Python linear algebra module -- requesting comments on interface

Discussion in 'Python' started by C. Barnes, Sep 9, 2005.

  1. C. Barnes

    C. Barnes Guest

    Hi, I'm in the process of writing a Python linear
    algebra module.

    The current targeted interface is:

    http://oregonstate.edu/~barnesc/temp/linalg/

    The interface was originally based on Raymond
    Hettinger's
    Matfunc [1]. However, it has evolved so that now it
    is
    nearly identical to JAMA [2], the Java matrix library.

    I am soliticing comments on this interface.

    Please post up any criticism that you have. Even
    small
    things -- if something isn't right, it's better to fix
    it now than later.

    I have not made source code available yet, since the
    current code is missing the decompositions and doesn't
    match the new interface. I'm in the process of
    rewritting the code to match the new interface. You
    can e-mail me and ask for the old code if you're
    curious
    or skeptical.

    [1]. http://users.rcn.com/python/download/python.htm
    [2]. http://math.nist.gov/javanumerics/jama/

    ---------------------------------------------
    Brief comparison with Numeric
    ---------------------------------------------

    Numeric and linalg serve different purposes.

    Numeric is intended to be a general purpose array
    extension. It takes a "kitchen sink" approach,
    and includes every function which could potentially
    be useful for array manipulations.

    Linalg is intended to handle real/complex vectors
    and matrices, for scientific and 3D applications.
    It has a more restricted scope. Because it is
    intended for 3D applications, it is optimized
    for dimension 2, 3, 4 operations.

    For the typical matrix operations, the linalg
    interface is much intuitive than Numeric's. Real
    and imaginary components are always cast to
    doubles, so no headaches are created if a matrix
    is instantiated from a list of integers. Unlike
    Numeric, the * operator performs matrix
    multiplication, A**-1 computes the matrix inverse,
    A == B returns True or False, and the 2-norm and
    cross product functions exist.

    As previously stated, linalg is optimized for
    matrix arithmetic with small matrices (size 2, 3, 4).

    A (somewhat out of date) set of microbenchmarks [3]
    [4]
    show that linalg is roughly an order of magnitude
    faster than Numeric for dimension 3 vectors and
    matrices.

    [3].
    Microbenchmarks without psyco:
    http://oregonstate.edu/~barnesc/temp/
    numeric_vs_linalg_prelim-2005-09-07.pdf

    [4].
    Microbenchmarks with psyco:
    http://oregonstate.edu/~barnesc/temp/
    numeric_vs_linalg_prelim_psyco-2005-09-07.pdf



    __________________________________________________
    Do You Yahoo!?
    Tired of spam? Yahoo! Mail has the best spam protection around
    http://mail.yahoo.com
     
    C. Barnes, Sep 9, 2005
    #1
    1. Advertising

  2. C. Barnes wrote:
    > Hi, I'm in the process of writing a Python linear
    > algebra module.
    >
    > The current targeted interface is:
    > http://oregonstate.edu/~barnesc/temp/linalg/


    Is this going to become free software. If yes, what license
    will you use?


    So my suggestions:

    In cases like these ones:

    random_matrix(m, n=-1)
    zero_matrix(m, n=-1)

    ... I think it's better to set the default value to "None"
    instead of a number:

    random_matrix(m, n=None)
    zero_matrix(m, n=None)

    IMHO, this is more intuitive and more "pythonic".

    I also suggest to make the "random function" choosable:

    random_matrix(m, n=None, randfunc=random.random)
    random_vector(n, randfunc=random.random)

    This way it's more easy for those who want another range
    of numbers, or want another kind of distribution of the
    random numbers.


    At the top of your documentation, there is a link "overview",
    which is broken:

    See _overview_ for a quick start.


    Greets,

    Volker

    --
    Volker Grabsch
    ---<<(())>>---
    \frac{\left|\vartheta_0\times\{\ell,\kappa\in\Re\}\right|}{\sqrt
    [G]{-\Gamma(\alpha)\cdot\mathcal{B}^{\left[\oint\!c_\hbar\right]}}}
     
    Volker Grabsch, Sep 9, 2005
    #2
    1. Advertising

  3. Since one of the module's targeted applications is for 3D applications,
    I think there should be some specific support for applying the
    Matrix-vector product operation to a sequence of vectors instead of
    only one at a time -- and it should be possible to optimize the
    module's code for this common case.

    I'd also like to see some special specific errors defined and raised
    from the Matrix det(), inverse(), and transpose() methods when the
    operation is attempted on an ill-formed matrices (e.g. for non-square,
    non-invertible, singular cases). This would allow client code to handle
    errors better.

    Very nice work overall, IMHO.

    Best,
    -Martin
     
    Martin Miller, Sep 9, 2005
    #3
  4. Connelly,

    Apologies, my first message was sent in error.

    I like your general setup. You appear to permit matrix operations,
    which the folk at Numeric and, later, numarray did not.

    My own package, PyMatrix, has similar aims to yours but it may be slower
    as it is based on numarray.

    My package is just about ready for another release but I'm toiling to
    improve the documentation. I felt that it could be of value to
    newcomers to matrices and so my new documentation is more long-winded
    than yours. Your overview sets the whole thing out very neatly.

    I have made use of Python's properties for transpose, inverse etc. This
    uses abbreviations and avoids redundant parentheses.

