
+  let c' = covarianceMatrix cov' inputs
+  let (epValue, epState) = 
+         gpClassifierEPLearn c' targets generateRandomSiteOrder stopEP
+  let classify = 
+          gpClassifierEPPredict (siteState epValue) inputs targets c' cov'  
+  let newOuts = classify points
+  
+  fprintfVector "gpml-hasgp-outputs.txt" "%g" newOuts
+
+  putStrLn $ "Done"
+  
+
+  return ()
+   
diff --git a/src/HasGP/Demos/RegressionDemo1.hs b/src/HasGP/Demos/RegressionDemo1.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Demos/RegressionDemo1.hs
@@ -0,0 +1,70 @@
+{- | Demonstration of Gaussian process regression using the simple
+     data from "Neural Networks for Pattern Recognition," by Chris
+     Bishop. This version estimates the hyperparameters using the
+     optimization algorithm from HMatrix.
+
+     For details of the algorithms involved see www.gaussianprocesses.org.
+
+     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.Demos.RegressionDemo1 where 
+
+import Numeric.LinearAlgebra
+import Numeric.GSL.Minimization
+
+import HasGP.Data.BishopData
+import HasGP.Types.MainTypes
+import HasGP.Support.Linear
+import HasGP.Support.Random
+import HasGP.Regression.Regression
+import HasGP.Covariance.SquaredExp
+
+demo = do 
+  putStrLn "Generating the training data..."
+
+  let (inputs, targets) = bishopData
+  saveMatrix "inputs.txt" "%g" inputs
+  fprintfVector "targets.txt" "%g" targets
+
+  putStrLn "Searching for best hyperparameters..."
+  let f = gpRLogHyperToEvidence (SquaredExponential 0 0) inputs targets 
+  let ev = fst . f
+  let gev = snd . f
+  let (solution, path) = 
+       minimizeVD ConjugatePR 0.0001 50 0.01 0.1 ev gev 
+                      (constant (log 0.1) 3)
+          
+  putStrLn $ "Found: " ++ (show solution)
+  putStrLn $ "Path: "
+  putStrLn $ show path
+
+  putStrLn "Learning and predicting..."
+  
+  let newPoints = fromColumns $ [linspace 100 (0.0,1.0)] 
+
+  let (newOuts, var) = 
+       gpRPredict' (SquaredExponential (solution @> 1) (solution @> 2)) 
+                   (solution @> 0) inputs targets newPoints
+
+  fprintfVector "outputs.txt" "%g" newOuts
+  fprintfVector "variances.txt" "%g" var  
+
+  putStrLn "Done"
+  return ()
+   
+
diff --git a/src/HasGP/Likelihood/Basic.hs b/src/HasGP/Likelihood/Basic.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Likelihood/Basic.hs
@@ -0,0 +1,40 @@
+{- | HasGP Gaussian Process Library. This module contains the class definition 
+     for log likelihoods.
+
+     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.Likelihood.Basic 
+   (
+     LogLikelihood(..)
+   ) where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Functions
+
+{- | The following allows arbitrary likelihoods with or without parameters 
+     to be wrapped up with their derivatives (with respect to f) and passed 
+     to a function.
+-}
+class LogLikelihood b where
+    likelihood    :: b -> Double -> Double -> Double 
+    dLikelihood   :: b -> Double -> Double -> Double
+    ddLikelihood  :: b -> Double -> Double -> Double
+    dddLikelihood :: b -> Double -> Double -> Double
+
diff --git a/src/HasGP/Likelihood/LogLogistic.hs b/src/HasGP/Likelihood/LogLogistic.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Likelihood/LogLogistic.hs
@@ -0,0 +1,46 @@
+{- | HasGP Gaussian Process Library. This module contains the definition 
+     for the standard log logistic likelihood function.
+
+     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.Likelihood.LogLogistic 
+   (
+     LogLogistic(..)
+   ) where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Functions
+import HasGP.Likelihood.Basic
+
+{- | Value and first three derivatives of log sigmoid with respect to its 
+     parameter f. log p(y|f) = log sigmoid (yf) where y is +1 or -1.
+-}
+data LogLogistic = LogLogistic
+
+instance LogLikelihood LogLogistic where
+    likelihood    LogLogistic y f = log (1 / (1 + (exp (-(f * y)))))
+    dLikelihood   LogLogistic y f = ((y + 1) / 2) - (sigmoid f)
+    ddLikelihood  LogLogistic y f = (-x) * (1 - x)
+        where x = sigmoid f
+    dddLikelihood LogLogistic y f = (exp (-f)) * (x^2) * ((2 * x) - 1)
+        where x = sigmoid f
+
+
+
diff --git a/src/HasGP/Likelihood/LogPhi.hs b/src/HasGP/Likelihood/LogPhi.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Likelihood/LogPhi.hs
@@ -0,0 +1,51 @@
+{- | HasGP Gaussian Process Library. This module contains the definition 
+     for the standard log Phi likelihood.
+
+     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.Likelihood.LogPhi
+   (
+     LogPhi(..)
+   ) where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Functions
+import HasGP.Likelihood.Basic
+
+{- | Value and first three derivatives of log Phi with respect to its
+     parameter f.  log p(y|f) = log \Phi (yf) where y is +1 or -1.  
