Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 101 insertions, 0 deletions

diff --git a/src/ml/dlib/examples/rvm_regression_ex.cpp b/src/ml/dlib/examples/rvm_regression_ex.cpp
new file mode 100644
index 000000000..d65cb5203
--- /dev/null
+++ b/src/ml/dlib/examples/rvm_regression_ex.cpp
@@ -0,0 +1,101 @@
+// 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 RVM 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 rvm 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 rvm_regression_trainer object.  This is the
+    // object that we will later use to do the training.
+    rvm_regression_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.   Note also that using an inappropriately large
+    // gamma will cause the RVM training algorithm to run extremely slowly.  What
+    // "large" means is relative to how spread out your data is.  So it is important
+    // to use a rule like this as a starting point for determining the gamma value
+    // if you want to use the RVM.  It is also probably a good idea to normalize your
+    // samples as shown in the rvm_ex.cpp example program.
+    const double gamma = 2.0/compute_mean_squared_distance(samples);
+    cout << "using gamma of " << gamma << endl;
+    trainer.set_kernel(kernel_type(gamma));
+
+    // One thing you can do to reduce the RVM training time is to make its
+    // stopping epsilon bigger.  However, this might make the outputs less
+    // reliable.  But sometimes it works out well.  0.001 is the default.
+    trainer.set_epsilon(0.001);
+
+    // 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 0.05
+    //0.239389   0.240989
+    //0.998334   0.999538
+    //-0.189201   -0.188453
+    //-0.191785   -0.226516
+
+
+    // The first column is the true value of the sinc function and the second
+    // column is the output from the rvm estimate.  
+
+
+
+    // 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;
+
+}
+
+