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<html><!-- Created using the cpp_pretty_printer from the dlib C++ library.  See http://dlib.net for updates. --><head><title>dlib C++ Library - custom_trainer_ex.cpp</title></head><body bgcolor='white'><pre>
<font color='#009900'>// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
</font><font color='#009900'>/*
    This example program shows you how to create your own custom binary classification
    trainer object and use it with the multiclass classification tools in the dlib C++
    library.  This example assumes you have already become familiar with the concepts
    introduced in the <a href="multiclass_classification_ex.cpp.html">multiclass_classification_ex.cpp</a> example program.


    In this example we will create a very simple trainer object that takes a binary
    classification problem and produces a decision rule which says a test point has the
    same class as whichever centroid it is closest to.  

    The multiclass training dataset will consist of four classes.  Each class will be a blob 
    of points in one of the quadrants of the cartesian plane.   For fun, we will use 
    std::string labels and therefore the labels of these classes will be the following:
        "upper_left",
        "upper_right",
        "lower_left",
        "lower_right"
*/</font>

<font color='#0000FF'>#include</font> <font color='#5555FF'>&lt;</font>dlib<font color='#5555FF'>/</font>svm_threaded.h<font color='#5555FF'>&gt;</font>

<font color='#0000FF'>#include</font> <font color='#5555FF'>&lt;</font>iostream<font color='#5555FF'>&gt;</font>
<font color='#0000FF'>#include</font> <font color='#5555FF'>&lt;</font>vector<font color='#5555FF'>&gt;</font>

<font color='#0000FF'>#include</font> <font color='#5555FF'>&lt;</font>dlib<font color='#5555FF'>/</font>rand.h<font color='#5555FF'>&gt;</font>

<font color='#0000FF'>using</font> <font color='#0000FF'>namespace</font> std;
<font color='#0000FF'>using</font> <font color='#0000FF'>namespace</font> dlib;

<font color='#009900'>// Our data will be 2-dimensional data. So declare an appropriate type to contain these points.
</font><font color='#0000FF'>typedef</font> matrix<font color='#5555FF'>&lt;</font><font color='#0000FF'><u>double</u></font>,<font color='#979000'>2</font>,<font color='#979000'>1</font><font color='#5555FF'>&gt;</font> sample_type;

<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>
<font color='#0000FF'>struct</font> <b><a name='custom_decision_function'></a>custom_decision_function</b>
<b>{</b>
    <font color='#009900'>/*!
        WHAT THIS OBJECT REPRESENTS
            This object is the representation of our binary decision rule.  
    !*/</font>

    <font color='#009900'>// centers of the two classes
</font>    sample_type positive_center, negative_center;

    <font color='#0000FF'><u>double</u></font> <b><a name='operator'></a>operator</b><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font> <font face='Lucida Console'>(</font>
        <font color='#0000FF'>const</font> sample_type<font color='#5555FF'>&amp;</font> x
    <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>
    <b>{</b>
        <font color='#009900'>// if x is closer to the positive class then return +1 
</font>        <font color='#0000FF'>if</font> <font face='Lucida Console'>(</font><font color='#BB00BB'>length</font><font face='Lucida Console'>(</font>positive_center <font color='#5555FF'>-</font> x<font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font> <font color='#BB00BB'>length</font><font face='Lucida Console'>(</font>negative_center <font color='#5555FF'>-</font> x<font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>
            <font color='#0000FF'>return</font> <font color='#5555FF'>+</font><font color='#979000'>1</font>;
        <font color='#0000FF'>else</font>
            <font color='#0000FF'>return</font> <font color='#5555FF'>-</font><font color='#979000'>1</font>;
    <b>}</b>
<b>}</b>;

<font color='#009900'>// Later on in this example we will save our decision functions to disk.  This
</font><font color='#009900'>// pair of routines is needed for this functionality.
</font><font color='#0000FF'><u>void</u></font> <b><a name='serialize'></a>serialize</b> <font face='Lucida Console'>(</font><font color='#0000FF'>const</font> custom_decision_function<font color='#5555FF'>&amp;</font> item, std::ostream<font color='#5555FF'>&amp;</font> out<font face='Lucida Console'>)</font>
<b>{</b>
    <font color='#009900'>// write the state of item to the output stream
</font>    <font color='#BB00BB'>serialize</font><font face='Lucida Console'>(</font>item.positive_center, out<font face='Lucida Console'>)</font>;
    <font color='#BB00BB'>serialize</font><font face='Lucida Console'>(</font>item.negative_center, out<font face='Lucida Console'>)</font>;
<b>}</b>

