private static 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.
var samples = new List <SampleType>();
var labels = new List <double>();
// 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 (var r = -20; r <= 20; ++r)
{
for (var c = -20; c <= 20; ++c)
{
var samp = new SampleType();
samp.SetSize(2, 1);
samp[0] = r;
samp[1] = c;
samples.Add(samp);
// if this point is less than 10 from the origin
if (Math.Sqrt((double)r * r + c * c) <= 10)
{
labels.Add(+1);
}
else
{
labels.Add(-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.
using (var normalizer = new VectorNormalizer <SampleType>())
{
// let the normalizer learn the mean and standard deviation of the samples
normalizer.Train(samples);
// now normalize each sample
for (var i = 0; i < samples.Count; ++i)
{
var ret = normalizer.Operator(samples[i]);
samples[i].Dispose();
samples[i] = ret;
}
// 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.
Dlib.RandomizeSamples(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.
double maxNu = Dlib.MaximumNu(labels);
// here we make an instance of the svm_nu_trainer object that uses our kernel type.
using (var trainer = new SvmNuTrainer <double, RadialBasisKernel <double, Matrix <double> > >())
{
// 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.
Console.WriteLine("doing cross validation");
for (var gamma = 0.00001; gamma <= 1; gamma *= 5)
{
for (var nu = 0.00001; nu < maxNu; nu *= 5)
{
// tell the trainer the parameters we want to use
using (var kernel = new RadialBasisKernel <double, Matrix <double> >(gamma, 0, 0))
{
trainer.Kernel = kernel;
trainer.Nu = nu;
Console.Write($"gamma: {gamma} nu: {nu}");
// Print out the cross validation accuracy for 3-fold cross validation using
// the current gamma and nu. cross_validate_trainer() returns a row vector.
// The first element of the vector is the fraction of +1 training examples
// correctly classified and the second number is the fraction of -1 training
// examples correctly classified.
using (var ret = Dlib.CrossValidateTrainer(trainer, samples, labels, 3))
Console.Write($" cross validation accuracy: {ret}");
}
}
}
// 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.
using (var kernel = new RadialBasisKernel <double, Matrix <double> >(0.15625, 0, 0))
{
trainer.Kernel = kernel;
trainer.Nu = 0.15625;
// 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.
var learnedFunction = new NormalizedFunction <double, DecisionFunction <double, RadialBasisKernel <double, Matrix <double> > > >();
learnedFunction.Normalizer = normalizer; // save normalization information
using (var function = trainer.Train(samples, labels))
{
learnedFunction.Function = function; // perform the actual SVM training and save the results
// print out the number of support vectors in the resulting decision function
Console.WriteLine();
// ToDo: must support nested matrix
//Console.WriteLine($"number of support vectors in our learned_function is {learnedFunction.Function.basis_vectors.size()}");
}
// Now let's try this decision_function on some samples we haven't seen before.
using (var sample = new SampleType())
{
sample.SetSize(2, 1);
sample[0] = 3.123;
sample[1] = 2;
Console.WriteLine($"This is a +1 class example, the classifier output is {learnedFunction.Operator(sample)}");
sample[0] = 3.123;
sample[1] = 9.3545;
Console.WriteLine($"This is a +1 class example, the classifier output is {learnedFunction.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 9.3545;
Console.WriteLine($"This is a -1 class example, the classifier output is {learnedFunction.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 0;
Console.WriteLine($"This is a -1 class example, the classifier output is {learnedFunction.Operator(sample)}");
}
// We can also train a decision function that reports a well conditioned probability
// instead of just a number > 0 for the +1 class and < 0 for the -1 class. An example
// of doing that follows:
var learnedProbabilisticFunction = new NormalizedFunction <double, ProbabilisticDecisionFunction <double, RadialBasisKernel <double, Matrix <double> > > >();
learnedProbabilisticFunction.Normalizer = normalizer;
using (var function = Dlib.TrainProbabilisticDecisionFunction <double,
RadialBasisKernel <double, Matrix <double> >,
SvmNuTrainer <double, RadialBasisKernel <double, Matrix <double> > > >(trainer, samples, labels, 3))
{
learnedProbabilisticFunction.Function = function;
// Now we have a function that returns the probability that a given sample is of the +1 class.
