@ -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 = 0.0000 1; gamma < = 1 ; gamma += 0.1 )
for ( double gamma = 0.0000 0 1; 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 ;