Documentation re-generated, a lot of minor stuff

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blacklight 2009-08-16 20:57:15 +02:00
parent d52976e74e
commit 7861e56f35
144 changed files with 2589 additions and 851 deletions

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<li><a href="namespaces.html"><span>Namespaces</span></a></li>
<li class="current"><a href="annotated.html"><span>Classes</span></a></li>
<li><a href="files.html"><span>Files</span></a></li>
<li><a href="examples.html"><span>Examples</span></a></li>
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</div>
<div class="tabs">
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<tr><td class="memItemLeft" nowrap align="right" valign="top">&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#92b145f2f6f00bf1ba645ce2235882c2">NeuralNet</a> ()</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Empty constructor for the class - it just makes nothing. <a href="#92b145f2f6f00bf1ba645ce2235882c2"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#c79534c7c0dfb20d1d03be2ad7569b78">NeuralNet</a> (size_t in_size, size_t hidden_size, size_t out_size, double l, int e)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#3d602f3988a9a3e2c77dc6955674f412">NeuralNet</a> (size_t in_size, size_t hidden_size, size_t out_size, double l, int e, double th=0.0, double(*a)(double)=__actv)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Constructor. <a href="#c79534c7c0dfb20d1d03be2ad7569b78"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#7fc7fc3e3220c138ffa5356fef6b9757">NeuralNet</a> (const string file) throw (NetworkFileNotFoundException)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Constructor. <a href="#3d602f3988a9a3e2c77dc6955674f412"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#b4bfa407d28bb17abf7f735a049987d9">NeuralNet</a> (const std::string file) throw (NetworkFileNotFoundException)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Constructor. <a href="#7fc7fc3e3220c138ffa5356fef6b9757"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#0c16df2e0701503052c63749930b238e">NeuralNet</a> (size_t in_size, size_t hidden_size, size_t out_size, double(*actv)(double), double l, int e)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Constructor. <a href="#0c16df2e0701503052c63749930b238e"></a><br></td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Constructor. <a href="#b4bfa407d28bb17abf7f735a049987d9"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#961dce8913264bf64c899dce4e25f810">getOutput</a> () const </td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It gets the output of the network (note: the layer output should contain an only neuron). <a href="#961dce8913264bf64c899dce4e25f810"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">vector&lt; double &gt;&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#a6b8bf3800b43b58843c65fc431207ae">getOutputs</a> ()</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e08cdcf4b70f987700e553d9914f6179">getThreshold</a> () const </td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It gets the output of the network in case the output layer contains more neurons. <a href="#a6b8bf3800b43b58843c65fc431207ae"></a><br></td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Get the threshold of the neurons in the network. <a href="#e08cdcf4b70f987700e553d9914f6179"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">std::vector&lt; double &gt;&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e6d2215ecc8b560db2f6797db642191c">getOutputs</a> ()</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It gets the output of the network in case the output layer contains more neurons. <a href="#e6d2215ecc8b560db2f6797db642191c"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#562dfe9fb8d73bf25a23ce608451d3aa">expected</a> () const </td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It gets the value expected. <a href="#562dfe9fb8d73bf25a23ce608451d3aa"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#b6475762b7e9eab086befdc511f7c236">setExpected</a> (double <a class="el" href="classneuralpp_1_1NeuralNet.html#261f5f68fcc5be54250cfa03945266dd">ex</a>)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Get the expected value (in case you have an only neuron in output layer). <a href="#562dfe9fb8d73bf25a23ce608451d3aa"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">std::vector&lt; double &gt;&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#51a1851ed07b85bec091c9053ae99cf7">getExpected</a> () const </td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It sets the value you expect from your network. <a href="#b6475762b7e9eab086befdc511f7c236"></a><br></td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Get the expected value (in case you have an only neuron in output layer). <a href="#51a1851ed07b85bec091c9053ae99cf7"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#b6475762b7e9eab086befdc511f7c236">setExpected</a> (double ex)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It sets the value you expect from your network (in case the network has an only neuron in its output layer). <a href="#b6475762b7e9eab086befdc511f7c236"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e649edc3d86bec7c0e178d5c892b4fd7">setExpected</a> (std::vector&lt; double &gt; ex)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Set the values you expect from your network. <a