// print out the number of support vectors in the resulting decision function.
// (it should be the same as in the one above)
Console.WriteLine();
// ToDo: must support nested matrix
//Console.WriteLine($"number of support vectors in our learned_pfunct is {learnedProbabilisticFunction.Function.DecisionFunct.BasisVectors.size()}");
}
using (var sample = new SampleType())
{
sample.SetSize(2, 1);
sample[0] = 3.123;
sample[1] = 2;
Console.WriteLine($"This +1 class example should have high probability. Its probability is: {learnedProbabilisticFunction.Operator(sample)}");
sample[0] = 3.123;
sample[1] = 9.3545;
Console.WriteLine($"This +1 class example should have high probability. Its probability is: {learnedProbabilisticFunction.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 9.3545;
Console.WriteLine($"This -1 class example should have low probability. Its probability is: {learnedProbabilisticFunction.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 0;
Console.WriteLine($"This -1 class example should have low probability. Its probability is: {learnedProbabilisticFunction.Operator(sample)}");
}
// 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:
NormalizedFunction <double, ProbabilisticDecisionFunction <double, RadialBasisKernel <double, Matrix <double> > > > .Serialize("saved_function.dat", learnedProbabilisticFunction);
// Now let's open that file back up and load the function object it contains.
learnedProbabilisticFunction.Dispose();
learnedProbabilisticFunction = NormalizedFunction <double, ProbabilisticDecisionFunction <double, RadialBasisKernel <double, Matrix <double> > > > .Deserialize("saved_function.dat");
// 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.
// 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.
using (var reduced = Dlib.Reduced2 <double,
RadialBasisKernel <double, Matrix <double> >,
SvmNuTrainer <double, RadialBasisKernel <double, Matrix <double> > > >(trainer, 10))
{
Console.WriteLine();
using (var ret = Dlib.CrossValidateTrainer(reduced, samples, labels, 3))
Console.Write($"cross validation accuracy with only 10 support vectors: {ret}");
}
// Let's print out the original cross validation score too for comparison.
using (var ret2 = Dlib.CrossValidateTrainer(trainer, samples, labels, 3))
Console.Write($"cross validation accuracy with all the original support vectors: {ret2}");
// 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:
using (var reduced = Dlib.Reduced2 <double,
RadialBasisKernel <double, Matrix <double> >,
SvmNuTrainer <double, RadialBasisKernel <double, Matrix <double> > > >(trainer, 10))
using (var function = reduced.Train(samples, labels))
learnedFunction.Function = function;
// And similarly for the probabilistic_decision_function:
using (var reduced = Dlib.Reduced2 <double,
RadialBasisKernel <double, Matrix <double> >,
SvmNuTrainer <double, RadialBasisKernel <double, Matrix <double> > > >(trainer, 10))
using (var function = Dlib.TrainProbabilisticDecisionFunction(reduced, samples, labels, 3))
learnedProbabilisticFunction.Function = function;
learnedFunction.Dispose();
learnedProbabilisticFunction.Dispose();
}
}
}
return(0);
}
private static 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;
// Here we create an instance of the pegasos svm trainer object we will be using.
using (var trainer = new SvmPegasos <double, RadialBasisKernel <double, Matrix <double> > >())
using (var kernel = new RadialBasisKernel <double, Matrix <double> >(0.005, 0, 0))
{
// Here we setup the parameters to this object. See the dlib documentation for a
// description of what these parameters are.
trainer.SetLambda(0.00001);
trainer.Kernel = kernel;
// Set the maximum number of support vectors we want the trainer object to use
// in representing the decision function it is going to learn. In general,
// supplying a bigger number here will only ever give you a more accurate
// answer. However, giving a smaller number will make the algorithm run
// faster and decision rules that involve fewer support vectors also take
// less time to evaluate.
trainer.MaxNumSupportVector = 10;
var samples = new List <SampleType>();
var labels = new List <double>();
// make an instance of a sample matrix so we can use it below
var center = new SampleType();
center.SetSize(2, 1);
center.Assign(new[] { 20d, 20d });
// Now let's go into a loop and randomly generate 1000 samples.
