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Hey I can't believe it, I fixed it...
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113 changed files with 673 additions and 776 deletions
doc/html
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<tr><td class="memItemLeft" nowrap align="right" valign="top"> </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>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#c79534c7c0dfb20d1d03be2ad7569b78"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#88380cb002edcccf11b59f6d3f6c94c9">NeuralNet</a> (const char *file) throw (NetworkFileNotFoundException)</td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#7fc7fc3e3220c138ffa5356fef6b9757">NeuralNet</a> (const string file) throw (NetworkFileNotFoundException)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#88380cb002edcccf11b59f6d3f6c94c9"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#b4b261f7f7fa93c45855288fd66cfdca">NeuralNet</a> (size_t in_size, size_t hidden_size, size_t out_size, double(*actv)(double), double(*<a class="el" href="classneuralpp_1_1NeuralNet.html#df44689f4e6201ca1ddc67655cce3576">deriv</a>)(double), double l, int e)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#7fc7fc3e3220c138ffa5356fef6b9757"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"> </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>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#b4b261f7f7fa93c45855288fd66cfdca"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#7de7ee318eeb791d21a01e9e9e0e8c5a">getOutput</a> ()</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <a href="#0c16df2e0701503052c63749930b238e"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#961dce8913264bf64c899dce4e25f810">getOutput</a> () const </td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It gets the output of the network (note: the layer output should contain an only neuron). <a href="#7de7ee318eeb791d21a01e9e9e0e8c5a"></a><br></td></tr>
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<tr><td class="mdescLeft"> </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>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">vector< double > </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#a6b8bf3800b43b58843c65fc431207ae">getOutputs</a> ()</td></tr>
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<tr><td class="mdescLeft"> </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>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#423fd38a61d79905dcc12da84c805114">expected</a> ()</td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#562dfe9fb8d73bf25a23ce608451d3aa">expected</a> () const </td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It gets the value expected. <a href="#423fd38a61d79905dcc12da84c805114"></a><br></td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It gets the value expected. <a href="#562dfe9fb8d73bf25a23ce608451d3aa"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void </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>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It sets the value you expect from your network. <a href="#b6475762b7e9eab086befdc511f7c236"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#46f23f462318a4ffc037a4e806364c3f">link</a> ()</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It links the layers of the network (input, hidden, output). <a href="#46f23f462318a4ffc037a4e806364c3f"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#5db8d6ba4785f732da6e642b4f8f11a0">save</a> (const char *fname)</td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#fdf94c276720c25e565cac834fe8a407">save</a> (const char *fname) throw (NetworkFileWriteException)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Save a trained neural network to a binary file. <a href="#5db8d6ba4785f732da6e642b4f8f11a0"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e8b8741d28bec1354db555eabe418cb6">train</a> (string xml, <a class="el" href="classneuralpp_1_1NeuralNet.html#94c36c94060e785ea67a0014c4182f8f">source</a> xrc) throw (InvalidXMLException)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Save a trained neural network to a binary file. <a href="#fdf94c276720c25e565cac834fe8a407"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void </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>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Train a network using a training set loaded from an XML file. <a href="#e8b8741d28bec1354db555eabe418cb6"></a><br></td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Train a network using a training set loaded from an XML file. <a href="#ead4bdef0602a5cadbe3beb685e01f5f"></a><br></td></tr>
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<tr><td colspan="2"><br><h2>Static Public Member Functions</h2></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#45c7645d4affe65752d37cd230afba24">initXML</a> (string &xml)</td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#28b9966c5f197b8e86d57dd104aa32a6">closeXML</a> (string &xml)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Closes an open XML document generated by "initXML" and "XMLFromSet". <a href="#28b9966c5f197b8e86d57dd104aa32a6"></a><br></td></tr>
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<tr><td colspan="2"><br><h2>Public Attributes</h2></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> * </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"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#bbdaa1b6c0a1a95d2b18cd25fda2a266">hidden</a></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#fa9b2dbcbb39d0fc70f790ac24069a74">output</a></td></tr>
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<tr><td colspan="2"><br><h2>Private Member Functions</h2></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#94169c89a7cd47122ab5dbf1d5c5e108">updateWeights</a> ()</td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">void </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>
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<tr><td class="mdescLeft"> </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's synapsis through back-propagation.">updateWeights()</a> to the layer l. <a href="#62695a82dfb1df758a44150921aec8e0"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#8a140d28e6dd4097470c7c138801ad01">error</a> (double <a class="el" href="classneuralpp_1_1NeuralNet.html#261f5f68fcc5be54250cfa03945266dd">ex</a>)</td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double </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>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It get the error made on the expected result as |v-v'|/v. <a href="#8a140d28e6dd4097470c7c138801ad01"></a><br></td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Get the error made on the expected result as |v-v'|/v. <a href="#0616c51404efaca2714e37dd7478997e"></a><br></td></tr>
