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Is it time to release 1.0 version?
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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="#961dce8913264bf64c899dce4e25f810"></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#e08cdcf4b70f987700e553d9914f6179">getThreshold</a> () const </td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Get the threshold of the neurons in the network. <a href="#e08cdcf4b70f987700e553d9914f6179"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">std::vector< double > </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#e6d2215ecc8b560db2f6797db642191c">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="#e6d2215ecc8b560db2f6797db642191c"></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#562dfe9fb8d73bf25a23ce608451d3aa">expected</a> () const </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#e08cdcf4b70f987700e553d9914f6179">getThreshold</a> () const </td></tr>
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<tr><td class="mdescLeft"> </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>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">std::vector< double > </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#51a1851ed07b85bec091c9053ae99cf7">getExpected</a> () const </td></tr>
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<tr><td class="mdescLeft"> </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>
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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 ex)</td></tr>
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<tr><td class="mdescLeft"> </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>
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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#e649edc3d86bec7c0e178d5c892b4fd7">setExpected</a> (std::vector< double > ex)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Set the values you expect from your network. <a href="#e649edc3d86bec7c0e178d5c892b4fd7"></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#b0bd1daadb06980dff1f50d33a7c098e">update</a> ()</td></tr>
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<tr><td class="mdescLeft"> </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>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Get the threshold of the neurons in the network. <a href="#e08cdcf4b70f987700e553d9914f6179"></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#c129c180647362da963758bfd1ba6890">propagate</a> ()</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It propagates values through the network. <a href="#c129c180647362da963758bfd1ba6890"></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#405b32d2928344314ecf0469070b0f17">setInput</a> (std::vector< double > v)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It sets the input for the network. <a href="#405b32d2928344314ecf0469070b0f17"></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">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="#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#a060e28b438613a6cc9e0895ddbc292b">loadFromBinary</a> (const std::string fname) throw (NetworkFileNotFoundException)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">DEPRECATED. <a href="#a060e28b438613a6cc9e0895ddbc292b"></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#520147d9b47b69565567bd3fdcfd8897">saveToBinary</a> (const char *fname) throw (NetworkFileWriteException)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">DEPRECATED. <a href="#520147d9b47b69565567bd3fdcfd8897"></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#1c9e17437d41a7048611e21a3cc1c7dd">train</a> (std::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="#1c9e17437d41a7048611e21a3cc1c7dd"></a><br></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#96da6712a72051cf34ad961761ef6e08">initXML</a> (std::string &xml)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Initialize the training XML for the neural network. <a href="#96da6712a72051cf34ad961761ef6e08"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">static std::vector< double > </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#83c6555520856d5867752904349ab6ca">split</a> (char delim, std::string str)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Splits a string into a vector of doubles, given a delimitator. <a href="#83c6555520856d5867752904349ab6ca"></a><br></td></tr>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">static std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#0a2733037af912b3e6a10146e7b7172f">XMLFromSet</a> (int &id, std::string set)</td></tr>
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<tr><td class="mdescLeft"> </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: '<training id="0"><input id="0">2</input><input id="1">3</input><output id="0">5</output> &lt/training>'. <a href="#0a2733037af912b3e6a10146e7b7172f"></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#94169c89a7cd47122ab5dbf1d5c5e108">updateWeights</a> ()</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">It updates the weights of the net's synapsis through back-propagation. <a href="#94169c89a7cd47122ab5dbf1d5c5e108"></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#f697a8d9967ad8f03e5a16a42cd110c5">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="#f697a8d9967ad8f03e5a16a42cd110c5"></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 ex)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Get the error made on the expected result as squared deviance. <a href="#8a140d28e6dd4097470c7c138801ad01"></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#562dfe9fb8d73bf25a23ce608451d3aa">expected</a> () const </td></tr>
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<tr><td class="mdescLeft"> </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>
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<tr><td class="memItemLeft" nowrap align="right" valign="top">std::vector< double > </td><td class="memItemRight" valign="bottom"><a class="el" href="classneuralpp_1_1NeuralNet.html#51a1851ed07b85bec091c9053ae99cf7">getExpected</a> () const </td></tr>
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<tr><td class="mdescLeft"> </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>
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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 ex)</td></tr>
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<tr><td class="mdescLeft"> </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>
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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#e649edc3d86bec7c0e178d5c892b4fd7">setExpected</a> (std::vector< double > ex)</td></tr>
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<tr><td class="mdescLeft"> </td><td class="mdescRight">Set the values you expect from your network. <a href="#e649edc3d86bec7c0e178d5c892b4fd7"></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#b0bd1daadb06980dff1f50d33a7c098e">update</a> ()</td></tr>
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<tr><td class="mdescLeft"> </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>
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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 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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Use *ONLY* this class, unless you know what you're doing <dl compact><dt><b>Examples: </b></dt><dd>
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<p>
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<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>
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<a class="el" href="examples_2adderFromString_8cpp-example.html#_a0">examples/adderFromString.cpp</a>, <a class="el" href="examples_2doAdd_8cpp-example.html#_a0">examples/doAdd.cpp</a>, <a class="el" href="examples_2learnAdd_8cpp-example.html#_a0">examples/learnAdd.cpp</a>, and <a class="el" href="examples_2networkForSumsAndSubtractions_8cpp-example.html#_a0">examples/networkForSumsAndSubtractions.cpp</a>.</dl><hr><h2>Member Enumeration Documentation</h2>
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<a class="anchor" name="94c36c94060e785ea67a0014c4182f8f"></a><!-- doxytag: member="neuralpp::NeuralNet::source" ref="94c36c94060e785ea67a0014c4182f8f" args="" -->
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<div class="memitem">
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<div class="memproto">
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It updates the weights of the net's synapsis through back-propagation.
