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<a href="gmm_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>&#160;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="preprocessor">#ifndef MLPACK_METHODS_MOG_MOG_EM_HPP</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="preprocessor">#define MLPACK_METHODS_MOG_MOG_EM_HPP</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="prereqs_8hpp.html">mlpack/prereqs.hpp</a>&gt;</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment">// This is the default fitting method class.</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="em__fit_8hpp.html">em_fit.hpp</a>&quot;</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacemlpack.html">mlpack</a> {</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="keyword">namespace </span>gmm {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html"> 79</a></span>&#160;<span class="keyword">class </span><a class="code" href="classmlpack_1_1gmm_1_1GMM.html">GMM</a></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;{</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; <span class="keyword">private</span>:</div><div class="line"><a name="l00083"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#aa56ad9ae3ab3239bb4eeca657800e39c"> 83</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aa56ad9ae3ab3239bb4eeca657800e39c">gaussians</a>;</div><div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a050283bded04ed3a4560d9687968827e"> 85</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a050283bded04ed3a4560d9687968827e">dimensionality</a>;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a0ebf41f54695be5dac0141f661527c12"> 88</a></span>&#160; std::vector&lt;distribution::GaussianDistribution&gt; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a0ebf41f54695be5dac0141f661527c12">dists</a>;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#aaaa49f6ce741d6925648b05481e8eafd"> 91</a></span>&#160; arma::vec <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aaaa49f6ce741d6925648b05481e8eafd">weights</a>;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <span class="keyword">public</span>:</div><div class="line"><a name="l00097"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#af13b579d8aca40510534154aa3e57c98"> 97</a></span>&#160; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#af13b579d8aca40510534154aa3e57c98">GMM</a>() :</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; gaussians(0),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; dimensionality(0)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; <span class="comment">// Warn the user. They probably don&#39;t want to do this. If this constructor</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; <span class="comment">// is being used (because it is required by some template classes), the user</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="comment">// should know that it is potentially dangerous.</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <a class="code" href="classmlpack_1_1Log.html#a80ba817a1abcf742c7463b1e74bf55da">Log::Debug</a> &lt;&lt; <span class="stringliteral">&quot;GMM::GMM(): no parameters given; Estimate() may fail &quot;</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; &lt;&lt; <span class="stringliteral">&quot;unless parameters are set.&quot;</span> &lt;&lt; std::endl;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#af13b579d8aca40510534154aa3e57c98">GMM</a>(<span class="keyword">const</span> <span class="keywordtype">size_t</span> gaussians, <span class="keyword">const</span> <span class="keywordtype">size_t</span> dimensionality);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a403d2b1d78fc749d2764704b5cc635d7"> 123</a></span>&#160; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a403d2b1d78fc749d2764704b5cc635d7">GMM</a>(<span class="keyword">const</span> std::vector&lt;distribution::GaussianDistribution&gt; &amp; dists,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; <span class="keyword">const</span> arma::vec&amp; weights) :</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; gaussians(dists.size()),</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; dimensionality((!dists.empty()) ? dists[0].Mean().n_elem : 0),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; dists(dists),</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; weights(weights) { <span class="comment">/* Nothing to do. */</span> }</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#af13b579d8aca40510534154aa3e57c98">GMM</a>(<span class="keyword">const</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html">GMM</a>&amp; other);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html">GMM</a>&amp; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#af1d99d49d10b2b7c09055a8932e1b30e">operator=</a>(<span class="keyword">const</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html">GMM</a>&amp; other);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;</div><div class="line"><a name="l00137"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a8efcb0139531931d570de77328297846"> 