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<doc>
   <title>Algorithms</title>

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   <body>
      <br/><br/>

         <p>
            This page documents library components that are all basically just implementations of 
            mathematical functions or algorithms without any really significant data structures 
            associated with them. So this includes things like checksums, cryptographic hashes, 
            machine learning algorithms, sorting, etc...
         </p>

   </body>

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   <menu width="150">
    <top>
      <section>
         <name>Tools</name>
         <item>bigint</item> 
         <item>crc32</item> 
         <item>rand</item> 
         <item>running_stats</item> 
         <item>vector_normalizer</item> 
         <item>mlp</item> 
         <item nolink="true">
            <name>Optimization</name>
            <sub>
               <item>derivative</item> 
               <item>make_line_search_function</item> 
               <item>poly_min_extrap</item> 
               <item>line_search</item> 
               <item>find_min_quasi_newton</item> 
               <item>find_min_conjugate_gradient</item> 
               <item>find_min_quasi_newton2</item> 
               <item>find_min_conjugate_gradient2</item> 
            </sub>
         </item>
         <item nolink="true">
            <name>Geometry</name>
            <sub>
               <item>rectangle</item> 
               <item>vector</item> 
               <item>point</item> 
               <item>centered_rect</item> 
               <item>translate_rect</item> 
               <item>resize_rect</item> 
               <item>resize_rect_width</item> 
               <item>resize_rect_height</item> 
               <item>move_rect</item> 
               <item>nearest_point</item> 
            </sub>
         </item>
         <item nolink="true">
            <name>Bayes Utilities</name>
            <sub>
               <item>assignment</item>
               <item>joint_probability_table</item>
               <item>conditional_probability_table</item>
               <item>bayes_node</item>
               <item>bayesian_network_gibbs_sampler</item>
               <item>bayesian_network_join_tree</item>
               <item nolink="true">
                  <name>Bayes Node Utilities</name>
                  <sub>
                     <item>set_node_value</item>
                     <item>node_value</item>
                     <item>node_is_evidence</item>
                     <item>set_node_as_evidence</item>
                     <item>set_node_as_nonevidence</item>
                     <item>set_node_num_values</item>
                     <item>node_num_values</item>
                     <item>node_probability</item>
                     <item>set_node_probability</item>
                     <item>node_first_parent_assignment</item>
                     <item>node_next_parent_assignment</item>
                     <item>node_cpt_filled_out</item>
                  </sub>
               </item>
            </sub>
         </item>
         <item nolink="true">
            <name>Graph Utilities</name>
            <sub>
               <item>graph_contains_directed_cycle</item> 
               <item>graph_contains_undirected_cycle</item> 
               <item>create_moral_graph</item> 
               <item>triangulate_graph_and_find_cliques</item> 
               <item>graph_contains_length_one_cycle</item> 
               <item>find_connected_nodes</item> 
               <item>graph_is_connected</item> 
               <item>is_clique</item> 
               <item>is_maximal_clique</item> 
               <item>copy_graph_structure</item> 
               <item>edge</item> 
               <item>is_join_tree</item> 
               <item>create_join_tree</item> 
            </sub>
         </item>
         <item nolink="true">
            <name>Kernel_Methods</name>
            <sub>
               <item nolink="true">
                  <name>Kernels</name>
                  <sub>
                     <item>radial_basis_kernel</item>
                     <item>polynomial_kernel</item>
                     <item>sigmoid_kernel</item>
                     <item>linear_kernel</item>
                  </sub>
               </item>
               <item nolink="true">
                  <name>Function_Objects</name>
                  <sub>
                     <item>decision_function</item>
                     <item>distance_function</item>
                     <item>probabilistic_decision_function</item>
                     <item>normalized_function</item>
                  </sub>
               </item>
               <item>pick_initial_centers</item> 
               <item>krls</item>
               <item>kcentroid</item>
               <item>linearly_independent_subset_finder</item>
               <item>kkmeans</item>
               <item>svm_nu_trainer</item> 
               <item>rvm_trainer</item> 
               <item>rvm_regression_trainer</item> 
               <item>rbf_network_trainer</item> 
               <item>train_probabilistic_decision_function</item> 
               <item>cross_validate_trainer</item> 
               <item>test_binary_decision_function</item> 
               <item>cross_validate_trainer_threaded</item> 
               <item>rank_features</item> 
               <item>reduced_decision_function_trainer</item> 
               <item>reduced</item> 
               <item>reduced_decision_function_trainer2</item> 
               <item>reduced2</item> 
            </sub>
         </item>
         <item>randomize_samples</item> 
         <item>is_binary_classification_problem</item> 
         <item>hsort_array</item> 
         <item>isort_array</item> 
         <item>put_in_range</item> 
         <item>md5</item> 
         <item>median</item> 
         <item>qsort_array</item> 
         <item>square_root</item> 
         <item nolink="true">
            <name>Set Utilities</name>
            <sub>
               <item>set_intersection_size</item> 
               <item>set_intersection</item> 
               <item>set_union</item> 
               <item>set_difference</item> 
            </sub>
         </item>
      </section>