+-}
+data LogPhi = LogPhi
+
+instance LogLikelihood LogPhi where 
+    likelihood    LogPhi y f = logPhi (y * f)
+    dLikelihood   LogPhi y f = y * nOverP
+        where
+          nOverP = (n f) / (phiIntegral (y * f))
+    ddLikelihood  LogPhi y f = -(((nOverP)^2) + ((y * f) * nOverP))
+        where
+          nOverP = (n f) / (phiIntegral (y * f))
+    dddLikelihood LogPhi y f = 
+        (2 * y * (nOverP^3)) + (((2 * f) + (y^2)) * 
+                                (nOverP ^2)) - (y * (1 - (f^2)) * nOverP)
+            where
+              nOverP = (n f) / (phiIntegral (y * f))
+
+
diff --git a/src/HasGP/Parsers/SvmLight.hs b/src/HasGP/Parsers/SvmLight.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Parsers/SvmLight.hs
@@ -0,0 +1,404 @@
+{- | Parser implemented using the Parsec library for reading from files in the 
+     format used by SVMLight.
+
+     Currently assumes your file is a text file in Unix format. The extra 
+     characters in Windows text files confuse it.
+     
+     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.Parsers.SvmLight 
+   (
+     analyse,
+     getMatrixExamplesFromFileC
+   ) where
+
+import Text.ParserCombinators.Parsec
+import qualified IO
+import Data.List
+
+import qualified Numeric.LinearAlgebra as M
+
+type FullTarget = (Int,Double,Bool)
+type FullFeature = ((Int,String),Double)
+type FullExample = (FullTarget,[FullFeature],String)
+
+-- | Classes are denoted by plus or minus 1 or 0
+oneZero :: Parser Int
+oneZero = do
+  n <- oneOf "10"
+  return (read [n])
+
+-- | Targets either correspond to classes or to doubles for regression 
+--   problems. 
+classTarget :: Parser Int
+classTarget = 
+    do 
+      n <- oneZero
+      return n 
+    <|> do 
+      char '+'
+      n <- oneZero
+      return n
+    <|> do 
+      char '-' 
+      n <- oneZero
+      return (-n)
+
+-- | Feature values are doubles. 
+positiveDouble :: Parser Double
+positiveDouble = 
+    do 
+      char '.'
+      s <- many1 digit
+      return (read("0." ++ s))
+    <|> try ( do 
+              s1 <- many1 digit
+              char '.'
+              s2 <- many digit
+              return (read(s1 ++ "." ++ if (s2=="") then "0" else s2))
+            )
+            <|> do 
+                s1 <- many1 digit
+                return (read s1)
+    
+-- | We may or may not want to automatically convert an integer,
+--   depending on the circumstances. In particular targets for 
+--   regression problems can be doubles, so we dont want to read a 
+--   class as a double.
+positiveDoubleNotInt :: Parser Double
+positiveDoubleNotInt = 
+    do 
+      char '.'
+      s <- many1 digit
+      return (read("0." ++ s))      
+    <|> do 
+      s1 <- many1 digit
+      char '.'
+      s2 <- many digit
+      return (read(s1 ++ "." ++ if (s2=="") then "0" else s2))
+
+signedDouble :: Parser Double
+signedDouble = 
+    do 
+      n <- positiveDouble
+      return n 
+    <|> do 
+      char '+'
+      n <- positiveDouble
+      return n
+    <|> do 
+      char '-' 
+      n <- positiveDouble
+      return (-n)
+
+signedDoubleNotInt :: Parser Double
+signedDoubleNotInt = 
+    do 
+      n <- positiveDoubleNotInt
+      return n 
+    <|> do 
+      char '+'
+      n <- positiveDoubleNotInt
+      return n
+    <|> do 
+      char '-' 
+      n <- positiveDoubleNotInt
+      return (-n)
+
+-- | A target is -1, +1, 0 or a floating point number. The
+--   Boolean is True for one of the first three and false
+--   for the fourth. A look at Joachims' examples suggests
+--   the + is optional.
+target :: Parser FullTarget              
+target = try ( 
+              do                                
+                n <- signedDoubleNotInt
+                return (0, n, False)        
+             )
+         <|> do 
+           n <- classTarget
+           return (n, 0.0, True)
+
+-- | We need integers to number the features.
+integer :: Parser Int
+integer = 
+    do 
+      n <- many1 digit
+      return (read n)
+    <|> do 
+      char '+'
+      n <- many1 digit
+      return (read n)
+    <|> do 
+      char '-'
+      n <-many1 digit
+      return (read ('-':n))
+
+-- | A feature in this format can in fact be either numbered or 
+--   labelled as "qid".
+feature :: Parser (Int, String)
+feature = 
+    do 
+      s <- string "qid"
+      return (0,"qid")
+    <|> do 
+      i <- integer
+      return (i,"")
+
+-- | Most of a line is taken up with : separated features and their values.
+featureValuePair :: Parser FullFeature
+featureValuePair = 
+    do 
+      f <- feature
+      char ':'
+      v <- signedDouble
+      return (f,v)
+
+-- | This is needed so that we can read pretty much anything we like from 
+--   comments.
+generalLetter :: Parser Char
+generalLetter = alphaNum 
+                 <|> oneOf ('"':'\\':"[]{};:'@#~/?.>,<|`¬!£$%^&*()-_=+")
+
+-- | The format describes comments in two ways. At the start of the file they 
+--   are ignored, but after a line they need to be read.
