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diff --git a/ml/dlib/examples/krr_regression_ex.cpp b/ml/dlib/examples/krr_regression_ex.cpp
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-// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-/*
-    This is an example illustrating the use of the kernel ridge regression 
-    object from the dlib C++ Library.
-
-    This example will train on data from the sinc function.
-
-*/
-
-#include <iostream>
-#include <vector>
-
-#include <dlib/svm.h>
-
-using namespace std;
-using namespace dlib;
-
-// Here is the sinc function we will be trying to learn with kernel ridge regression 
-double sinc(double x)
-{
-    if (x == 0)
-        return 1;
-    return sin(x)/x;
-}
-
-int main()
-{
-    // Here we declare that our samples will be 1 dimensional column vectors.  
-    typedef matrix<double,1,1> sample_type;
-
-    // Now sample some points from the sinc() function
-    sample_type m;
-    std::vector<sample_type> samples;
-    std::vector<double> labels;
-    for (double x = -10; x <= 4; x += 1)
-    {
-        m(0) = x;
-        samples.push_back(m);
-        labels.push_back(sinc(x));
-    }
-
-    // Now we are making a typedef for the kind of kernel we want to use.  I picked the
-    // radial basis kernel because it only has one parameter and generally gives good
-    // results without much fiddling.
-    typedef radial_basis_kernel<sample_type> kernel_type;
-
-    // Here we declare an instance of the krr_trainer object.  This is the
-    // object that we will later use to do the training.
-    krr_trainer<kernel_type> trainer;
-
-    // Here we set the kernel we want to use for training.   The radial_basis_kernel 
-    // has a parameter called gamma that we need to determine.  As a rule of thumb, a good 
-    // gamma to try is 1.0/(mean squared distance between your sample points).  So 
-    // below we are using a similar value computed from at most 2000 randomly selected
-    // samples.
-    const double gamma = 3.0/compute_mean_squared_distance(randomly_subsample(samples, 2000));
-    cout << "using gamma of " << gamma << endl;
-    trainer.set_kernel(kernel_type(gamma));
-
-    // now train a function based on our sample points
-    decision_function<kernel_type> test = trainer.train(samples, labels);
-
-    // now we output the value of the sinc function for a few test points as well as the 
-    // value predicted by our regression.
-    m(0) = 2.5; cout << sinc(m(0)) << "   " << test(m) << endl;
-    m(0) = 0.1; cout << sinc(m(0)) << "   " << test(m) << endl;
-    m(0) = -4;  cout << sinc(m(0)) << "   " << test(m) << endl;
-    m(0) = 5.0; cout << sinc(m(0)) << "   " << test(m) << endl;
-
-    // The output is as follows:
-    //using gamma of 0.075
-    //    0.239389   0.239389
-    //    0.998334   0.998362
-    //    -0.189201   -0.189254
-    //    -0.191785   -0.186618
-
-    // The first column is the true value of the sinc function and the second
-    // column is the output from the krr estimate.  
-
-
-    // Note that the krr_trainer has the ability to tell us the leave-one-out predictions
-    // for each sample.  
-    std::vector<double> loo_values;
-    trainer.train(samples, labels, loo_values);
-    cout << "mean squared LOO error: " << mean_squared_error(labels,loo_values) << endl;
-    cout << "R^2 LOO value:          " << r_squared(labels,loo_values) << endl;
-    // Which outputs the following:
-    // mean squared LOO error: 8.29575e-07
-    // R^2 LOO value:          0.999995
-
-
-
-
-
-    // Another thing that is worth knowing is that just about everything in dlib is serializable.
-    // So for example, you can save the test object to disk and recall it later like so:
-    serialize("saved_function.dat") << test;
-
-    // Now let's open that file back up and load the function object it contains.
-    deserialize("saved_function.dat") >> test;
-
-}
-
-