{-# LANGUAGE CPP #-}
{-# LANGUAGE ForeignFunctionInterface #-}
{-# LANGUAGE RecordWildCards #-}
{- |
The problem: You have a system of equations, that you have written as a single matrix equation
@Ax = b@
Where A and b are matrices (b could be a vector, as a special case). You want to find a solution x.
The solution: You can choose between various decompositions, depending on what your matrix A looks like, and depending on whether you favor speed or accuracy. However, let's start with an example that works in all cases, and is a good compromise:
@
import Data.Eigen.Matrix
import Data.Eigen.LA
main = do
let
a :: MatrixXd
a = fromList [[1,2,3], [4,5,6], [7,8,10]]
b = fromList [[3],[3],[4]]
x = solve ColPivHouseholderQR a b
putStrLn \"Here is the matrix A:\" >> print a
putStrLn \"Here is the vector b:\" >> print b
putStrLn \"The solution is:\" >> print x
@
produces the following output
@
Here is the matrix A:
Matrix 3x3
1.0 2.0 3.0
4.0 5.0 6.0
7.0 8.0 10.0
Here is the vector b:
Matrix 3x1
3.0
3.0
4.0
The solution is:
Matrix 3x1
-2.0000000000000004
1.0000000000000018
0.9999999999999989
@
Checking if a solution really exists: Only you know what error margin you want to allow for a solution to be considered valid.
You can compute relative error using @'norm' (ax - b) / 'norm' b@ formula or use 'relativeError' function which provides the same calculation implemented slightly more efficient.
-}
module Data.Eigen.LA (
-- * Basic linear solving
Decomposition(..),
solve,
relativeError,
-- * Rank-revealing decompositions
{- |
Certain decompositions are rank-revealing, i.e. are able to compute the 'rank' of a matrix. These are typically also the decompositions that behave best in the face of a non-full-rank matrix (which in the 'square' case means a singular matrix).
@
import Data.Eigen.Matrix
import Data.Eigen.LA
main = do
let a = fromList [[1,2,5],[2,1,4],[3,0,3]] :: MatrixXd
putStrLn "Here is the matrix A:" >> print a
putStrLn "The rank of A is:" >> print (rank FullPivLU a)
putStrLn "Here is a matrix whose columns form a basis of the null-space of A:" >> print (kernel FullPivLU a)
putStrLn "Here is a matrix whose columns form a basis of the column-space of A:" >> print (image FullPivLU a)
@
produces the following output
@
Here is the matrix A:
Matrix 3x3
1.0 2.0 5.0
2.0 1.0 4.0
3.0 0.0 3.0
The rank of A is:
2
Here is a matrix whose columns form a basis of the null-space of A:
Matrix 3x1
0.5000000000000001
1.0
-0.5
Here is a matrix whose columns form a basis of the column-space of A:
Matrix 3x2
5.0 1.0
4.0 2.0
3.0 3.0
@
-}
rank,
kernel,
image,
-- * Multiple linear regression
{- | A linear regression model that contains more than one predictor variable. -}
linearRegression
) where
import Prelude as P
import Foreign.Storable
import Foreign.Marshal.Alloc
import qualified Foreign.Concurrent as FC
#if __GLASGOW_HASKELL__ >= 710
#else
import Control.Applicative
#endif
import Data.Eigen.Matrix
import qualified Data.Eigen.Internal as I
import qualified Data.Eigen.Matrix.Mutable as M
import qualified Data.Vector.Storable as VS
{- |
@
Decomposition Requirements on the matrix Speed Accuracy Rank Kernel Image
PartialPivLU Invertible ++ + - - -
FullPivLU None - +++ + + +
HouseholderQR None ++ + - - -
ColPivHouseholderQR None + ++ + - -
FullPivHouseholderQR None - +++ + - -
LLT Positive definite +++ + - - -
LDLT Positive or negative semidefinite +++ ++ - - -
JacobiSVD None - +++ + - -
@
The best way to do least squares solving for square matrices is with a SVD decomposition ('JacobiSVD')
-}
data Decomposition
-- | LU decomposition of a matrix with partial pivoting.
