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Changed the way the parameter search is done so that it will produce more reasonable results 

for a wider range of datasets.  This way people who cut and paste from this code should have
an easier time getting it to work with their data.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403752
This commit is contained in:
Davis King 2010-07-17 16:47:50 +00:00
parent 22c58d2b2b
commit 5a019f0a04
1 changed files with 4 additions and 4 deletions
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8
examples/rvm_ex.cpp
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@ -104,7 +104,7 @@ int main()
// should look at the model_selection_ex.cpp program for examples of more sophisticated
// strategies for determining good parameter choices.
cout << "doing cross validation" << endl;
for (double gamma = 0.00001; gamma <= 1; gamma += 0.1)
for (double gamma = 0.000001; gamma <= 1; gamma *= 5)
{
// tell the trainer the parameters we want to use
trainer.set_kernel(kernel_type(gamma));
@ -119,12 +119,12 @@ int main()
// From looking at the output of the above loop it turns out that a good value for
// gamma for this problem is 0.1. So that is what we will use.
// gamma for this problem is 0.08. So that is what we will use.
// Now we train on the full set of data and obtain the resulting decision function. We use the
// value of 0.1 for gamma. The decision function will return values >= 0 for samples it predicts
// value of 0.08 for gamma. The decision function will return values >= 0 for samples it predicts
// are in the +1 class and numbers < 0 for samples it predicts to be in the -1 class.
trainer.set_kernel(kernel_type(0.1));
trainer.set_kernel(kernel_type(0.08));
typedef decision_function<kernel_type> dec_funct_type;
typedef normalized_function<dec_funct_type> funct_type;