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<html>
<head>
<title>Docs For Class NeuralNetwork</title>
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<body>
<h1>Class: NeuralNetwork</h1>
<table width="100%" border="0">
<tr><td valign="top">
<h3><a href="#class_details">Class Overview</a></h3>
<pre></pre><br />
<div class="description"><strong>Multi-layer Neural Network in PHP</strong></div><br /><br />
<h4>Author(s):</h4>
<ul>
<li>E. Akerboom</li>
<li><a href="http://www.tremani.nl/">Tremani</a>, <a href="http://maps.google.com/maps?f=q&hl=en&q=delft%2C+the+netherlands&ie=UTF8&t=k&om=1&ll=53.014783%2C4.921875&spn=36.882665%2C110.566406&z=4">Delft</a>, The Netherlands</li>
</ul>
<h4>Version:</h4>
<ul>
<li>1.0</li>
</ul>
</td>
<td valign="top">
<h3><a href="#class_methods">Methods</a></h3>
<ul>
<li><a href="#methodNeuralNetwork">NeuralNetwork</a></li>
<li><a href="#methodactivation">activation</a></li>
<li><a href="#methodaddControlData">addControlData</a></li>
<li><a href="#methodaddTestData">addTestData</a></li>
<li><a href="#methodcalculate">calculate</a></li>
<li><a href="#methodderivative_activation">derivative_activation</a></li>
<li><a href="#methodgetControlDataIDs">getControlDataIDs</a></li>
<li><a href="#methodgetLearningRate">getLearningRate</a></li>
<li><a href="#methodgetMomentum">getMomentum</a></li>
<li><a href="#methodgetRandomWeight">getRandomWeight</a></li>
<li><a href="#methodgetTestDataIDs">getTestDataIDs</a></li>
<li><a href="#methodisVerbose">isVerbose</a></li>
<li><a href="#methodload">load</a></li>
<li><a href="#methodsave">save</a></li>
<li><a href="#methodsetLearningRate">setLearningRate</a></li>
<li><a href="#methodsetMomentum">setMomentum</a></li>
<li><a href="#methodsetVerbose">setVerbose</a></li>
<li><a href="#methodshowWeights">showWeights</a></li>
<li><a href="#methodtrain">train</a></li>
</ul>
</td>
</tr></table>
<hr />
<table width="100%" border="0"><tr>
</tr></table>
<hr />
<a name="class_details"></a>
<h3>Class Details</h3>
<div class="tags">
[line 75]<br />
<strong>Multi-layer Neural Network in PHP</strong><br /><br /><p>Loosely based on source code by <a href="http://www.philbrierley.com">Phil Brierley</a>, that was translated into PHP by 'dspink' in sep 2005</p><p>Algorithm was obtained from the excellent introductory book "<a href="http://www.amazon.com/link/dp/0321204662">Artificial Intelligence - a guide to intelligent systems</a>" by Michael Negnevitsky (ISBN 0-201-71159-1)</p><p><strong>Example: learning the 'XOR'-function</strong> <ol><li><div class="src-line"> <span class="src-inc">require_once</span><span class="src-sym">(</span><span class="src-str">"class_neuralnetwork.php"</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// Create a new neural network with 3 input neurons,</span></div></li>
<li><div class="src-line"> <span class="src-comm">// 4 hidden neurons, and 1 output neuron</span></div></li>
<li><div class="src-line"> <span class="src-var">$n </span>= <span class="src-key">new </span><a href="#methodNeuralNetwork">NeuralNetwork</a><span class="src-sym">(</span><span class="src-num">3</span><span class="src-sym">, </span><span class="src-num">4</span><span class="src-sym">, </span><span class="src-num">1</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> <span class="src-var">$n</span><span class="src-sym">-></span><a href="#methodsetVerbose">setVerbose</a><span class="src-sym">(</span><span class="src-id">false</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// Add test-data to the network. In this case,</span></div></li>
<li><div class="src-line"> <span class="src-comm">// we want the network to learn the 'XOR'-function.</span></div></li>
<li><div class="src-line"> <span class="src-comm">// The third input-parameter is the 'bias'.</span></div></li>
<li><div class="src-line"> <span class="src-var">$n</span><span class="src-sym">-></span><a href="#methodaddTestData">addTestData</a><span class="src-sym">( </span><span class="src-key">array </span><span class="src-sym">(</span>-<span class="src-num">1</span><span class="src-sym">, </span>-<span class="src-num">1</span><span class="src-sym">, </span><span class="src-num">1</span><span class="src-sym">)</span><span class="src-sym">, </span><span class="src-key">array </span><span class="src-sym">(</span>-<span class="src-num">1</span><span class="src-sym">))</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> <span class="src-var">$n</span><span class="src-sym">-></span><a href="#methodaddTestData">addTestData</a><span class="src-sym">( </span><span class="src-key">array </span><span class="src-sym">(</span>-<span class="src-num">1</span><span class="src-sym">, </span><span class="src-num">1</span><span class="src-sym">, </span><span class="src-num">1</span><span class="src-sym">)</span><span class="src-sym">, </span><span class="src-key">array </span><span class="src-sym">( </span><span class="src-num">1</span><span class="src-sym">))</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> <span class="src-var">$n</span><span class="src-sym">-></span><a href="#methodaddTestData">addTestData</a><span class="src-sym">( </span><span class="src-key">array </span><span class="src-sym">( </span><span class="src-num">1</span><span class="src-sym">, </span>-<span class="src-num">1</span><span class="src-sym">, </span><span class="src-num">1</span><span class="src-sym">)</span><span class="src-sym">, </span><span class="src-key">array </span><span class="src-sym">( </span><span class="src-num">1</span><span class="src-sym">))</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> <span class="src-var">$n</span><span class="src-sym">-></span><a href="#methodaddTestData">addTestData</a><span class="src-sym">( </span><span class="src-key">array </span><span class="src-sym">( </span><span class="src-num">1</span><span class="src-sym">, </span><span class="src-num">1</span><span class="src-sym">, </span><span class="src-num">1</span><span class="src-sym">)</span><span class="src-sym">, </span><span class="src-key">array </span><span class="src-sym">(</span>-<span class="src-num">1</span><span class="src-sym">))</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// we try training the network for at most $max times</span></div></li>
<li><div class="src-line"> <span class="src-var">$max </span>= <span class="src-num">3</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// train the network in max 1000 epochs, with a max squared error of 0.01</span></div></li>
<li><div class="src-line"> <span class="src-key">while </span><span class="src-sym">(</span><span class="src-sym">!</span><span class="src-sym">(</span><span class="src-var">$success</span>=<span class="src-var">$n</span><span class="src-sym">-></span><a href="#methodtrain">train</a><span class="src-sym">(</span><span class="src-num">1000</span><span class="src-sym">, </span><span class="src-num">0.01</span><span class="src-sym">)) </span>&& <span class="src-var">$max</span>--><span class="src-num">0</span><span class="src-sym">) </span><span class="src-sym">{</span></div></li>
<li><div class="src-line"> <span class="src-comm">// training failed:</span></div></li>
<li><div class="src-line"> <span class="src-comm">// 1. re-initialize the weights in the network</span></div></li>
<li><div class="src-line"> <span class="src-var">$n</span><span class="src-sym">-></span><span class="src-id">initWeights</span><span class="src-sym">(</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// 2. display message</span></div></li>
<li><div class="src-line"> echo <span class="src-str">"Nothing found...<hr />"</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> <span class="src-sym">}</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// print a message if the network was succesfully trained</span></div></li>
<li><div class="src-line"> <span class="src-key">if </span><span class="src-sym">(</span><span class="src-var">$success</span><span class="src-sym">) </span><span class="src-sym">{</span></div></li>
<li><div class="src-line"> <span class="src-var">$epochs </span>= <span class="src-var">$n</span><span class="src-sym">-></span><span class="src-id">getEpoch</span><span class="src-sym">(</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> echo <span class="src-str">"</span><span class="src-str"><span class="src-id">Success</span> <span class="src-id">in</span> <span class="src-var">$epochs</span> <span class="src-id">training</span> <span class="src-id">rounds</span>!<<span class="src-id">hr</span> /></span><span class="src-str">"</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> <span class="src-sym">}</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// in any case, we print the output of the neural network</span></div></li>
<li><div class="src-line"> <span class="src-key">for </span><span class="src-sym">(</span><span class="src-var">$i </span>= <span class="src-num">0</span><span class="src-sym">; </span><span class="src-var">$i </span>< <a href="http://www.php.net/count">count</a><span class="src-sym">(</span><span class="src-var">$n</span><span class="src-sym">-></span><span class="src-id">trainInputs</span><span class="src-sym">)</span><span class="src-sym">; </span><span class="src-var">$i </span>++<span class="src-sym">) </span><span class="src-sym">{</span></div></li>