href="#e649edc3d86bec7c0e178d5c892b4fd7"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#b0bd1daadb06980dff1f50d33a7c098e">update</a> ()</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It updates through back-propagation the weights of the synapsis and computes again the output value for <em>epochs</em> times, calling back updateWeights and commitChanges functions. <a href="#b0bd1daadb06980dff1f50d33a7c098e"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#c129c180647362da963758bfd1ba6890">propagate</a> ()</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It propagates values through the network. <a href="#c129c180647362da963758bfd1ba6890"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#0de170e8ab561ad63d0739b4c4b74f68">setInput</a> (vector&lt; double &gt; &amp;v)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#405b32d2928344314ecf0469070b0f17">setInput</a> (std::vector&lt; double &gt; v)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It sets the input for the network. <a href="#0de170e8ab561ad63d0739b4c4b74f68"></a><br></td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It sets the input for the network. <a href="#405b32d2928344314ecf0469070b0f17"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#46f23f462318a4ffc037a4e806364c3f">link</a> ()</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It links the layers of the network (input, hidden, output). <a href="#46f23f462318a4ffc037a4e806364c3f"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#fdf94c276720c25e565cac834fe8a407">save</a> (const char *fname) throw (NetworkFileWriteException)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Save a trained neural network to a binary file. <a href="#fdf94c276720c25e565cac834fe8a407"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#ead4bdef0602a5cadbe3beb685e01f5f">train</a> (string xml, <a class="el" href="classneuralpp_1_1NeuralNet.html#94c36c94060e785ea67a0014c4182f8f">source</a> src) throw (InvalidXMLException)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#1c9e17437d41a7048611e21a3cc1c7dd">train</a> (std::string xml, <a class="el" href="classneuralpp_1_1NeuralNet.html#94c36c94060e785ea67a0014c4182f8f">source</a> src) throw (InvalidXMLException)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Train a network using a training set loaded from an XML file. <a href="#ead4bdef0602a5cadbe3beb685e01f5f"></a><br></td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Train a network using a training set loaded from an XML file. <a href="#1c9e17437d41a7048611e21a3cc1c7dd"></a><br></td></tr>
<tr><td colspan="2"><br><h2>Static Public Member Functions</h2></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#45c7645d4affe65752d37cd230afba24">initXML</a> (string &amp;xml)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#96da6712a72051cf34ad961761ef6e08">initXML</a> (std::string &amp;xml)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Initialize the training XML for the neural network. <a href="#45c7645d4affe65752d37cd230afba24"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static vector&lt; double &gt;&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e07af23ceb8666518da0c035bf1e0376">split</a> (char delim, string str)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Initialize the training XML for the neural network. <a href="#96da6712a72051cf34ad961761ef6e08"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static std::vector&lt; double &gt;&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#83c6555520856d5867752904349ab6ca">split</a> (char delim, std::string str)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Splits a string into a vector of doubles, given a delimitator. <a href="#e07af23ceb8666518da0c035bf1e0376"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static string&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#4be31ecb0b543a192997bd83c6995ccb">XMLFromSet</a> (int id, string set)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Splits a string into a vector of doubles, given a delimitator. <a href="#83c6555520856d5867752904349ab6ca"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static std::string&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#0a2733037af912b3e6a10146e7b7172f">XMLFromSet</a> (int &amp;id, std::string set)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Get a training set from a string and copies it to an XML For example, these strings could be training sets for making sums: "2,3;5" - "5,6;11" - "2,2;4" - "4,5:9" This method called on the first string will return an XML such this: '&lt;training id="0"&gt;&lt;input id="0"&gt;2&lt;/input&gt;&lt;input id="1"&gt;3&lt;/input&gt;&lt;output id="0"&gt;5&lt;/output&gt; &amp;lt/training&gt;'. <a href="#4be31ecb0b543a192997bd83c6995ccb"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#28b9966c5f197b8e86d57dd104aa32a6">closeXML</a> (string &amp;xml)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Get a training set from a string and copies it to an XML For example, these strings could be training sets for making sums: "2,3;5" - "5,6;11" - "2,2;4" - "4,5:9" This method called on the first string will return an XML such this: '&lt;training id="0"&gt;&lt;input id="0"&gt;2&lt;/input&gt;&lt;input id="1"&gt;3&lt;/input&gt;&lt;output id="0"&gt;5&lt;/output&gt; &amp;lt/training&gt;'. <a href="#0a2733037af912b3e6a10146e7b7172f"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">static void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e17732ed578bc4bd6032bfae58a5cf51">closeXML</a> (std::string &amp;xml)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Closes an open XML document generated by "initXML" and "XMLFromSet". <a href="#28b9966c5f197b8e86d57dd104aa32a6"></a><br></td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Closes an open XML document generated by "initXML" and "XMLFromSet". <a href="#e17732ed578bc4bd6032bfae58a5cf51"></a><br></td></tr>