<font color='#0000FF'><u>void</u></font> <b><a name='deserialize'></a>deserialize</b> <font face='Lucida Console'>(</font>custom_decision_function<font color='#5555FF'>&amp;</font> item, std::istream<font color='#5555FF'>&amp;</font> in<font face='Lucida Console'>)</font>
<b>{</b>
    <font color='#009900'>// read the data from the input stream and store it in item
</font>    <font color='#BB00BB'>deserialize</font><font face='Lucida Console'>(</font>item.positive_center, in<font face='Lucida Console'>)</font>;
    <font color='#BB00BB'>deserialize</font><font face='Lucida Console'>(</font>item.negative_center, in<font face='Lucida Console'>)</font>;
<b>}</b>

<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>
<font color='#0000FF'>class</font> <b><a name='simple_custom_trainer'></a>simple_custom_trainer</b>
<b>{</b>
    <font color='#009900'>/*!
        WHAT THIS OBJECT REPRESENTS
            This is our example custom binary classifier trainer object.  It simply 
            computes the means of the +1 and -1 classes, puts them into our 
            custom_decision_function, and returns the results.

            Below we define the train() function.  I have also included the
            requires/ensures definition for a generic binary classifier's train()
    !*/</font>
<font color='#0000FF'>public</font>:


    custom_decision_function <b><a name='train'></a>train</b> <font face='Lucida Console'>(</font>
        <font color='#0000FF'>const</font> std::vector<font color='#5555FF'>&lt;</font>sample_type<font color='#5555FF'>&gt;</font><font color='#5555FF'>&amp;</font> samples,
        <font color='#0000FF'>const</font> std::vector<font color='#5555FF'>&lt;</font><font color='#0000FF'><u>double</u></font><font color='#5555FF'>&gt;</font><font color='#5555FF'>&amp;</font> labels
    <font face='Lucida Console'>)</font> <font color='#0000FF'>const</font>
    <font color='#009900'>/*!
        requires
            - is_binary_classification_problem(samples, labels) == true
              (e.g. labels consists of only +1 and -1 values, samples.size() == labels.size())
        ensures
            - returns a decision function F with the following properties:
                - if (new_x is a sample predicted have +1 label) then
                    - F(new_x) &gt;= 0
                - else
                    - F(new_x) &lt; 0
    !*/</font>
    <b>{</b>
        sample_type positive_center, negative_center;

        <font color='#009900'>// compute sums of each class 
</font>        positive_center <font color='#5555FF'>=</font> <font color='#979000'>0</font>;
        negative_center <font color='#5555FF'>=</font> <font color='#979000'>0</font>;
        <font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font> i <font color='#5555FF'>=</font> <font color='#979000'>0</font>; i <font color='#5555FF'>&lt;</font> samples.<font color='#BB00BB'>size</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>i<font face='Lucida Console'>)</font>
        <b>{</b>
            <font color='#0000FF'>if</font> <font face='Lucida Console'>(</font>labels[i] <font color='#5555FF'>=</font><font color='#5555FF'>=</font> <font color='#5555FF'>+</font><font color='#979000'>1</font><font face='Lucida Console'>)</font>
                positive_center <font color='#5555FF'>+</font><font color='#5555FF'>=</font> samples[i];
            <font color='#0000FF'>else</font> <font color='#009900'>// this is a -1 sample
</font>                negative_center <font color='#5555FF'>+</font><font color='#5555FF'>=</font> samples[i];
        <b>}</b>

