Function main

examples/krr_classification_ex.cpp:27–204

int main()
{
    // 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 (double r = -20; r <= 20; r += 0.4)
    {
        for (double c = -20; c <= 20; c += 0.4)
        {
            sample_type samp;
            samp(0) = r;
            samp(1) = c;
            samples.push_back(samp);

            // if this point is less than 13 from the origin
            if (sqrt((double)r*r + c*c) <= 13)
                labels.push_back(+1);
            else
                labels.push_back(-1);

        }
    }

    cout << "samples generated: " << samples.size() << endl;
    cout << "  number of +1 samples: " << sum(mat(labels) > 0) << endl;
    cout << "  number of -1 samples: " << sum(mat(labels) < 0) << endl;

    // 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]); 


    // here we make an instance of the krr_trainer object that uses our kernel type.