Name:           tensor
Version:        0.1
Cabal-Version:  >= 1.8
License:        GPL-3
License-File:   LICENSE
Author:         Federico Squartini, Nicola Squartini
Maintainer:     Nicola Squartini <tensor5@gmail.com>
Stability:      experimental
Synopsis:       A completely type-safe library for linear algebra
Category:       Data, Math
Build-Type:     Simple
Description:
  .
  This library defines data types and classes for fixed dimension
  vectors and tensors. The main objects are:
  .
  [@'Data.Ordinal.Ordinal'@] A totally ordered set with fixed
  size. The @'Data.Ordinal.Ordinal'@ type @'Data.Ordinal.One'@
  contains 1 element, @'Data.Ordinal.Succ' 'Data.Ordinal.One'@
  contains 2 elements, @'Data.Ordinal.Succ' 'Data.Ordinal.Succ'
  'Data.Ordinal.One'@ contains 3 elements, and so on (see
  "Data.Ordinal" for more details). The type @'Data.Ordinal.Two'@ is
  an alias for @'Data.Ordinal.Succ' 'Data.Ordinal.One'@,
  @'Data.Ordinal.Three'@ is an alias for @'Data.Ordinal.Succ'
  'Data.Ordinal.Succ' 'Data.Ordinal.One'@, and so on.
  .
  [@'Data.TypeList.MultiIndex.MultiIndex'@] The index set. It can be
  linear, rectangular, parallelepipedal, etc. The dimensions of the
  sides are expressed using @'Data.Ordinal.Ordinal'@ types and the
  type constructor @'Data.TypeList.MultiIndex.:|:'@,
  e.g. @('Data.Ordinal.Two' 'Data.TypeList.MultiIndex.:|:'
  ('Data.Ordinal.Three' 'Data.TypeList.MultiIndex.:|:'
  'Data.TypeList.MultiIndex.Nil'))@ is a rectangular index set with 2
  rows and 3 columns. The index set also contains elements, for
  example @('Data.Ordinal.Two' 'Data.TypeList.MultiIndex.:|:'
  ('Data.Ordinal.Three' 'Data.TypeList.MultiIndex.:|:'
  'Data.TypeList.MultiIndex.Nil'))@ contains all the pairs @(i
  'Data.TypeList.MultiIndex.:|:' (j 'Data.TypeList.MultiIndex.:|:'
  Nil))@ where i is in @'Data.Ordinal.Two'@ and j is in
  @'Data.Ordinal.Three'@. See "Data.TypeList.MultiIndex" for more
  details.
  .
  [@'Data.Tensor.Tensor'@] It is an assignment of elements to each
  element of its @'Data.TypeList.MultiIndex.MultiIndex'@.
  .
  Objects like vectors and matrices are special cases of tensors.
  Most of the functions to manipulate tensors are grouped into type
  classes.  This allow the possibility of having different internal
  representations (backends) of a tensor, and act on these with the
  same functions. At the moment we only provide one backend based on
  <http://hackage.haskell.org/package/vector>, which is accessible by
  importing the module "Data.Tensor.Vector". More backends will be
  provided in future releases.
  .
  Here is a usage example:
  .

  >>> :m Data.Ordinal Data.TypeList.MultiIndex Data.Tensor.Vector
  >>> fromList [2,3,5,1,3,6,0,5,4,2,1,3] :: Tensor (Four :|: (Three :|: Nil)) Int
  [[2,3,5],[1,3,6],[0,5,4],[2,1,3]]
  .
  The above defines a tensor with 4 rows and 3 columns (a matrix) and
  @'Int'@ coefficients. The entries of this matrix are taken from a
  list using @'Data.Tensor.fromList'@ which is a method of the class
  @'Data.Tensor.FromList'@. Notice the output: the @'Show'@ instance
  is defined in such a way to give a readable representation as list
  of lists. The is equivalent but slightly more readable code:
  .
  >>> fromList [2,3,5,1,3,6,0,5,4,2,1,3] :: Matrix Four Three Int
  [[2,3,5],[1,3,6],[0,5,4],[2,1,3]]
  .
  Analogously
  .
  >>> fromList [7,3,-6] :: Tensor (Three :|: Nil) Int
  [7,3,-6]
  .
  and
  .
  >>> fromList [7,3,-6] :: Vector Three Int
  [7,3,-6]
  .
  are the same. In order to access an entry of a
  @'Data.Tensor.Tensor'@ we use the @'Data.Tensor.!'@ operator, which
  takes the same @'Data.TypeList.MultiIndex.MultiIndex'@ of the
  @'Data.Tensor.Tensor'@ as its second argument:
  .
  >>> let a = fromList [2,3,5,1,3,6,0,5,4,2,1,3] :: Matrix Four Three Int
  >>> let b = fromList [7,3,-6] :: Vector Three Int
  >>> a ! (toMultiIndex [1,3] :: (Four :|: (Three :|: Nil)))
  5
  >>> b ! (toMultiIndex [2] :: (Three :|: Nil))
  3
  .
  it returns the element at the coordinate (1,3) of the matrix @a@,
  and the element at the coordinate 2 of the vector b. In fact, thanks
  to type inference, we could simply write
  .
  >>> a ! toMultiIndex [1,3]
  5
  >>> b ! toMultiIndex [2]
  2
  .
  And now a couple of examples of algebraic operations (requires
  adding "Data.Tensor.LinearAlgebra.Vector" to the import list):
  .
  >>> :m Data.Ordinal Data.TypeList.MultiIndex Data.Tensor.Vector Data.Tensor.LinearAlgebra.Vector
  >>> let a = fromList [2,3,5,1,3,6,0,5,4,2,1,3] :: Matrix Four Three Int
  >>> let b = fromList [7,3,-6] :: Vector Three Int
  >>> a .*. b
  [-7,-20,-9,-1]
  .
  is the product of matrix @a@ and vector @b@, while
  .
  >>> let c = fromList [3,4,0,-1,4,5,6,2,1] :: Matrix Three Three Int
  >>> c
  [[3,4,0],[-1,4,5],[6,2,1]]
  >>> charPoly c
  [106,13,8]
  .
  gives the coefficients of the characteristic polynomial of the
  matrix @c@.

Library
  Build-Depends:   base == 4.*, vector
  Build-Tools:     ghc >= 7.2.1
  GHC-Options:     -Wall -O2
  Exposed-Modules: Data.Cardinal
                   Data.Ordinal
                   Data.Tensor.LinearAlgebra
                   Data.Tensor.LinearAlgebra.Vector
                   Data.TypeAlgebra
                   Data.TypeList
                   Data.TypeList.MultiIndex
                   Data.Tensor
                   Data.Tensor.Vector
  Other-modules:   Data.Tensor.Vector.Internal
                   Data.TypeList.MultiIndex.Internal