        <font color='#009900'>// divide by number of +1 samples
</font>        positive_center <font color='#5555FF'>/</font><font color='#5555FF'>=</font> <font color='#BB00BB'>sum</font><font face='Lucida Console'>(</font><font color='#BB00BB'>mat</font><font face='Lucida Console'>(</font>labels<font face='Lucida Console'>)</font> <font color='#5555FF'>=</font><font color='#5555FF'>=</font> <font color='#5555FF'>+</font><font color='#979000'>1</font><font face='Lucida Console'>)</font>;
        <font color='#009900'>// divide by number of -1 samples
</font>        negative_center <font color='#5555FF'>/</font><font color='#5555FF'>=</font> <font color='#BB00BB'>sum</font><font face='Lucida Console'>(</font><font color='#BB00BB'>mat</font><font face='Lucida Console'>(</font>labels<font face='Lucida Console'>)</font> <font color='#5555FF'>=</font><font color='#5555FF'>=</font> <font color='#5555FF'>-</font><font color='#979000'>1</font><font face='Lucida Console'>)</font>;

        custom_decision_function df;
        df.positive_center <font color='#5555FF'>=</font> positive_center;
        df.negative_center <font color='#5555FF'>=</font> negative_center;

        <font color='#0000FF'>return</font> df;
    <b>}</b>
<b>}</b>;

<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>
<font color='#0000FF'><u>void</u></font> <b><a name='generate_data'></a>generate_data</b> <font face='Lucida Console'>(</font>
    std::vector<font color='#5555FF'>&lt;</font>sample_type<font color='#5555FF'>&gt;</font><font color='#5555FF'>&amp;</font> samples,
    std::vector<font color='#5555FF'>&lt;</font>string<font color='#5555FF'>&gt;</font><font color='#5555FF'>&amp;</font> labels
<font face='Lucida Console'>)</font>;
<font color='#009900'>/*!
    ensures
        - make some four class data as described above.  
        - each class will have 50 samples in it
!*/</font>

<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>
<font color='#0000FF'><u>int</u></font> <b><a name='main'></a>main</b><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>
<b>{</b>
    std::vector<font color='#5555FF'>&lt;</font>sample_type<font color='#5555FF'>&gt;</font> samples;
    std::vector<font color='#5555FF'>&lt;</font>string<font color='#5555FF'>&gt;</font> labels;

    <font color='#009900'>// First, get our labeled set of training data
</font>    <font color='#BB00BB'>generate_data</font><font face='Lucida Console'>(</font>samples, labels<font face='Lucida Console'>)</font>;

    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>samples.size(): </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> samples.<font color='#BB00BB'>size</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;

    <font color='#009900'>// Define the trainer we will use.  The second template argument specifies the type
</font>    <font color='#009900'>// of label used, which is string in this case.
</font>    <font color='#0000FF'>typedef</font> one_vs_one_trainer<font color='#5555FF'>&lt;</font>any_trainer<font color='#5555FF'>&lt;</font>sample_type<font color='#5555FF'>&gt;</font>, string<font color='#5555FF'>&gt;</font> ovo_trainer;


    ovo_trainer trainer;

    <font color='#009900'>// Now tell the one_vs_one_trainer that, by default, it should use the simple_custom_trainer
</font>    <font color='#009900'>// to solve the individual binary classification subproblems.
</font>    trainer.<font color='#BB00BB'>set_trainer</font><font face='Lucida Console'>(</font><font color='#BB00BB'>simple_custom_trainer</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>;

    <font color='#009900'>// Next, to make things a little more interesting, we will setup the one_vs_one_trainer
</font>    <font color='#009900'>// to use kernel ridge regression to solve the upper_left vs lower_right binary classification
</font>    <font color='#009900'>// subproblem.  
</font>    <font color='#0000FF'>typedef</font> radial_basis_kernel<font color='#5555FF'>&lt;</font>sample_type<font color='#5555FF'>&gt;</font> rbf_kernel;
    krr_trainer<font color='#5555FF'>&lt;</font>rbf_kernel<font color='#5555FF'>&gt;</font> rbf_trainer;
    rbf_trainer.<font color='#BB00BB'>set_kernel</font><font face='Lucida Console'>(</font><font color='#BB00BB'>rbf_kernel</font><font face='Lucida Console'>(</font><font color='#979000'>0.1</font><font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>;
    trainer.<font color='#BB00BB'>set_trainer</font><font face='Lucida Console'>(</font>rbf_trainer, "<font color='#CC0000'>upper_left</font>", "<font color='#CC0000'>lower_right</font>"<font face='Lucida Console'>)</font>;