    </top>  
   </menu>

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   <components>
   
   <!-- ************************************************************************* -->
      
      <component>
         <name>bayesian_network_join_tree</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This object represents an implementation of the join tree algorithm 
            (a.k.a. the junction tree algorithm)
            for inference in bayesian networks.  
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
            <example>bayes_net_gui_ex.cpp.html</example>
            <example>bayes_net_from_disk_ex.cpp.html</example>
         </examples>
                                 
      </component>
   
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      <component>
         <name>bayesian_network_gibbs_sampler</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This object performs Markov Chain Monte Carlo sampling of a bayesian
            network using the Gibbs sampling technique. 
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
         </examples>
                                 
      </component>
   
   <!-- ************************************************************************* -->
      
      <component>
         <name>bayes_node</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This object represents a node in a bayesian network.  It is
            intended to be used inside the <a href="containers.html#directed_graph">directed_graph</a> object to
            represent bayesian networks.
         </description>
                                 
         <examples>
            <example>bayes_net_ex.cpp.html</example>
            <example>bayes_net_gui_ex.cpp.html</example>
         </examples>
      </component>
   
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      <component>
         <name>conditional_probability_table</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This object represents a conditional probability table.
         </description>
                                 
      </component>
   
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      <component>
         <name>joint_probability_table</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This object represents a joint probability table.  
         </description>
                                 
      </component>

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      <component>
         <name>derivative</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
            This is a function that takes another function as input and returns
            a function object that numerically computes the derivative of the input function.
         </description>
                                 
      </component>


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      <component>
         <name>make_line_search_function</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
            This is a function that takes another function f(x) as input and returns
            a function object l(z) = f(start + z*direction). 
         </description>
                                 
      </component>


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      <component>
         <name>pick_initial_centers</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
         <description>
            This is a function that you can use to seed data clustering algorithms
            like the <a href="#kkmeans">kkmeans</a> clustering method.  What it 
            does is pick reasonable starting points for clustering by basically
            trying to find a set of points that are all far away from each other.
         </description>
         <examples>
            <example>kkmeans_ex.cpp.html</example>
         </examples>
                                 
      </component>


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      <component>
         <name>poly_min_extrap</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
            This function finds the 3rd degree polynomial that interpolates a 
            set of points and returns you the minimum of that polynomial.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->
      
      <component>
         <name>line_search</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
            Performs a line search on a given function and returns the input
            that makes the function significantly smaller.
         </description>
                                 
      </component>


   <!-- ************************************************************************* -->
      
      <component>
         <name>find_min_quasi_newton</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
             Performs an unconstrained minimization of the potentially nonlinear function f() using the 
             BFGS quasi newton method.  
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->
      
      <component>
         <name>find_min_conjugate_gradient</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
             Performs an unconstrained minimization of the potentially nonlinear function f() using a 
              conjugate gradient method. 
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->
      
      <component>
         <name>find_min_quasi_newton2</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
             Performs an unconstrained minimization of the potentially nonlinear function f() using the
             BFGS quasi newton method.  This version doesn't take a gradient function of f()
              but instead numerically approximates the gradient.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->
      
      <component>
         <name>find_min_conjugate_gradient2</name>
         <file>dlib/optimization.h</file>
         <spec_file link="true">dlib/optimization/optimization_abstract.h</spec_file>
         <description>
             Performs an unconstrained minimization of the potentially nonlinear function f() using a 
              conjugate gradient method. This version doesn't take a gradient function of f()
              but instead numerically approximates the gradient.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->
      
      <component>
         <name>assignment</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This object models an assignment of random variables to particular values.
            It is used with the <a href="#joint_probability_table">joint_probability_table</a> and 
            <a href="#conditional_probability_table">conditional_probability_table</a>
            objects to represent assignments of various random variables to actual values.
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
            <example>bayes_net_gui_ex.cpp.html</example>
         </examples>
                                 