+stringToLineEnd :: Parser [String]
+stringToLineEnd = 
+    do 
+      words <- sepEndBy (many1 generalLetter) (many (char ' '))
+      newline
+      return words
+
+-- | At the end of a line, a # followed by text needs to be read.          
+--   I assume you only take the string up to the end of the line.            
+info :: Parser String
+info = 
+    do 
+      char '#'
+      spaces
+      s <- stringToLineEnd
+      return (drop 1 (foldl (++) [] (map (' ':) s)))
+    <|> do 
+      newline
+      return ""
+
+-- | This reads a single line denoting a single example.
+line :: Parser FullExample
+line = 
+    do  
+      many (char ' ')
+      t <- target
+      many1 (char ' ')
+      fvp <- sepEndBy (featureValuePair) (many (char ' '))
+      s <- info
+      return (t, fvp, s)
+
+-- | This reads a file, ignoring comments at the beginning.
+file:: Parser [FullExample]
+file = 
+    do 
+      many info
+      l <- many1 line
+      spaces
+      eof
+      return l
+
+-- | A bunch of basic functions for extracting interesting things from
+--   the output of the parser in a format
+--   that's likely to be a bit easier to use.
+split1 :: (a,b,c) -> a
+split1 (a,_,_) = a
+
+split2 :: (a,b,c) -> b
+split2 (_,b,_) = b
+
+split3 :: (a,b,c) -> c
+split3 (_,_,c) = c
+
+fullExamplesSeparate :: [FullExample] 
+                       -> ([FullTarget],[[FullFeature]],[String])
+fullExamplesSeparate l = unzip3 l
+
+classificationProblem :: [FullTarget] -> Bool
+classificationProblem l = 
+    all (\x -> (split3 x) && (((split1 x)==1) || ((split1 x)==(-1)))) l
+
+getClassificationTargets :: [FullTarget] -> [Int]
+getClassificationTargets [] = []
+getClassificationTargets ((c,_,_):rest) 
+    = c:(getClassificationTargets rest)
+
+regressionProblem :: [FullTarget] -> Bool
+regressionProblem l = all (\x -> (not $ split3 x)) l
+
+getRegressionTargets :: [FullTarget] -> [Double]
+getRegressionTargets [] = []
+getRegressionTargets ((_,r,_):rest) 
+    = r:(getRegressionTargets rest)
+
+noQid :: [[FullFeature]] -> Bool
+noQid l = all no l
+    where no l2 = all (\x -> ((snd $ fst x)/="quid")) l2
+
+getExampleRange:: [FullExample] -> (Int,Int)
+getExampleRange [] = (0,0)
+getExampleRange x 
+    = ((minimum result), (maximum result))
+      where y = split2 $ fullExamplesSeparate x -- just the attribute vectors
+            z = map (map (fst . fst)) y -- attribute numbers from vectors
+            result = foldl (++) [] z
+
+-- | Now sort numbers and attributes at the same time 
+--   so that the numbers are ascending.
+comp :: (Int,Double) -> (Int,Double) -> Ordering
+comp (a,b) (c,d) 
+    | (a > c) = LT
+    | (a < c) = GT
+    | otherwise = error "Numbers should not be equal."
+
+sortExamples :: [Int] -> [Double] -> [(Int, Double)]  
+sortExamples numbers values = sortBy comp (zip numbers values) 
+      
+insertZeros :: (Int, Int) -> ([Int], [Double]) -> [Double]
+insertZeros (min_num, max_num) (i, d) 
+    = iz (unzip (sortExamples i d)) [] min_num max_num
+      where 
+        iz ([], []) r m n           = if (n == (m-1))
+                                      then r
+                                      else iz ([], []) (0:r) m (n-1)
+        iz ([], _) _ _ _            
+            = error "insertZeros: arguments need to have equal lengths"
+        iz (_, []) _ _ _            
+            = error "insertZeros: arguments need to have equal lengths"
+        iz ((h1:t1), (h2:t2)) r m n = if (h1 == n) 
+                                      then iz (t1, t2) (h2:r) m (n-1)
+                                      else iz (h1:t1, h2:t2) (0:r) m (n-1)
+
+-- | Get the attribute vectors as a list of lists.
+--   Care required here as we need to insert 0 where there is no attribute.
+getExamples :: [FullExample] -> [[Double]]
+getExamples [] = []
+getExamples x = map (insertZeros (getExampleRange x)) (zip numbers values) 
+    where 
+      y = split2 $ 
+          fullExamplesSeparate x        -- attribute vectors
+      numbers = map (map (fst . fst)) y -- attribute numbers from vectors
+      values = map (map snd) y          -- attribute values from the vectors
+
+-- | Does a matrix of Doubles make sense: that is, are all the rows the 
+--   same length?
+dimensionsCorrect :: [[Double]] -> Bool
+dimensionsCorrect [] = True
+dimensionsCorrect [r] = True
+dimensionsCorrect (h:t) = all (==(length h)) (map length t)
+
+-- | Find the dimensions of a matrix represented as a list of lists of Doubles.
+dimensions :: [[a]] -> (Int, Int)
+dimensions [] = (0,0)
+dimensions m@(h:t) = (length m, length h)
+          
+-- | Parse a file in SvmLight format and print some information about it.   