137</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a8efcb0139531931d570de77328297846">Gaussians</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aa56ad9ae3ab3239bb4eeca657800e39c">gaussians</a>; }</div><div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#ab4de2999f32be33f4a0945647df8fe8b"> 139</a></span>&#160; <span class="keywordtype">size_t</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#ab4de2999f32be33f4a0945647df8fe8b">Dimensionality</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a050283bded04ed3a4560d9687968827e">dimensionality</a>; }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a17e101975b7496e499eb4cf8bff50b89"> 146</a></span>&#160; <span class="keyword">const</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html">distribution::GaussianDistribution</a>&amp; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a17e101975b7496e499eb4cf8bff50b89">Component</a>(<span class="keywordtype">size_t</span> i)<span class="keyword"> const </span>{</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; <span class="keywordflow">return</span> dists[i]; }</div><div class="line"><a name="l00153"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a586b59080e6a53ca3de8b5e6a39b5955"> 153</a></span>&#160; <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html">distribution::GaussianDistribution</a>&amp; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a586b59080e6a53ca3de8b5e6a39b5955">Component</a>(<span class="keywordtype">size_t</span> i) { <span class="keywordflow">return</span> dists[i]; }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;</div><div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a20f4e112944e1299107f0b5ff6071c20"> 156</a></span>&#160; <span class="keyword">const</span> arma::vec&amp; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a20f4e112944e1299107f0b5ff6071c20">Weights</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aaaa49f6ce741d6925648b05481e8eafd">weights</a>; }</div><div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="classmlpack_1_1gmm_1_1GMM.html#a526fb59ae2c52f09e5c4c9a81e3f1b8f"> 158</a></span>&#160; arma::vec&amp; <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a526fb59ae2c52f09e5c4c9a81e3f1b8f">Weights</a>() { <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aaaa49f6ce741d6925648b05481e8eafd">weights</a>; }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#ac6e14ce071d6a315487e8cc01dde70d2">Probability</a>(<span class="keyword">const</span> arma::vec&amp; observation) <span class="keyword">const</span>;</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#ac6e14ce071d6a315487e8cc01dde70d2">Probability</a>(<span class="keyword">const</span> arma::vec&amp; observation,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> component) <span class="keyword">const</span>;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; arma::vec <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a3037b0529e22a4005c694c4bf297a7e2">Random</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="keyword">template</span>&lt;<span class="keyword">typename</span> FittingType = EMFit&lt;&gt;&gt;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aaff2ee51dbb56489878aaca88f3a972b">Train</a>(<span class="keyword">const</span> arma::mat&amp; observations,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> trials = 1,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> useExistingModel = <span class="keyword">false</span>,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; FittingType fitter = FittingType());</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; <span class="keyword">template</span>&lt;<span class="keyword">typename</span> FittingType = EMFit&lt;&gt;&gt;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aaff2ee51dbb56489878aaca88f3a972b">Train</a>(<span class="keyword">const</span> arma::mat&amp; observations,</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <span class="keyword">const</span> arma::vec&amp; probabilities,</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; <span class="keyword">const</span> <span class="keywordtype">size_t</span> trials = 1,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> useExistingModel = <span class="keyword">false</span>,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; FittingType fitter = FittingType());</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#aa32a2615534ec6f21c2e749be1c4e851">Classify</a>(<span class="keyword">const</span> arma::mat&amp; observations,</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; arma::Row&lt;size_t&gt;&amp; labels) <span class="keyword">const</span>;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; <span class="keyword">template</span>&lt;<span class="keyword">typename</span> Archive&gt;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#a42f747d2ffcb22d510a7e4092ae0b7e8">Serialize</a>(Archive&amp; ar, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <span class="comment">/* version */</span>);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; <span