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<tr><td colspan="2"><br><h2>Private Attributes</h2></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#4cb52dae7b43d03fac73afca7b9f3a51">epochs</a></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#261f5f68fcc5be54250cfa03945266dd">ex</a></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> * </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"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#bbdaa1b6c0a1a95d2b18cd25fda2a266">hidden</a></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> * </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#fa9b2dbcbb39d0fc70f790ac24069a74">output</a></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double(* </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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<tr><td class="mdescLeft"> </td><td class="mdescRight">Private pointer to function, containing the function to be used as activation function. <a href="#c1469e6afd87d85b82f14bc246f82457"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">double(* </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#df44689f4e6201ca1ddc67655cce3576">deriv</a> )(double)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Private pointer to function, containing the function to be used as derivate of the activation function. <a href="#df44689f4e6201ca1ddc67655cce3576"></a><br></td></tr>
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</table>
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<hr><a name="_details"></a><h2>Detailed Description</h2>
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Main project's class.
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</div>
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</div><p>
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<a class="anchor" name="88380cb002edcccf11b59f6d3f6c94c9"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="88380cb002edcccf11b59f6d3f6c94c9" args="(const char *file)" -->
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<a class="anchor" name="7fc7fc3e3220c138ffa5356fef6b9757"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="7fc7fc3e3220c138ffa5356fef6b9757" args="(const string file)" -->
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">neuralpp::NeuralNet::NeuralNet </td>
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<td>(</td>
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<td class="paramtype">const char * </td>
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<td class="paramtype">const string </td>
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<td class="paramname"> <em>file</em> </td>
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<td> ) </td>
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<td> throw (NetworkFileNotFoundException)</td>
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<p>
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<dl compact><dt><b>Parameters:</b></dt><dd>
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<table border="0" cellspacing="2" cellpadding="0">
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<tr><td valign="top"></td><td valign="top"><em>file</em> </td><td>Binary file containing a neural network previously saved by <a class="el" href="classneuralpp_1_1NeuralNet.html#5db8d6ba4785f732da6e642b4f8f11a0" title="Save a trained neural network to a binary file.">save()</a> method </td></tr>
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<tr><td valign="top"></td><td valign="top"><em>file</em> </td><td>Binary file containing a neural network previously saved by <a class="el" href="classneuralpp_1_1NeuralNet.html#fdf94c276720c25e565cac834fe8a407" title="Save a trained neural network to a binary file.">save()</a> method </td></tr>
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</table>
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</dl>
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<dl compact><dt><b>Exceptions:</b></dt><dd>
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</div>
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</div><p>
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<a class="anchor" name="b4b261f7f7fa93c45855288fd66cfdca"></a><!-- doxytag: member="neuralpp::NeuralNet::NeuralNet" ref="b4b261f7f7fa93c45855288fd66cfdca" args="(size_t in_size, size_t hidden_size, size_t out_size, double(*actv)(double), double(*deriv)(double), double l, int e)" -->
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<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="paramtype">double(*)(double) </td>
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<td class="paramname"> <em>actv</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">double(*)(double) </td>
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<td class="paramname"> <em>deriv</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<tr><td valign="top"></td><td valign="top"><em>hidden_size</em> </td><td>Size of the hidden layer </td></tr>
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<tr><td valign="top"></td><td valign="top"><em>out_size</em> </td><td>Size of the output layer </td></tr>
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<tr><td valign="top"></td><td valign="top"><em>actv</em> </td><td>Activation function to use (default: f(x)=x) </td></tr>
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<tr><td valign="top"></td><td valign="top"><em>deriv</em> </td><td>Derivate for the activation function to use (default: f'(x)=1) </td></tr>
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<tr><td valign="top"></td><td valign="top"><em>l</em> </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>
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<tr><td valign="top"></td><td valign="top"><em>e</em> </td><td>Epochs (cycles) to execute (the most you execute, the most the network can be accurate for its purpose) </td></tr>
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</table>
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</div>
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</div><p>
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<a class="anchor" name="8a140d28e6dd4097470c7c138801ad01"></a><!-- doxytag: member="neuralpp::NeuralNet::error" ref="8a140d28e6dd4097470c7c138801ad01" args="(double ex)" -->
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<a class="anchor" name="0616c51404efaca2714e37dd7478997e"></a><!-- doxytag: member="neuralpp::NeuralNet::error" ref="0616c51404efaca2714e37dd7478997e" args="(double ex) const " -->
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<td class="paramtype">double </td>
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<td class="paramname"> <em>ex</em> </td>
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<td> ) </td>
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<td><code> [private]</code></td>
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<td> const<code> [private]</code></td>
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</table>
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</div>
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<div class="memdoc">
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<p>
|
||||
It get the error made on the expected result as |v-v'|/v.