+analyse :: String -> IO ()
+analyse file_name = 
+    do
+      x <- parseFromFile file file_name
+      case x of
+        Left error -> IO.putStrLn $ show error
+        Right result -> do
+               IO.putStrLn ("-------------------------------------------")
+               IO.putStrLn ("Analysing SVMLight input file: " ++ file_name)
+               IO.putStrLn ("-------------------------------------------")
+               IO.putStrLn ("Classification problem: " 
+                            ++ (if (classificationProblem r1) 
+                                then "Yes" 
+                                else "No"))
+               IO.putStrLn ("Regression Problem:     " 
+                            ++ (if (regressionProblem r1) 
+                                then "Yes" 
+                                else "No"))
+               IO.putStrLn ("First five classification target values: " 
+                            ++ (show $ 
+                                take 5 (getClassificationTargets $ r1)))
+               IO.putStrLn ("First five regression target values:     " 
+                            ++ (show $ take 5 (getRegressionTargets $ r1)))
+               IO.putStrLn ("-------------------------------------------")
+               IO.putStrLn ("Maximum attribute number: " ++ (show $ snd $ r2))
+               IO.putStrLn ("Minimum attribute number: " ++ (show $ fst $ r2))
+               IO.putStrLn ("-------------------------------------------")
+               IO.putStrLn ("Maximum attribute value:  " 
+                            ++ (show $ maximum $ map maximum r3))
+               IO.putStrLn ("Minimum attribute value:  " 
+                            ++ (show $ minimum $ map minimum r3))
+               IO.putStrLn ("qid present:              " 
+                            ++ (if (noQid $ r4) 
+                                then "No" 
+                                else "Yes"))
+               IO.putStrLn ("Matrix correctly sized:   " 
+                            ++ (if (dimensionsCorrect r3) 
+                                then "Yes" 
+                                else "No"))
+               IO.putStrLn ("Number of examples:       " 
+                            ++ (show $ fst $ dimensions r3))
+               IO.putStrLn ("Number of attributes:     " 
+                            ++ (show $ snd $ dimensions r3))
+               IO.putStrLn ("Beginning of first five examples: ") 
+               IO.putStrLn (show $ map (take 5) (take 5 r3))
+               IO.putStrLn ("------------------------------------------")
+                   where 
+                     r = fullExamplesSeparate result
+                     r1 = split1 r
+                     r2 = getExampleRange result
+                     r3 = getExamples result
+                     r4 = split2 r
+
+getExamplesFromFile :: String -> IO (Either ParseError [FullExample])
+getExamplesFromFile string = 
+    do 
+      x <- parseFromFile file string
+      return x
+
+fullExampleToMatrixC::[FullExample] -> (M.Matrix Double, M.Vector Double)
+fullExampleToMatrixC fullExamples 
+    | classificationProblem targets = 
+        if (dimensionsCorrect attributes)
+        then (M.fromRows attributes', 
+              M.fromList $ map convert intTargets)
+        else error "Dimension not correct for making a matrix"
+    | otherwise = error "This is not a classification problem."
+    where 
+      targets = split1 $ fullExamplesSeparate fullExamples
+      attributes = getExamples fullExamples
+      attributes' = map M.fromList attributes
+      intTargets = getClassificationTargets targets
+      convert x 
+          | x==1 = 1.0
+          | x==(-1) = (-1.0)
+          | otherwise = error "Unexpected class label."
+
+-- | Read examples from a file in SvmLight format and produce a corresponding 
+--   matrix and vector, for a classification problem. Includes checks 
+--   that all examples have the same number of attributes, and that the file 
+--   does in fact correspond to a classification problem.
+getMatrixExamplesFromFileC::String -> IO (M.Matrix Double, M.Vector Double)
+getMatrixExamplesFromFileC fileName = 
+    do
+      contents <- getExamplesFromFile fileName
+      case contents of
+        Left error' -> error $ show error'
+        Right result -> return $ fullExampleToMatrixC result
+                    
diff --git a/src/HasGP/Regression/Regression.hs b/src/HasGP/Regression/Regression.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Regression/Regression.hs
@@ -0,0 +1,142 @@
+{- | Regression is a module in the HasGP Gaussian process
+   library. It implements basic Gaussian process regression.
+   For the technical details see www.gaussianprocesses.org.
+
+   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.Regression.Regression 
+   (
+     gpRMain,
+     gpRPredict,
+     gpRPredict',
+     gpRLogEvidence,
+     gpRGradLogEvidence,
+     gpRLogHyperToEvidence
+   ) where 
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Covariance.Basic
+import HasGP.Support.MatrixFunction
+import HasGP.Support.Solve
+import HasGP.Support.Linear
+
+-- | Compute the main quantities required to do regression, specifically:
+--    the Cholesky decomposition L of the covariance matrix, and the parameters 
+--    \alpha such that L L^t y = alpha.
+gpRMain :: CovarianceFunction cF => cF  
+        -> Double                        -- ^ The log noise variance
+        -> Inputs                        
+        -> Targets                       
+        -> (DMatrix,DVector)             -- ^ L and alpha.
+gpRMain cov lognvar d t = (l,alpha)
+    where
+      n = rows d
+      k = covarianceMatrix cov d
+      m = (k + (scale (exp lognvar) (ident n)))
+      l = trans $ chol m
+      alpha = upperSolve (trans l) (lowerSolve l t)
+
+-- | Compute the expected value and variance for a collection of 
+--   new points supplied as the rows of a matrix. Differs from 
+--   gpRPredict' as l and alpha need to be computed in advance.