<li><div class="src-line"> <span class="src-var">$output </span>= <span class="src-var">$n</span><span class="src-sym">-></span><a href="#methodcalculate">calculate</a><span class="src-sym">(</span><span class="src-var">$n</span><span class="src-sym">-></span><span class="src-id">trainInputs</span><span class="src-sym">[</span><span class="src-var">$i</span><span class="src-sym">]</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> print <span class="src-str">"</span><span class="src-str"><<span class="src-id">br</span> /><span class="src-id">Testset</span> <span class="src-var">$i</span>; </span><span class="src-str">"</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> print <span class="src-str">"expected output = ("</span>.<a href="http://www.php.net/implode">implode</a><span class="src-sym">(</span><span class="src-str">", "</span><span class="src-sym">, </span><span class="src-var">$n</span><span class="src-sym">-></span><span class="src-id">trainOutput</span><span class="src-sym">[</span><span class="src-var">$i</span><span class="src-sym">]</span><span class="src-sym">)</span>.<span class="src-str">") "</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> print <span class="src-str">"output from neural network = ("</span>.<a href="http://www.php.net/implode">implode</a><span class="src-sym">(</span><span class="src-str">", "</span><span class="src-sym">, </span><span class="src-var">$output</span><span class="src-sym">)</span>.<span class="src-str">")\n"</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> <span class="src-sym">}</span></div></li>
</ol></p><p>The resulting output could for example be something along the following lines:</p><p><ol><li><div class="src-line"> <span class="src-id">Success in </span><span class="src-num">719 </span><span class="src-id">training rounds</span><span class="src-sym">!</span></div></li>
<li><div class="src-line"> <span class="src-id">Testset </span><span class="src-num">0</span><span class="src-sym">; </span><span class="src-id">expected output </span>= <span class="src-sym">(</span>-<span class="src-num">1</span><span class="src-sym">) </span><span class="src-id">output from neural network </span>= <span class="src-sym">(</span>-<span class="src-num">0.986415991978</span><span class="src-sym">)</span></div></li>
<li><div class="src-line"> <span class="src-id">Testset </span><span class="src-num">1</span><span class="src-sym">; </span><span class="src-id">expected output </span>= <span class="src-sym">(</span><span class="src-num">1</span><span class="src-sym">) </span><span class="src-id">output from neural network </span>= <span class="src-sym">(</span><span class="src-num">0.992121412998</span><span class="src-sym">)</span></div></li>
<li><div class="src-line"> <span class="src-id">Testset </span><span class="src-num">2</span><span class="src-sym">; </span><span class="src-id">expected output </span>= <span class="src-sym">(</span><span class="src-num">1</span><span class="src-sym">) </span><span class="src-id">output from neural network </span>= <span class="src-sym">(</span><span class="src-num">0.992469534962</span><span class="src-sym">)</span></div></li>
<li><div class="src-line"> <span class="src-id">Testset </span><span class="src-num">3</span><span class="src-sym">; </span><span class="src-id">expected output </span>= <span class="src-sym">(</span>-<span class="src-num">1</span><span class="src-sym">) </span><span class="src-id">output from neural network </span>= <span class="src-sym">(</span>-<span class="src-num">0.990224120384</span><span class="src-sym">)</span></div></li>
</ol></p><p>...which indicates the network has learned the task.</p><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>version:</b> </td><td>1.0</td>
</tr>
<tr>
<td><b>since:</b> </td><td>feb 2007</td>
</tr>
<tr>
<td><b>author:</b> </td><td>E. Akerboom</td>
</tr>
<tr>
<td><b>author:</b> </td><td><a href="http://www.tremani.nl/">Tremani</a>, <a href="http://maps.google.com/maps?f=q&hl=en&q=delft%2C+the+netherlands&ie=UTF8&t=k&om=1&ll=53.014783%2C4.921875&spn=36.882665%2C110.566406&z=4">Delft</a></td>
</tr>
<tr>
<td><b>license:</b> </td><td><a href="http://opensource.org/licenses/bsd-license.php">BSD License</a></td>
</tr>
</table>
</div>
</div><br /><br />
<div class="top">[ <a href="#top">Top</a> ]</div><br />
<hr />
<a name="class_methods"></a>
<h3>Class Methods</h3>
<div class="tags">