    <font color='#009900'>// Now let's do 5-fold cross-validation using the one_vs_one_trainer we just setup.
</font>    <font color='#009900'>// As an aside, always shuffle the order of the samples before doing cross validation.  
</font>    <font color='#009900'>// For a discussion of why this is a good idea see the <a href="svm_ex.cpp.html">svm_ex.cpp</a> example.
</font>    <font color='#BB00BB'>randomize_samples</font><font face='Lucida Console'>(</font>samples, labels<font face='Lucida Console'>)</font>;
    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>cross validation: \n</font>" <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> <font color='#BB00BB'>cross_validate_multiclass_trainer</font><font face='Lucida Console'>(</font>trainer, samples, labels, <font color='#979000'>5</font><font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
    <font color='#009900'>// This dataset is very easy and everything is correctly classified.  Therefore, the output of 
</font>    <font color='#009900'>// cross validation is the following confusion matrix.
</font>    <font color='#009900'>/*
        50  0  0  0 
         0 50  0  0 
         0  0 50  0 
         0  0  0 50 
    */</font>


    <font color='#009900'>// We can also obtain the decision rule as always.
</font>    one_vs_one_decision_function<font color='#5555FF'>&lt;</font>ovo_trainer<font color='#5555FF'>&gt;</font> df <font color='#5555FF'>=</font> trainer.<font color='#BB00BB'>train</font><font face='Lucida Console'>(</font>samples, labels<font face='Lucida Console'>)</font>;

    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>predicted label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> <font color='#BB00BB'>df</font><font face='Lucida Console'>(</font>samples[<font color='#979000'>0</font>]<font face='Lucida Console'>)</font>  <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>, true label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> labels[<font color='#979000'>0</font>] <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>predicted label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> <font color='#BB00BB'>df</font><font face='Lucida Console'>(</font>samples[<font color='#979000'>90</font>]<font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>, true label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> labels[<font color='#979000'>90</font>] <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
    <font color='#009900'>// The output is:
</font>    <font color='#009900'>/*
        predicted label: upper_right, true label: upper_right
        predicted label: lower_left, true label: lower_left
    */</font>


    <font color='#009900'>// Finally, let's save our multiclass decision rule to disk.  Remember that we have
</font>    <font color='#009900'>// to specify the types of binary decision function used inside the one_vs_one_decision_function.
</font>    one_vs_one_decision_function<font color='#5555FF'>&lt;</font>ovo_trainer, 
            custom_decision_function,                             <font color='#009900'>// This is the output of the simple_custom_trainer 
</font>            decision_function<font color='#5555FF'>&lt;</font>radial_basis_kernel<font color='#5555FF'>&lt;</font>sample_type<font color='#5555FF'>&gt;</font> <font color='#5555FF'>&gt;</font>  <font color='#009900'>// This is the output of the rbf_trainer
</font>        <font color='#5555FF'>&gt;</font> df2, df3;

    df2 <font color='#5555FF'>=</font> df;
    <font color='#009900'>// save to a file called df.dat
</font>    <font color='#BB00BB'>serialize</font><font face='Lucida Console'>(</font>"<font color='#CC0000'>df.dat</font>"<font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> df2;

    <font color='#009900'>// load the function back in from disk and store it in df3.  
</font>    <font color='#BB00BB'>deserialize</font><font face='Lucida Console'>(</font>"<font color='#CC0000'>df.dat</font>"<font face='Lucida Console'>)</font> <font color='#5555FF'>&gt;</font><font color='#5555FF'>&gt;</font> df3;


    <font color='#009900'>// Test df3 to see that this worked.
</font>    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>predicted label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> <font color='#BB00BB'>df3</font><font face='Lucida Console'>(</font>samples[<font color='#979000'>0</font>]<font face='Lucida Console'>)</font>  <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>, true label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> labels[<font color='#979000'>0</font>] <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>predicted label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> <font color='#BB00BB'>df3</font><font face='Lucida Console'>(</font>samples[<font color='#979000'>90</font>]<font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>, true label: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> labels[<font color='#979000'>90</font>] <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
    <font color='#009900'>// Test df3 on the samples and labels and print the confusion matrix.
</font>    cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>test deserialized function: \n</font>" <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> <font color='#BB00BB'>test_multiclass_decision_function</font><font face='Lucida Console'>(</font>df3, samples, labels<font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;

