[ quantlib-Patches-3139291 ] Enhancement to linear least squares regression

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Patches item #3139291, was opened at 2010-12-17 20:13
Message generated for change (Comment added) made by klausspanderen
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Category: None
Group: None
Status: Open
Resolution: None
Priority: 5
Private: No
Submitted By: Slava Mazur (shlagbaum)
Assigned to: Nobody/Anonymous (nobody)
Summary: Enhancement to linear least squares regression

Initial Comment:
The idea behind proposed enhancement is application of the current linear least squares regression to a wider range of containers. Currently the input data must be in std::vector<double>. However, in my particular case for example, i need to perform linear least squares on the data in boost::circular_buffer<double> and it is highly undesirable to create vector copies of the original data just to satisfy rather rigid requirements of the current linear least squares design. A proposed patch relaxes such a requirement and at the same time preserves backward compatibility. A correspondent test case is added to the test_suite.

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Comment By: Klaus Spanderen (klausspanderen)
Date: 2010-12-28 15:06

Message:
I have removed the swap method. Please find the recent version in the SVN
head. Even though it breaks backward compatibility I would now also remove
the ArgumentType as it is redundant. Or should we introduce a backward
compatibility stub, Luigi?

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Comment By: Klaus Spanderen (klausspanderen)
Date: 2010-12-28 12:49

Message:
Good solution! I have submitted your last version (with a small change from
my side) to the SVN head. IMO the original intention for two classes -
LinearLeastSquaresRegression and LinearRegression - was that
LinearLeastSquaresRegression is the generic class that can take a list of
functions used for the regression whereas LinearRegression takes only
linear functions. WIth the second constructor in the current version
        template <class xContainer, class yContainer, class vContainer>
        LinearRegression(const xContainer& x,
                         const yContainer& y, const vContainer &v);
LinearRegression now becomes a swiss jackknife. IMHO I'd either remove
this constructor or - as you already mentioned - remove the class
LinearLeastSquaresRegression completely (leaving a stub for backward
compatibility)

I've removed the second usage of the swap method already and I think we
can remove the other swap invocation as well if LinearFcts becomes a list
of functions.

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Comment By: Slava Mazur (shlagbaum)
Date: 2010-12-24 18:51

Message:
With a little help from mpl i managed to simplify design significantly (see
the attached). A template argument "ArgumentType" apparently becomes
redundant (perhaps as well as the whole class
LinearLeastSquaresRegression), but they left unchanged for backward
compatibility. It is recommended to remove them in due time though - this
will simplify design even further (for example, an ugly cast in swap method
will go away).

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Comment By: Slava Mazur (shlagbaum)
Date: 2010-12-20 18:13

Message:
I agree, but the reason for the proposed design was the current design of
the LinearRegression class, which handles both 1d and multi-dimensional
regressions. In addition to generalize wrt input data type, my goal was to
optimize the existing solution, since currently LinearRegression class
treats a one-dimensional regression as being a multi-dimensional one, which
introduces although small, but an overhead in 1d case. Such an overhead may
become significant if LinearRegression is used repeatedly in a loop. If the
LinearRegression class were a template instead, things would be much
simpler, but this would break the backward compatibility.

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Comment By: Klaus Spanderen (klausspanderen)
Date: 2010-12-18 16:22

Message:
Enhancing the flexibilty of the class LinearLeastSquaresRegression is a
good idea. Just a design question: instead of adding more classes would it
be better for the "end user" to stay with one LinearLeastSquaresRegression
class but add two "templatized" constructors to this class? These
constructors would allow  e.g. boost::circular_buffer<double> to be used as
input parameters.

----------------------------------------------------------------------

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