Data.Eigen.Matrix

Matrix type

data Matrix

constant Matrix class to be used in pure computations, uses the same column major memory layout as Eigen MatrixXd

Constructors

Matrix

Fields

m_rows :: Int

m_cols :: Int

m_vals :: Vector CDouble

Instances

Num Matrix

only the following functions are defined for Num instance: (*), (+), (-)

Show Matrix

pretty prints the matrix

Matrix conversions

fromList :: [[Double]] -> Matrix

construct matrix from a list of rows, column count is detected as maximum row length

toList :: Matrix -> [[Double]]

converts matrix to a list of its rows

Standard matrices and special cases

empty :: Matrix

empty 0x0 matrix

zero :: Int -> Int -> Matrix

matrix where all coeff are 0

ones :: Int -> Int -> Matrix

matrix where all coeff are 1

identity :: Int -> Matrix

square matrix with 1 on main diagonal and 0 elsewhere

constant :: Int -> Int -> Double -> Matrix

matrix where all coeffs are filled with given value

Accessing matrix data

cols :: Matrix -> Int

number of columns for the matrix

rows :: Matrix -> Int

number of rows for the matrix

coeff :: Int -> Int -> Matrix -> Double

matrix coefficient at specific row and col

minCoeff :: Matrix -> Double

the minimum of all coefficients of matrix

maxCoeff :: Matrix -> Double

the maximum of all coefficients of matrix

col :: Int -> Matrix -> [Double]

list of coefficients for the given col

row :: Int -> Matrix -> [Double]

list of coefficients for the given row

block :: Int -> Int -> Int -> Int -> Matrix -> Matrix

extract rectangular block from matrix defined by startRow startCol blockRows blockCols

topRows :: Int -> Matrix -> Matrix

top n rows of matrix

bottomRows :: Int -> Matrix -> Matrix

bottom n rows of matrix

leftCols :: Int -> Matrix -> Matrix

left n columns of matrix

rightCols :: Int -> Matrix -> Matrix

right n columns of matrix

Matrix properties

norm :: Matrix -> Double

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.

squaredNorm :: Matrix -> Double

for vectors, the squared l2 norm, and for matrices the Frobenius norm. In both cases, it consists in the sum of the square of all the matrix entries. For vectors, this is also equals to the dot product of this with itself.

determinant :: Matrix -> Double

the determinant of the matrix

Matrix transformations

inverse :: Matrix -> Matrix

inverse of the matrix

For small fixed sizes up to 4x4, this method uses cofactors. In the general case, this method uses class PartialPivLU

adjoint :: Matrix -> Matrix

adjoint of the matrix

transpose :: Matrix -> Matrix

transpose of the matrix

normalize :: Matrix -> Matrix

nomalize the matrix by deviding it on its norm

Mutable operations

freeze :: MMatrix -> IO Matrix

create a snapshot of mutable matrix

thaw :: Matrix -> IO MMatrix

create mutable copy of the matrix

modify :: (MMatrix -> IO ()) -> Matrix -> Matrix

apply mutable operation to the mutable copy of the matrix and snapshot of this copy

with :: Matrix -> (Ptr C_MatrixXd -> IO a) -> IO a

apply foreign operation to the mutable copy of the matrix and operation result