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diff --git a/ml/dlib/examples/kcentroid_ex.cpp b/ml/dlib/examples/kcentroid_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 kcentroid object 
-    from the dlib C++ Library.
-
-    The kcentroid object is an implementation of an algorithm that recursively
-    computes the centroid (i.e. average) of a set of points.  The interesting
-    thing about dlib::kcentroid is that it does so in a kernel induced feature
-    space.  This means that you can use it as a non-linear one-class classifier.
-    So you might use it to perform online novelty detection (although, it has
-    other uses, see the svm_pegasos or kkmeans examples for example).  
-    
-    This example will train an instance of it on points from the sinc function.
-
-*/
-
-#include <iostream>
-#include <vector>
-
-#include <dlib/svm.h>
-#include <dlib/statistics.h>
-
-using namespace std;
-using namespace dlib;
-
-// Here is the sinc function we will be trying to learn with the kcentroid 
-// object.
-double sinc(double x)
-{
-    if (x == 0)
-        return 1;
-    return sin(x)/x;
-}
-
-int main()
-{
-    // Here we declare that our samples will be 2 dimensional column vectors.  
-    // (Note that if you don't know the dimensionality of your vectors at compile time
-    // you can change the 2 to a 0 and then set the size at runtime)
-    typedef matrix<double,2,1> sample_type;
-
-    // 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 kcentroid object.  The kcentroid has 3 parameters 
-    // you need to set.  The first argument to the constructor is the kernel we wish to 
-    // use.  The second is a parameter that determines the numerical accuracy with which 
-    // the object will perform the centroid estimation.  Generally, smaller values 
-    // give better results but cause the algorithm to attempt to use more dictionary vectors 
-    // (and thus run slower and use more memory).  The third argument, however, is the 
-    // maximum number of dictionary vectors a kcentroid is allowed to use.  So you can use
-    // it to control the runtime complexity.  
-    kcentroid<kernel_type> test(kernel_type(0.1),0.01, 15);
-
-
-    // now we train our object on a few samples of the sinc function.
-    sample_type m;
-    for (double x = -15; x <= 8; x += 1)
-    {
-        m(0) = x;
-        m(1) = sinc(x);
-        test.train(m);
-    }
-
-    running_stats<double> rs;
-
-    // Now let's output the distance from the centroid to some points that are from the sinc function.
-    // These numbers should all be similar.  We will also calculate the statistics of these numbers
-    // by accumulating them into the running_stats object called rs.  This will let us easily
-    // find the mean and standard deviation of the distances for use below.
-    cout << "Points that are on the sinc function:\n";
-    m(0) = -1.5; m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
-    m(0) = -1.5; m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
-    m(0) = -0;   m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
-    m(0) = -0.5; m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
-    m(0) = -4.1; m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
-    m(0) = -1.5; m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
-    m(0) = -0.5; m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
-
-    cout << endl;
-    // Let's output the distance from the centroid to some points that are NOT from the sinc function.
-    // These numbers should all be significantly bigger than previous set of numbers.  We will also
-    // use the rs.scale() function to find out how many standard deviations they are away from the 
-    // mean of the test points from the sinc function.  So in this case our criterion for "significantly bigger"
-    // is > 3 or 4 standard deviations away from the above points that actually are on the sinc function.
-    cout << "Points that are NOT on the sinc function:\n";
-    m(0) = -1.5; m(1) = sinc(m(0))+4;   cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
-    m(0) = -1.5; m(1) = sinc(m(0))+3;   cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
-    m(0) = -0;   m(1) = -sinc(m(0));    cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
-    m(0) = -0.5; m(1) = -sinc(m(0));    cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
-    m(0) = -4.1; m(1) = sinc(m(0))+2;   cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
-    m(0) = -1.5; m(1) = sinc(m(0))+0.9; cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
-    m(0) = -0.5; m(1) = sinc(m(0))+1;   cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
-
-    // And finally print out the mean and standard deviation of points that are actually from sinc().  
-    cout << "\nmean: " << rs.mean() << endl;
-    cout << "standard deviation: " << rs.stddev() << endl;
-
-    // The output is as follows:
-    /*
-        Points that are on the sinc function:
-            0.869913
-            0.869913
-            0.873408
-            0.872807
-            0.870432
-            0.869913
-            0.872807
-
-        Points that are NOT on the sinc function:
-            1.06366 is 119.65 standard deviations from sinc.
-            1.02212 is 93.8106 standard deviations from sinc.
-            0.921382 is 31.1458 standard deviations from sinc.
-            0.918439 is 29.3147 standard deviations from sinc.
-            0.931428 is 37.3949 standard deviations from sinc.
-            0.898018 is 16.6121 standard deviations from sinc.
-            0.914425 is 26.8183 standard deviations from sinc.
-
-            mean: 0.871313
-            standard deviation: 0.00160756
-    */
-
-    // So we can see that in this example the kcentroid object correctly indicates that 
-    // the non-sinc points are definitely not points from the sinc function.
-}
-
-