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<div class="navpath"><a class="el" href="namespaceitk.html">itk</a>::<a class="el" href="classitk_1_1KalmanLinearEstimator.html">KalmanLinearEstimator</a>
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<div class="contents">
<h1>itk::KalmanLinearEstimator< T, VEstimatorDimension > Class Template Reference<br>
<small>
[<a class="el" href="group__Numerics.html">Numerics</a>]</small>
</h1><!-- doxytag: class="itk::KalmanLinearEstimator" -->Implement a linear recursive estimator.
<a href="#_details">More...</a>
<p>
<code>#include <<a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>></code>
<p>
<p>
<a href="classitk_1_1KalmanLinearEstimator-members.html">List of all members.</a><table border="0" cellpadding="0" cellspacing="0">
<tr><td></td></tr>
<tr><td colspan="2"><br><h2>Public Types</h2></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">typedef vnl_matrix_fixed< T, <br class="typebreak">
VEstimatorDimension, <br class="typebreak">
VEstimatorDimension > </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#382cc82efea13f4ab17f916c35984998">MatrixType</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">typedef T </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#468b3ca27a0eb9ffa1d75915f2ca6ebf">ValueType</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">typedef vnl_vector_fixed< T, <br class="typebreak">
VEstimatorDimension > </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#55b0a9c4bc0c6c7cb0e8bc210f782904">VectorType</a></td></tr>
<tr><td colspan="2"><br><h2>Public Member Functions</h2></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#7f1aac845de4ef5d7f138daa254111df">ClearEstimation</a> (void)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#093e8151b4df23d6b5f0d08772d6284f">ClearVariance</a> (void)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#55b0a9c4bc0c6c7cb0e8bc210f782904">VectorType</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#1687c8a60f545adecd53ea196b695824">GetEstimator</a> (void) const </td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#382cc82efea13f4ab17f916c35984998">MatrixType</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#1fd8710fc2360298fa09f357c8fcf83d">GetVariance</a> (void) const </td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#8a5c0456504be1a996e9812fe8acfe9d">itkStaticConstMacro</a> (Dimension, unsigned int, VEstimatorDimension)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#a08648904bdcba50ce211dc703528e93">SetVariance</a> (const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#382cc82efea13f4ab17f916c35984998">MatrixType</a> &m)</td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#c3e42f962023400ca7aea8546672f762">UpdateWithNewMeasure</a> (const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#468b3ca27a0eb9ffa1d75915f2ca6ebf">ValueType</a> &newMeasure, const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#55b0a9c4bc0c6c7cb0e8bc210f782904">VectorType</a> &newPredictor)</td></tr>
<tr><td colspan="2"><div class="groupHeader"></div></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1KalmanLinearEstimator.html#d08e4f6de0bb106fbbac6de33588f289">SetVariance</a> (const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#468b3ca27a0eb9ffa1d75915f2ca6ebf">ValueType</a> &var=1.0)</td></tr>
</table>
<hr><a name="_details"></a><h2>Detailed Description</h2>
<h3>template<class T, unsigned int VEstimatorDimension><br>
class itk::KalmanLinearEstimator< T, VEstimatorDimension ></h3>
Implement a linear recursive estimator.
<p>
<a class="el" href="classitk_1_1KalmanLinearEstimator.html" title="Implement a linear recursive estimator.">KalmanLinearEstimator</a> class implements a linear recursive estimator. The class is templated over the type of the parameters to be estimated and over the number of parameters. Recursive estimation is a fast mechanism for getting information about a system for which we only have access to measures that are linearly related with the parameters we want to estimate.
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00041">41</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
<hr><h2>Member Typedef Documentation</h2>
<a class="anchor" name="382cc82efea13f4ab17f916c35984998"></a><!-- doxytag: member="itk::KalmanLinearEstimator::MatrixType" ref="382cc82efea13f4ab17f916c35984998" args="" -->
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template<class T , unsigned int VEstimatorDimension> </div>
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<td class="memname">typedef vnl_matrix_fixed<T,VEstimatorDimension,VEstimatorDimension> <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::<a class="el" href="classitk_1_1KalmanLinearEstimator.html#382cc82efea13f4ab17f916c35984998">MatrixType</a> </td>
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<p>
<a class="el" href="classitk_1_1Matrix.html" title="A templated class holding a M x N size Matrix This class contains a vnl_matrix_fixed...">Matrix</a> type defines a generic matrix type that is used for the matricial operations performed during estimation.
