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Diffstat (limited to 'ml/dlib/examples/svr_ex.cpp')
-rw-r--r-- | ml/dlib/examples/svr_ex.cpp | 96 |
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 -} - - |