<b>}</b>

<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>
<font color='#0000FF'><u>void</u></font> <b><a name='generate_data'></a>generate_data</b> <font face='Lucida Console'>(</font>
    std::vector<font color='#5555FF'>&lt;</font>sample_type<font color='#5555FF'>&gt;</font><font color='#5555FF'>&amp;</font> samples,
    std::vector<font color='#5555FF'>&lt;</font>string<font color='#5555FF'>&gt;</font><font color='#5555FF'>&amp;</font> labels
<font face='Lucida Console'>)</font>
<b>{</b>
    <font color='#0000FF'>const</font> <font color='#0000FF'><u>long</u></font> num <font color='#5555FF'>=</font> <font color='#979000'>50</font>;

    sample_type m;

    dlib::rand rnd;


    <font color='#009900'>// add some points in the upper right quadrant
</font>    m <font color='#5555FF'>=</font> <font color='#979000'>10</font>, <font color='#979000'>10</font>;
    <font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>long</u></font> i <font color='#5555FF'>=</font> <font color='#979000'>0</font>; i <font color='#5555FF'>&lt;</font> num; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>i<font face='Lucida Console'>)</font>
    <b>{</b>
        samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>m <font color='#5555FF'>+</font> <font color='#BB00BB'>randm</font><font face='Lucida Console'>(</font><font color='#979000'>2</font>,<font color='#979000'>1</font>,rnd<font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>;
        labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>"<font color='#CC0000'>upper_right</font>"<font face='Lucida Console'>)</font>;
    <b>}</b>

    <font color='#009900'>// add some points in the upper left quadrant
</font>    m <font color='#5555FF'>=</font> <font color='#5555FF'>-</font><font color='#979000'>10</font>, <font color='#979000'>10</font>;
    <font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>long</u></font> i <font color='#5555FF'>=</font> <font color='#979000'>0</font>; i <font color='#5555FF'>&lt;</font> num; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>i<font face='Lucida Console'>)</font>
    <b>{</b>
        samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>m <font color='#5555FF'>+</font> <font color='#BB00BB'>randm</font><font face='Lucida Console'>(</font><font color='#979000'>2</font>,<font color='#979000'>1</font>,rnd<font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>;
        labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>"<font color='#CC0000'>upper_left</font>"<font face='Lucida Console'>)</font>;
    <b>}</b>

    <font color='#009900'>// add some points in the lower right quadrant
</font>    m <font color='#5555FF'>=</font> <font color='#979000'>10</font>, <font color='#5555FF'>-</font><font color='#979000'>10</font>;
    <font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>long</u></font> i <font color='#5555FF'>=</font> <font color='#979000'>0</font>; i <font color='#5555FF'>&lt;</font> num; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>i<font face='Lucida Console'>)</font>
    <b>{</b>
        samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>m <font color='#5555FF'>+</font> <font color='#BB00BB'>randm</font><font face='Lucida Console'>(</font><font color='#979000'>2</font>,<font color='#979000'>1</font>,rnd<font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>;
        labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>"<font color='#CC0000'>lower_right</font>"<font face='Lucida Console'>)</font>;
    <b>}</b>

    <font color='#009900'>// add some points in the lower left quadrant
</font>    m <font color='#5555FF'>=</font> <font color='#5555FF'>-</font><font color='#979000'>10</font>, <font color='#5555FF'>-</font><font color='#979000'>10</font>;
    <font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>long</u></font> i <font color='#5555FF'>=</font> <font color='#979000'>0</font>; i <font color='#5555FF'>&lt;</font> num; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>i<font face='Lucida Console'>)</font>
    <b>{</b>
        samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>m <font color='#5555FF'>+</font> <font color='#BB00BB'>randm</font><font face='Lucida Console'>(</font><font color='#979000'>2</font>,<font color='#979000'>1</font>,rnd<font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>;
        labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font>"<font color='#CC0000'>lower_left</font>"<font face='Lucida Console'>)</font>;
    <b>}</b>

<b>}</b>

<font color='#009900'>// ----------------------------------------------------------------------------------------
</font>

</pre></body></html>