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<p>
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In-class use only
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</div>
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</div><p>
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<a class="anchor" name="f697a8d9967ad8f03e5a16a42cd110c5"></a><!-- doxytag: member="neuralpp::NeuralNet::commitChanges" ref="f697a8d9967ad8f03e5a16a42cd110c5" args="(Layer &l)" -->
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<td class="memname">void neuralpp::NeuralNet::commitChanges </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="classneuralpp_1_1Layer.html">Layer</a> & </td>
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<td class="paramname"> <em>l</em> </td>
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<td> ) </td>
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<td><code> [private]</code></td>
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</tr>
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</table>
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</div>
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<div class="memdoc">
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<p>
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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.
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<p>
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In-class use only <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>l</em> </td><td><a class="el" href="classneuralpp_1_1Layer.html" title="Class for managing layers of neurons.">Layer</a> to commit the changes </td></tr>
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</table>
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</dl>
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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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</dl>
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<dl class="return" compact><dt><b>Returns:</b></dt><dd>Mean error </dd></dl>
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</div>
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</div><p>
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<a class="anchor" name="961dce8913264bf64c899dce4e25f810"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutput" ref="961dce8913264bf64c899dce4e25f810" args="() const " -->
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<td class="memname">double neuralpp::NeuralNet::getOutput </td>
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<td>(</td>
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<td class="paramname"> </td>
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<td> ) </td>
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<td> const</td>
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<div class="memdoc">
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<p>
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It gets the output of the network (note: the layer output should contain an only neuron).
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<p>
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<dl class="return" compact><dt><b>Returns:</b></dt><dd>The output value of the network </dd></dl>
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<dl compact><dt><b>Examples: </b></dt><dd>
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<a class="el" href="examples_2doAdd_8cpp-example.html#a5">examples/doAdd.cpp</a>.</dl>
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</div>
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</div><p>
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<a class="anchor" name="e08cdcf4b70f987700e553d9914f6179"></a><!-- doxytag: member="neuralpp::NeuralNet::getThreshold" ref="e08cdcf4b70f987700e553d9914f6179" args="() const " -->
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<td class="memname">double neuralpp::NeuralNet::getThreshold </td>
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<td>(</td>
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<td class="paramname"> </td>
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<td> ) </td>
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<td> const</td>
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<p>
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Get the threshold of the neurons in the network.
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<p>
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<dl class="return" compact><dt><b>Returns:</b></dt><dd>The threshold of the neurons </dd></dl>
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</div>
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</div><p>
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<a class="anchor" name="e6d2215ecc8b560db2f6797db642191c"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutputs" ref="e6d2215ecc8b560db2f6797db642191c" args="()" -->
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<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">std::vector<double> neuralpp::NeuralNet::getOutputs </td>
|
||||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
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 " -->
|
||||
|
@ -446,7 +351,7 @@ It gets the output of the network in case the output layer contains more neurons
|
|||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td> const</td>
|
||||
<td> const<code> [private]</code></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -468,7 +373,7 @@ Of course you should specify this when you build your network by using setExpect
|
|||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td> const</td>
|
||||
<td> const<code> [private]</code></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -491,7 +396,7 @@ Of course you should specify this when you build your network by using setExpect
|
|||
<td class="paramtype">double </td>
|
||||
<td class="paramname"> <em>ex</em> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
<td><code> [private]</code></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -518,7 +423,7 @@ It sets the value you expect from your network (in case the network has an only
|
|||
<td class="paramtype">std::vector< double > </td>
|
||||
<td class="paramname"> <em>ex</em> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
<td><code> [private]</code></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -544,7 +449,7 @@ Set the values you expect from your network.
|
|||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
<td><code> [private]</code></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
@ -554,6 +459,95 @@ Set the values you expect from your network.
|
|||
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.