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00055">55</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="468b3ca27a0eb9ffa1d75915f2ca6ebf"></a><!-- doxytag: member="itk::KalmanLinearEstimator::ValueType" ref="468b3ca27a0eb9ffa1d75915f2ca6ebf" args="" -->
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template<class T , unsigned int VEstimatorDimension> </div>
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<td class="memname">typedef T <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::<a class="el" href="classitk_1_1KalmanLinearEstimator.html#468b3ca27a0eb9ffa1d75915f2ca6ebf">ValueType</a> </td>
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<p>
Type is the type associated with the parameters to be estimated. All the parameters are of the same type. Natural choices could be floats and doubles, because Type also is used for all the internal computations.
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00061">61</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="55b0a9c4bc0c6c7cb0e8bc210f782904"></a><!-- doxytag: member="itk::KalmanLinearEstimator::VectorType" ref="55b0a9c4bc0c6c7cb0e8bc210f782904" args="" -->
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template<class T , unsigned int VEstimatorDimension> </div>
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<td class="memname">typedef vnl_vector_fixed<T,VEstimatorDimension> <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::<a class="el" href="classitk_1_1KalmanLinearEstimator.html#55b0a9c4bc0c6c7cb0e8bc210f782904">VectorType</a> </td>
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<p>
<a class="el" href="classitk_1_1Vector.html" title="A templated class holding a n-Dimensional vector.">Vector</a> type defines a generic vector type that is used for the matricial operations performed during estimation.
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00051">51</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<hr><h2>Member Function Documentation</h2>
<a class="anchor" name="7f1aac845de4ef5d7f138daa254111df"></a><!-- doxytag: member="itk::KalmanLinearEstimator::ClearEstimation" ref="7f1aac845de4ef5d7f138daa254111df" args="(void)" -->
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template<class T , unsigned int VEstimatorDimension> </div>
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<td class="memname">void <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::ClearEstimation </td>
<td>(</td>
<td class="paramtype">void </td>
<td class="paramname"> </td>
<td> ) </td>
<td><code> [inline]</code></td>
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<p>
This method resets the estimator. It set all the parameters to null. The covariance matrix is not changed. <dl class="see" compact><dt><b>See also:</b></dt><dd>Estimator <p>
Variance <p>
<a class="el" href="classitk_1_1KalmanLinearEstimator.html#093e8151b4df23d6b5f0d08772d6284f">ClearVariance</a> </dd></dl>
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00073">73</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="093e8151b4df23d6b5f0d08772d6284f"></a><!-- doxytag: member="itk::KalmanLinearEstimator::ClearVariance" ref="093e8151b4df23d6b5f0d08772d6284f" args="(void)" -->
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template<class T , unsigned int VEstimatorDimension> </div>
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<td class="memname">void <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::ClearVariance </td>
<td>(</td>
<td class="paramtype">void </td>
<td class="paramname"> </td>
<td> ) </td>
<td><code> [inline]</code></td>
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<p>
This method resets the covariance matrix. It is set to an identity matrix <dl class="see" compact><dt><b>See also:</b></dt><dd>Estimator <p>
Variance <p>
<a class="el" href="classitk_1_1KalmanLinearEstimator.html#7f1aac845de4ef5d7f138daa254111df">ClearEstimation</a> </dd></dl>
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00078">78</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="1687c8a60f545adecd53ea196b695824"></a><!-- doxytag: member="itk::KalmanLinearEstimator::GetEstimator" ref="1687c8a60f545adecd53ea196b695824" args="(void) const " -->
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template<class T , unsigned int VEstimatorDimension> </div>
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<td class="memname">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#55b0a9c4bc0c6c7cb0e8bc210f782904">VectorType</a>& <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::GetEstimator </td>
<td>(</td>
<td class="paramtype">void </td>
<td class="paramname"> </td>
<td> ) </td>
<td> const<code> [inline]</code></td>
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<p>
This method returns the vector of estimated parameters <dl class="see" compact><dt><b>See also:</b></dt><dd>Estimator </dd></dl>
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00106">106</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="1fd8710fc2360298fa09f357c8fcf83d"></a><!-- doxytag: member="itk::KalmanLinearEstimator::GetVariance" ref="1fd8710fc2360298fa09f357c8fcf83d" args="(void) const " -->
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<td class="memname">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#382cc82efea13f4ab17f916c35984998">MatrixType</a>& <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::GetVariance </td>
<td>(</td>
<td class="paramtype">void </td>
<td class="paramname"> </td>
<td> ) </td>
<td> const<code> [inline]</code></td>
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<p>
This method returns the covariance matrix of the estimated parameters <dl class="see" compact><dt><b>See also:</b></dt><dd>Variance </dd></dl>
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00111">111</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="8a5c0456504be1a996e9812fe8acfe9d"></a><!-- doxytag: member="itk::KalmanLinearEstimator::itkStaticConstMacro" ref="8a5c0456504be1a996e9812fe8acfe9d" args="(Dimension, unsigned int, VEstimatorDimension)" -->
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<td class="memname"><a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::itkStaticConstMacro </td>
<td>(</td>
<td class="paramtype">Dimension </td>
<td class="paramname">, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">unsigned </td>
<td class="paramname"> <em>int</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">VEstimatorDimension </td>
<td class="paramname"></td><td> </td>
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<p>
Dimension of the vector of parameters to be estimated. It is equivalent to the number of parameters to estimate.