+gpRPredict :: CovarianceFunction cF => cF
+           -> DMatrix                    -- ^ l
+           -> DVector                    -- ^ alpha
+           -> Inputs                     
+           -> Targets                    
+           -> Inputs                     -- ^ The new inputs
+           -> (DVector, DVector)         -- ^ Mean, variance
+gpRPredict cov l alpha d t xStars = 
+    ((fromRows kxxStar) <> alpha, 
+     fromList $ zipWith (-) kxStarxStar (zipWith dot vs vs))
+    where
+      new = toRows xStars
+      kxxStar = covarianceWithPoints cov d new
+      kxStarxStar = zipWith (covariance cov) new new
+      vs = map (lowerSolve l) kxxStar
+
+-- | Compute the expected value and variance for a collection of 
+--   new points supplied as the rows of a matrix.       
+gpRPredict' :: CovarianceFunction cF => cF 
+            -> Double                      -- ^ The log noise variance
+            -> Inputs                      
+            -> Targets                     
+            -> Inputs                      -- ^ The new inputs
+            -> (DVector, DVector)          -- ^ Mean, variance
+gpRPredict' cov lognvar d t xStars = 
+    ((fromRows kxxStar) <> alpha, 
+     fromList $ zipWith (-) kxStarxStar (zipWith dot vs vs))
+    where
+      (l, alpha) = gpRMain cov lognvar d t 
+      new = toRows xStars
+      kxxStar = covarianceWithPoints cov d new
+      kxStarxStar = zipWith (covariance cov) new new
+      vs = map (lowerSolve l) kxxStar
+
+-- | Compute the log of the marginal likelihood.
+gpRLogEvidence :: DMatrix    -- ^ l
+               -> DVector    -- ^ alpha
+               -> Targets    
+               -> Double     -- ^ log marginal likelihood
+gpRLogEvidence l alpha t = 
+    (-0.5) * ((t <.> alpha) + 
+              (2.0 * (sum $ map log (toList $ takeDiag l))) + 
+              ((fromIntegral n) * (log (2 * pi))))
+    where
+      n = rows l
+
+-- | Compute the gradient of the log marginal likelihood.
+--   Output contains derivative with respect to noise variance 
+--   followed by the derivatives with respect to the hyperparameters 
+--   in the covariance function.
+gpRGradLogEvidence :: CovarianceFunction cF => cF
+                   -> Double  -- ^ the log noise variance
+                   -> DMatrix -- ^ l
+                   -> DVector -- ^ alpha 
+                   -> Inputs  
+                   -> DVector -- ^ Derivatives
+gpRGradLogEvidence cov lognvar l alpha i = 
+    join [fromList [dZdtheta $ scale (exp lognvar) $ ident (rows l)], 
+          fromList $ map dZdtheta dKdthetaList]
+        where
+          invK = HasGP.Support.Solve.cholSolve l
+          dKdthetaList = makeMatricesFromPairs (dCovarianceDParameters cov) i
+          dZdtheta dKdtheta = 
+              (0.5) * (sum $ toList $ abDiagOnly 
+                       ((asColumn alpha <> asRow alpha) - invK) dKdtheta)
+
+-- | Given the log parameters and other necessary inputs, compute 
+--   the NEGATIVE of the log marginal likelihood and its derivatives with 
+--   respect to the LOG hyperparameters.
+gpRLogHyperToEvidence :: CovarianceFunction cF => cF 
+                      -> Inputs  
+                      -> Targets 
+                      -> DVector -- ^ log hyperparameters, noise variance first
+                      -> (Double, DVector)
+gpRLogHyperToEvidence cov inputs targets parameters = 
+    (-(gpRLogEvidence l alpha targets), 
+     zipVectorWith (*) negGradLE (mapVector exp parameters))
+        where
+          lognvar = parameters @> 0
+          loghyper = subVector 1 ((dim parameters) - 1) parameters 
+          newCov = (makeCovarianceFromList cov) $ toList loghyper
+          (l, alpha) = gpRMain newCov lognvar inputs targets
+          negGradLE = -(gpRGradLogEvidence newCov lognvar l alpha inputs)
+
diff --git a/src/HasGP/Support/Functions.hs b/src/HasGP/Support/Functions.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Support/Functions.hs
@@ -0,0 +1,74 @@
+{- | HasGP Gaussian Process Library. This module contains assorted functions 
+     that support GP calculations but are more general-purpose than 
+     GP-specific.
+
+     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.Support.Functions where
+
+import HasGP.Types.MainTypes
+import Numeric.LinearAlgebra
+import Numeric.GSL.Special.Erf
+
+square :: Double -> Double
+square x = (x * x)
+
+trace :: DMatrix -> Double
+trace = sum . toList . takeDiag
+
+-- | Standard delta function - 0/1 valued.
+delta :: (Eq a) => a -> a -> Double
+delta a b 
+    | (a==b) = 1.0
+    | otherwise = 0.0
+
+-- | Standard delta function - boolean valued.
+deltaBool :: (Eq a) => a -> a -> Bool
+deltaBool a b = (a==b)
+
+-- | General sigmoid function with variable slope.
