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);
}