|
||||
<p>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="46f23f462318a4ffc037a4e806364c3f"></a><!-- doxytag: member="neuralpp::NeuralNet::link" ref="46f23f462318a4ffc037a4e806364c3f" args="()" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">void neuralpp::NeuralNet::link </td>
|
||||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td><code> [private]</code></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
It links the layers of the network (input, hidden, output).
|
||||
<p>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="961dce8913264bf64c899dce4e25f810"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutput" ref="961dce8913264bf64c899dce4e25f810" args="() const " -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">double neuralpp::NeuralNet::getOutput </td>
|
||||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td> const</td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
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_2adderFromString_8cpp-example.html#a4">examples/adderFromString.cpp</a>, and <a class="el" href="examples_2doAdd_8cpp-example.html#a5">examples/doAdd.cpp</a>.</dl>
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="e6d2215ecc8b560db2f6797db642191c"></a><!-- doxytag: member="neuralpp::NeuralNet::getOutputs" ref="e6d2215ecc8b560db2f6797db642191c" args="()" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">std::vector<double> neuralpp::NeuralNet::getOutputs </td>
|
||||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
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_2networkForSumsAndSubtractions_8cpp-example.html#a4">examples/networkForSumsAndSubtractions.cpp</a>.</dl>
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="e08cdcf4b70f987700e553d9914f6179"></a><!-- doxytag: member="neuralpp::NeuralNet::getThreshold" ref="e08cdcf4b70f987700e553d9914f6179" args="() const " -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">double neuralpp::NeuralNet::getThreshold </td>
|
||||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </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="c129c180647362da963758bfd1ba6890"></a><!-- doxytag: member="neuralpp::NeuralNet::propagate" ref="c129c180647362da963758bfd1ba6890" args="()" -->
|
||||
|
@ -575,7 +569,7 @@ It updates through back-propagation the weights of the synapsis and computes aga
|
|||
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 <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>
|
||||
<a class="el" href="examples_2adderFromString_8cpp-example.html#a3">examples/adderFromString.cpp</a>, <a class="el" href="examples_2doAdd_8cpp-example.html#a4">examples/doAdd.cpp</a>, and <a class="el" href="examples_2networkForSumsAndSubtractions_8cpp-example.html#a3">examples/networkForSumsAndSubtractions.cpp</a>.</dl>
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="405b32d2928344314ecf0469070b0f17"></a><!-- doxytag: member="neuralpp::NeuralNet::setInput" ref="405b32d2928344314ecf0469070b0f17" args="(std::vector< double > v)" -->
|
||||
|
@ -603,28 +597,7 @@ It sets the input for the network.
|
|||
</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="()" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">void neuralpp::NeuralNet::link </td>
|
||||
<td>(</td>
|
||||
<td class="paramname"> </td>
|
||||
<td> ) </td>
|
||||
<td></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
It links the layers of the network (input, hidden, output).
|
||||
<p>
|
||||
Don't use unless you exactly know what you're doing, it is already called by the constructor
|
||||
<a class="el" href="examples_2adderFromString_8cpp-example.html#a2">examples/adderFromString.cpp</a>, <a class="el" href="examples_2doAdd_8cpp-example.html#a3">examples/doAdd.cpp</a>, and <a class="el" href="examples_2networkForSumsAndSubtractions_8cpp-example.html#a2">examples/networkForSumsAndSubtractions.cpp</a>.</dl>
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="fdf94c276720c25e565cac834fe8a407"></a><!-- doxytag: member="neuralpp::NeuralNet::save" ref="fdf94c276720c25e565cac834fe8a407" args="(const char *fname)" -->
|
||||
|
@ -658,6 +631,70 @@ Save a trained neural network to a binary file.
|
|||
</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="a060e28b438613a6cc9e0895ddbc292b"></a><!-- doxytag: member="neuralpp::NeuralNet::loadFromBinary" ref="a060e28b438613a6cc9e0895ddbc292b" args="(const std::string fname)" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">void neuralpp::NeuralNet::loadFromBinary </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">const std::string </td>
|
||||
<td class="paramname"> <em>fname</em> </td>
|
||||
<td> ) </td>
|
||||
<td> throw (<a class="el" href="classneuralpp_1_1NetworkFileNotFoundException.html">NetworkFileNotFoundException</a>)</td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
DEPRECATED.
