Commit 28219262 authored by Davis King's avatar Davis King

merged changes and updated abstract file.

parents 7bb67fc1 31078ade
......@@ -147,8 +147,21 @@ namespace dlib
typedef decision_function<kernel_type> trained_function_type;
rvm_trainer (
) : eps(0.001)
) : eps(0.001), max_iterations(2000)
{
}
void set_max_iterations (
unsigned long max_iterations_
)
{
max_iterations = max_iterations_;
}
unsigned long get_max_iterations (
) const
{
return max_iterations;
}
void set_epsilon (
......@@ -288,9 +301,11 @@ namespace dlib
bool search_all_alphas = false;
unsigned long ticker = 0;
const unsigned long rounds_of_narrow_search = 100;
unsigned long iterations = 0;
while (true)
while (iterations != max_iterations)
{
iterations++;
if (recompute_beta)
{
// calculate the current t_estimate. (this is the predicted t value for each sample according to the
......@@ -572,6 +587,7 @@ namespace dlib
// private member variables
kernel_type kernel;
scalar_type eps;
unsigned long max_iterations;
const static scalar_type tau;
......
......@@ -50,6 +50,7 @@ namespace dlib
- This object is properly initialized and ready to be used
to train a relevance vector machine.
- #get_epsilon() == 0.001
- #get_max_iterations() == 2000
!*/
void set_epsilon (
......@@ -86,6 +87,22 @@ namespace dlib
- returns a copy of the kernel function in use by this object
!*/
unsigned long get_max_iterations (
) const;
/*!
ensures
- returns the maximum number of iterations the RVM optimizer is allowed to
run before it is required to stop and return a result.
!*/
void set_max_iterations (
unsigned long max_iter
);
/*!
ensures
- #get_max_iterations() == max_iter
!*/
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
......
......@@ -103,6 +103,9 @@ int main()
// stopping epsilon bigger. However, this might make the outputs less
// reliable. But sometimes it works out well. 0.001 is the default.
trainer.set_epsilon(0.001);
// You can also set an explicit limit on the number of iterations used by the numeric
// solver. The default is 2000.
trainer.set_max_iterations(2000);
// Now we loop over some different gamma values to see how good they are. Note
// that this is a very simple way to try out a few possible parameter choices. You
......
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