class="keyword">private</span>:</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1gmm_1_1GMM.html#af65d34a4697118184206c46ebf5c7360">LogLikelihood</a>(</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <span class="keyword">const</span> arma::mat&amp; dataPoints,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; <span class="keyword">const</span> std::vector&lt;distribution::GaussianDistribution&gt;&amp; distsL,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; <span class="keyword">const</span> arma::vec&amp; weights) <span class="keyword">const</span>;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160;};</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160;} <span class="comment">// namespace gmm</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160;} <span class="comment">// namespace mlpack</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160;<span class="comment">// Include implementation.</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160;<span class="preprocessor">#include &quot;gmm_impl.hpp&quot;</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160;<span class="preprocessor">#endif</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160;</div><div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a3037b0529e22a4005c694c4bf297a7e2"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a3037b0529e22a4005c694c4bf297a7e2">mlpack::gmm::GMM::Random</a></div><div class="ttdeci">arma::vec Random() const </div><div class="ttdoc">Return a randomly generated observation according to the probability distribution defined by this obj...</div></div>
<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html">mlpack::distribution::GaussianDistribution</a></div><div class="ttdoc">A single multivariate Gaussian distribution. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00024">gaussian_distribution.hpp:24</a></div></div>
<div class="ttc" id="em__fit_8hpp_html"><div class="ttname"><a href="em__fit_8hpp.html">em_fit.hpp</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_aa32a2615534ec6f21c2e749be1c4e851"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#aa32a2615534ec6f21c2e749be1c4e851">mlpack::gmm::GMM::Classify</a></div><div class="ttdeci">void Classify(const arma::mat &amp;observations, arma::Row&lt; size_t &gt; &amp;labels) const </div><div class="ttdoc">Classify the given observations as being from an individual component in this GMM. </div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a403d2b1d78fc749d2764704b5cc635d7"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a403d2b1d78fc749d2764704b5cc635d7">mlpack::gmm::GMM::GMM</a></div><div class="ttdeci">GMM(const std::vector&lt; distribution::GaussianDistribution &gt; &amp;dists, const arma::vec &amp;weights)</div><div class="ttdoc">Create a GMM with the given dists and weights. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00123">gmm.hpp:123</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_af65d34a4697118184206c46ebf5c7360"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#af65d34a4697118184206c46ebf5c7360">mlpack::gmm::GMM::LogLikelihood</a></div><div class="ttdeci">double LogLikelihood(const arma::mat &amp;dataPoints, const std::vector&lt; distribution::GaussianDistribution &gt; &amp;distsL, const arma::vec &amp;weights) const </div><div class="ttdoc">This function computes the loglikelihood of the given model. </div></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>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a20f4e112944e1299107f0b5ff6071c20"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a20f4e112944e1299107f0b5ff6071c20">mlpack::gmm::GMM::Weights</a></div><div class="ttdeci">const arma::vec &amp; Weights() const </div><div class="ttdoc">Return a const reference to the a priori weights of each Gaussian. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00156">gmm.hpp:156</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a17e101975b7496e499eb4cf8bff50b89"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a17e101975b7496e499eb4cf8bff50b89">mlpack::gmm::GMM::Component</a></div><div class="ttdeci">const distribution::GaussianDistribution &amp; Component(size_t i) const </div><div class="ttdoc">Return a const reference to a component distribution. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00146">gmm.hpp:146</a></div></div>
<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>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a0ebf41f54695be5dac0141f661527c12"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a0ebf41f54695be5dac0141f661527c12">mlpack::gmm::GMM::dists</a></div><div class="ttdeci">std::vector&lt; distribution::GaussianDistribution &gt; dists</div><div class="ttdoc">Vector of Gaussians. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00088">gmm.hpp:88</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_aa56ad9ae3ab3239bb4eeca657800e39c"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#aa56ad9ae3ab3239bb4eeca657800e39c">mlpack::gmm::GMM::gaussians</a></div><div class="ttdeci">size_t gaussians</div><div class="ttdoc">The number of Gaussians in the model. