eigen 1.2.4 → 1.2.5

raw patch · 3 files changed

+117/−25 lines, 3 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• Data/Eigen/LA.hs +7/−6
• Data/Eigen/Matrix.hs +109/−18
• eigen.cabal +1/−1

@@ -58,7 +58,7 @@     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).  @@@ -105,6 +105,7 @@     linearRegression ) where +import Prelude as P import Foreign.Storable import Foreign.Marshal.Alloc import qualified Foreign.Concurrent as FC@@ -116,7 +117,7 @@  {- | @-Decomposition           Requirements on the matrix          Speed   Accuracy  Rank  Kernel  Image +Decomposition           Requirements on the matrix          Speed   Accuracy  Rank  Kernel  Image  PartialPivLU            Invertible                          ++      +         -     -       - FullPivLU               None                                -       +++       +     +       +@@ -200,7 +201,7 @@  -- | Return a matrix whose columns form a basis of the column-space of @A@ image :: Decomposition -> Matrix -> Matrix-image d m1 = I.performIO $ +image d m1 = I.performIO $     alloca $ \pvals ->     alloca $ \prows ->     alloca $ \pcols ->@@ -243,11 +244,11 @@ -} linearRegression :: [[Double]] -> ([Double], Double) linearRegression points = (coeffs, e) where-    a = fromList $ map ((1:).tail) points-    b = fromList $ map ((:[]).head) points+    a = fromList $ P.map ((1:).tail) points+    b = fromList $ P.map ((:[]).head) points     x = solve ColPivHouseholderQR a b     e = relativeError x a b-    coeffs = map head $ toList x+    coeffs = P.map head $ toList x   