<tr><td colspan="2"><br><h2>Public Attributes</h2></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> *&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e2b4e8405f9d25edab395d61502bdba9">input</a></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#94169c89a7cd47122ab5dbf1d5c5e108">updateWeights</a> ()</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It updates the weights of the net's synapsis through back-propagation. <a href="#94169c89a7cd47122ab5dbf1d5c5e108"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#62695a82dfb1df758a44150921aec8e0">commitChanges</a> (<a class="el" href="classneuralpp_1_1Layer.html">Layer</a> *l)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#f697a8d9967ad8f03e5a16a42cd110c5">commitChanges</a> (<a class="el" href="classneuralpp_1_1Layer.html">Layer</a> &amp;l)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It commits the changes made by <a class="el" href="classneuralpp_1_1NeuralNet.html#94169c89a7cd47122ab5dbf1d5c5e108" title="It updates the weights of the net&#39;s synapsis through back-propagation.">updateWeights()</a> to the layer l. <a href="#62695a82dfb1df758a44150921aec8e0"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#0616c51404efaca2714e37dd7478997e">error</a> (double <a class="el" href="classneuralpp_1_1NeuralNet.html#261f5f68fcc5be54250cfa03945266dd">ex</a>) const </td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">It commits the changes made by <a class="el" href="classneuralpp_1_1NeuralNet.html#94169c89a7cd47122ab5dbf1d5c5e108" title="It updates the weights of the net&#39;s synapsis through back-propagation.">updateWeights()</a> to the layer l. <a href="#f697a8d9967ad8f03e5a16a42cd110c5"></a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#8a140d28e6dd4097470c7c138801ad01">error</a> (double ex)</td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Get the error made on the expected result as |v-v'|/v. <a href="#0616c51404efaca2714e37dd7478997e"></a><br></td></tr>
<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Get the error made on the expected result as squared deviance. <a href="#8a140d28e6dd4097470c7c138801ad01"></a><br></td></tr>
<tr><td colspan="2"><br><h2>Private Attributes</h2></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">int&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#4cb52dae7b43d03fac73afca7b9f3a51">epochs</a></td></tr>
@ -119,7 +126,9 @@
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#6bd7be443e46b2fdbf1da2edb8e611ab">l_rate</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#261f5f68fcc5be54250cfa03945266dd">ex</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#327dbfdd72b0a74293f8f29630525aa3">threshold</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">std::vector&lt; double &gt;&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#a9e4ff43427f56663739c4c7450de8ee">expect</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">double(*&nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#c1469e6afd87d85b82f14bc246f82457">actv_f</a> )(double)</td></tr>
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<hr><a name="_details"></a><h2>Detailed Description</h2>
Main project's class.
<p>
Use *ONLY* this class, unless you know what you're doing <hr><h2>Member Enumeration Documentation</h2>
Use *ONLY* this class, unless you know what you're doing <dl compact><dt><b>Examples: </b></dt><dd>
<p>
<a class="el" href="examples_2adderFromScratch_8cpp-example.html#_a0">examples/adderFromScratch.cpp</a>, <a class="el" href="examples_2doAdd_8cpp-example.html#_a0">examples/doAdd.cpp</a>, and <a class="el" href="examples_2learnAdd_8cpp-example.html#_a0">examples/learnAdd.cpp</a>.</dl><hr><h2>Member Enumeration Documentation</h2>
<a class="anchor" name="94c36c94060e785ea67a0014c4182f8f"></a><!-- doxytag: member="neuralpp::NeuralNet::source" ref="94c36c94060e785ea67a0014c4182f8f" args="" -->
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@ -176,7 +188,7 @@ Empty constructor for the class - it just makes nothing.
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</div><p>
<a class="anchor" name="c79534c7c0dfb20d1d03be2ad7569b78"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="c79534c7c0dfb20d1d03be2ad7569b78" args="(size_t in_size, size_t hidden_size, size_t out_size, double l, int e)" -->
<a class="anchor" name="3d602f3988a9a3e2c77dc6955674f412"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="3d602f3988a9a3e2c77dc6955674f412" args="(size_t in_size, size_t hidden_size, size_t out_size, double l, int e, double th=0.0, double(*a)(double)=__actv)" -->
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@ -208,7 +220,19 @@ Empty constructor for the class - it just makes nothing.
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&nbsp;</td>
<td class="paramname"> <em>e</em></td><td>&nbsp;</td>
<td class="paramname"> <em>e</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">double&nbsp;</td>
<td class="paramname"> <em>th</em> = <code>0.0</code>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">double(*)(double)&nbsp;</td>
<td class="paramname"> <em>a</em> = <code>__actv</code></td><td>&nbsp;</td>
</tr>
<tr>
<td></td>
@ -229,22 +253,24 @@ Constructor.
