updated the docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402231

docs/docs/algorithms.xml

@@ -12,14 +12,8 @@
          <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, sorting, etc...
-         </p>
-
-         <p>
-         Everything in this section basically follows the same conventions as 
-         the rest of the library.   So to get a bigint for example you would need to write something 
-         like <tt>typedef dlib::bigint::kernel_2a bint;</tt> and from then on make your big ints like 
-         <tt>bint my_bigint;</tt>.  
+            associated with them. So this includes things like checksums, cryptographic hashes, 
+            machine learning algorithms, sorting, etc...
          </p>
 
    </body>
@@ -61,6 +55,7 @@
                <item>linear_kernel</item>
                <item>decision_function</item>
                <item>probabilistic_decision_function</item>
+               <item>krls</item>
             </sub>
          </item>
       </section>
@@ -664,6 +659,25 @@
          </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>
+                                 
+      </component>
+      
+      
    <!-- ************************************************************************* -->
       
       <component>

docs/docs/index.xml

@@ -115,6 +115,7 @@
                <ul>
                   <li><a href="algorithms.html#mlp">multi layer perceptrons</a> </li>  
                   <li><a href="algorithms.html#svm_nu_train">nu support vector machines</a></li>
+                  <li><a href="algorithms.html#krls">kernel RLS regression</a></li>
                   <li>Bayesian Network inference algorithms such as the 
                      <a href="algorithms.html#bayesian_network_join_tree">join tree</a> algorithm and 
                      <a href="algorithms.html#bayesian_network_gibbs_sampler">Gibbs sampler</a> Markov Chain Monte Carlo algorithm</li>

docs/docs/release_notes.xml

@@ -12,12 +12,23 @@
 
 <current>
 New Stuff:
+   - Added an implementation of the kernel recursive least squares algorithm
 
 Non-Backwards Compatible Changes:
+   - Broke backwards compatability in the directed_graph_drawer's serialization
+     format when I fixed the bug below.  
 
 Bug fixes:
+   - Fixed two bugs in the directed_graph_drawer widget.  First, it sometimes 
+     threw a dlib::fatal_error due to a race condition.  Second, the color of 
+     the nodes wasn't being serialized when save_graph() was called.  
+   - Made vector_to_matrix() work for std::vector objects that have non-default 
+     allocators.
 
 Other:
+   - Added some stuff to make people get a really obvious error message
+     when they set up the include path incorrectly.
+
 </current>
 
 <!-- *******************************************************************************  -->

docs/docs/term_index.xml

@@ -374,6 +374,7 @@
          <term link="algorithms.html#svm_nu_train" name="support vector machine"/>
          <term link="algorithms.html#vector" name="vector"/>
          <term link="algorithms.html#point" name="point"/>
+         <term link="algorithms.html#krls" name="krls"/>
          
          <term link="dlib/svm/svm_abstract.h.html#maximum_nu" name="maximum_nu"/>