<hr />
<a name="methodNeuralNetwork"></a>
<h3>constructor NeuralNetwork <span class="smalllinenumber">[line 124]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>NeuralNetwork NeuralNetwork(
array
$nodecount)</code>
</td></tr></table>
</td></tr></table><br />
Creates a neural network.<br /><br /><p>Example: <ol><li><div class="src-line"> <span class="src-comm">// create a network with 4 input nodes, 10 hidden nodes, and 4 output nodes</span></div></li>
<li><div class="src-line"> <span class="src-var">$n </span>= <span class="src-key">new </span><a href="#methodNeuralNetwork">NeuralNetwork</a><span class="src-sym">(</span><span class="src-num">4</span><span class="src-sym">, </span><span class="src-num">10</span><span class="src-sym">, </span><span class="src-num">4</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// create a network with 4 input nodes, 1 hidden layer with 10 nodes,</span></div></li>
<li><div class="src-line"> <span class="src-comm">// another hidden layer with 10 nodes, and 4 output nodes</span></div></li>
<li><div class="src-line"> <span class="src-var">$n </span>= <span class="src-key">new </span><a href="#methodNeuralNetwork">NeuralNetwork</a><span class="src-sym">(</span><span class="src-num">4</span><span class="src-sym">, </span><span class="src-num">10</span><span class="src-sym">, </span><span class="src-num">10</span><span class="src-sym">, </span><span class="src-num">4</span><span class="src-sym">)</span><span class="src-sym">;</span></div></li>
<li><div class="src-line"> </div></li>
<li><div class="src-line"> <span class="src-comm">// alternative syntax</span></div></li>
<li><div class="src-line"> <span class="src-var">$n </span>= <span class="src-key">new </span><a href="#methodNeuralNetwork">NeuralNetwork</a><span class="src-sym">(</span><span class="src-key">array</span><span class="src-sym">(</span><span class="src-num">4</span><span class="src-sym">, </span><span class="src-num">10</span><span class="src-sym">, </span><span class="src-num">10</span><span class="src-sym">, </span><span class="src-num">4</span><span class="src-sym">))</span><span class="src-sym">;</span></div></li>
</ol></p><br /><br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">array </td>
<td><b>$nodecount</b> </td>
<td>The number of nodes in the consecutive layers.</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodactivation"></a>
<h3>method activation <span class="smalllinenumber">[line 234]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>float activation(
float
$value)</code>
</td></tr></table>
</td></tr></table><br />
Implements the standard (default) activation function for backpropagation networks, the 'tanh' activation function.<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>The final output of the node</td>
</tr>
</table>
</div>
<br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">float </td>
<td><b>$value</b> </td>
<td>The preliminary output to apply this function to</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodaddControlData"></a>
<h3>method addControlData <span class="smalllinenumber">[line 294]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>void addControlData(
array
$input, array
$output, [int
$id = null])</code>
</td></tr></table>
</td></tr></table><br />
Add a set of control data to the network.<br /><br /><p>This set of data is used to prevent 'overlearning' of the network. The network will stop training if the results obtained for the control data are worsening.</p><p>The data added as control data is not used for training.</p><br /><br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">array </td>
<td><b>$input</b> </td>
<td>An input vector</td>
</tr>
<tr>
<td class="type">array </td>
<td><b>$output</b> </td>
<td>The corresponding output</td>
</tr>
<tr>
<td class="type">int </td>
<td><b>$id</b> </td>
<td>(optional) An identifier for this piece of data</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodaddTestData"></a>
<h3>method addTestData <span class="smalllinenumber">[line 259]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>void addTestData(
array
$input, array
$output, [int
$id = null])</code>
</td></tr></table>
</td></tr></table><br />
Add a test vector and its output<br /><br /><br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">array </td>
<td><b>$input</b> </td>
<td>An input vector</td>
</tr>
<tr>
<td class="type">array </td>
<td><b>$output</b> </td>
<td>The corresponding output</td>
</tr>
<tr>
<td class="type">int </td>
<td><b>$id</b> </td>