<tr><td valign="top"></td><td valign="top"><em>out_size</em>&nbsp;</td><td>Size of the output layer </td></tr>
<tr><td valign="top"></td><td valign="top"><em>l</em>&nbsp;</td><td>learn rate (get it after doing some experiments, but generally try to keep its value quite low to be more accurate) </td></tr>
<tr><td valign="top"></td><td valign="top"><em>e</em>&nbsp;</td><td>Epochs (cycles) to execute (the most you execute, the most the network can be accurate for its purpose) </td></tr>
<tr><td valign="top"></td><td valign="top"><em>th</em>&nbsp;</td><td>Threshold, value in [0,1] that establishes how much a neuron must be 'sensitive' on variations of the input values </td></tr>
<tr><td valign="top"></td><td valign="top"><em>a</em>&nbsp;</td><td>Activation function to use (default: f(x)=x) </td></tr>
</table>
</dl>
</div>
</div><p>
<a class="anchor" name="7fc7fc3e3220c138ffa5356fef6b9757"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="7fc7fc3e3220c138ffa5356fef6b9757" args="(const string file)" -->
<a class="anchor" name="b4bfa407d28bb17abf7f735a049987d9"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="b4bfa407d28bb17abf7f735a049987d9" args="(const std::string file)" -->
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<td class="memname">neuralpp::NeuralNet::NeuralNet </td>
<td>(</td>
<td class="paramtype">const string&nbsp;</td>
<td class="paramtype">const std::string&nbsp;</td>
<td class="paramname"> <em>file</em> </td>
<td>&nbsp;)&nbsp;</td>
<td> throw (NetworkFileNotFoundException)</td>
<td> throw (<a class="el" href="classneuralpp_1_1NetworkFileNotFoundException.html">NetworkFileNotFoundException</a>)</td>
</tr>
</table>
</div>
@ -260,72 +286,7 @@ Constructor.
</dl>
<dl compact><dt><b>Exceptions:</b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
<tr><td valign="top"></td><td valign="top"><em>NetworkFileNotFoundException</em>&nbsp;</td><td></td></tr>
</table>
</dl>
</div>
</div><p>
<a class="anchor" name="0c16df2e0701503052c63749930b238e"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="0c16df2e0701503052c63749930b238e" args="(size_t in_size, size_t hidden_size, size_t out_size, double(*actv)(double), double l, int e)" -->
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<td class="memname">neuralpp::NeuralNet::NeuralNet </td>
<td>(</td>
<td class="paramtype">size_t&nbsp;</td>
<td class="paramname"> <em>in_size</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">size_t&nbsp;</td>
<td class="paramname"> <em>hidden_size</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">size_t&nbsp;</td>
<td class="paramname"> <em>out_size</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">double(*)(double)&nbsp;</td>
<td class="paramname"> <em>actv</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">double&nbsp;</td>
<td class="paramname"> <em>l</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&nbsp;</td>
<td class="paramname"> <em>e</em></td><td>&nbsp;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td><td></td>
</tr>
</table>
</div>
<div class="memdoc">
<p>
Constructor.
<p>
<dl compact><dt><b>Parameters:</b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
<tr><td valign="top"></td><td valign="top"><em>in_size</em>&nbsp;</td><td>Size of the input layer </td></tr>
<tr><td valign="top"></td><td valign="top"><em>hidden_size</em>&nbsp;</td><td>Size of the hidden layer </td></tr>
<tr><td valign="top"></td><td valign="top"><em>out_size</em>&nbsp;</td><td>Size of the output layer </td></tr>
<tr><td valign="top"></td><td valign="top"><em>actv</em>&nbsp;</td><td>Activation function to use (default: f(x)=x) </td></tr>
<tr><td valign="top"></td><td valign="top"><em>l</em>&nbsp;</td><td>learn rate (get it after doing some experiments, but generally try to keep its value quite low to be more accurate) </td></tr>
<tr><td valign="top"></td><td valign="top"><em>e</em>&nbsp;</td><td>Epochs (cycles) to execute (the most you execute, the most the network can be accurate for its purpose) </td></tr>
<tr><td valign="top"></td><td valign="top"><em><a class="el" href="classneuralpp_1_1NetworkFileNotFoundException.html" title="Exception thrown when doing an attempt to load a network from an invalid file.">NetworkFileNotFoundException</a></em>&nbsp;</td><td></td></tr>
</table>
</dl>
@ -353,14 +314,14 @@ It updates the weights of the net's synapsis through back-propagation.