      </component>
   
   <!-- ************************************************************************* -->
      
      <component>
         <name>set_node_probability</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily set the probability of a  
            <a href="#bayes_node">bayes_node</a> given its parents when it is inside 
            a <a href="containers.html#directed_graph">directed_graph</a> object. 
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
         </examples>
                                 
      </component>
   
   <!-- ************************************************************************* -->
      
      <component>
         <name>node_first_parent_assignment</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily obtain an <a href="#assignment">assignment</a>
            that contains all the parents of a node in a bayesian network.
         </description>
         <examples>
            <example>bayes_net_gui_ex.cpp.html</example>
         </examples>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      <component>
         <name>node_next_parent_assignment</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily loop through all the parent <a href="#assignment">assignments</a>
            of a node in a bayesian network.
         </description>
         <examples>
            <example>bayes_net_gui_ex.cpp.html</example>
         </examples>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      
      <component>
         <name>node_cpt_filled_out</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily verify that a node
            in a bayesian network has its <a href="#conditional_probability_table">conditional_probability_table</a> 
            completely filled out.
         </description>
         <examples>
            <example>bayes_net_gui_ex.cpp.html</example>
         </examples>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      
      <component>
         <name>node_probability</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily obtain the probability of a  
            <a href="#bayes_node">bayes_node</a> given its parents when it is inside 
            a <a href="containers.html#directed_graph">directed_graph</a> object. 
         </description>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      <component>
         <name>node_num_values</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily obtain the number of values of a  
            <a href="#bayes_node">bayes_node</a> when it is inside 
            a <a href="containers.html#directed_graph">directed_graph</a> object. 
         </description>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      <component>
         <name>set_node_num_values</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily set the number of values of a  
            <a href="#bayes_node">bayes_node</a> when it is inside 
            a <a href="containers.html#directed_graph">directed_graph</a> object. 
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
         </examples>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      <component>
         <name>set_node_as_nonevidence</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily remove the evidence flag of a  
            <a href="#bayes_node">bayes_node</a> when it is inside 
            a <a href="containers.html#directed_graph">directed_graph</a> object. 
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
         </examples>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      <component>
         <name>set_node_as_evidence</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily set the evidence flag of a  
            <a href="#bayes_node">bayes_node</a> when it is inside 
            a <a href="containers.html#directed_graph">directed_graph</a> object. 
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
         </examples>
                                 
      </component>
   <!-- ************************************************************************* -->
      
      <component>
         <name>node_is_evidence</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily determine if a  
            <a href="#bayes_node">bayes_node</a> is evidence when it is inside 
            a <a href="containers.html#directed_graph"> directed_graph</a> object. 
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>node_value</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily obtain the value of a 
            <a href="#bayes_node">bayes_node</a> when it is inside a <a href="containers.html#directed_graph">
            directed_graph</a> object. 
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>set_node_value</name>
         <file>dlib/bayes_utils.h</file>
         <spec_file link="true">dlib/bayes_utils/bayes_utils_abstract.h</spec_file>
         <description>
            This is a function declared in the dlib::bayes_node_utils namespace.  It 
            is a convenience function that allows you to easily modify the value of a 
            <a href="#bayes_node">bayes_node</a> when it is inside a <a href="containers.html#directed_graph">
            directed_graph</a> object. 
         </description>
         <examples>
            <example>bayes_net_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      <component checked="true">
         <name>bigint</name>
         <file>dlib/bigint.h</file>
         <spec_file>dlib/bigint/bigint_kernel_abstract.h</spec_file>
         <description>
            This object represents an arbitrary precision unsigned integer.  It's pretty simple.  
            It's interface is just like a normal int, you don't have to tell it how much memory
            to use or anything unusual.  It just goes :)       
         </description>
         
         <implementations>
            <implementation>
               <name>bigint_kernel_1</name>
               <file>dlib/bigint/bigint_kernel_1.h</file>
               <description> 
                  This implementation is done using an array of unsigned shorts.  It is also reference counted.  
                  For further details see the above link.  Also note that kernel_2 should be 
      faster in almost every case so you should really just use that version of the bigint object.
               </description> 
    
               <typedefs>
                  <typedef>
                     <name>kernel_1a</name>
                     <description>is a typedef for bigint_kernel_1</description>
                  </typedef>
               </typedefs>                
               