+generalSigmoid :: Double -> Double -> Double
+generalSigmoid theta x = 1 / (1 + (exp (-(theta * x))))
+
+-- | Standard sigmoid function.
+sigmoid :: Double -> Double
+sigmoid = generalSigmoid 1
+
+-- | Integral of Gaussian density of mean 0 and variance 1 
+--   from -infinity to x
+phiIntegral :: Double -> Double
+phiIntegral x = 1 - (erf_Q x)
+
+-- | Value of Gaussian density function for mean 0 and 
+--   variance 1.
+n :: Double -> Double
+n x = erf_Z x
+
+-- | DANGER! You can't compute the ratio (n x) / (phiIntegral x) directly, 
+--   as although it has sensible values for negative x the denominator gets 
+--   small so fast that you quickly get Infinity turning up. GSL has the 
+--   inverse Mill's function/hazard function for the Gaussian distribution, 
+--   and the ratio is equal to hazard(-x).
+nOverPhi :: Double -> Double
+nOverPhi x = hazard(-x)
+
+-- | DANGER! See nOverPhi - you have to compute this carefully as 
+--   well.
+logPhi :: Double -> Double
+logPhi x = log $ (n x) / (hazard (-x))
+
diff --git a/src/HasGP/Support/Iterate.hs b/src/HasGP/Support/Iterate.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Support/Iterate.hs
@@ -0,0 +1,74 @@
+{- | We often need to iterate some update equation until convergence is 
+   detected. This module uses the State monad to provide a very general way of 
+   expressing computations of this kind.
+
+   Copyright (C) Sean Holden 2011. 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.Support.Iterate where
+
+import Control.Monad.State
+
+{- | iterateOnce takes a function to update a state and another 
+     to compute a value associated with a given state.
+
+     It returns a state transformer performing the corresponding 
+     update - that is, one iteration.
+-}
+iterateOnce::(s -> s) -> (s -> a) -> State s a
+iterateOnce updateState stateValue = 
+    do currentState <- get
+       let newState = updateState currentState
+       put newState
+       return $ stateValue newState
+
+{- | iterateToConvergence takes a state transformer typically generated 
+     using iterateOnce, a convergence test that compares two values 
+     associated with the current and next states returning True if 
+     we've converged, and an initial value.
+
+     It returns a state transformer that performs iteration until 
+     convergence. When run from an initial state it returns the state 
+     at convergence and the corresponding value.
+-}
+iterateToConvergence::State s a -> (a -> a -> Bool) -> a -> State s a
+iterateToConvergence doOnce converged currentValue = 
+    do newValue <- doOnce
+       if (converged currentValue newValue) 
+         then return newValue
+         else iterateToConvergence doOnce converged newValue
+
+{- | The same as iterateToConvergence, but takes the state update and 
+     state value functions directly, so the resulting state transformer 
+     only requires a start state to be run.
+-}
+iterateToConvergence'::(s -> s) -> (s -> a) -> (a -> a -> Bool) -> State s a
+iterateToConvergence' updateState stateValue converged = 
+    do startState <- get
+       let initialValue = stateValue startState
+       let itOnce = iterateOnce updateState stateValue
+       iterateToConvergence itOnce converged initialValue
+
+{- | The same as iterateToConvergence, but does one update to obtain an 
+     initial value and continues from there. Consequently, no initial 
+     value is required, but you do one extra update.
+-}
+iterateToConvergence''::State s a -> (a -> a -> Bool) -> State s a
+iterateToConvergence'' doOnce converged = 
+    do newValue <- doOnce
+       iterateToConvergence doOnce converged newValue
+
diff --git a/src/HasGP/Support/Linear.hs b/src/HasGP/Support/Linear.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Support/Linear.hs
@@ -0,0 +1,95 @@
+{- | HasGP Gaussian Process Library. This module contains assorted
+     functions that support GP calculations and are specifically
+     related to linear algebra.
+
+     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.Support.Linear where
+
+import Data.Packed.ST
+import Control.Monad (mapM_,zipWithM_)
+import Control.Monad.ST
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Functions as F
+
+-- | Sum the elements in a vector.
+sumVector :: DVector -> Double
+sumVector = foldVector (+) 0.0
+
+-- | Sum of elements in a vector, divided by an Int.
+sumVectorDiv :: Int -> DVector -> Double
+sumVectorDiv d v = (sumVector v)/(fromIntegral d)
+
+-- | Length of a vector.
+lengthV :: (Normed a b) => a b -> RealOf b
+lengthV = pnorm PNorm2
+
+-- | Generate a vector equal to the first column of a matrix.
+toVector :: Matrix Double -> Vector Double
+toVector x = head $ toColumns x
+
+-- | Replace the element at a specified position in a vector.
+--   NOTE: hmatrix numbers from 0, which is odd. This numbers from 1.
+--   The result is returned by overwriting v. This is implemented 
+--   via runSTVector because the increase in efficiency is HUGE.