|
||||
Get the error made on the expected result as |v-v'|/v.
|
||||
<p>
|
||||
<dl compact><dt><b>Parameters:</b></dt><dd>
|
||||
<table border="0" cellspacing="2" cellpadding="0">
|
||||
|
@ -417,7 +408,7 @@ It get the error made on the expected result as |v-v'|/v.
|
|||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="7de7ee318eeb791d21a01e9e9e0e8c5a"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutput" ref="7de7ee318eeb791d21a01e9e9e0e8c5a" args="()" -->
|
||||
<a class="anchor" name="961dce8913264bf64c899dce4e25f810"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutput" ref="961dce8913264bf64c899dce4e25f810" args="() const " -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
|
@ -426,7 +417,7 @@ It get the error made on the expected result as |v-v'|/v.
|
|||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
<td> const</td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -461,7 +452,7 @@ It gets the output of the network in case the output layer contains more neurons
|
|||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="423fd38a61d79905dcc12da84c805114"></a><!-- doxytag: member="neuralpp::NeuralNet::expected" ref="423fd38a61d79905dcc12da84c805114" args="()" -->
|
||||
<a class="anchor" name="562dfe9fb8d73bf25a23ce608451d3aa"></a><!-- doxytag: member="neuralpp::NeuralNet::expected" ref="562dfe9fb8d73bf25a23ce608451d3aa" args="() const " -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
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||||
<table class="memname">
|
||||
|
@ -470,7 +461,7 @@ It gets the output of the network in case the output layer contains more neurons
|
|||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
<td> const</td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -600,17 +591,17 @@ It links the layers of the network (input, hidden, output).
|
|||
Don't use unless you exactly know what you're doing, it is already called by the constructor
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="5db8d6ba4785f732da6e642b4f8f11a0"></a><!-- doxytag: member="neuralpp::NeuralNet::save" ref="5db8d6ba4785f732da6e642b4f8f11a0" args="(const char *fname)" -->
|
||||
<a class="anchor" name="fdf94c276720c25e565cac834fe8a407"></a><!-- doxytag: member="neuralpp::NeuralNet::save" ref="fdf94c276720c25e565cac834fe8a407" args="(const char *fname)" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">bool neuralpp::NeuralNet::save </td>
|
||||
<td class="memname">void neuralpp::NeuralNet::save </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">const char * </td>
|
||||
<td class="paramname"> <em>fname</em> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
<td> throw (NetworkFileWriteException)</td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -624,10 +615,15 @@ Save a trained neural network to a binary file.
|
|||
<tr><td valign="top"></td><td valign="top"><em>fname</em> </td><td>Binary file where you're going to save your network </td></tr>
|
||||
</table>
|
||||
</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> </td><td>When you get an error writing the network's information to a file </td></tr>
|
||||
</table>
|
||||
</dl>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="e8b8741d28bec1354db555eabe418cb6"></a><!-- doxytag: member="neuralpp::NeuralNet::train" ref="e8b8741d28bec1354db555eabe418cb6" args="(string xml, source xrc)" -->
|
||||
<a class="anchor" name="ead4bdef0602a5cadbe3beb685e01f5f"></a><!-- doxytag: member="neuralpp::NeuralNet::train" ref="ead4bdef0602a5cadbe3beb685e01f5f" args="(string xml, source src)" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
|
@ -641,7 +637,7 @@ Save a trained neural network to a binary file.