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00083">gmm.hpp:83</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_ab4de2999f32be33f4a0945647df8fe8b"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#ab4de2999f32be33f4a0945647df8fe8b">mlpack::gmm::GMM::Dimensionality</a></div><div class="ttdeci">size_t Dimensionality() const </div><div class="ttdoc">Return the dimensionality of the model. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00139">gmm.hpp:139</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_af1d99d49d10b2b7c09055a8932e1b30e"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#af1d99d49d10b2b7c09055a8932e1b30e">mlpack::gmm::GMM::operator=</a></div><div class="ttdeci">GMM &amp; operator=(const GMM &amp;other)</div><div class="ttdoc">Copy operator for GMMs. </div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html">mlpack::gmm::GMM</a></div><div class="ttdoc">A Gaussian Mixture Model (GMM). </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00079">gmm.hpp:79</a></div></div>
<div class="ttc" id="classmlpack_1_1Log_html_a80ba817a1abcf742c7463b1e74bf55da"><div class="ttname"><a href="classmlpack_1_1Log.html#a80ba817a1abcf742c7463b1e74bf55da">mlpack::Log::Debug</a></div><div class="ttdeci">static MLPACK_EXPORT util::NullOutStream Debug</div><div class="ttdoc">MLPACK_EXPORT is required for global variables, so that they are properly exported by the Windows com...</div><div class="ttdef"><b>Definition:</b> <a href="log_8hpp_source.html#l00079">log.hpp:79</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_aaaa49f6ce741d6925648b05481e8eafd"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#aaaa49f6ce741d6925648b05481e8eafd">mlpack::gmm::GMM::weights</a></div><div class="ttdeci">arma::vec weights</div><div class="ttdoc">Vector of a priori weights for each Gaussian. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00091">gmm.hpp:91</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_aaff2ee51dbb56489878aaca88f3a972b"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#aaff2ee51dbb56489878aaca88f3a972b">mlpack::gmm::GMM::Train</a></div><div class="ttdeci">double Train(const arma::mat &amp;observations, const size_t trials=1, const bool useExistingModel=false, FittingType fitter=FittingType())</div><div class="ttdoc">Estimate the probability distribution directly from the given observations, using the given algorithm...</div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_ac6e14ce071d6a315487e8cc01dde70d2"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#ac6e14ce071d6a315487e8cc01dde70d2">mlpack::gmm::GMM::Probability</a></div><div class="ttdeci">double Probability(const arma::vec &amp;observation) const </div><div class="ttdoc">Return the probability that the given observation came from this distribution. </div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a42f747d2ffcb22d510a7e4092ae0b7e8"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a42f747d2ffcb22d510a7e4092ae0b7e8">mlpack::gmm::GMM::Serialize</a></div><div class="ttdeci">void Serialize(Archive &amp;ar, const unsigned int)</div><div class="ttdoc">Serialize the GMM. </div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a526fb59ae2c52f09e5c4c9a81e3f1b8f"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a526fb59ae2c52f09e5c4c9a81e3f1b8f">mlpack::gmm::GMM::Weights</a></div><div class="ttdeci">arma::vec &amp; Weights()</div><div class="ttdoc">Return a reference to the a priori weights of each Gaussian. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00158">gmm.hpp:158</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a050283bded04ed3a4560d9687968827e"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a050283bded04ed3a4560d9687968827e">mlpack::gmm::GMM::dimensionality</a></div><div class="ttdeci">size_t dimensionality</div><div class="ttdoc">The dimensionality of the model. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00085">gmm.hpp:85</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a586b59080e6a53ca3de8b5e6a39b5955"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a586b59080e6a53ca3de8b5e6a39b5955">mlpack::gmm::GMM::Component</a></div><div class="ttdeci">distribution::GaussianDistribution &amp; Component(size_t i)</div><div class="ttdoc">Return a reference to a component distribution. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00153">gmm.hpp:153</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_a8efcb0139531931d570de77328297846"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#a8efcb0139531931d570de77328297846">mlpack::gmm::GMM::Gaussians</a></div><div class="ttdeci">size_t Gaussians() const </div><div class="ttdoc">Return the number of gaussians in the model. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00137">gmm.hpp:137</a></div></div>
<div class="ttc" id="classmlpack_1_1gmm_1_1GMM_html_af13b579d8aca40510534154aa3e57c98"><div class="ttname"><a href="classmlpack_1_1gmm_1_1GMM.html#af13b579d8aca40510534154aa3e57c98">mlpack::gmm::GMM::GMM</a></div><div class="ttdeci">GMM()</div><div class="ttdoc">Create an empty Gaussian Mixture Model, with zero gaussians. </div><div class="ttdef"><b>Definition:</b> <a href="gmm_8hpp_source.html#l00097">gmm.hpp:97</a></div></div>
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