In-class use only
</div>
</div><p>
<a class="anchor" name="62695a82dfb1df758a44150921aec8e0"></a><!-- doxytag: member="neuralpp::NeuralNet::commitChanges" ref="62695a82dfb1df758a44150921aec8e0" args="(Layer *l)" -->
<a class="anchor" name="f697a8d9967ad8f03e5a16a42cd110c5"></a><!-- doxytag: member="neuralpp::NeuralNet::commitChanges" ref="f697a8d9967ad8f03e5a16a42cd110c5" args="(Layer &amp;l)" -->
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<td class="memname">void neuralpp::NeuralNet::commitChanges </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> *&nbsp;</td>
<td class="paramtype"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> &amp;&nbsp;</td>
<td class="paramname"> <em>l</em> </td>
<td>&nbsp;)&nbsp;</td>
<td><code> [private]</code></td>
@ -380,7 +341,7 @@ In-class use only <dl compact><dt><b>Parameters:</b></dt><dd>
</div>
</div><p>
<a class="anchor" name="0616c51404efaca2714e37dd7478997e"></a><!-- doxytag: member="neuralpp::NeuralNet::error" ref="0616c51404efaca2714e37dd7478997e" args="(double ex) const " -->
<a class="anchor" name="8a140d28e6dd4097470c7c138801ad01"></a><!-- doxytag: member="neuralpp::NeuralNet::error" ref="8a140d28e6dd4097470c7c138801ad01" args="(double ex)" -->
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@ -390,14 +351,14 @@ In-class use only <dl compact><dt><b>Parameters:</b></dt><dd>
<td class="paramtype">double&nbsp;</td>
<td class="paramname"> <em>ex</em> </td>
<td>&nbsp;)&nbsp;</td>
<td> const<code> [private]</code></td>
<td><code> [private]</code></td>
</tr>
</table>
</div>
<div class="memdoc">
<p>
Get the error made on the expected result as |v-v'|/v.
Get the error made on the expected result as squared deviance.
<p>
<dl compact><dt><b>Parameters:</b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
@ -427,15 +388,38 @@ Get the error made on the expected result as |v-v'|/v.
It gets the output of the network (note: the layer output should contain an only neuron).
<p>
<dl class="return" compact><dt><b>Returns:</b></dt><dd>The output value of the network </dd></dl>
<dl compact><dt><b>Examples: </b></dt><dd>
<a class="el" href="examples_2doAdd_8cpp-example.html#a5">examples/doAdd.cpp</a>.</dl>
</div>
</div><p>
<a class="anchor" name="a6b8bf3800b43b58843c65fc431207ae"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutputs" ref="a6b8bf3800b43b58843c65fc431207ae" args="()" -->
<a class="anchor" name="e08cdcf4b70f987700e553d9914f6179"></a><!-- doxytag: member="neuralpp::NeuralNet::getThreshold" ref="e08cdcf4b70f987700e553d9914f6179" args="() const " -->
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<td class="memname">vector&lt;double&gt; neuralpp::NeuralNet::getOutputs </td>
<td class="memname">double neuralpp::NeuralNet::getThreshold </td>
<td>(</td>
<td class="paramname"> </td>
<td>&nbsp;)&nbsp;</td>
<td> const</td>
</tr>
</table>
</div>
<div class="memdoc">
<p>
Get the threshold of the neurons in the network.
<p>
<dl class="return" compact><dt><b>Returns:</b></dt><dd>The threshold of the neurons </dd></dl>
</div>
</div><p>
<a class="anchor" name="e6d2215ecc8b560db2f6797db642191c"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutputs" ref="e6d2215ecc8b560db2f6797db642191c" args="()" -->
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<td class="memname">std::vector&lt;double&gt; neuralpp::NeuralNet::getOutputs </td>
<td>(</td>
<td class="paramname"> </td>
<td>&nbsp;)&nbsp;</td>
@ -449,7 +433,8 @@ It gets the output of the network (note: the layer output should contain an only
It gets the output of the network in case the output layer contains more neurons.
<p>
<dl class="return" compact><dt><b>Returns:</b></dt><dd>A vector containing the output values of the network </dd></dl>
<dl compact><dt><b>Examples: </b></dt><dd>
<a class="el" href="examples_2adderFromScratch_8cpp-example.html#a4">examples/adderFromScratch.cpp</a>.</dl>
</div>
</div><p>
<a class="anchor" name="562dfe9fb8d73bf25a23ce608451d3aa"></a><!-- doxytag: member="neuralpp::NeuralNet::expected" ref="562dfe9fb8d73bf25a23ce608451d3aa" args="() const " -->
@ -468,7 +453,29 @@ It gets the output of the network in case the output layer contains more neurons
<div class="memdoc">
<p>
It gets the value expected.
Get the expected value (in case you have an only neuron in output layer).