            </implementation> 
                     
            <implementation>
               <name>bigint_kernel_2</name>
               <file>dlib/bigint/bigint_kernel_2.h</file>
               <description> 
                  This implementation is basically the same as kernel_1 except it uses the 
                  Fast Fourier Transform to perform multiplcations much faster.  
               </description> 
    
               <typedefs>
                  <typedef>
                     <name>kernel_2a</name>
                     <description>is a typedef for bigint_kernel_2</description>
                  </typedef>
               </typedefs>                
               
            </implementation> 
                     
         </implementations>
                           
                        
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>crc32</name>
         <file>dlib/crc32.h</file>
         <spec_file>dlib/crc32/crc32_kernel_abstract.h</spec_file>
         <description>
            This object represents the CRC-32 algorithm for calculating
                        checksums.   
         </description>
         
         <implementations>
            <implementation>
               <name>crc32_kernel_1</name>
               <file>dlib/crc32/crc32_kernel_1.h</file>
               <description> 
                  This implementation uses the polynomial 0xedb88320. 
               </description> 
    
               <typedefs>
                  <typedef>
                     <name>kernel_1a</name>
                     <description>is a typedef for crc32_kernel_1</description>
                  </typedef>
               </typedefs>                
               
            </implementation>                                  
         </implementations>
                        
      </component>
            
   <!-- ************************************************************************* -->
      
      <component>
         <name>rand</name>
         <file>dlib/rand.h</file>
         <spec_file>dlib/rand/rand_kernel_abstract.h</spec_file>
         <description>
            This object represents a pseudorandom number generator.
         </description>
         
         <implementations>
            <implementation>
               <name>rand_kernel_1</name>
               <file>dlib/rand/rand_kernel_1.h</file>
               <description> 
                  This implementation is done using the Mersenne Twister algorithm.
               </description> 
    
               <typedefs>
                  <typedef>
                     <name>kernel_1a</name>
                     <description>is a typedef for rand_kernel_1</description>
                  </typedef>
               </typedefs>                
               
            </implementation> 
                     
         </implementations>
         
         <extensions>
      
            <extension>
               <name>rand_float</name>
               <spec_file>dlib/rand/rand_float_abstract.h</spec_file>
               <description> 
                  This extension gives rand the ability to generate random floating point numbers.
               </description> 

               <implementations>
                  <implementation>
                     <name>rand_float_1</name>
                     <file>dlib/rand/rand_float_1.h</file>
                     <description> 
                        The implementation is obvious.  Click on the link if you want to see.                        
                     </description> 

                     <typedefs>
                        <typedef>
                           <name>float_1a</name>
                           <description>is a typedef for rand_kernel_1a extended by rand_float_1</description>
                        </typedef>
                     </typedefs>                
                     
                  </implementation> 
                           
               </implementations>
   
               
            </extension>
         </extensions>
               
                        
      </component>
            
   <!-- ************************************************************************* -->
      
      <component>
         <name>rectangle</name>
         <file>dlib/geometry.h</file>
         <spec_file>dlib/geometry/rectangle_abstract.h</spec_file>
         <description>
            This object represents a rectangular region inside a cartesian 
            coordinate system.  It allows you to easily represent and manipulate
            rectangles.
         </description>
      </component>
            
   <!-- ************************************************************************* -->
      
      <component checked="true">
         <name>mlp</name>
         <file>dlib/mlp.h</file>
         <spec_file>dlib/mlp/mlp_kernel_abstract.h</spec_file>
         <description>
                This object represents a multilayer layer perceptron network that is
                trained using the back propagation algorithm.  The training algorithm also
                incorporates the momentum method.  That is, each round of back propagation
                training also adds a fraction of the previous update.  This fraction
                is controlled by the momentum term set in the constructor.  
         </description>

         <examples>
            <example>mlp_ex.cpp.html</example>
         </examples>
         
         <implementations>
            <implementation>
               <name>mlp_kernel_1</name>
               <file>dlib/mlp/mlp_kernel_1.h</file>
               <description> 
                  This is implemented in the obvious way.
               </description> 
    
               <typedefs>
                  <typedef>
                     <name>kernel_1a</name>
                     <description>is a typedef for mlp_kernel_1</description>
                  </typedef>
               </typedefs>                
               
            </implementation> 
                     
         </implementations>
                        
      </component>
            
   <!-- ************************************************************************* -->
      
