Merging upstream version 1.46.3.

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

1 files changed, 0 insertions, 160 deletions

diff --git a/ml/dlib/examples/svm_pegasos_ex.cpp b/ml/dlib/examples/svm_pegasos_ex.cpp
deleted file mode 100644
index e69b485fc..000000000
--- a/ml/dlib/examples/svm_pegasos_ex.cpp
+++ /dev/null
@@ -1,160 +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 dlib C++ library's
-    implementation of the pegasos algorithm for online training of support 
-    vector machines.   
-
-    This example creates a simple binary classification problem and shows
-    you how to train a support vector machine on that data.
-
-    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 <ctime>
-#include <vector>
-#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;
-
-
-    // Here we create an instance of the pegasos svm trainer object we will be using.
-    svm_pegasos<kernel_type> trainer;
-    // Here we setup the parameters to this object.  See the dlib documentation for a 
-    // description of what these parameters are. 
-    trainer.set_lambda(0.00001);
-    trainer.set_kernel(kernel_type(0.005));
-
-    // 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.set_max_num_sv(10);
-
-    std::vector<sample_type> samples;
-    std::vector<double> labels;
-
-    // make an instance of a sample matrix so we can use it below
-    sample_type sample, center;
-
-    center = 20, 20;
-
-    // Now let's go into a loop and randomly generate 1000 samples.
-    srand(time(0));
-    for (int i = 0; i < 10000; ++i)
-    {
-        // Make a random sample vector. 
-        sample = randm(2,1)*40 - center;
-
-        // Now if that random vector is less than 10 units from the origin then it is in 
-        // the +1 class.
-        if (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.push_back(sample);
-            labels.push_back(+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.push_back(sample);
-            labels.push_back(-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.
-
-    sample(0) = 3.123;
-    sample(1) = 4;
-    cout << "This is a +1 example, its SVM output is: " << trainer(sample) << endl;
-
-    sample(0) = 13.123;
-    sample(1) = 9.3545;
-    cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
-
-    sample(0) = 13.123;
-    sample(1) = 0;
-    cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
-
-
-
-
-
-    // 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
-    cout << "cross validation: " << cross_validate_trainer(batch_cached(trainer,0.1), samples, labels, 4);
-
-    // 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.
-    decision_function<kernel_type> df = verbose_batch_cached(trainer,0.1).train(samples, labels);
-
-    // At this point we have obtained a decision function from the above batch mode training.
-    // Now we can use it on some test samples exactly as we did above.
-
-    sample(0) = 3.123;
-    sample(1) = 4;
-    cout << "This is a +1 example, its SVM output is: " << df(sample) << endl;
-
-    sample(0) = 13.123;
-    sample(1) = 9.3545;
-    cout << "This is a -1 example, its SVM output is: " << df(sample) << endl;
-
-    sample(0) = 13.123;
-    sample(1) = 0;
-    cout << "This is a -1 example, its SVM output is: " << df(sample) << endl;
-
-
-}
-