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diff --git a/ml/dlib/examples/svm_sparse_ex.cpp b/ml/dlib/examples/svm_sparse_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 showing how to use sparse feature vectors with
-    the dlib C++ library's machine learning tools.
-
-    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 100 dimensional data and will
-    come from a simple linearly separable distribution.  
-*/
-
-
-#include <iostream>
-#include <ctime>
-#include <vector>
-#include <dlib/svm.h>
-
-using namespace std;
-using namespace dlib;
-
-
-int main()
-{
-    // In this example program we will be dealing with feature vectors that are sparse (i.e. most
-    // of the values in each vector are zero).  So rather than using a dlib::matrix we can use
-    // one of the containers from the STL to represent our sample vectors.  In particular, we 
-    // can use the std::map to represent sparse vectors.  (Note that you don't have to use std::map.
-    // Any STL container of std::pair objects that is sorted can be used.  So for example, you could 
-    // use a std::vector<std::pair<unsigned long,double> > here so long as you took care to sort every vector)
-    typedef std::map<unsigned long,double> sample_type;
-
-
-    // This is a typedef for the type of kernel we are going to use in this example.
-    // Since our data is linearly separable I picked the linear kernel.  Note that if you
-    // are using a sparse vector representation like std::map then you have to use a kernel
-    // meant to be used with that kind of data type.  
-    typedef sparse_linear_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 a parameter to this object.  See the dlib documentation for a 
-    // description of what this parameter does. 
-    trainer.set_lambda(0.00001);
-
-    // Let's also use the svm trainer specially optimized for the linear_kernel and
-    // sparse_linear_kernel.
-    svm_c_linear_trainer<kernel_type> linear_trainer;
-    // This trainer solves the "C" formulation of the SVM.  See the documentation for
-    // details.
-    linear_trainer.set_c(10);
-
-    std::vector<sample_type> samples;
-    std::vector<double> labels;
-
-    // make an instance of a sample vector so we can use it below
-    sample_type sample;
-
-
-    // Now let's go into a loop and randomly generate 10000 samples.
-    srand(time(0));
-    double label = +1;
-    for (int i = 0; i < 10000; ++i)
-    {
-        // flip this flag
-        label *= -1;
-
-        sample.clear();
-
-        // now make a random sparse sample with at most 10 non-zero elements
-        for (int j = 0; j < 10; ++j)
-        {
-            int idx = std::rand()%100;
-            double value = static_cast<double>(std::rand())/RAND_MAX;
-
-            sample[idx] = label*value;
-        }
-
-        // let the svm_pegasos learn about this sample.  
-        trainer.train(sample,label);
-
-        // Also save the samples we are generating so we can let the svm_c_linear_trainer
-        // learn from them below.  
-        samples.push_back(sample);
-        labels.push_back(label);
-    }
-
-    // In addition to the rule we learned with the pegasos trainer, let's also use our
-    // linear_trainer to learn a decision rule.
-    decision_function<kernel_type> df = linear_trainer.train(samples, labels);
-
-    // Now we have trained our SVMs.  Let's test them out a bit.  
-    // Each of these statements prints the output of the SVMs given a particular sample.  
-    // Each 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.clear();
-    sample[4] = 0.3;
-    sample[10] = 0.9;
-    cout << "This is a +1 example, its SVM output is: " << trainer(sample) << endl;
-    cout << "df: " << df(sample) << endl;
-
-    sample.clear();
-    sample[83] = -0.3;
-    sample[26] = -0.9;
-    sample[58] = -0.7;
-    cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
-    cout << "df: " << df(sample) << endl;
-
-    sample.clear();
-    sample[0] = -0.2;
-    sample[9] = -0.8;
-    cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
-    cout << "df: " << df(sample) << endl;
-
-}
-