<td>(optional) An identifier for this piece of data</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodcalculate"></a>
<h3>method calculate <span class="smalllinenumber">[line 187]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>mixed calculate(
array
$input)</code>
</td></tr></table>
</td></tr></table><br />
Calculate the output of the neural network for a given input vector<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>The output of the network</td>
</tr>
</table>
</div>
<br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">array </td>
<td><b>$input</b> </td>
<td>The vector to calculate</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodderivative_activation"></a>
<h3>method derivative_activation <span class="smalllinenumber">[line 246]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>$float derivative_activation(
float
$value)</code>
</td></tr></table>
</td></tr></table><br />
Implements the derivative of the activation function. By default, this is the inverse of the 'tanh' activation function: 1.0 - tanh($value)*tanh($value);<br /><br /><br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">float </td>
<td><b>$value</b> </td>
<td>'X'</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodgetControlDataIDs"></a>
<h3>method getControlDataIDs <span class="smalllinenumber">[line 313]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>array getControlDataIDs(
)</code>
</td></tr></table>
</td></tr></table><br />
Returns the identifiers of the control data used during the training of the network (if available)<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>An array of identifiers</td>
</tr>
</table>
</div>
<br /><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodgetLearningRate"></a>
<h3>method getLearningRate <span class="smalllinenumber">[line 155]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>float getLearningRate(
int
$layer)</code>
</td></tr></table>
</td></tr></table><br />
Gets the learning rate for a specific layer<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>The learning rate for that layer</td>
</tr>
</table>
</div>
<br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">int </td>
<td><b>$layer</b> </td>
<td>The layer to obtain the learning rate for</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodgetMomentum"></a>
<h3>method getMomentum <span class="smalllinenumber">[line 177]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>float getMomentum(
)</code>
</td></tr></table>
</td></tr></table><br />
Gets the momentum.<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>The momentum</td>
</tr>
</table>
</div>
<br /><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodgetRandomWeight"></a>
<h3>method getRandomWeight <span class="smalllinenumber">[line 647]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>float getRandomWeight(
$layer)</code>
</td></tr></table>
</td></tr></table><br />
Gets a random weight between [-0.25 .. 0.25]. Used to initialize the network.<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>A random weight</td>
</tr>
</table>
</div>
<br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type"> </td>
<td><b>$layer</b> </td>
<td></td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodgetTestDataIDs"></a>
<h3>method getTestDataIDs <span class="smalllinenumber">[line 277]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>array getTestDataIDs(
)</code>
</td></tr></table>
</td></tr></table><br />
Returns the identifiers of the data used to train the network (if available)<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>An array of identifiers</td>
</tr>
</table>
</div>
<br /><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodisVerbose"></a>
<h3>method isVerbose <span class="smalllinenumber">[line 344]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>boolean isVerbose(
)</code>
</td></tr></table>
</td></tr></table><br />
Returns whether or not the network displays status and error messages.<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>'true' if status and error messages are displayed, 'false' otherwise</td>
</tr>
</table>
</div>
<br /><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodload"></a>
<h3>method load <span class="smalllinenumber">[line 355]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>boolean load(
string
$filename)</code>
</td></tr></table>
</td></tr></table><br />
Loads a neural network from a file saved by the 'save()' function. Clears the training and control data added so far.<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>'true' on success, 'false' otherwise</td>
</tr>
</table>