|
|||
<td class="paramkey"></td>
|
||||
<td></td>
|
||||
<td class="paramtype"><a class="el" href="classneuralpp_1_1NeuralNet.html#94c36c94060e785ea67a0014c4182f8f">source</a> </td>
|
||||
<td class="paramname"> <em>xrc</em></td><td> </td>
|
||||
<td class="paramname"> <em>src</em></td><td> </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td></td>
|
||||
|
@ -858,51 +854,6 @@ Closes an open XML document generated by "initXML" and "XMLFromSet".
|
|||
|
||||
<p>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="e2b4e8405f9d25edab395d61502bdba9"></a><!-- doxytag: member="neuralpp::NeuralNet::input" ref="e2b4e8405f9d25edab395d61502bdba9" args="" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a>* <a class="el" href="classneuralpp_1_1NeuralNet.html#e2b4e8405f9d25edab395d61502bdba9">neuralpp::NeuralNet::input</a><code> [private]</code> </td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="bbdaa1b6c0a1a95d2b18cd25fda2a266"></a><!-- doxytag: member="neuralpp::NeuralNet::hidden" ref="bbdaa1b6c0a1a95d2b18cd25fda2a266" args="" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a>* <a class="el" href="classneuralpp_1_1NeuralNet.html#bbdaa1b6c0a1a95d2b18cd25fda2a266">neuralpp::NeuralNet::hidden</a><code> [private]</code> </td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="fa9b2dbcbb39d0fc70f790ac24069a74"></a><!-- doxytag: member="neuralpp::NeuralNet::output" ref="fa9b2dbcbb39d0fc70f790ac24069a74" args="" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a>* <a class="el" href="classneuralpp_1_1NeuralNet.html#fa9b2dbcbb39d0fc70f790ac24069a74">neuralpp::NeuralNet::output</a><code> [private]</code> </td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="c1469e6afd87d85b82f14bc246f82457"></a><!-- doxytag: member="neuralpp::NeuralNet::actv_f" ref="c1469e6afd87d85b82f14bc246f82457" args=")(double)" -->
|
||||
|
@ -922,19 +873,47 @@ Private pointer to function, containing the function to be used as activation fu
|
|||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="df44689f4e6201ca1ddc67655cce3576"></a><!-- doxytag: member="neuralpp::NeuralNet::deriv" ref="df44689f4e6201ca1ddc67655cce3576" args=")(double)" -->
|
||||
<a class="anchor" name="e2b4e8405f9d25edab395d61502bdba9"></a><!-- doxytag: member="neuralpp::NeuralNet::input" ref="e2b4e8405f9d25edab395d61502bdba9" args="" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">double(* <a class="el" href="classneuralpp_1_1NeuralNet.html#df44689f4e6201ca1ddc67655cce3576">neuralpp::NeuralNet::deriv</a>)(double)<code> [private]</code> </td>
|
||||
<td class="memname"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a>* <a class="el" href="classneuralpp_1_1NeuralNet.html#e2b4e8405f9d25edab395d61502bdba9">neuralpp::NeuralNet::input</a> </td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
Private pointer to function, containing the function to be used as derivate of the activation function.
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="bbdaa1b6c0a1a95d2b18cd25fda2a266"></a><!-- doxytag: member="neuralpp::NeuralNet::hidden" ref="bbdaa1b6c0a1a95d2b18cd25fda2a266" args="" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a>* <a class="el" href="classneuralpp_1_1NeuralNet.html#bbdaa1b6c0a1a95d2b18cd25fda2a266">neuralpp::NeuralNet::hidden</a> </td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="fa9b2dbcbb39d0fc70f790ac24069a74"></a><!-- doxytag: member="neuralpp::NeuralNet::output" ref="fa9b2dbcbb39d0fc70f790ac24069a74" args="" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a>* <a class="el" href="classneuralpp_1_1NeuralNet.html#fa9b2dbcbb39d0fc70f790ac24069a74">neuralpp::NeuralNet::output</a> </td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
|
||||
</div>
|
||||
|
@ -942,7 +921,7 @@ Private pointer to function, containing the function to be used as derivate of t
|
|||
<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>
|
||||
</div>
|
||||
<hr size="1"><address style="text-align: right;"><small>Generated on Sun Aug 9 11:11:18 2009 for Neural++ by
|
||||
<hr size="1"><address style="text-align: right;"><small>Generated on Sat Aug 15 02:56:02 2009 for Neural++ by
|
||||
<a href="http://www.doxygen.org/index.html">
|
||||
<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
|
||||
</body>
|
||||
|
|
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