+replaceInVector :: DVector -> Int -> Double -> DVector
+replaceInVector v i n 
+    | (1 <= i) && (i <= (dim v)) = runSTVector $ do
+                                     v2 <- thawVector v
+                                     writeVector v2 (i-1) n
+                                     return v2
+    | otherwise                  = error "Index out of range in replaceInVector"
+       
+-- | Efficiently pre multiply by a diagonal matrix (passed as a vector)
+preMultiply :: DVector -> DMatrix -> DMatrix
+preMultiply v m = fromRows $ zipWith scale (toList v) (toRows m) 
+
+-- | Efficiently post multiply by a diagonal matrix (passed as a vector)
+postMultiply :: DMatrix -> DVector -> DMatrix
+postMultiply m v = fromColumns $ zipWith scale (toList v) (toColumns m) 
+
+-- | Compute x^T A x when A is diagonal. The second argument is the 
+--   diagonal of A.
+xAxDiag :: DVector -> DVector -> Double
+xAxDiag x a 
+    | (d == dim a) = a <.> (x * x)      
+    | otherwise = error "Incorrect dimensions in xAxDiag"
+    where
+      d = dim x
+
+-- | Compute the diagonal only of the product of two square matrices
+abDiagOnly :: DMatrix -> DMatrix -> DVector
+abDiagOnly a b = fromList $ zipWith (<.>) (toRows a) (toColumns b)
+
+-- | Compute ABA where A is diagonal. The first argument is the diagonal of A.
+abaDiagDiag :: DVector -> DMatrix -> DMatrix
+abaDiagDiag a b = (d><d) (zipWith (*) bL aA)
+    where
+      d = dim a
+      aL = toList a
+      aA = [(a1 * a2) | a1 <- aL, a2 <- aL]
+      bL = toList $ join $ toRows b
+
+-- | Compute aBa where a is a vector and B is a matrix
+abaVV :: DVector -> DMatrix -> Double
+abaVV a b = (flatten ((asRow a) <> b)) <.> a
+
+
diff --git a/src/HasGP/Support/MatrixFunction.hs b/src/HasGP/Support/MatrixFunction.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Support/MatrixFunction.hs
@@ -0,0 +1,64 @@
+{- | HasGP Gaussian Process Library. This module contains assorted
+     functions that support the construction of matrices from
+     functions.
+
+     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.Support.MatrixFunction where
+
+import Numeric.LinearAlgebra
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Functions as F
+
+-- | Take two vectors and a function. The vectors contain inputs 1 and 2. 
+--   The function maps a pair of inputs to a value. Produce a matrix 
+--   containing the values of the function at the relevant points.
+makeMatrixFromFunction2 :: (DVector -> Double) 
+                       -> DVector 
+                       -> DVector 
+                       -> DMatrix
+makeMatrixFromFunction2 f x1 x2 = 
+    flipud $ ((length x1L)><(length x2L)) (map f l)
+        where 
+          x1L = toList x1
+          x2L = toList x2
+          l = map fromList [[a,b] | a <- x1L, b <-x2L ]
+      
+-- | Take a function and a matrix of instance vectors. Apply the function to 
+--   each possible pair of instance vectors and return the result as a matrix.
+makeMatrixFromPairs2 :: (DVector -> DVector -> Double) -> DMatrix -> DMatrix
+makeMatrixFromPairs2 f i = (d><d) [f x1 x2 | x1 <- x, x2 <- x]
+    where 
+      d = rows i
+      x = toRows i
+
+-- | Same as makeMatrixFromPairs but the function returns a vector. In this 
+-- case the output is a list of matrices, one for each element of the 
+-- function value.
+makeMatricesFromPairs :: (DVector -> DVector -> DVector) -> DMatrix -> [DMatrix]
+makeMatricesFromPairs f i = map (d><d) (reArrange lists [])
+    where
+      d = rows i
+      x = toRows i
+      lists = map toList [f x1 x2 | x1 <- x, x2 <- x]
+      reArrange [] r = reverse r
+      reArrange l@(h:t) r 
+                | (h == []) = reverse r
+                | otherwise = reArrange (map tail l) ((map head l):r)
+
diff --git a/src/HasGP/Support/Random.hs b/src/HasGP/Support/Random.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Support/Random.hs
@@ -0,0 +1,87 @@
+{- | Gaussian Process Library. This module contains assorted functions
+     that support random number generation and the construction of basic
+     standard training sets.
+
+     Note: these are mostly calls to functions now (but not originally)
+     supplied by HMatrix. Originally different random sources were used, 
+     hence the current format.
+
+     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.Support.Random where
+
+import Numeric.LinearAlgebra
+import Numeric.Container
+
+import HasGP.Types.MainTypes
+
+-- | Make a random matrix. Elements are uniformly distributed between 
+--   specified bounds. Returns the matrix and a new generator.
+uniformMatrix :: Int                 -- ^ Seed
+              -> (Double,Double)     -- ^ Range for the elements
+              -> Int                 -- ^ Number of rows
+              -> Int                 -- ^ Number of columns
+              -> DMatrix
+uniformMatrix seed (low,high) rows columns = 
+        uniformSample seed rows [(low,high) | x <- [1..columns]] 
+
+-- | Produce vectors with normally distributed, independent elements of
+--   zero mean and specified variance.
+normalVectorSimple :: Int       -- ^ Seed 
+                   -> Double    -- ^ Variance
+                   -> Int       -- ^ Number of elements in the vector.
+                   -> DVector
+normalVectorSimple seed v n = 
+        flatten $ gaussianSample seed 1 (constant 0.0 n) 
+                    (scale (1/v) (ident n)::DMatrix)
+
+-- | Produce lists with normally distributed independent elements of
+--   zero mean and specified variance.