<p>
Of course you should specify this when you build your network by using setExpected. <dl class="return" compact><dt><b>Returns:</b></dt><dd>The expected output value for a certain training phase </dd></dl>
</div>
</div><p>
<a class="anchor" name="51a1851ed07b85bec091c9053ae99cf7"></a><!-- doxytag: member="neuralpp::NeuralNet::getExpected" ref="51a1851ed07b85bec091c9053ae99cf7" args="() const " -->
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<td class="memname">std::vector&lt;double&gt; neuralpp::NeuralNet::getExpected </td>
<td>(</td>
<td class="paramname"> </td>
<td>&nbsp;)&nbsp;</td>
<td> const</td>
</tr>
</table>
</div>
<div class="memdoc">
<p>
Get the expected value (in case you have an only neuron in output layer).
<p>
Of course you should specify this when you build your network by using setExpected. <dl class="return" compact><dt><b>Returns:</b></dt><dd>The expected output value for a certain training phase </dd></dl>
@ -491,7 +498,7 @@ Of course you should specify this when you build your network by using setExpect
<div class="memdoc">
<p>
It sets the value you expect from your network.
It sets the value you expect from your network (in case the network has an only neuron in its output layer).
<p>
<dl compact><dt><b>Parameters:</b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
@ -499,6 +506,33 @@ It sets the value you expect from your network.
</table>
</dl>
</div>
</div><p>
<a class="anchor" name="e649edc3d86bec7c0e178d5c892b4fd7"></a><!-- doxytag: member="neuralpp::NeuralNet::setExpected" ref="e649edc3d86bec7c0e178d5c892b4fd7" args="(std::vector&lt; double &gt; ex)" -->
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<td class="memname">void neuralpp::NeuralNet::setExpected </td>
<td>(</td>
<td class="paramtype">std::vector&lt; double &gt;&nbsp;</td>
<td class="paramname"> <em>ex</em> </td>
<td>&nbsp;)&nbsp;</td>
<td></td>
</tr>
</table>
</div>
<div class="memdoc">
<p>
Set the values you expect from your network.
<p>
<dl compact><dt><b>Parameters:</b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
<tr><td valign="top"></td><td valign="top"><em>ex</em>&nbsp;</td><td>Expected output values </td></tr>
</table>
</dl>
</div>
</div><p>
<a class="anchor" name="b0bd1daadb06980dff1f50d33a7c098e"></a><!-- doxytag: member="neuralpp::NeuralNet::update" ref="b0bd1daadb06980dff1f50d33a7c098e" args="()" -->
@ -540,17 +574,18 @@ It updates through back-propagation the weights of the synapsis and computes aga
<p>
It propagates values through the network.
<p>
Use this when you want to give an already trained network some new values the get to the output
Use this when you want to give an already trained network some new values the get to the output <dl compact><dt><b>Examples: </b></dt><dd>
<a class="el" href="examples_2adderFromScratch_8cpp-example.html#a3">examples/adderFromScratch.cpp</a>, and <a class="el" href="examples_2doAdd_8cpp-example.html#a4">examples/doAdd.cpp</a>.</dl>
</div>
</div><p>
<a class="anchor" name="0de170e8ab561ad63d0739b4c4b74f68"></a><!-- doxytag: member="neuralpp::NeuralNet::setInput" ref="0de170e8ab561ad63d0739b4c4b74f68" args="(vector&lt; double &gt; &amp;v)" -->
<a class="anchor" name="405b32d2928344314ecf0469070b0f17"></a><!-- doxytag: member="neuralpp::NeuralNet::setInput" ref="405b32d2928344314ecf0469070b0f17" args="(std::vector&lt; double &gt; v)" -->
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<td class="memname">void neuralpp::NeuralNet::setInput </td>
<td>(</td>
<td class="paramtype">vector&lt; double &gt; &amp;&nbsp;</td>
<td class="paramtype">std::vector&lt; double &gt;&nbsp;</td>
<td class="paramname"> <em>v</em> </td>
<td>&nbsp;)&nbsp;</td>
<td></td>
@ -567,7 +602,8 @@ It sets the input for the network.
