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<div class="title">exact_svd_method.hpp</div> </div>
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<a href="exact__svd__method_8hpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#ifndef MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_EXACT_SVD_METHOD_HPP</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#define MLPACK_METHODS_PCA_DECOMPOSITION_POLICIES_EXACT_SVD_METHOD_HPP</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <<a class="code" href="prereqs_8hpp.html">mlpack/prereqs.hpp</a>></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">namespace </span><a class="code" href="namespacemlpack.html">mlpack</a> {</div><div class="line"><a name="l00022"></a><span class="lineno"><a class="line" href="namespacemlpack_1_1pca.html"> 22</a></span> <span class="keyword">namespace </span>pca {</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00027"></a><span class="lineno"><a class="line" href="classmlpack_1_1pca_1_1ExactSVDPolicy.html"> 27</a></span> <span class="keyword">class </span><a class="code" href="classmlpack_1_1pca_1_1ExactSVDPolicy.html">ExactSVDPolicy</a></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> {</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classmlpack_1_1pca_1_1ExactSVDPolicy.html#ac7be12f95e69f36b608a1668be8e8105"> 41</a></span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1pca_1_1ExactSVDPolicy.html#ac7be12f95e69f36b608a1668be8e8105">Apply</a>(<span class="keyword">const</span> arma::mat& data,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> arma::mat& centeredData,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  arma::mat& transformedData,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  arma::vec& eigVal,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  arma::mat& eigvec,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> <span class="comment">/* rank */</span>)</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  {</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// This matrix will store the right singular values; we do not need them.</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  arma::mat v;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// Do singular value decomposition. Use the economical singular value</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// decomposition if the columns are much larger than the rows.</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">if</span> (data.n_rows < data.n_cols)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="comment">// Do economical singular value decomposition and compute only the left</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="comment">// singular vectors.</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  arma::svd_econ(eigvec, eigVal, v, centeredData, <span class="charliteral">'l'</span>);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  }</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  arma::svd(eigvec, eigVal, v, centeredData);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  }</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Now we must square the singular values to get the eigenvalues.</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="comment">// In addition we must divide by the number of points, because the</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// covariance matrix is X * X' / (N - 1).</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  eigVal %= eigVal / (data.n_cols - 1);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="comment">// Project the samples to the principals.</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  transformedData = arma::trans(eigvec) * centeredData;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  }</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> };</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> } <span class="comment">// namespace pca</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> } <span class="comment">// namespace mlpack</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="preprocessor">#endif</span></div><div class="ttc" id="namespacemlpack_html"><div class="ttname"><a href="namespacemlpack.html">mlpack</a></div><div class="ttdoc">Linear algebra utility functions, generally performed on matrices or vectors. </div><div class="ttdef"><b>Definition:</b> <a href="binarize_8hpp_source.html#l00018">binarize.hpp:18</a></div></div>
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<div class="ttc" id="prereqs_8hpp_html"><div class="ttname"><a href="prereqs_8hpp.html">prereqs.hpp</a></div><div class="ttdoc">The core includes that mlpack expects; standard C++ includes and Armadillo. </div></div>
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<div class="ttc" id="classmlpack_1_1pca_1_1ExactSVDPolicy_html"><div class="ttname"><a href="classmlpack_1_1pca_1_1ExactSVDPolicy.html">mlpack::pca::ExactSVDPolicy</a></div><div class="ttdoc">Implementation of the exact SVD policy. </div><div class="ttdef"><b>Definition:</b> <a href="exact__svd__method_8hpp_source.html#l00027">exact_svd_method.hpp:27</a></div></div>
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<div class="ttc" id="classmlpack_1_1pca_1_1ExactSVDPolicy_html_ac7be12f95e69f36b608a1668be8e8105"><div class="ttname"><a href="classmlpack_1_1pca_1_1ExactSVDPolicy.html#ac7be12f95e69f36b608a1668be8e8105">mlpack::pca::ExactSVDPolicy::Apply</a></div><div class="ttdeci">void Apply(const arma::mat &data, const arma::mat &centeredData, arma::mat &transformedData, arma::vec &eigVal, arma::mat &eigvec, const size_t)</div><div class="ttdoc">Apply Principal Component Analysis to the provided data set using the exact SVD method. </div><div class="ttdef"><b>Definition:</b> <a href="exact__svd__method_8hpp_source.html#l00041">exact_svd_method.hpp:41</a></div></div>
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