+normalList :: Int        -- ^ Seed 
+           -> Double     -- ^ Variance
+           -> Int        -- ^ Number of elements in the list
+           -> [Double]
+normalList seed v n = toList $ normalVectorSimple seed v n
+                          
+-- | Produce normally distributed vectors with mean and covariance
+--   specified.
+normalVector :: Int       -- ^ Seed 
+             -> DVector   -- ^ Mean vector
+             -> DMatrix   -- ^ Covariance matrix
+             -> DVector  
+normalVector seed m c = flatten $ gaussianSample seed 1 m c
+                             
+-- | Make a matrix with normally distributed, independent elements of 
+--   zero mean and specified variance.
+normalMatrix :: Int       -- ^ Seed 
+             -> Double    -- ^ Variance
+             -> Int       -- ^ Rows 
+             -> Int       -- ^ Columns
+             -> DMatrix
+normalMatrix seed variance rows columns = 
+        gaussianSample seed rows (constant 0.0 columns) 
+                           (scale variance ((ident columns)::DMatrix))
+                                       
+
+
+
+
+
+
+
diff --git a/src/HasGP/Support/Solve.hs b/src/HasGP/Support/Solve.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Support/Solve.hs
@@ -0,0 +1,100 @@
+{- | HasGP Gaussian Process Library. This module contains assorted functions 
+     that support the efficient solution of sets of linear equations
+
+     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.Support.Solve where
+
+import Numeric.LinearAlgebra
+import Data.Packed.ST
+
+import HasGP.Types.MainTypes
+import HasGP.Support.Functions as F
+
+import Control.Monad.ST
+
+{- | It's not clear whether the use of linearSolve from HMatrix will induce
+     a performance hit when the matrix is upper or lower triangular. Pro: 
+     it's a call to something presumably from LaPack. Con: we've got some 
+     structure that should allow us to make it O(n^2) instead of O(n^3).
+
+     To do: try some timed runs to see if these are needed.
+-}
+
+-- | Solve an upper triangular system. 
+upperSolve :: DMatrix -> DVector -> DVector
+upperSolve m y = uS mR yL n x
+    where 
+      n = rows m
+      x = constant 0.0 n
+      mR = reverse $ toRows m
+      yL = reverse $ toList y
+
+-- | Solve a lower triangular system.
+lowerSolve :: DMatrix -> DVector -> DVector
+lowerSolve m y = lS mR yL 1 x
+    where 
+      x = constant 0.0 $ rows m
+      mR = toRows m
+      yL = toList y
+
+-- | Used by lowerSolve.
+lS [] [] n x = x
+lS (row:rows) (y:ys) n x = lS rows ys (n+1) $ computeNthElement row y n x
+lS _ _ _ x = x 
+
+-- | Used by upperSolve.
+uS [] [] n x = x
+uS (row:rows) (y:ys) n x = uS rows ys (n-1) $ computeNthElement row y n x
+uS _ _ _ x = x 
+    
+-- | Compute the value of x_n when solving a lower triangular 
+--   set of equations Mx=y. It is assumed that all values x_i where 
+--   i < n are already in the vector x and that the rest of the 
+--   elements of x are 0.
+computeNthElement::DVector   -- ^ nth row of M
+                 -> Double   -- ^ y_n    
+                 -> Int      -- ^ n
+                 -> DVector  -- ^ current x vector
+                 -> DVector  -- ^ x vector with x_n computed.
+computeNthElement row y n x = 
+     runSTVector $ do
+          let inner = row <.> x
+          row' <- thawVector row 
+          mN <- readVector row' (n-1)
+          x' <- thawVector x
+          writeVector x' (n-1) ((y - inner)/mN)
+          return x'
+
+-- | General solver for linear equations of the relevant kind. 
+--
+--   First parameter is either upperSolve or lowerSolve. Next two parameters 
+--   are the upper/lower triangular matrix from the Cholesky decomposition, 
+--   then another matrix. Returns the solution as a matrix.
+generalSolve :: (DMatrix -> DVector -> DVector) 
+             -> DMatrix 
+             -> DMatrix 
+             -> DMatrix
+generalSolve solver l m = fromColumns $ map (solver l) (toColumns m) 
+
+-- | Find the inverse of a matrix from its Cholesky decomposition
+cholSolve :: DMatrix -> DMatrix
+cholSolve l = fromColumns $ map (upperSolve l) (toColumns $ ident $ rows l)
+
+
+
diff --git a/src/HasGP/Types/MainTypes.hs b/src/HasGP/Types/MainTypes.hs
new file mode 100644
--- /dev/null
+++ b/src/HasGP/Types/MainTypes.hs
@@ -0,0 +1,37 @@
+{- | MainTypes is a module in the HasGP Gaussian process library. It implements 
+     basic types for the entire library. 
+
+     Note: some more specific classes and types are defined elsewhere, 
+     in particular in HasGP.Likelihood and HasGP.Covariance.
+
+     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.Types.MainTypes where
+
+import Numeric.LinearAlgebra
+
+-- | These are defined to make functions more readable.
+type DVector = Vector Double
+type DMatrix = Matrix Double   
+
+type Input = Vector Double
+type Inputs = Matrix Double
+type CovarianceMatrix = Matrix Double
+type Targets = Vector Double
+type Outputs = Vector Double
+