Function main

examples/svm_ex.cpp:28–254

int main()
{
    // The svm functions use column vectors to contain a lot of the data on which they
    // operate. So the first thing we do here is declare a convenient typedef.  

    // This typedef declares a matrix with 2 rows and 1 column.  It will be the object that
    // contains each of our 2 dimensional samples.   (Note that if you wanted more than 2
    // features in this vector you can simply change the 2 to something else.  Or if you
    // don't know how many features you want until runtime then you can put a 0 here and
    // use the matrix.set_size() member function)
    typedef matrix<double, 2, 1> sample_type;

    // This is a typedef for the type of kernel we are going to use in this example.  In
    // this case I have selected the radial basis kernel that can operate on our 2D
    // sample_type objects
    typedef radial_basis_kernel<sample_type> kernel_type;


    // Now we make objects to contain our samples and their respective labels.
    std::vector<sample_type> samples;
    std::vector<double> labels;

    // Now let's put some data into our samples and labels objects.  We do this by looping
    // over a bunch of points and labeling them according to their distance from the
    // origin.
    for (int r = -20; r <= 20; ++r)
    {
        for (int c = -20; c <= 20; ++c)
        {
            sample_type samp;
            samp(0) = r;
            samp(1) = c;
            samples.push_back(samp);

            // if this point is less than 10 from the origin
            if (sqrt((double)r*r + c*c) <= 10)
                labels.push_back(+1);
            else
                labels.push_back(-1);

        }
    }


    // Here we normalize all the samples by subtracting their mean and dividing by their
    // standard deviation.  This is generally a good idea since it often heads off
    // numerical stability problems and also prevents one large feature from smothering
    // others.  Doing this doesn't matter much in this example so I'm just doing this here
    // so you can see an easy way to accomplish this with the library.  
    vector_normalizer<sample_type> normalizer;
    // let the normalizer learn the mean and standard deviation of the samples
    normalizer.train(samples);
    // now normalize each sample
    for (unsigned long i = 0; i < samples.size(); ++i)
        samples[i] = normalizer(samples[i]); 


    // Now that we have some data we want to train on it.  However, there are two