1 files changed, 0 insertions, 96 deletions
diff --git a/ml/dlib/examples/svr_ex.cpp b/ml/dlib/examples/svr_ex.cpp
deleted file mode 100644
index a18edf24d..000000000
--- a/ml/dlib/examples/svr_ex.cpp
+++ /dev/null
@@ -1,96 +0,0 @@
-// 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 epsilon-insensitive support vector 
-    regression object from the dlib C++ Library.
-
-    In this example we will draw some points from the sinc() function and do a
-    non-linear regression on them.
-*/
-
-#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 the svr_trainer 
-// object.
-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 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;
-
-
-    std::vector<sample_type> samples;
-    std::vector<double> targets;
-
-    // The first thing we do is pick a few training points from the sinc() function.
-    sample_type m;
-    for (double x = -10; x <= 4; x += 1)
-    {
-        m(0) = x;
-
-        samples.push_back(m);
-        targets.push_back(sinc(x));
-    }
-
-    // Now setup a SVR trainer object.  It has three parameters, the kernel and
-    // two parameters specific to SVR.  
-    svr_trainer<kernel_type> trainer;
-    trainer.set_kernel(kernel_type(0.1));
-
-    // This parameter is the usual regularization parameter.  It determines the trade-off 
-    // between trying to reduce the training error or allowing more errors but hopefully 
-    // improving the generalization of the resulting function.  Larger values encourage exact 
-    // fitting while smaller values of C may encourage better generalization.
-    trainer.set_c(10);
-
-    // Epsilon-insensitive regression means we do regression but stop trying to fit a data 
-    // point once it is "close enough" to its target value.  This parameter is the value that 
-    // controls what we mean by "close enough".  In this case, I'm saying I'm happy if the
-    // resulting regression function gets within 0.001 of the target value.
-    trainer.set_epsilon_insensitivity(0.001);
-
-    // Now do the training and save the results
-    decision_function<kernel_type> df = trainer.train(samples, targets);
-
-    // now we output the value of the sinc function for a few test points as well as the 
-    // value predicted by SVR.
-    m(0) = 2.5; cout << sinc(m(0)) << "   " << df(m) << endl;
-    m(0) = 0.1; cout << sinc(m(0)) << "   " << df(m) << endl;
-    m(0) = -4;  cout << sinc(m(0)) << "   " << df(m) << endl;
-    m(0) = 5.0; cout << sinc(m(0)) << "   " << df(m) << endl;
-
-    // The output is as follows:
-    //  0.239389   0.23905
-    //  0.998334   0.997331
-    // -0.189201   -0.187636
-    // -0.191785   -0.218924
-
-    // The first column is the true value of the sinc function and the second
-    // column is the output from the SVR estimate.  
-
-    // We can also do 5-fold cross-validation and find the mean squared error and R-squared
-    // values.  Note that we need to randomly shuffle the samples first.  See the svm_ex.cpp 
-    // for a discussion of why this is important. 
-    randomize_samples(samples, targets);
-    cout << "MSE and R-Squared: "<< cross_validate_regression_trainer(trainer, samples, targets, 5) << endl;
-    // The output is: 
-    // MSE and R-Squared: 1.65984e-05    0.999901
-}
-
-