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Added a function to set the minimum tolerance after the object has been constructed. 

Also made the spec more clear.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403664
This commit is contained in:
Davis King 2010-06-02 23:28:34 +00:00
parent 78e4869542
commit a929b06633
2 changed files with 43 additions and 14 deletions
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14
dlib/svm/linearly_independent_subset_finder.h
View File

@ -98,6 +98,20 @@ namespace dlib
return min_tolerance;
}
void set_minimum_tolerance (
scalar_type min_tol
)
{
// make sure requires clause is not broken
DLIB_ASSERT(min_tol > 0,
"\tlinearly_independent_subset_finder::set_minimum_tolerance()"
<< "\n\tinvalid argument to this function"
<< "\n\tmin_tol: " << min_tol
<< "\n\tthis: " << this
);
min_tolerance = min_tol;
}
void clear_dictionary ()
{
dictionary.clear();

43
dlib/svm/linearly_independent_subset_finder_abstract.h
View File

@ -24,19 +24,24 @@ namespace dlib
WHAT THIS OBJECT REPRESENTS
This is an implementation of an online algorithm for recursively finding a
set of linearly independent vectors in a kernel induced feature space. To
use it you decide how large you would like the set to be and then you feed it
sample points.
Each time you present it with a new sample point (via this->add()) it either
keeps the current set of independent points unchanged, or if the new point
is "more linearly independent" than one of the points it already has,
it replaces the weakly linearly independent point with the new one.
set (aka dictionary) of linearly independent vectors in a kernel induced
feature space. To use it you decide how large you would like the dictionary
to be and then you feed it sample points.
This object uses the Approximately Linearly Dependent metric described in the paper
The Kernel Recursive Least Squares Algorithm by Yaakov Engel to decide which
points are more linearly independent than others.
The implementation uses the Approximately Linearly Dependent metric described
in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel to
decide which points are more linearly independent than others. The metric is
simply the squared distance between a test point and the subspace spanned by
the current set of dictionary vectors.
Each time you present this object with a new sample point (via this->add())
it calculates the projection distance and if it is sufficiently large then this
new point is included into the current dictionary. Note that this object can
be configured to have a maximum size. Once the max dictionary size is reached
each new point kicks out a previous point. This is done by selecting the current
dictionary vector that has the smallest projection error onto the others. That
is, the "least linearly independent" vector is removed to make room for the
new one.
!*/
public:
@ -92,7 +97,7 @@ namespace dlib
- returns the minimum tolerance to use for the approximately linearly dependent
test used for dictionary vector selection (see KRLS paper for ALD details).
In other words, this is the minimum threshold for how linearly independent
a sample must be for it to even be considered for addition to the dictionary.
a sample must be for it to be considered for addition to the dictionary.
Moreover, bigger values of this field will make the algorithm run faster but
might give less accurate results.
- The exact meaning of the tolerance parameter is the following:
@ -105,6 +110,16 @@ namespace dlib
of the dictionary.
!*/
void set_minimum_tolerance (
scalar_type min_tolerance
);
/*!
requires
- min_tolerance > 0
ensures
- #minimum_tolerance() == min_tol
!*/
void clear_dictionary (
);
/*!
@ -117,7 +132,7 @@ namespace dlib
);
/*!
ensures
- if (x is linearly independent of the vectors already in this object) then
- if (x is sufficiently linearly independent of the vectors already in this object) then
- adds x into the dictionary
- (*this)[#dictionary_size()-1] == x
- returns true