      <component>
         <name>vector</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/vector_abstract.h</spec_file>
         <description>
            This object represents a three dimensional vector.
         </description>
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>point</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/vector_abstract.h</spec_file>
         <description>
               This object represents a point inside a Cartesian coordinate system.
         </description>
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>krls</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/krls_abstract.h</spec_file>
         <description>
                This is an implementation of the kernel recursive least squares algorithm 
                described in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel.
            <p>
                The long and short of this algorithm is that it is an online kernel based 
                regression algorithm.  You give it samples (x,y) and it learns the function
                f(x) == y.  For a detailed description of the algorithm read the above paper.
            </p>
         </description>

         <examples>
            <example>krls_ex.cpp.html</example>
            <example>krls_filter_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>kkmeans</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
         <description>
                This is an implementation of a kernelized k-means clustering algorithm.  
                It performs k-means clustering by using the <a href="#kcentroid">kcentroid</a> object.  
         </description>

         <examples>
            <example>kkmeans_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>vector_normalizer</name>
         <file>dlib/statistics.h</file>
         <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
         <description>
                This object represents something that can learn to normalize a set 
                of vectors.  In particular, normalized vectors should have zero
                mean and a variance of one.  

                Also, if desired, this object can also use principal component 
                analysis for the purposes of reducing the number of elements in a 
                vector.  
         </description>

         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
      
   <!-- ************************************************************************* -->
      <component>
         <name>running_stats</name>
         <file>dlib/statistics.h</file>
         <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
         <description>
                This object represents something that can compute the running mean and
                variance of a stream of real numbers.  
         </description>

         <examples>
            <example>kcentroid_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
      
   <!-- ************************************************************************* -->

      <component>
         <name>linearly_independent_subset_finder</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/linearly_independent_subset_finder_abstract.h</spec_file>
         <description>
            <p>
                This is an implementation of an online algorithm for recursively finding a
                set of linearly independent vectors in a kernel induced feature space.  To 
                use it you decide how large you would like the set to be and then you feed it 
                sample points.  
            </p>
            <p>
                
                Each time you present it with a new sample point it either 
                keeps the current set of independent points unchanged, or if the new point 
                is "more linearly independent" than one of the points it already has,  
                it replaces the weakly linearly independent point with the new one.
            </p>

            <p>
                
                This object uses the Approximately Linearly Dependent metric described in the paper 
                The Kernel Recursive Least Squares Algorithm by Yaakov Engel to decide which
                points are more linearly independent than others.
            </p>
         </description>

      </component>
      
      
   <!-- ************************************************************************* -->
      
      
      <component>
         <name>kcentroid</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/kcentroid_abstract.h</spec_file>
         <description>
                This is an implementation of an online algorithm for recursively estimating the
                centroid of a sequence of training points.  It uses the sparsification technique
                described in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel.

            <p>
                This object then allows you to compute the distance between the center of mass
                and any test points.  So you can use this object to predict how similar a test
                point is to the data this object has been trained on (larger distances from the
                centroid indicate dissimilarity/anomalous points).
            </p>
         </description>

         <examples>
            <example>kcentroid_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>train_probabilistic_decision_function</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
         <description>
            <p>
               Trains a <a href="#probabilistic_decision_function">probabilistic_decision_function</a> using 
               some sort of trainer object such as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or
               <a href="#rbf_network_trainer">rbf_network_trainer</a>.
            </p>
            The probability model is created by using the technique described in the paper:
            <blockquote>
                Probabilistic Outputs for Support Vector Machines and
                Comparisons to Regularized Likelihood Methods by 
                John C. Platt.  Match 26, 1999
            </blockquote>
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>rbf_network_trainer</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/rbf_network_abstract.h</spec_file>
         <description>
               Trains a radial basis function network and outputs a <a href="#decision_function">decision_function</a>. 
         </description>
      </component>
      
   <!-- ************************************************************************* -->

