Merging upstream version 1.45.3+dfsg.

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

1 files changed, 255 insertions, 0 deletions

diff --git a/src/ml/dlib/examples/svm_ex.cpp b/src/ml/dlib/examples/svm_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 support vector machine
+    utilities from the dlib C++ Library.  
+
+    This example creates a simple set of data to train on and then shows
+    you how to use the cross validation and svm training functions
+    to find a good decision function that can classify examples in our
+    data set.
+
+
+    The data used in this example will be 2 dimensional data and will
+    come from a distribution where points with a distance less than 10
+    from the origin are labeled +1 and all other points are labeled
+    as -1.
+        
+*/
+
+
+#include <iostream>
+#include <dlib/svm.h>
+
+using namespace std;
+using namespace dlib;
+
+
+int main()
+{
+    // The svm functions use column vectors to contain a lot of the data on which they
+    // operate. So the first thing we do here is declare a convenient typedef.  
+
+    // This typedef declares a matrix with 2 rows and 1 column.  It will be the object that
+    // contains each of our 2 dimensional samples.   (Note that if you wanted more than 2
+    // features in this vector you can simply change the 2 to something else.  Or if you
+    // don't know how many features you want until runtime then you can put a 0 here and
+    // use the matrix.set_size() member function)
+    typedef matrix<double, 2, 1> sample_type;
+
+    // This is a typedef for the type of kernel we are going to use in this example.  In
+    // this case I have selected the radial basis kernel that can operate on our 2D
+    // sample_type objects
+    typedef radial_basis_kernel<sample_type> kernel_type;
+
+
+    // Now we make objects to contain our samples and their respective labels.
+    std::vector<sample_type> samples;
+    std::vector<double> labels;
+
+    // Now let's put some data into our samples and labels objects.  We do this by looping
+    // over a bunch of points and labeling them according to their distance from the
+    // origin.
+    for (int r = -20; r <= 20; ++r)
+    {
+        for (int c = -20; c <= 20; ++c)
+        {
+            sample_type samp;
+            samp(0) = r;
+            samp(1) = c;
+            samples.push_back(samp);
+
+            // if this point is less than 10 from the origin
+            if (sqrt((double)r*r + c*c) <= 10)
+                labels.push_back(+1);
+            else
+                labels.push_back(-1);
+
+        }
+    }
+
+
+    // Here we normalize all the samples by subtracting their mean and dividing by their
+    // standard deviation.  This is generally a good idea since it often heads off
+    // numerical stability problems and also prevents one large feature from smothering
+    // others.  Doing this doesn't matter much in this example so I'm just doing this here
+    // so you can see an easy way to accomplish this with the library.  
+    vector_normalizer<sample_type> normalizer;
+    // let the normalizer learn the mean and standard deviation of the samples
+    normalizer.train(samples);
+    // now normalize each sample
+    for (unsigned long i = 0; i < samples.size(); ++i)
+        samples[i] = normalizer(samples[i]); 
+
+
+    // Now that we have some data we want to train on it.  However, there are two
+    // parameters to the training.  These are the nu and gamma parameters.  Our choice for
+    // these parameters will influence how good the resulting decision function is.  To
+    // test how good a particular choice of these parameters is we can use the
+    // cross_validate_trainer() function to perform n-fold cross validation on our training
+    // data.  However, there is a problem with the way we have sampled our distribution
+    // above.  The problem is that there is a definite ordering to the samples.  That is,
+    // the first half of the samples look like they are from a different distribution than
+    // the second half.  This would screw up the cross validation process but we can fix it
+    // by randomizing the order of the samples with the following function call.
+    randomize_samples(samples, labels);
+
+
+    // The nu parameter has a maximum value that is dependent on the ratio of the +1 to -1
+    // labels in the training data.  This function finds that value.
+    const double max_nu = maximum_nu(labels);
+
+    // here we make an instance of the svm_nu_trainer object that uses our kernel type.
+    svm_nu_trainer<kernel_type> trainer;
+
+    // Now we loop over some different nu and gamma values to see how good they are.  Note
+    // that this is a very simple way to try out a few possible parameter choices.  You
+    // should look at the model_selection_ex.cpp program for examples of more sophisticated
+    // strategies for determining good parameter choices.
+    cout << "doing cross validation" << endl;
+    for (double gamma = 0.00001; gamma <= 1; gamma *= 5)
+    {
+        for (double nu = 0.00001; nu < max_nu; nu *= 5)
+        {
+            // tell the trainer the parameters we want to use
+            trainer.set_kernel(kernel_type(gamma));
+            trainer.set_nu(nu);
+
+            cout << "gamma: " << gamma << "    nu: " << nu;
+            // Print out the cross validation accuracy for 3-fold cross validation using
+            // the current gamma and nu.  cross_validate_trainer() returns a row vector.
+            // The first element of the vector is the fraction of +1 training examples
+            // correctly classified and the second number is the fraction of -1 training
+            // examples correctly classified.
+            cout << "     cross validation accuracy: " << cross_validate_trainer(trainer, samples, labels, 3);
+        }
+    }
+
+
+    // From looking at the output of the above loop it turns out that a good value for nu
+    // and gamma for this problem is 0.15625 for both.  So that is what we will use.
