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<a href="Scientific-module.html">Package Scientific</a> ::
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Class AveragedAutoRegressiveModel
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class AveragedAutoRegressiveModel</h1><p class="nomargin-top"></p>
<pre class="base-tree">
<a href="Scientific.Signals.Models.AutoRegressiveModel-class.html">AutoRegressiveModel</a> --+
|
<strong class="uidshort">AveragedAutoRegressiveModel</strong>
</pre>
<hr />
<p>Averaged auto-regressive model for stochastic process</p>
<p>An averaged model is constructed by averaging the model coefficients
of several auto-regressive models of the same order. An averaged model is
created empty, then individual models are added.</p>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Scientific.Signals.Models.AveragedAutoRegressiveModel-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">order</span>,
<span class="summary-sig-arg">delta_t</span>)</span></td>
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<td><span class="summary-sig"><a href="Scientific.Signals.Models.AveragedAutoRegressiveModel-class.html#add" class="summary-sig-name">add</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">model</span>,
<span class="summary-sig-arg">weight</span>=<span class="summary-sig-default">1</span>)</span><br />
Adds the coefficients of an autoregressive model to the average.</td>
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<p class="indent-wrapped-lines"><b>Inherited from <code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html">AutoRegressiveModel</a></code></b>:
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#correlation">correlation</a></code>,
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#frictionConstant">frictionConstant</a></code>,
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#memoryFunction">memoryFunction</a></code>,
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#memoryFunctionZ">memoryFunctionZ</a></code>,
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#memoryFunctionZapprox">memoryFunctionZapprox</a></code>,
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#poles">poles</a></code>,
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#predictStep">predictStep</a></code>,
<code><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#spectrum">spectrum</a></code>
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<a name="__init__"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">order</span>,
<span class="sig-arg">delta_t</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
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>
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<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>order</code></strong> (<code>int</code>) - the order of the model</li>
<li><strong class="pname"><code>delta_t</code></strong> (<code>float</code>) - the sampling interval for the time series</li>
</ul></dd>
<dt>Overrides:
<a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#__init__">AutoRegressiveModel.__init__</a>
</dt>
</dl>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">add</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">model</span>,
<span class="sig-arg">weight</span>=<span class="sig-default">1</span>)</span>
</h3>
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<p>Adds the coefficients of an autoregressive model to the average.</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>model</code></strong> (<a
href="Scientific.Signals.Models.AutoRegressiveModel-class.html"
class="link">AutoRegressiveModel</a>) - an autoregressive model</li>
<li><strong class="pname"><code>weight</code></strong> (<code>float</code>) - the weight of the model in the average</li>
</ul></dd>
<dt>Raises:</dt>
<dd><ul class="nomargin-top">
<li><code><strong class='fraise'>ValueError</strong></code> - if the order of the model does not match the order of the average
model</li>
</ul></dd>
</dl>
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