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<title>Initialize the weights using Widrow + Nguyen’s algorithm</title>
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<h1 class="refname">fann_init_weights</h1>
<p class="verinfo">(PECL fann >= 1.0.0)</p><p class="refpurpose"><span class="refname">fann_init_weights</span> — <span class="dc-title">Initialize the weights using Widrow + Nguyen’s algorithm</span></p>
</div>
<div class="refsect1 description" id="refsect1-function.fann-init-weights-description">
<h3 class="title">Description</h3>
<div class="methodsynopsis dc-description">
<span class="type">bool</span> <span class="methodname"><strong>fann_init_weights</strong></span>
( <span class="methodparam"><span class="type">resource</span> <code class="parameter">$ann</code></span>
, <span class="methodparam"><span class="type">resource</span> <code class="parameter">$train_data</code></span>
)</div>
<p class="para rdfs-comment">
Initialize the weights using Widrow + Nguyen’s algorithm.
</p>
<p class="para">
This function behaves similarly to <span class="function"><a href="function.fann-randomize-weights.html" class="function">fann_randomize_weights()</a></span>. It will use the algorithm developed by
Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training. This technique is not always
successful, and in some cases can be less efficient than a purely random initialization.
</p>
<p class="para">
The algorithm requires access to the range of the input data (for example largest and smallest input), and therefore
accepts a second argument, data, which is the training data that will be used to train the network.
</p>
</div>
<div class="refsect1 parameters" id="refsect1-function.fann-init-weights-parameters">
<h3 class="title">Parameters</h3>
<dl>
<dt>
<span class="term"><em><code class="parameter">ann</code></em></span>
<dd>
<p class="para">Neural network <span class="type"><a href="language.types.resource.html" class="type resource">resource</a></span>.</p>
</dd>
</dt>
<dt>
<span class="term"><em><code class="parameter">train_data</code></em></span>
<dd>
<p class="para">Neural network training data <span class="type"><a href="language.types.resource.html" class="type resource">resource</a></span>.</p>
</dd>
</dt>
</dl>
</div>
<div class="refsect1 returnvalues" id="refsect1-function.fann-init-weights-returnvalues">
<h3 class="title">Return Values</h3>
<p class="para">Returns <strong><code>TRUE</code></strong> on success, or <strong><code>FALSE</code></strong> otherwise.</p>
</div>
<div class="refsect1 seealso" id="refsect1-function.fann-init-weights-seealso">
<h3 class="title">See Also</h3>
<p class="para">
<ul class="simplelist">
<li class="member"> <span class="function"><a href="function.fann-randomize-weights.html" class="function" rel="rdfs-seeAlso">fann_randomize_weights()</a> - Give each connection a random weight between min_weight and max_weight</span></li>
<li class="member"> <span class="function"><a href="function.fann-read-train-from-file.html" class="function" rel="rdfs-seeAlso">fann_read_train_from_file()</a> - Reads a file that stores training data</span></li>
</ul>
</p>
</div>
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