+
+    // Now we train on the full set of data and obtain the resulting decision function.  We
+    // use the value of 0.15625 for nu and gamma.  The decision function will return values
+    // >= 0 for samples it predicts are in the +1 class and numbers < 0 for samples it
+    // predicts to be in the -1 class.
+    trainer.set_kernel(kernel_type(0.15625));
+    trainer.set_nu(0.15625);
+    typedef decision_function<kernel_type> dec_funct_type;
+    typedef normalized_function<dec_funct_type> funct_type;
+
+    // Here we are making an instance of the normalized_function object.  This object
+    // provides a convenient way to store the vector normalization information along with
+    // the decision function we are going to learn.  
+    funct_type learned_function;
+    learned_function.normalizer = normalizer;  // save normalization information
+    learned_function.function = trainer.train(samples, labels); // perform the actual SVM training and save the results
+
+    // print out the number of support vectors in the resulting decision function
+    cout << "\nnumber of support vectors in our learned_function is " 
+         << learned_function.function.basis_vectors.size() << endl;
+
+    // Now let's try this decision_function on some samples we haven't seen before.
+    sample_type sample;
+
+    sample(0) = 3.123;
+    sample(1) = 2;
+    cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl;
+
+    sample(0) = 3.123;
+    sample(1) = 9.3545;
+    cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 9.3545;
+    cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 0;
+    cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl;
+
+
+    // We can also train a decision function that reports a well conditioned probability
+    // instead of just a number > 0 for the +1 class and < 0 for the -1 class.  An example
+    // of doing that follows:
+    typedef probabilistic_decision_function<kernel_type> probabilistic_funct_type;  
+    typedef normalized_function<probabilistic_funct_type> pfunct_type;
+
+    pfunct_type learned_pfunct; 
+    learned_pfunct.normalizer = normalizer;
+    learned_pfunct.function = train_probabilistic_decision_function(trainer, samples, labels, 3);
+    // Now we have a function that returns the probability that a given sample is of the +1 class.  
+
+    // print out the number of support vectors in the resulting decision function.  
+    // (it should be the same as in the one above)
+    cout << "\nnumber of support vectors in our learned_pfunct is " 
+         << learned_pfunct.function.decision_funct.basis_vectors.size() << endl;
+
+    sample(0) = 3.123;
+    sample(1) = 2;
+    cout << "This +1 class example should have high probability.  Its probability is: " 
+         << learned_pfunct(sample) << endl;
+
+    sample(0) = 3.123;
+    sample(1) = 9.3545;
+    cout << "This +1 class example should have high probability.  Its probability is: " 
+         << learned_pfunct(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 9.3545;
+    cout << "This -1 class example should have low probability.  Its probability is: " 
+         << learned_pfunct(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 0;
+    cout << "This -1 class example should have low probability.  Its probability is: " 
+         << learned_pfunct(sample) << endl;
+
+
+
+    // Another thing that is worth knowing is that just about everything in dlib is
+    // serializable.  So for example, you can save the learned_pfunct object to disk and
+    // recall it later like so:
+    serialize("saved_function.dat") << learned_pfunct;
+
+    // Now let's open that file back up and load the function object it contains.
+    deserialize("saved_function.dat") >> learned_pfunct;
+
+    // Note that there is also an example program that comes with dlib called the
+    // file_to_code_ex.cpp example.  It is a simple program that takes a file and outputs a
+    // piece of C++ code that is able to fully reproduce the file's contents in the form of
+    // a std::string object.  So you can use that along with the std::istringstream to save
+    // learned decision functions inside your actual C++ code files if you want.  
+
+
+
+
+    // Lastly, note that the decision functions we trained above involved well over 200
+    // basis vectors.  Support vector machines in general tend to find decision functions
+    // that involve a lot of basis vectors.  This is significant because the more basis
+    // vectors in a decision function, the longer it takes to classify new examples.  So
+    // dlib provides the ability to find an approximation to the normal output of a trainer
+    // using fewer basis vectors.  
+
+    // Here we determine the cross validation accuracy when we approximate the output using
+    // only 10 basis vectors.  To do this we use the reduced2() function.  It takes a
+    // trainer object and the number of basis vectors to use and returns a new trainer
+    // object that applies the necessary post processing during the creation of decision
+    // function objects.
+    cout << "\ncross validation accuracy with only 10 support vectors: " 
+         << cross_validate_trainer(reduced2(trainer,10), samples, labels, 3);
+
+    // Let's print out the original cross validation score too for comparison.
+    cout << "cross validation accuracy with all the original support vectors: " 
+         << cross_validate_trainer(trainer, samples, labels, 3);
+
+    // When you run this program you should see that, for this problem, you can reduce the
+    // number of basis vectors down to 10 without hurting the cross validation accuracy. 
+
+
+    // To get the reduced decision function out we would just do this:
+    learned_function.function = reduced2(trainer,10).train(samples, labels);
+    // And similarly for the probabilistic_decision_function: 
+    learned_pfunct.function = train_probabilistic_decision_function(reduced2(trainer,10), samples, labels, 3);
+}
+