<tr><td valign="top"></td><td valign="top"><em>v</em>&nbsp;</td><td>Vector of doubles, containing the values to give to your network </td></tr>
</table>
</dl>
<dl compact><dt><b>Examples: </b></dt><dd>
<a class="el" href="examples_2adderFromScratch_8cpp-example.html#a2">examples/adderFromScratch.cpp</a>, and <a class="el" href="examples_2doAdd_8cpp-example.html#a3">examples/doAdd.cpp</a>.</dl>
</div>
</div><p>
<a class="anchor" name="46f23f462318a4ffc037a4e806364c3f"></a><!-- doxytag: member="neuralpp::NeuralNet::link" ref="46f23f462318a4ffc037a4e806364c3f" args="()" -->
@ -601,7 +637,7 @@ Don't use unless you exactly know what you're doing, it is already called by the
<td class="paramtype">const char *&nbsp;</td>
<td class="paramname"> <em>fname</em> </td>
<td>&nbsp;)&nbsp;</td>
<td> throw (NetworkFileWriteException)</td>
<td> throw (<a class="el" href="classneuralpp_1_1NetworkFileWriteException.html">NetworkFileWriteException</a>)</td>
</tr>
</table>
</div>
@ -617,20 +653,21 @@ Save a trained neural network to a binary file.
</dl>
<dl compact><dt><b>Exceptions:</b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
<tr><td valign="top"></td><td valign="top"><em>NetworkFileWriteException</em>&nbsp;</td><td>When you get an error writing the network's information to a file </td></tr>
<tr><td valign="top"></td><td valign="top"><em><a class="el" href="classneuralpp_1_1NetworkFileWriteException.html" title="Exception thrown when trying to write the network&#39;s information to a file that...">NetworkFileWriteException</a></em>&nbsp;</td><td>When you get an error writing the network's information to a file </td></tr>
</table>
</dl>
<dl compact><dt><b>Examples: </b></dt><dd>
<a class="el" href="examples_2learnAdd_8cpp-example.html#a2">examples/learnAdd.cpp</a>.</dl>
</div>
</div><p>
<a class="anchor" name="ead4bdef0602a5cadbe3beb685e01f5f"></a><!-- doxytag: member="neuralpp::NeuralNet::train" ref="ead4bdef0602a5cadbe3beb685e01f5f" args="(string xml, source src)" -->
<a class="anchor" name="1c9e17437d41a7048611e21a3cc1c7dd"></a><!-- doxytag: member="neuralpp::NeuralNet::train" ref="1c9e17437d41a7048611e21a3cc1c7dd" args="(std::string xml, source src)" -->
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<td class="memname">void neuralpp::NeuralNet::train </td>
<td>(</td>
<td class="paramtype">string&nbsp;</td>
<td class="paramtype">std::string&nbsp;</td>
<td class="paramname"> <em>xml</em>, </td>
</tr>
<tr>
@ -642,7 +679,7 @@ Save a trained neural network to a binary file.
<tr>
<td></td>
<td>)</td>
<td></td><td></td><td> throw (InvalidXMLException)</td>
<td></td><td></td><td> throw (<a class="el" href="classneuralpp_1_1InvalidXMLException.html">InvalidXMLException</a>)</td>
</tr>
</table>
</div>
@ -659,20 +696,21 @@ A sample XML file is available in examples/adder.xml <dl compact><dt><b>Paramete
</dl>
<dl compact><dt><b>Exceptions:</b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
<tr><td valign="top"></td><td valign="top"><em>InvalidXMLException</em>&nbsp;</td><td></td></tr>
<tr><td valign="top"></td><td valign="top"><em><a class="el" href="classneuralpp_1_1InvalidXMLException.html" title="Exception thrown when trying parsing an invalid XML.">InvalidXMLException</a></em>&nbsp;</td><td></td></tr>
</table>
</dl>
<dl compact><dt><b>Examples: </b></dt><dd>
<a class="el" href="examples_2adderFromScratch_8cpp-example.html#a1">examples/adderFromScratch.cpp</a>, and <a class="el" href="examples_2learnAdd_8cpp-example.html#a1">examples/learnAdd.cpp</a>.</dl>
</div>
</div><p>
<a class="anchor" name="45c7645d4affe65752d37cd230afba24"></a><!-- doxytag: member="neuralpp::NeuralNet::initXML" ref="45c7645d4affe65752d37cd230afba24" args="(string &amp;xml)" -->
<a class="anchor" name="96da6712a72051cf34ad961761ef6e08"></a><!-- doxytag: member="neuralpp::NeuralNet::initXML" ref="96da6712a72051cf34ad961761ef6e08" args="(std::string &amp;xml)" -->
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<td class="memname">static void neuralpp::NeuralNet::initXML </td>
<td>(</td>
<td class="paramtype">string &amp;&nbsp;</td>
<td class="paramtype">std::string &amp;&nbsp;</td>
<td class="paramname"> <em>xml</em> </td>
<td>&nbsp;)&nbsp;</td>
<td><code> [static]</code></td>
@ -692,12 +730,12 @@ Initialize the training XML for the neural network.