      <component>
         <name>rvm_regression_trainer</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/rvm_abstract.h</spec_file>
         <description>
            <p>
               Trains a relevance vector machine for solving regression problems.  
               Outputs a <a href="#decision_function">decision_function</a> that represents the learned 
               regression function. 
            </p>
               The implementation of the RVM training algorithm used by this library is based
               on the following paper:
               <blockquote>
                Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation 
                for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings 
                of the Ninth International Workshop on Artificial Intelligence and Statistics, 
                Key West, FL, Jan 3-6.
               </blockquote>
         </description>
         <examples>
            <example>rvm_regression_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      
      <component>
         <name>rvm_trainer</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/rvm_abstract.h</spec_file>
         <description>
            <p>
               Trains a relevance vector machine for solving binary classification problems.  
               Outputs a <a href="#decision_function">decision_function</a> that represents the learned classifier. 
            </p>
               The implementation of the RVM training algorithm used by this library is based
               on the following paper:
               <blockquote>
                Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation 
                for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings 
                of the Ninth International Workshop on Artificial Intelligence and Statistics, 
                Key West, FL, Jan 3-6.
               </blockquote>
         </description>
         <examples>
            <example>rvm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      
      <component>
         <name>svm_nu_trainer</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
         <description>
            <p>
               Trains a nu support vector classifier and outputs a <a href="#decision_function">decision_function</a>. 
            </p>
               The implementation of the nu-svm training algorithm used by this library is based
               on the following excellent papers:
               <ul>
                  <li>Chang and Lin, Training {nu}-Support Vector Classifiers: Theory and Algorithms</li>
                  <li>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector 
                     machines, 2001. Software available at 
                     <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">http://www.csie.ntu.edu.tw/~cjlin/libsvm</a></li>
               </ul>
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      <component>
         <name>normalized_function</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/function_abstract.h</spec_file>
         <description>
                This object represents a container for another function
                object and an instance of the <a href="#vector_normalizer">vector_normalizer</a> object.  

                It automatically noramlizes all inputs before passing them
                off to the contained function object.
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->


      <component>
         <name>probabilistic_decision_function</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/function_abstract.h</spec_file>
         <description>
                This object represents a binary decision function for use with
                support vector machines.  It returns an 
                estimate of the probability that a given sample is in the +1 class. 
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      <component>
         <name>distance_function</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/function_abstract.h</spec_file>
         <description>
                This object represents a point in kernel induced feature space. 
                You may use this object to find the distance from the point it 
                represents to points in input space.
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      <component>
         <name>decision_function</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/function_abstract.h</spec_file>
         <description>
                This object represents a decision or regression function that was 
                learned by a kernel based learning algorithm.  
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>linear_kernel</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
         <description>
                This object represents a linear function kernel for use with
                kernel learning machines.
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      <component>
         <name>sigmoid_kernel</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
         <description>
                This object represents a sigmoid kernel for use with
                kernel learning machines.
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->

      <component>
         <name>polynomial_kernel</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
         <description>
                This object represents a polynomial kernel for use with
                kernel learning machines.
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>radial_basis_kernel</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
         <description>
                This object represents a radial basis function kernel for use with
                kernel learning machines.
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>is_binary_classification_problem</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
         <description>
             This function simply takes two vectors, the first containing feature vectors and
             the second containing labels, and reports back if the two could possibly 
             contain data for a well formed classification problem.
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>randomize_samples</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
         <description>
               Randomizes the order of samples in a column vector containing sample data.
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>rank_features</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/feature_ranking_abstract.h</spec_file>
         <description>
             Finds a ranking of the top N (a user supplied parameter) features in a set of data 
             from a two class classification problem.  It  
              does this by computing the distance between the centroids of both classes in kernel defined 
              feature space.  Good features are then ones that result in the biggest separation between
              the two centroids. 
         </description>
         <examples>
            <example>rank_features_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>reduced</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
         <description>
            This is a convenience function for creating 
            <a href="#reduced_decision_function_trainer">reduced_decision_function_trainer</a>
            objects.
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>reduced_decision_function_trainer</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
         <description>
               This is a trainer object that is meant to wrap other trainer objects 
               that create <a href="#decision_function">decision_function</a> objects.
               It performs post processing on the output decision_function objects 
               with the intent of representing the decision_function with fewer 
               support vectors.
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>reduced2</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
         <description>
            This is a convenience function for creating 
            <a href="#reduced_decision_function_trainer2">reduced_decision_function_trainer2</a>
            objects.
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>reduced_decision_function_trainer2</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
         <description>
               <p>
               This is a trainer object that is meant to wrap other trainer objects 
               that create <a href="#decision_function">decision_function</a> objects.
               It performs post processing on the output decision_function objects 
               with the intent of representing the decision_function with fewer 
               support vectors.  
               </p>
               <p>
               It begins by performing the same post processing as
               the <a href="#reduced_decision_function_trainer">reduced_decision_function_trainer</a>
               object but it also performs a global gradient based optimization 
               to further improve the results.
               </p>
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>test_binary_decision_function</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
         <description>
            Tests a <a href="#decision_function">decision_function</a> that represents a binary decision function and
            returns the test accuracy.  

