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Commit f311e46d authored by Davis King's avatar Davis King
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Fixed some potential bugs in the rvm 

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402480
parent c0754e3b
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Showing with 35 additions and 83 deletions
dlib/svm/rvm.h
View file @ f311e46d
......@@ -59,25 +59,6 @@ namespace dlib
return kernel;
}
void set_epsilon (
scalar_type eps_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(eps_ > 0,
"\tvoid rvm_trainer::set_epsilon(eps_)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t eps: " << eps_
);
eps = eps_;
}
const scalar_type get_epsilon (
) const
{
return eps;
}
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
......@@ -289,7 +270,7 @@ namespace dlib
// if we just did a search of all the alphas and it still put is back
// into the current active set then we are done.
if (search_all_alphas == true && active_bases(selected_idx) != -1)
if (search_all_alphas == true && selected_idx != -1 && active_bases(selected_idx) != -1)
{
break;
}
......@@ -327,7 +308,7 @@ namespace dlib
alpha(idx) = s*s/(q*q-s);
}
else
else if (phi.nc() > 1) // don't ever remove the last basis vector
{
// the new alpha value is infinite so remove the selected alpha from our model
active_bases(selected_idx) = -1;
......@@ -348,6 +329,21 @@ namespace dlib
// recompute the beta vector next time around the main loop.
recompute_beta = true;
}
else
{
if (search_all_alphas == true)
{
// In this case we are saying we are done because the wide search
// told us to remove the only basis function we have. So just stop
break;
}
else
{
// we tried to remove the last basis vector in phi
// so lets make sure we do a round of wide search next time.
ticker = rounds_of_narrow_search;
}
}
}
else
{
......@@ -629,25 +625,6 @@ namespace dlib
return kernel;
}
void set_epsilon (
scalar_type eps_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(eps_ > 0,
"\tvoid rvm_regression_trainer::set_epsilon(eps_)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t eps: " << eps_
);
eps = eps_;
}
const scalar_type get_epsilon (
) const
{
return eps;
}
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
......@@ -799,7 +776,7 @@ namespace dlib
// if we just did a search of all the alphas and it still put is back
// into the current active set then we are done.
if (search_all_alphas == true && active_bases(selected_idx) != -1)
if (search_all_alphas == true && selected_idx != -1 && active_bases(selected_idx) != -1)
{
break;
}
......@@ -838,7 +815,7 @@ namespace dlib
alpha(idx) = s*s/(q*q-s);
}
else
else if (phi.nc() > 1) // don't ever remove the last basis vector from phi
{
// the new alpha value is infinite so remove the selected alpha from our model
active_bases(selected_idx) = -1;
......@@ -855,6 +832,21 @@ namespace dlib
}
}
}
else
{
if (search_all_alphas == true)
{
// In this case we are saying we are done because the wide search
// told us to remove the only basis function we have. So just stop
break;
}
else
{
// we tried to remove the last basis vector in phi
// so lets make sure we do a round of wide search next time.
ticker = rounds_of_narrow_search;
}
}
}
else
{
......
dlib/svm/rvm_abstract.h
View file @ f311e46d
......@@ -50,7 +50,6 @@ namespace dlib
ensures
- This object is properly initialized and ready to be used
to train a relevance vector machine.
- #get_epsilon() == 0.001
!*/
void set_kernel (
......@@ -68,25 +67,6 @@ namespace dlib
- returns a copy of the kernel function in use by this object
!*/
void set_epsilon (
scalar_type eps_
);
/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/
const scalar_type get_epsilon (
) const;
/*!
ensures
- returns the error epsilon that determines when training should stop.
Generally a good value for this is 0.001. Smaller values may result
in a more accurate solution but take longer to execute.
!*/
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
......@@ -104,7 +84,7 @@ namespace dlib
Also, y should contain scalar_type objects.
ensures
- trains a relevance vector classifier given the training samples in x and
labels in y. Training is done when the error is less than get_epsilon().
labels in y.
- returns a decision function F with the following properties:
- if (new_x is a sample predicted have +1 label) then
- F(new_x) >= 0
......@@ -173,7 +153,6 @@ namespace dlib
ensures
- This object is properly initialized and ready to be used
to train a relevance vector machine.
- #get_epsilon() == 0.001
!*/
void set_kernel (
......@@ -191,25 +170,6 @@ namespace dlib
- returns a copy of the kernel function in use by this object
!*/
void set_epsilon (
scalar_type eps_
);
/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/
const scalar_type get_epsilon (
) const;
/*!
ensures
- returns the error epsilon that determines when training should stop.
Generally a good value for this is 0.001. Smaller values may result
in a more accurate solution but take longer to execute.
!*/
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
......
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