Update documentation for 0.3.0 release.

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 C++ library with efficient implementations of selected machine learning algorithms.
 
+Current version: 0.3.0
+
 (C) 2020 <a href="mailto:roman.werpachowski@gmail.com">Roman Werpachowski</a>.
 
 Project webpage: https://romanwerpachowski.github.io/ML/

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+<div class="title">RecursiveMultivariateOLS.hpp</div>  </div>
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+<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/* (C) 2020 Roman Werpachowski. */</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="preprocessor">#include &lt;Eigen/Cholesky&gt;</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="preprocessor">#include &quot;LinearRegression.hpp&quot;</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespaceml.html">ml</a></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;{</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;    <span class="keyword">namespace </span>LinearRegression</div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;    {</div><div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html">   24</a></span>&#160;        <span class="keyword">class </span><a class="code" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html">RecursiveMultivariateOLS</a></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;        {</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;        <span class="keyword">public</span>:</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;            DLL_DECLSPEC <a class="code" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#abeef4f383bd42d1c42aa455512f3c471">RecursiveMultivariateOLS</a>();</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;            DLL_DECLSPEC <a class="code" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#abeef4f383bd42d1c42aa455512f3c471">RecursiveMultivariateOLS</a>(Eigen::Ref&lt;const Eigen::MatrixXd&gt; X, Eigen::Ref&lt;const Eigen::VectorXd&gt; y);</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;            DLL_DECLSPEC <span class="keywordtype">void</span> <a class="code" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a459f258fd086528bf1e06f3eac5b99d5">update</a>(Eigen::Ref&lt;const Eigen::MatrixXd&gt; X, Eigen::Ref&lt;const Eigen::VectorXd&gt; y);</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;</div><div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a29fea80582f61a1235ed0efedb0a72f4">   46</a></span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a29fea80582f61a1235ed0efedb0a72f4">n</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="keyword">            </span>{</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;                <span class="keywordflow">return</span> n_;</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;            }</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a55e966b13a29c52c5dacf0295948b574">   52</a></span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a55e966b13a29c52c5dacf0295948b574">d</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="keyword">            </span>{</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;                <span class="keywordflow">return</span> d_;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;            }</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#ab5db1361876165a60fa6ccd4c519d29c">   58</a></span>&#160;            <span class="keyword">const</span> Eigen::VectorXd&amp; <a class="code" href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#ab5db1361876165a60fa6ccd4c519d29c">beta</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="keyword">            </span>{</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;                <span class="keywordflow">return</span> beta_;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;            }</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;        <span class="keyword">private</span>:</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;            Eigen::LDLT&lt;Eigen::MatrixXd&gt; helper_decomp_; </div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;            Eigen::MatrixXd P_; </div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;            Eigen::MatrixXd K_; </div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;            Eigen::MatrixXd W_; </div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;            Eigen::MatrixXd V_; </div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;            Eigen::VectorXd beta_; </div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;            Eigen::VectorXd residuals_; </div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n_; </div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d_; </div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;            <span class="keywordtype">void</span> initialise(Eigen::Ref&lt;const Eigen::MatrixXd&gt; X, Eigen::Ref&lt;const Eigen::VectorXd&gt; y);</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        };</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    }</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;}</div><div class="ttc" id="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS_html_a29fea80582f61a1235ed0efedb0a72f4"><div class="ttname"><a href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a29fea80582f61a1235ed0efedb0a72f4">ml::LinearRegression::RecursiveMultivariateOLS::n</a></div><div class="ttdeci">unsigned int n() const</div><div class="ttdoc">Returns the number of data points seen so far. </div><div class="ttdef"><b>Definition:</b> RecursiveMultivariateOLS.hpp:46</div></div>
+<div class="ttc" id="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS_html_a55e966b13a29c52c5dacf0295948b574"><div class="ttname"><a href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a55e966b13a29c52c5dacf0295948b574">ml::LinearRegression::RecursiveMultivariateOLS::d</a></div><div class="ttdeci">unsigned int d() const</div><div class="ttdoc">Returns the dimension of data points. If n() == 0, returs 0. </div><div class="ttdef"><b>Definition:</b> RecursiveMultivariateOLS.hpp:52</div></div>
+<div class="ttc" id="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS_html_abeef4f383bd42d1c42aa455512f3c471"><div class="ttname"><a href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#abeef4f383bd42d1c42aa455512f3c471">ml::LinearRegression::RecursiveMultivariateOLS::RecursiveMultivariateOLS</a></div><div class="ttdeci">RecursiveMultivariateOLS()</div><div class="ttdoc">Initialises without data. </div></div>
+<div class="ttc" id="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS_html_a459f258fd086528bf1e06f3eac5b99d5"><div class="ttname"><a href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#a459f258fd086528bf1e06f3eac5b99d5">ml::LinearRegression::RecursiveMultivariateOLS::update</a></div><div class="ttdeci">void update(Eigen::Ref&lt; const Eigen::MatrixXd &gt; X, Eigen::Ref&lt; const Eigen::VectorXd &gt; y)</div><div class="ttdoc">Updates the beta estimate with a new sample. </div></div>
+<div class="ttc" id="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS_html"><div class="ttname"><a href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html">ml::LinearRegression::RecursiveMultivariateOLS</a></div><div class="ttdoc">Recursive multivariate Ordinary Least Squares. </div><div class="ttdef"><b>Definition:</b> RecursiveMultivariateOLS.hpp:24</div></div>
+<div class="ttc" id="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS_html_ab5db1361876165a60fa6ccd4c519d29c"><div class="ttname"><a href="classml_1_1LinearRegression_1_1RecursiveMultivariateOLS.html#ab5db1361876165a60fa6ccd4c519d29c">ml::LinearRegression::RecursiveMultivariateOLS::beta</a></div><div class="ttdeci">const Eigen::VectorXd &amp; beta() const</div><div class="ttdoc">Returns the current estimate of beta. If n() == 0, returns an empty vector. </div><div class="ttdef"><b>Definition:</b> RecursiveMultivariateOLS.hpp:58</div></div>
+<div class="ttc" id="namespaceml_html"><div class="ttname"><a href="namespaceml.html">ml</a></div><div class="ttdef"><b>Definition:</b> Clustering.hpp:8</div></div>
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