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Reduce the size of the section labels

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Tiago Gomes 2013-04-13 00:49:59 +01:00
parent cb6715664b
commit 745a034d43
3 changed files with 23 additions and 11 deletions
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21
packages/CLPBN/html/index.html
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@ -16,10 +16,15 @@
<div class="header"> <div class="header">
<div id="leftcolumn"><h1>Prolog Factor Language</h1></div> <div id="leftcolumn"><h1>Prolog Factor Language</h1></div>
<div id="rightcolumn"> <div id="rightcolumn">
<table> <div>
<tr><td>vsc</td><td>at gmail.com</td></tr> <div id=name>Vítor Costa</div>
<tr><td>tiago.avv</td><td>at gmail.com</td></tr> <div id=email>vsc at gmail.com </div>
</table> </div>
<div style="padding-top:10px">
<div id=name>Tiago Gomes</div>
<div id=email>tiago.avv at gmail.com</div>
</div>
</div> </div>
<div style="clear: both"></div> <div style="clear: both"></div>
<nav id="menu"> <nav id="menu">
@ -28,8 +33,8 @@
<li><a href="#installation">Installation</a></li> <li><a href="#installation">Installation</a></li>
<li><a href="#language">Language</a></li> <li><a href="#language">Language</a></li>
<li><a href="#querying">Querying</a></li> <li><a href="#querying">Querying</a></li>
<li><a href="#inference_options">Inference Options</a></li> <li><a href="#options">Options</a></li>
<li><a href="#parameter_learning">Parameter Learning</a></li> <li><a href="#learning">Learning</a></li>
<li><a href="#external_interface">External Interface</a></li> <li><a href="#external_interface">External Interface</a></li>
<li><a href="#papers">Papers</a></li> <li><a href="#papers">Papers</a></li>
</ul> </ul>
@ -238,7 +243,7 @@ In this section we demonstrate how to use PFL to solve probabilistic queries. We
<!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++--> <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
<!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++--> <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
<!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++--> <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
<h2 id="inference_options">Inference Options</h2> <h2 id="options">Options</h2>
PFL supports both ground and lifted inference methods. The inference algorithm can be chosen by calling <span class=texttt>set_solver/1</span>. The following are supported: PFL supports both ground and lifted inference methods. The inference algorithm can be chosen by calling <span class=texttt>set_solver/1</span>. The following are supported:
<ul> <ul>
@ -352,7 +357,7 @@ This option allows to print a textual representation of the factor graph.
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<!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++--> <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
<!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++--> <!--+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-->
<h2 id="parameter_learning">Parameter Learning</h2> <h2 id="learning">Learning</h2>
PFL is capable to learn the parameters for bayesian networks, through an implementation of the expectation-maximization algorithm. PFL is capable to learn the parameters for bayesian networks, through an implementation of the expectation-maximization algorithm.
<p>Next we show an example of parameter learning for the sprinkler network.</p> <p>Next we show an example of parameter learning for the sprinkler network.</p>

9
packages/CLPBN/html/pfl.css
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@ -43,9 +43,16 @@ body {
padding-top:15px; padding-top:15px;
padding-right:0px; padding-right:0px;
margin-right:0px; margin-right:0px;
}
#name {
color:#2798CA; color:#2798CA;
} }
#email {
color:gray;
}
.mainbody { .mainbody {
padding-top:10px; padding-top:10px;
} }
@ -58,7 +65,7 @@ body {
#menu ul { #menu ul {
padding:0px; padding:0px;
margin:0px; margin: 20px 0px;
background-color:#EDEDED; background-color:#EDEDED;
list-style:none; list-style:none;
text-align: center; text-align: center;

4
packages/CLPBN/pfl.tex
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@ -274,7 +274,7 @@ PFL also supports calculating joint probability distributions. For instance, we
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
\section{Inference Options} \section{Options}
PFL supports both ground and lifted inference methods. The inference algorithm can be chosen by calling \texttt{set\_solver/1}. The following are supported: PFL supports both ground and lifted inference methods. The inference algorithm can be chosen by calling \texttt{set\_solver/1}. The following are supported:
\begin{itemize} \begin{itemize}
\item \texttt{ve}, variable elimination (written in Prolog) \item \texttt{ve}, variable elimination (written in Prolog)
@ -397,7 +397,7 @@ This option allows to print a textual representation of the factor graph.
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
%------------------------------------------------------------------------------ %------------------------------------------------------------------------------
\section{Parameter Learning} \section{Learning}
PFL is capable to learn the parameters for bayesian networks, through an implementation of the expectation-maximization algorithm. PFL is capable to learn the parameters for bayesian networks, through an implementation of the expectation-maximization algorithm.
Next we show an example of parameter learning for the sprinkler network. Next we show an example of parameter learning for the sprinkler network.