         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      
      <component>
         <name>cross_validate_trainer_threaded</name>
         <file>dlib/svm_threaded.h</file>
         <spec_file link="true">dlib/svm/svm_threaded_abstract.h</spec_file>
         <description>
               Performs k-fold cross validation on a user supplied trainer object such
               as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>.  This function does the same thing as <a href="#cross_validate_trainer">cross_validate_trainer</a>
               except this function also allows you to specify how many threads of execution to use.
               So you can use this function to take advantage of a multi-core system to perform
               cross validation faster.
         </description>
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>cross_validate_trainer</name>
         <file>dlib/svm.h</file>
         <spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
         <description>
               Performs k-fold cross validation on a user supplied trainer object such
               as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>.
         </description>
         <examples>
            <example>svm_ex.cpp.html</example>
         </examples>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>hsort_array</name>
         <file>dlib/sort.h</file>
         <spec_file link="true">dlib/sort.h</spec_file>
         <description>
            hsort_array is an implementation of the heapsort algorithm.  It will sort anything that has an 
            array like operator[] interface.  
         </description>
                                 
      </component>
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>put_in_range</name>
         <file>dlib/algs.h</file>
         <spec_file link="true">dlib/algs.h</spec_file>
         <description>
            This is a simple function that takes a range and a value and returns the given
            value if it is within the range.  If it isn't in the range then it returns the  
            end of range value that is closest.
         </description>
                                 
      </component>
               
   <!-- ************************************************************************* -->
      
      <component>
         <name>isort_array</name>
         <file>dlib/sort.h</file>
         <spec_file link="true">dlib/sort.h</spec_file>
         <description>
            isort_array is an implementation of the insertion sort algorithm.  It will sort anything that has an 
            array like operator[] interface.   
         </description>
                                 
      </component>
               
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>qsort_array</name>
         <file>dlib/sort.h</file>
         <spec_file link="true">dlib/sort.h</spec_file>
         <description>
            qsort_array is an implementation of the QuickSort algorithm.  It will sort anything that has an array like 
            operator[] interface.  If the quick sort becomes unstable then it switches to a heap sort.  This 
            way sorting is guaranteed to take at most N*log(N) time.
         </description>
                                 
      </component>
            
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>md5</name>
         <file>dlib/md5.h</file>
         <spec_file>dlib/md5/md5_kernel_abstract.h</spec_file>
         <description>
            This is an implementation of The MD5 Message-Digest Algorithm as described in rfc1321.   
         </description>
                                 
      </component>
            
      
      
   <!-- ************************************************************************* -->
      
      <component>
         <name>median</name>
         <file>dlib/algs.h</file>
         <spec_file link="true">dlib/algs.h</spec_file>
         <description>
            This function takes three paramaters and finds the median of the three.  The median is swapped into
            the first parameter and the first parameter ends up in one of the other two, unless the first parameter was
            the median to begin with of course. 
         </description>
                                 
      </component>
            
   <!-- ************************************************************************* -->
      