Dlib.SRand((uint)(DateTime.UtcNow - new DateTime(1970, 1, 1, 0, 0, 0, 0)).TotalSeconds);
for (var i = 0; i < 10000; ++i)
{
// Make a random sample vector.
using (var r = Dlib.RandM(2, 1))
{
var sample = r * 40 - center;
// Now if that random vector is less than 10 units from the origin then it is in
// the +1 class.
if (Dlib.Length(sample) <= 10)
{
// let the svm_pegasos learn about this sample
trainer.Train(sample, +1);
// save this sample so we can use it with the batch training examples below
samples.Add(sample);
labels.Add(+1);
}
else
{
// let the svm_pegasos learn about this sample
trainer.Train(sample, -1);
// save this sample so we can use it with the batch training examples below
samples.Add(sample);
labels.Add(-1);
}
}
}
// Now we have trained our SVM. Let's see how well it did.
// Each of these statements prints out the output of the SVM given a particular sample.
// The SVM outputs a number > 0 if a sample is predicted to be in the +1 class and < 0
// if a sample is predicted to be in the -1 class.
// Now let's try this decision_function on some samples we haven't seen before.
using (var sample = new SampleType())
{
sample.SetSize(2, 1);
sample[0] = 3.123;
sample[1] = 4;
Console.WriteLine($"This is a +1 example, its SVM output is: {trainer.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 9.3545;
Console.WriteLine($"This is a -1 example, its SVM output is: {trainer.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 0;
Console.WriteLine($"This is a -1 example, its SVM output is: {trainer.Operator(sample)}");
}
// The previous part of this example program showed you how to perform online training
// with the pegasos algorithm. But it is often the case that you have a dataset and you
// just want to perform batch learning on that dataset and get the resulting decision
// function. To support this the dlib library provides functions for converting an online
// training object like svm_pegasos into a batch training object.
// First let's clear out anything in the trainer object.
trainer.Clear();
// Now to begin with, you might want to compute the cross validation score of a trainer object
// on your data. To do this you should use the batch_cached() function to convert the svm_pegasos object
// into a batch training object. Note that the second argument to batch_cached() is the minimum
// learning rate the trainer object must report for the batch_cached() function to consider training
// complete. So smaller values of this parameter cause training to take longer but may result
// in a more accurate solution.
// Here we perform 4-fold cross validation and print the results
using (var batchTrainer = Dlib.BatchCached <double,
RadialBasisKernel <double, Matrix <double> >,
SvmPegasos <double, RadialBasisKernel <double, Matrix <double> > > >(trainer, 0.1))
using (var ret = Dlib.CrossValidateTrainer(batchTrainer, samples, labels, 4))
Console.Write($"cross validation: {ret}");
// Here is an example of creating a decision function. Note that we have used the verbose_batch_cached()
// function instead of batch_cached() as above. They do the same things except verbose_batch_cached() will
// print status messages to standard output while training is under way.
using (var verboseBatchCached = Dlib.VerboseBatchCached <double,
RadialBasisKernel <double, Matrix <double> >,
SvmPegasos <double, RadialBasisKernel <double, Matrix <double> > > >(trainer, 0.1))
using (var df = verboseBatchCached.Train(samples, labels))
using (var sample = new SampleType())
{
sample.SetSize(2, 1);
sample[0] = 3.123;
sample[1] = 4;
Console.WriteLine($"This is a +1 example, its SVM output is: {df.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 9.3545;
Console.WriteLine($"This is a -1 example, its SVM output is: {df.Operator(sample)}");
sample[0] = 13.123;
sample[1] = 0;
Console.WriteLine($"This is a -1 example, its SVM output is: {df.Operator(sample)}");
}
}
return(0);
}