@@ -41,14 +41,24 @@     blueNorm,     hypotNorm,     determinant,+    -- * Generic reductions+    fold,+    fold',+    ifold,+    ifold',     -- * Boolean reductions     all,     any,     count,-    -- * Matrix operations+    -- * Basic matrix algebra     add,     sub,     mul,+    -- * Mapping over elements+    map,+    imap,+    filter,+    ifilter,     -- * Matrix transformations     diagonal,     transpose,@@ -57,6 +67,8 @@     conjugate,     normalize,     modify,+    upperTriangule,+    lowerTriangule,     -- * Mutable matrices     thaw,     freeze,@@ -65,7 +77,8 @@     unsafeWith, ) where -import Prelude hiding (null, sum, all, any)+import qualified Prelude as P+import Prelude hiding (null, sum, all, any, map, filter) import Data.List (intercalate) import Data.Tuple import Foreign.Ptr@@ -81,7 +94,7 @@ import qualified Data.Eigen.Internal as I import qualified Data.Eigen.Matrix.Mutable as M --- | Matrix to be used in pure computations, uses column major memory layout+-- | Matrix to be used in pure computations, uses column major memory layout, features copy-free FFI with C++ <http://eigen.tuxfamily.org Eigen> library. data Matrix = Matrix {     m_rows :: Int,     m_cols :: Int,@@ -92,16 +105,16 @@ instance Show Matrix where     show m@Matrix{..} = concat [         "Matrix ", show m_rows, "x", show m_cols, -        "\n", intercalate "\n" $ map (intercalate "\t" . map show) $ toList m, "\n"]+        "\n", intercalate "\n" $ P.map (intercalate "\t" . P.map show) $ toList m, "\n"] --- | Nm instance for the matrix+-- | Shortcuts for basic matrix math instance Num Matrix where     (*) = mul     (+) = add     (-) = sub     fromInteger = constant 1 1 . fromInteger-    signum m@Matrix{..} = m { m_vals = VS.map signum m_vals }-    abs m@Matrix{..} = m { m_vals = VS.map abs m_vals }+    signum = map signum+    abs = map abs  -- | Empty 0x0 matrix empty :: Matrix@@ -155,7 +168,7 @@ dims Matrix{..} = (m_rows, m_cols)  -- | Matrix coefficient at specific row and col-(!) :: Matrix -> (Int,Int) -> Double+(!) :: Matrix -> (Int, Int) -> Double (!) m (row,col) = coeff row col m  -- | Matrix coefficient at specific row and col@@ -188,27 +201,27 @@ valid :: Matrix -> Bool valid Matrix{..} = m_rows >= 0 && m_cols >= 0 && VS.length m_vals == m_rows * m_cols --- | The maximum of all coefficients of matrix+-- | The maximum coefficient of the matrix maxCoeff :: Matrix -> Double maxCoeff Matrix{..} = I.cast $ VS.maximum m_vals --- | The minimum of all coefficients of matrix+-- | The minimum coefficient of the matrix minCoeff :: Matrix -> Double minCoeff Matrix{..} = I.cast $ VS.minimum m_vals --- | Top n rows of matrix+-- | Top @N@ rows of matrix topRows :: Int -> Matrix -> Matrix topRows rows m@Matrix{..} = block 0 0 rows m_cols m --- | Bottom n rows of matrix+-- | Bottom @N@ rows of matrix bottomRows :: Int -> Matrix -> Matrix bottomRows rows m@Matrix{..} = block (m_rows - rows) 0 rows m_cols m --- | Left n columns of matrix+-- | Left @N@ columns of matrix leftCols :: Int -> Matrix -> Matrix leftCols cols m@Matrix{..} = block 0 0 m_rows cols m --- | Right n columns of matrix+-- | Right @N@ columns of matrix rightCols :: Int -> Matrix -> Matrix rightCols cols m@Matrix{..} = block 0 (m_cols - cols) m_rows cols m @@ -216,7 +229,7 @@ fromList :: [[Double]] -> Matrix fromList list = Matrix rows cols vals where     rows = length list-    cols = maximum $ map length list+    cols = maximum $ P.map length list     vals = VS.create $ do         vm <- VSM.replicate (rows * cols) 0         forM_ (zip [0..] list) $ \(row, vals) ->@@ -265,7 +278,10 @@ count :: (Double -> Bool) -> Matrix -> Int count f = VS.foldl' (\n x -> if f (I.cast x) then succ n else n) 0 . m_vals --- | For vectors, the l2 norm, and for matrices the Frobenius norm. In both cases, it consists in the square root of the sum of the square of all the matrix entries. For vectors, this is also equals to the square root of the dot product of this with itself.+{-| For vectors, the l2 norm, and for matrices the Frobenius norm.+    In both cases, it consists in the square root of the sum of the square of all the matrix entries.+    For vectors, this is also equals to the square root of the dot product of this with itself.+-} norm :: Matrix -> Double norm = _prop I.c_norm @@ -291,19 +307,94 @@ add :: Matrix -> Matrix -> Matrix add m1 m2     | dims m1 == dims m2 = _binop const I.c_add m1 m2-    | otherwise = error "Matrix.add: matrix should have the same size"+    | otherwise = error "Matrix.add: matrices should have the same size"  -- | Subtracting two matrices by subtracting the corresponding entries together. You can use @(-)@ function as well. sub :: Matrix -> Matrix -> Matrix sub m1 m2     | dims m1 == dims m2 = _binop const I.c_sub m1 m2-    | otherwise = error "Matrix.add: matrix should have the same size"+    | otherwise = error "Matrix.add: matrices should have the same size"  -- | Matrix multiplication. You can use @(*)@ function as well. mul :: Matrix -> Matrix -> Matrix mul m1 m2     | m_cols m1 == m_rows m2 = _binop (\(rows, _) (_, cols) -> (rows, cols)) I.c_mul m1 m2     | otherwise = error "Matrix.mul: number of columns for lhs matrix should be the same as number of rows for rhs matrix"++{- | Apply a given function to each element of the matrix.++Here is an example how to implement scalar matrix multiplication:++>>> let a = fromList [[1,2],[3,4]]++>>> a+Matrix 2x2+1.0 2.0+3.0 4.0++>>> map (*10) a+Matrix 2x2+10.0    20.0+30.0    40.0++-}+map :: (Double -> Double) -> Matrix -> Matrix+map f m@Matrix{..} = m { m_vals = VS.map (I.cast . f . I.cast) m_vals }++{- | Apply a given function to each element of the matrix.++Here is an example how getting upper triangular matrix can be implemented:++>>> let a = fromList [[1,2,3],[4,5,6],[7,8,9]]++>>> a+Matrix 3x3+1.0 2.0 3.0+4.0 5.0 6.0+7.0 8.0 9.0++>>> imap (\row col val -> if row <= col then val else 0) a+Matrix 3x3+1.0 2.0 3.0+0.0 5.0 6.0+0.0 0.0 9.0++-}++imap :: (Int -> Int -> Double -> Double) -> Matrix -> Matrix+imap f m@Matrix{..} = m { m_vals = VS.imap (\n -> let (c, r) = divMod n m_rows in I.cast . f r c . I.cast) m_vals }++-- | Upper trinagle of the matrix+upperTriangule :: Matrix -> Matrix+upperTriangule = imap (\row col val -> if row <= col then val else 0)++-- | Lower trinagle of the matrix+lowerTriangule :: Matrix -> Matrix+lowerTriangule = imap (\row col val -> if row >= col then val else 0)++-- | Filter elements in the matrix. Filtered elements will be replaced by 0+filter :: (Double -> Bool) -> Matrix -> Matrix+filter f = map (\x -> if f x then x else 0)++-- | Filter elements in the matrix. Filtered elements will be replaced by 0+ifilter :: (Int -> Int -> Double -> Bool) -> Matrix -> Matrix+ifilter f = imap (\r c x -> if f r c x then x else 0)++-- | Reduce matrix using user provided function applied to each element.+fold :: (a -> Double -> a) -> a -> Matrix -> a+fold f a Matrix{..} = VS.foldl (\a x -> f a (I.cast x)) a m_vals++-- | Reduce matrix using user provided function applied to each element. This is strict version of 'fold'+fold' :: (a -> Double -> a) -> a -> Matrix -> a+fold' f a Matrix{..} = VS.foldl' (\a x -> f a (I.cast x)) a m_vals++-- | Reduce matrix using user provided function applied to each element and it's index+ifold :: (Int -> Int -> a -> Double -> a) -> a -> Matrix -> a+ifold f a Matrix{..} = VS.ifoldl (\a n x -> let (c,r) = divMod n m_rows in f r c a (I.cast x)) a m_vals++-- | Reduce matrix using user provided function applied to each element and it's index. This is strict version of 'ifold'+ifold' :: (Int -> Int -> a -> Double -> a) -> a -> Matrix -> a+ifold' f a Matrix{..} = VS.ifoldl' (\a n x -> let (c,r) = divMod n m_rows in f r c a (I.cast x)) a m_vals  -- | Diagonal of the matrix diagonal :: Matrix -> Matrix

@@ -1,5 +1,5 @@ name:           eigen-version:        1.2.4+version:        1.2.5 homepage:       https://github.com/osidorkin/haskell-eigen synopsis:       Eigen C++ library (linear algebra: matrices, vectors, numerical solvers). description:    This module provides Haskell binding for Eigen C++ library.