      <component>
         <name>square_root</name>
         <file>dlib/algs.h</file>
         <spec_file link="true">dlib/algs.h</spec_file>
         <description>        
            square_root is a function which takes an unsigned long and returns the square root of it or
            if the root is not an integer then it is rounded up to the next integer.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>edge</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a> object and a 
            pair of indices.  It returns a reference to the edge object between the two nodes
            with the given indices. 
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>is_join_tree</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes two <a href="containers.html#graph">graph</a> objects and
            checks if the second of the two graphs is a valid join tree (aka tree decomposition)
            of the first graph.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>create_join_tree</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a> object and
            creates a join tree for that graph.  Or in other words, this function finds a 
            tree decomposition of the given graph.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>graph_contains_directed_cycle</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function checks a <a href="containers.html#directed_graph">directed_graph</a> for directed cycles.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>set_intersection</name>
         <file>dlib/set_utils.h</file>
         <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file>
         <description>        
            This function takes two <a href="containers.html#set">set</a> objects and 
            gives you their intersection.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>set_union</name>
         <file>dlib/set_utils.h</file>
         <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file>
         <description>        
            This function takes two <a href="containers.html#set">set</a> objects and 
            gives you their union.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>set_difference</name>
         <file>dlib/set_utils.h</file>
         <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file>
         <description>        
            This function takes two <a href="containers.html#set">set</a> objects and 
            gives you their difference.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>set_intersection_size</name>
         <file>dlib/set_utils.h</file>
         <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file>
         <description>        
            This function takes two <a href="containers.html#set">set</a> objects and tells you
            how many items they have in common.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>triangulate_graph_and_find_cliques</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a> and
            turns it into a chordal graph.  It also returns a 
            <a href="containers.html#set">set</a> that contains
            all the cliques present in the choral graph.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>create_moral_graph</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#directed_graph">directed_graph</a>
            and returns the moralized version of the graph in the form of a 
            <a href="containers.html#graph">graph</a> object.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>graph_contains_length_one_cycle</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a>
            or <a href="containers.html#directed_graph">directed_graph</a> object and
            returns true if and only if the graph contains a node that has an edge that
            links back to itself.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>find_connected_nodes</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a node from a <a href="containers.html#graph">graph</a>
            or <a href="containers.html#directed_graph">directed_graph</a> object and a 
            <a href="containers.html#set">set</a> of unsigned longs.  It finds all the
            nodes in the given graph that are connected to the given node by an 
            undirected path and returns them in the set (also note that the
            original query node is also returned in this set).
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>graph_is_connected</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a> or
            <a href="containers.html#directed_graph">directed_graph</a> object and
            determines if the graph is connected.  That is, it returns true if and only if
            there is an undirected path between any two nodes in the given graph.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>is_clique</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a> and a 
            <a href="containers.html#set">set</a> of node index values and checks 
            if the specified set of nodes is a clique in the graph.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>copy_graph_structure</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a> or 
            <a href="containers.html#directed_graph">directed_graph</a> and copies  
            its structure to another graph or directed_graph object.  The only 
            restriction is that you can't copy the structure of a graph into a
            directed_graph.  The three other possible combinations are allowed
            however.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>is_maximal_clique</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function takes a <a href="containers.html#graph">graph</a> and a 
            <a href="containers.html#set">set</a> of node index values and checks 
            if the specified set of nodes is a maximal clique in the graph.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>nearest_point</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/rectangle_abstract.h</spec_file>
         <description>        
            This function takes a <a href="#rectangle">rectangle</a> and a 
            <a href="#point">point</a> and returns the point in the given 
            rectangle that is nearst to the given point.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>move_rect</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/rectangle_abstract.h</spec_file>
         <description>        
            This function takes a <a href="#rectangle">rectangle</a> and moves
            it so that it's upper left corner occupies the given location.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>resize_rect_height</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/rectangle_abstract.h</spec_file>
         <description>        
            This function takes a <a href="#rectangle">rectangle</a> and 
            returns a new rectangle with the given height but otherwise with the 
            same edge points as the original rectangle. 
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>resize_rect_width</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/rectangle_abstract.h</spec_file>
         <description>        
            This function takes a <a href="#rectangle">rectangle</a> and 
            returns a new rectangle with the given width but otherwise with the 
            same edge points as the original rectangle. 
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>resize_rect</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/rectangle_abstract.h</spec_file>
         <description>        
            This function takes a <a href="#rectangle">rectangle</a> and 
            returns a new rectangle with the given size but with the same upper
            left corner as the original rectangle.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>translate_rect</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/rectangle_abstract.h</spec_file>
         <description>        
            This function takes a <a href="#rectangle">rectangle</a> and moves
            it by a given number of units along the x and y axis relative to
            where it was before the move.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>centered_rect</name>
         <file>dlib/geometry.h</file>
         <spec_file link="true">dlib/geometry/rectangle_abstract.h</spec_file>
         <description>        
            There are various overloads of this function but the basic idea is
            that it returns a <a href="#rectangle">rectangle</a> with a given 
            width and height and centered about a given point.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

      <component>
         <name>graph_contains_undirected_cycle</name>
         <file>dlib/graph_utils.h</file>
         <spec_file link="true">dlib/graph_utils/graph_utils_abstract.h</spec_file>
         <description>        
            This function checks a <a href="containers.html#directed_graph">directed_graph</a> for undirected cycles.
         </description>
                                 
      </component>

   <!-- ************************************************************************* -->

   </components>

   <!-- ************************************************************************* -->


</doc>