</div>
</div><p>
<a class="anchor" name="e07af23ceb8666518da0c035bf1e0376"></a><!-- doxytag: member="neuralpp::NeuralNet::split" ref="e07af23ceb8666518da0c035bf1e0376" args="(char delim, string str)" -->
<a class="anchor" name="83c6555520856d5867752904349ab6ca"></a><!-- doxytag: member="neuralpp::NeuralNet::split" ref="83c6555520856d5867752904349ab6ca" args="(char delim, std::string str)" -->
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<td class="memname">static vector&lt;double&gt; neuralpp::NeuralNet::split </td>
<td class="memname">static std::vector&lt;double&gt; neuralpp::NeuralNet::split </td>
<td>(</td>
<td class="paramtype">char&nbsp;</td>
<td class="paramname"> <em>delim</em>, </td>
@ -705,7 +743,7 @@ Initialize the training XML for the neural network.
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">string&nbsp;</td>
<td class="paramtype">std::string&nbsp;</td>
<td class="paramname"> <em>str</em></td><td>&nbsp;</td>
</tr>
<tr>
@ -730,20 +768,20 @@ Splits a string into a vector of doubles, given a delimitator.
</div>
</div><p>
<a class="anchor" name="4be31ecb0b543a192997bd83c6995ccb"></a><!-- doxytag: member="neuralpp::NeuralNet::XMLFromSet" ref="4be31ecb0b543a192997bd83c6995ccb" args="(int id, string set)" -->
<a class="anchor" name="0a2733037af912b3e6a10146e7b7172f"></a><!-- doxytag: member="neuralpp::NeuralNet::XMLFromSet" ref="0a2733037af912b3e6a10146e7b7172f" args="(int &amp;id, std::string set)" -->
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<td class="memname">static string neuralpp::NeuralNet::XMLFromSet </td>
<td class="memname">static std::string neuralpp::NeuralNet::XMLFromSet </td>
<td>(</td>
<td class="paramtype">int&nbsp;</td>
<td class="paramtype">int &amp;&nbsp;</td>
<td class="paramname"> <em>id</em>, </td>
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<td class="paramname"> <em>set</em></td><td>&nbsp;</td>
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@ -768,14 +806,14 @@ Get a training set from a string and copies it to an XML For example, these stri
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<a class="anchor" name="28b9966c5f197b8e86d57dd104aa32a6"></a><!-- doxytag: member="neuralpp::NeuralNet::closeXML" ref="28b9966c5f197b8e86d57dd104aa32a6" args="(string &amp;xml)" -->
<a class="anchor" name="e17732ed578bc4bd6032bfae58a5cf51"></a><!-- doxytag: member="neuralpp::NeuralNet::closeXML" ref="e17732ed578bc4bd6032bfae58a5cf51" args="(std::string &amp;xml)" -->
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<td class="memname">static void neuralpp::NeuralNet::closeXML </td>
<td>(</td>
<td class="paramtype">string &amp;&nbsp;</td>
<td class="paramtype">std::string &amp;&nbsp;</td>
<td class="paramname"> <em>xml</em> </td>
<td>&nbsp;)&nbsp;</td>
<td><code> [static]</code></td>
@ -841,12 +879,27 @@ Closes an open XML document generated by "initXML" and "XMLFromSet".
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<a class="anchor" name="261f5f68fcc5be54250cfa03945266dd"></a><!-- doxytag: member="neuralpp::NeuralNet::ex" ref="261f5f68fcc5be54250cfa03945266dd" args="" -->
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<td class="memname">double <a class="el" href="classneuralpp_1_1NeuralNet.html#261f5f68fcc5be54250cfa03945266dd">neuralpp::NeuralNet::ex</a><code> [private]</code> </td>
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<a class="anchor" name="a9e4ff43427f56663739c4c7450de8ee"></a><!-- doxytag: member="neuralpp::NeuralNet::expect" ref="a9e4ff43427f56663739c4c7450de8ee" args="" -->
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<td class="memname">std::vector&lt;double&gt; <a class="el" href="classneuralpp_1_1NeuralNet.html#a9e4ff43427f56663739c4c7450de8ee">neuralpp::NeuralNet::expect</a><code> [private]</code> </td>
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@ -921,7 +974,7 @@ Private pointer to function, containing the function to be used as activation fu
<hr>The documentation for this class was generated from the following file:<ul>
<li><a class="el" href="neural_09_09_8hpp-source.html">neural++.hpp</a></ul>
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<hr size="1"><address style="text-align: right;"><small>Generated on Sat Aug 15 02:56:02 2009 for Neural++ by&nbsp;
<hr size="1"><address style="text-align: right;"><small>Generated on Sun Aug 16 20:53:42 2009 for Neural++ by&nbsp;
<a href="http://www.doxygen.org/index.html">
<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
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