|
||||
<p>
|
||||
Load a trained neural network from a binary file. This function is deprecated and kept for back-compatibility. Use the XML format instead to load and neural networks and, respectly, the NeuralNetwork(const std::string) constructor or the <a class="el" href="classneuralpp_1_1NeuralNet.html#fdf94c276720c25e565cac834fe8a407" title="Save a trained neural network to a binary file.">save(const char*)</a> methods. <dl compact><dt><b>Parameters:</b></dt><dd>
|
||||
<table border="0" cellspacing="2" cellpadding="0">
|
||||
<tr><td valign="top"></td><td valign="top"><em>fname</em> </td><td>Name of the file to be loaded </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><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> </td><td>When you're trying to load an invalid network file </td></tr>
|
||||
</table>
|
||||
</dl>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="520147d9b47b69565567bd3fdcfd8897"></a><!-- doxytag: member="neuralpp::NeuralNet::saveToBinary" ref="520147d9b47b69565567bd3fdcfd8897" args="(const char *fname)" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">void neuralpp::NeuralNet::saveToBinary </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">const char * </td>
|
||||
<td class="paramname"> <em>fname</em> </td>
|
||||
<td> ) </td>
|
||||
<td> throw (<a class="el" href="classneuralpp_1_1NetworkFileWriteException.html">NetworkFileWriteException</a>)</td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
DEPRECATED.
|
||||
<p>
|
||||
Save a trained neural network to a binary file. This function is deprecated and kept for back-compatibility. Use the XML format instead to load and neural networks and, respectly, the NeuralNetwork(const std::string) constructor or the <a class="el" href="classneuralpp_1_1NeuralNet.html#fdf94c276720c25e565cac834fe8a407" title="Save a trained neural network to a binary file.">save(const char*)</a> methods. <dl compact><dt><b>Parameters:</b></dt><dd>
|
||||
<table border="0" cellspacing="2" cellpadding="0">
|
||||
<tr><td valign="top"></td><td valign="top"><em>fname</em> </td><td>Name of the file to be saved with the network information </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><a class="el" href="classneuralpp_1_1NetworkFileWriteException.html" title="Exception thrown when trying to write the network's information to a file that...">NetworkFileWriteException</a></em> </td><td>When you try to write the network information to an invalid file </td></tr>
|
||||
</table>
|
||||
</dl>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="1c9e17437d41a7048611e21a3cc1c7dd"></a><!-- doxytag: member="neuralpp::NeuralNet::train" ref="1c9e17437d41a7048611e21a3cc1c7dd" args="(std::string xml, source src)" -->
|
||||
|
@ -700,7 +737,7 @@ A sample XML file is available in examples/adder.xml <dl compact><dt><b>Paramete
|
|||
</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>
|
||||
<a class="el" href="examples_2adderFromString_8cpp-example.html#a1">examples/adderFromString.cpp</a>, <a class="el" href="examples_2learnAdd_8cpp-example.html#a1">examples/learnAdd.cpp</a>, and <a class="el" href="examples_2networkForSumsAndSubtractions_8cpp-example.html#a1">examples/networkForSumsAndSubtractions.cpp</a>.</dl>
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="96da6712a72051cf34ad961761ef6e08"></a><!-- doxytag: member="neuralpp::NeuralNet::initXML" ref="96da6712a72051cf34ad961761ef6e08" args="(std::string &xml)" -->
|
||||
|
@ -728,44 +765,6 @@ Initialize the training XML for the neural network.
|
|||
</table>
|
||||
</dl>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="83c6555520856d5867752904349ab6ca"></a><!-- doxytag: member="neuralpp::NeuralNet::split" ref="83c6555520856d5867752904349ab6ca" args="(char delim, std::string str)" -->
|
||||
<div class="memitem">
|
||||
<div class="memproto">
|
||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">static std::vector<double> neuralpp::NeuralNet::split </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">char </td>
|
||||
<td class="paramname"> <em>delim</em>, </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="paramkey"></td>
|
||||
<td></td>
|
||||
<td class="paramtype">std::string </td>
|
||||
<td class="paramname"> <em>str</em></td><td> </td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td>)</td>
|
||||
<td></td><td></td><td><code> [static]</code></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
<div class="memdoc">
|
||||
|
||||
<p>
|
||||
Splits a string into a vector of doubles, given a delimitator.
|
||||
<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>delim</em> </td><td>Delimitator </td></tr>
|
||||
<tr><td valign="top"></td><td valign="top"><em>str</em> </td><td>String to be splitted </td></tr>
|
||||
</table>
|
||||
</dl>
|
||||
<dl class="return" compact><dt><b>Returns:</b></dt><dd>Vector of doubles containing splitted values </dd></dl>
|
||||
|
||||
</div>
|
||||
</div><p>
|
||||
<a class="anchor" name="0a2733037af912b3e6a10146e7b7172f"></a><!-- doxytag: member="neuralpp::NeuralNet::XMLFromSet" ref="0a2733037af912b3e6a10146e7b7172f" args="(int &id, std::string set)" -->
|
||||
|
@ -974,7 +973,7 @@ 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>
|
||||
</div>
|
||||
<hr size="1"><address style="text-align: right;"><small>Generated on Sun Aug 16 20:53:42 2009 for Neural++ by
|
||||
<hr size="1"><address style="text-align: right;"><small>Generated on Fri Sep 4 11:25:49 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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