</div>
<br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">string </td>
<td><b>$filename</b> </td>
<td>The filename to load the network from</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodsave"></a>
<h3>method save <span class="smalllinenumber">[line 399]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>boolean save(
string
$filename)</code>
</td></tr></table>
</td></tr></table><br />
Saves a neural network to a file<br /><br /><br /><br />
<h4>Tags:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td><b>return:</b> </td><td>'true' on success, 'false' otherwise</td>
</tr>
</table>
</div>
<br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">string </td>
<td><b>$filename</b> </td>
<td>The filename to save the neural network to</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodsetLearningRate"></a>
<h3>method setLearningRate <span class="smalllinenumber">[line 141]</span></h3>
<div class="function">
<table width="90%" border="0" cellspacing="0" cellpadding="1"><tr><td class="code_border">
<table width="100%" border="0" cellspacing="0" cellpadding="2"><tr><td class="code">
<code>void setLearningRate(
array
$learningrate)</code>
</td></tr></table>
</td></tr></table><br />
Sets the learning rate between the different layers.<br /><br /><br /><br />
<h4>Parameters:</h4>
<div class="tags">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td class="type">array </td>
<td><b>$learningrate</b> </td>
<td>An array containing the learning rates [range 0.0 - 1.0]. The size of this array is 'layercount - 1'. You might also provide a single number. If that is the case, then this will be the learning rate for the whole network.</td>
</tr>
</table>
</div><br />
<div class="top">[ <a href="#top">Top</a> ]</div>
</div>
<hr />
<a name="methodsetMomentum"></a>
<h3>method setMomentum <span class="smalllinenumber">[line 168]</span></h3>
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<code>void setMomentum(
float
$momentum)</code>
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Sets the 'momentum' for the learning algorithm. The momentum should accelerate the learning process and help avoid local minima.<br /><br /><br /><br />
<h4>Parameters:</h4>
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<td class="type">float </td>
<td><b>$momentum</b> </td>
<td>The momentum. Must be between 0.0 and 1.0; Usually between 0.5 and 0.9</td>
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<a name="methodsetVerbose"></a>
<h3>method setVerbose <span class="smalllinenumber">[line 335]</span></h3>
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<code>void setVerbose(
boolean
$is_verbose)</code>
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Determines if the neural network displays status and error messages. By default, it does.<br /><br /><br /><br />
<h4>Parameters:</h4>
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<td class="type">boolean </td>
<td><b>$is_verbose</b> </td>
<td>'true' if you want to display status and error messages, 'false' if you don't</td>
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<a name="methodshowWeights"></a>
<h3>method showWeights <span class="smalllinenumber">[line 322]</span></h3>
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<code>void showWeights(
[boolean
$force = false])</code>
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Shows the current weights and thresholds<br /><br /><br /><br />
<h4>Parameters:</h4>
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<td class="type">boolean </td>
<td><b>$force</b> </td>
<td>Force the output, even if the network is <a href="#methodsetVerbose">not verbose</a>.</td>
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<a name="methodtrain"></a>
<h3>method train <span class="smalllinenumber">[line 424]</span></h3>
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<code>bool train(
[int
$maxEpochs = 500], [float
$maxError = 0.01])</code>
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Start the training process<br /><br /><br /><br />
<h4>Tags:</h4>
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<td><b>return:</b> </td><td>'true' if the training was successful, 'false' otherwise</td>
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<h4>Parameters:</h4>
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<td><b>$maxEpochs</b> </td>
<td>The maximum number of epochs</td>
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<td><b>$maxError</b> </td>
<td>The maximum squared error in the training data</td>
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