    My work was based on the ideas of Huaiyu Zhu, who developed MatPy:
    http://matpy.sourceforge.net/

    You might be interested in looking at PyMatrix:
    http://www3.sympatico.ca/cjw/PyMatrix/

    Best wishes,

    Colin W.

    C. Barnes wrote:
    > Hi, I'm in the process of writing a Python linear
    > algebra module.
    >
    > The current targeted interface is:
    >
    > http://oregonstate.edu/~barnesc/temp/linalg/
    >
    > The interface was originally based on Raymond
    > Hettinger's
    > Matfunc [1]. However, it has evolved so that now it
    > is
    > nearly identical to JAMA [2], the Java matrix library.
    >
    > I am soliticing comments on this interface.
    >
    > Please post up any criticism that you have. Even
    > small
    > things -- if something isn't right, it's better to fix
    > it now than later.
    >
    > I have not made source code available yet, since the
    > current code is missing the decompositions and doesn't
    > match the new interface. I'm in the process of
    > rewritting the code to match the new interface. You
    > can e-mail me and ask for the old code if you're
    > curious
    > or skeptical.
    >
    > [1]. http://users.rcn.com/python/download/python.htm
    > [2]. http://math.nist.gov/javanumerics/jama/
    >
    > ---------------------------------------------
    > Brief comparison with Numeric
    > ---------------------------------------------
    >
    > Numeric and linalg serve different purposes.
    >
    > Numeric is intended to be a general purpose array
    > extension. It takes a "kitchen sink" approach,
    > and includes every function which could potentially
    > be useful for array manipulations.
    >
    > Linalg is intended to handle real/complex vectors
    > and matrices, for scientific and 3D applications.
    > It has a more restricted scope. Because it is
    > intended for 3D applications, it is optimized
    > for dimension 2, 3, 4 operations.
    >
    > For the typical matrix operations, the linalg
    > interface is much intuitive than Numeric's. Real
    > and imaginary components are always cast to
    > doubles, so no headaches are created if a matrix
    > is instantiated from a list of integers. Unlike
    > Numeric, the * operator performs matrix
    > multiplication, A**-1 computes the matrix inverse,
    > A == B returns True or False, and the 2-norm and
    > cross product functions exist.
    >
    > As previously stated, linalg is optimized for
    > matrix arithmetic with small matrices (size 2, 3, 4).
    >
    > A (somewhat out of date) set of microbenchmarks [3]
    > [4]
    > show that linalg is roughly an order of magnitude
    > faster than Numeric for dimension 3 vectors and
    > matrices.
    >
    > [3].
    > Microbenchmarks without psyco:
    > http://oregonstate.edu/~barnesc/temp/
    > numeric_vs_linalg_prelim-2005-09-07.pdf
    >
    > [4].
    > Microbenchmarks with psyco:
    > http://oregonstate.edu/~barnesc/temp/
    > numeric_vs_linalg_prelim_psyco-2005-09-07.pdf
    >
    >
    >
    > __________________________________________________
    > Do You Yahoo!?
    > Tired of spam? Yahoo! Mail has the best spam protection around
    > http://mail.yahoo.com
     
    Colin J. Williams, Sep 9, 2005
    #4
  5. On Fri, 9 Sep 2005 04:58:43 -0700 (PDT), "C. Barnes" <> wrote:

    >
    >Hi, I'm in the process of writing a Python linear
    >algebra module.
    >
    >The current targeted interface is:
    >
    > http://oregonstate.edu/~barnesc/temp/linalg/
    >
    >The interface was originally based on Raymond
    >Hettinger's
    >Matfunc [1]. However, it has evolved so that now it
    >is
    >nearly identical to JAMA [2], the Java matrix library.
    >
    >I am soliticing comments on this interface.
    >
    >Please post up any criticism that you have. Even
    >small
    >things -- if something isn't right, it's better to fix
    >it now than later.
    >

    Wondering whether you will be supporting OpenGL-style matrices and
    operations for graphics. UIAM they permit optimizations in both
    storage and operations due to the known zero and one element values
    that would appear in full matrix representations of the same.

    http://www.rush3d.com/reference/opengl-redbook-1.1/appendixg.html

    Also wondering about some helper function to measure sensitivity of
    ..solve results when getting near-singular, but maybe that's an
    out-side-of-the package job.

    From a quick look, it looks quite nice ;-)

    Regards,
    Bengt Richter
     
    Bengt Richter, Sep 10, 2005
    #5
  6. Szabolcs Nagy, Sep 11, 2005
    #6
    1. Advertising

Want to reply to this thread or ask your own question?

It takes just 2 minutes to sign up (and it's free!). Just click the sign up button to choose a username and then you can ask your own questions on the forum.
Similar Threads
  1. ckumar
    Replies:
    2
    Views:
    461
    ckumar
    Jan 17, 2005
  2. Bernard Xhumga
    Replies:
    0
    Views:
    474
    Bernard Xhumga
    Nov 24, 2003
  3. Replies:
    0
    Views:
    304
  4. Terry Reedy
    Replies:
    1
    Views:
    325
  5. C. Barnes
    Replies:
    0
    Views:
    459
    C. Barnes
    Sep 14, 2005
Loading...

Share This Page