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Removed unnecessary restrictions on the rbf_network_trainer

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object.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402398
This commit is contained in:
Davis King 2008-07-10 02:22:54 +00:00
parent e32aa6cf90
commit 29b08623e8
2 changed files with 9 additions and 5 deletions
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3
dlib/svm/rbf_network.h
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@ -108,14 +108,13 @@ namespace dlib
typedef typename decision_function<kernel_type>::scalar_vector_type scalar_vector_type;
// make sure requires clause is not broken
DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
DLIB_ASSERT(x.nr() > 1 && x.nr() == y.nr() && x.nc() == 1 && y.nc() == 1,
"\tdecision_function rbf_network_trainer::train(x,y)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t x.nr(): " << x.nr()
<< "\n\t y.nr(): " << y.nr()
<< "\n\t x.nc(): " << x.nc()
<< "\n\t y.nc(): " << y.nc()
<< "\n\t is_binary_classification_problem(x,y): " << ((is_binary_classification_problem(x,y))? "true":"false")
);
// first run all the sampes through a kcentroid object to find the rbf centers

11
dlib/svm/rbf_network_abstract.h
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@ -27,8 +27,7 @@ namespace dlib
- get_tolerance() == 0.01
WHAT THIS OBJECT REPRESENTS
This object implements a trainer for radial basis function network for
solving binary classification problems.
This object implements a trainer for an radial basis function network.
The implementation of this algorithm follows the normal RBF training
process. For more details see the code or the Wikipedia article
@ -94,7 +93,13 @@ namespace dlib
) const
/*!
requires
- is_binary_classification_problem(x,y) == true
- in_sample_vector_type == a matrix or something convertable to a matrix
via vector_to_matrix()
- in_scalar_vector_type == a matrix or something convertable to a matrix
via vector_to_matrix()
- x.nr() > 1
- x.nr() == y.nr() && x.nc() == 1 && y.nc() == 1
(i.e. x and y are both column vectors of the same length)
ensures
- trains a RBF network given the training samples in x and
labels in y.