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<a class="anchor" name="a08648904bdcba50ce211dc703528e93"></a><!-- doxytag: member="itk::KalmanLinearEstimator::SetVariance" ref="a08648904bdcba50ce211dc703528e93" args="(const MatrixType &m)" -->
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<td class="memname">void <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::SetVariance </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#382cc82efea13f4ab17f916c35984998">MatrixType</a> & </td>
<td class="paramname"> <em>m</em> </td>
<td> ) </td>
<td><code> [inline]</code></td>
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<p>
This method sets the covariance matrix to known matrix. It is intended to initialize the estimator with a priori information about the statistical distribution of the parameters. It can also be used to resume the operation of a previously used estimator using it last known state. <dl class="see" compact><dt><b>See also:</b></dt><dd>Estimator <p>
Variance <p>
<a class="el" href="classitk_1_1KalmanLinearEstimator.html#7f1aac845de4ef5d7f138daa254111df">ClearEstimation</a> </dd></dl>
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00101">101</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="d08e4f6de0bb106fbbac6de33588f289"></a><!-- doxytag: member="itk::KalmanLinearEstimator::SetVariance" ref="d08e4f6de0bb106fbbac6de33588f289" args="(const ValueType &var=1.0)" -->
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<td class="memname">void <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::SetVariance </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#468b3ca27a0eb9ffa1d75915f2ca6ebf">ValueType</a> & </td>
<td class="paramname"> <em>var</em> = <code>1.0</code> </td>
<td> ) </td>
<td><code> [inline]</code></td>
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<p>
This method sets the covariance matrix to a diagonal matrix with equal values. It is useful when the variance of all the parameters be estimated are the same and the parameters are considered independents. <dl class="see" compact><dt><b>See also:</b></dt><dd>Estimator <p>
Variance <p>
<a class="el" href="classitk_1_1KalmanLinearEstimator.html#7f1aac845de4ef5d7f138daa254111df">ClearEstimation</a> </dd></dl>
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00089">89</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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<a class="anchor" name="c3e42f962023400ca7aea8546672f762"></a><!-- doxytag: member="itk::KalmanLinearEstimator::UpdateWithNewMeasure" ref="c3e42f962023400ca7aea8546672f762" args="(const ValueType &newMeasure, const VectorType &newPredictor)" -->
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<td class="memname">void <a class="el" href="classitk_1_1KalmanLinearEstimator.html">itk::KalmanLinearEstimator</a>< T, VEstimatorDimension >::UpdateWithNewMeasure </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#468b3ca27a0eb9ffa1d75915f2ca6ebf">ValueType</a> & </td>
<td class="paramname"> <em>newMeasure</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classitk_1_1KalmanLinearEstimator.html#55b0a9c4bc0c6c7cb0e8bc210f782904">VectorType</a> & </td>
<td class="paramname"> <em>newPredictor</em></td><td> </td>
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<p>
Update the estimation using the information provided by a new measure along with a new line of the linear predictor. This method is the one that should be called iteratively in order to estimate the parameter's vector. It internally updates the covariance matrix.
<p>Definition at line <a class="el" href="itkKalmanLinearEstimator_8h_source.html#l00139">139</a> of file <a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a>.</p>
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</div><p>
<hr>The documentation for this class was generated from the following file:<ul>
<li><a class="el" href="itkKalmanLinearEstimator_8h_source.html">itkKalmanLinearEstimator.h</a></ul>
</div>
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i586
>
media
>
contrib-release
>
by-pkgid
>
d5e74628f0e673bb8680aebce32b2c04
>
files
>
4778