= PartialPivLU
-- | LU decomposition of a matrix with complete pivoting.
| FullPivLU
-- | Householder QR decomposition of a matrix.
| HouseholderQR
-- | Householder rank-revealing QR decomposition of a matrix with column-pivoting.
| ColPivHouseholderQR
-- | Householder rank-revealing QR decomposition of a matrix with full pivoting.
| FullPivHouseholderQR
-- | Standard Cholesky decomposition (LL^T) of a matrix.
| LLT
-- | Robust Cholesky decomposition of a matrix with pivoting.
| LDLT
-- | Two-sided Jacobi SVD decomposition of a rectangular matrix.
| JacobiSVD deriving (Eq, Enum, Show, Read)
-- | [x = solve d a b] finds a solution @x@ of @ax = b@ equation using decomposition @d@
solve :: I.Elem a b => Decomposition -> Matrix a b -> Matrix a b -> Matrix a b
solve d a b = I.performIO $ do
x <- M.new (cols a) 1
M.unsafeWith x $ \x_vals x_rows x_cols ->
unsafeWith a $ \a_vals a_rows a_cols ->
unsafeWith b $ \b_vals b_rows b_cols ->
I.call $ I.solve (I.cast $ fromEnum d)
x_vals x_rows x_cols
a_vals a_rows a_cols
b_vals b_rows b_cols
unsafeFreeze x
-- | [e = relativeError x a b] computes @norm (ax - b) / norm b@ where @norm@ is L2 norm
relativeError :: I.Elem a b => Matrix a b -> Matrix a b -> Matrix a b -> a
relativeError x a b = I.performIO $
unsafeWith x $ \x_vals x_rows x_cols ->
unsafeWith a $ \a_vals a_rows a_cols ->
unsafeWith b $ \b_vals b_rows b_cols ->
alloca $ \pe -> do
I.call $ I.relativeError pe
x_vals x_rows x_cols
a_vals a_rows a_cols
b_vals b_rows b_cols
I.cast <$> peek pe
-- | The rank of the matrix
rank :: I.Elem a b => Decomposition -> Matrix a b -> Int
rank d m = I.performIO $ alloca $ \pr -> do
I.call $ unsafeWith m $ I.rank (I.cast $ fromEnum d) pr
I.cast <$> peek pr
-- | Return matrix whose columns form a basis of the null-space of @A@
kernel :: I.Elem a b => Decomposition -> Matrix a b -> Matrix a b
kernel d m1 = I.performIO $
alloca $ \pvals ->
alloca $ \prows ->
alloca $ \pcols ->
unsafeWith m1 $ \vals1 rows1 cols1 -> do
I.call $ I.kernel (I.cast $ fromEnum d)
pvals prows pcols
vals1 rows1 cols1
vals <- peek pvals
rows <- I.cast <$> peek prows
cols <- I.cast <$> peek pcols
fp <- FC.newForeignPtr vals $ I.free vals
return $ Matrix rows cols $ VS.unsafeFromForeignPtr0 fp $ rows * cols
-- | Return a matrix whose columns form a basis of the column-space of @A@
image :: I.Elem a b => Decomposition -> Matrix a b -> Matrix a b
image d m1 = I.performIO $
alloca $ \pvals ->
alloca $ \prows ->
alloca $ \pcols ->
unsafeWith m1 $ \vals1 rows1 cols1 -> do
I.call $ I.image (I.cast $ fromEnum d)
pvals prows pcols
vals1 rows1 cols1
vals <- peek pvals
rows <- I.cast <$> peek prows
cols <- I.cast <$> peek pcols
fp <- FC.newForeignPtr vals $ I.free vals
return $ Matrix rows cols $ VS.unsafeFromForeignPtr0 fp $ rows * cols
{- |
[(coeffs, error) = linearRegression points] computes multiple linear regression @y = a1 x1 + a2 x2 + ... + an xn + b@ using 'ColPivHouseholderQR' decomposition
* point format is @[y, x1..xn]@
* coeffs format is @[b, a1..an]@
* error is calculated using 'relativeError'
@
import Data.Eigen.LA
main = print $ linearRegression [
[-4.32, 3.02, 6.89],
[-3.79, 2.01, 5.39],
[-4.01, 2.41, 6.01],
[-3.86, 2.09, 5.55],
[-4.10, 2.58, 6.32]]
@
produces the following output
@
([-2.3466569233817127,-0.2534897541434826,-0.1749653335680988],1.8905965120153139e-3)
@
-}
linearRegression :: [[Double]] -> ([Double], Double)
linearRegression points = (coeffs, e) where
a = fromList $ P.map ((1:).tail) points
b = fromList $ P.map ((:[]).head) points
x = solve ColPivHouseholderQR a b
e = relativeError x a b
coeffs = P.map head $ toList x