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<a href="gaussian__distribution_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="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#ifndef MLPACK_CORE_DISTRIBUTIONS_GAUSSIAN_DISTRIBUTION_HPP</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#define MLPACK_CORE_DISTRIBUTIONS_GAUSSIAN_DISTRIBUTION_HPP</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="prereqs_8hpp.html">mlpack/prereqs.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace </span><a class="code" href="namespacemlpack.html">mlpack</a> {</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">namespace </span>distribution {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00024"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html"> 24</a></span> <span class="keyword">class </span><a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html">GaussianDistribution</a></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> {</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a792a82012b34799267f3840bbb980470"> 28</a></span>  arma::vec <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a792a82012b34799267f3840bbb980470">mean</a>;</div><div class="line"><a name="l00030"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a996e66f349b41a80685c867af5337d8e"> 30</a></span>  arma::mat <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a996e66f349b41a80685c867af5337d8e">covariance</a>;</div><div class="line"><a name="l00032"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a6939db7f1ae0111a399c00cee480f407"> 32</a></span>  arma::mat <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a6939db7f1ae0111a399c00cee480f407">covLower</a>;</div><div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a504a3081ccbd369ca8873a0473fd8e15"> 34</a></span>  arma::mat <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a504a3081ccbd369ca8873a0473fd8e15">invCov</a>;</div><div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ab25ba64193df7f17b0106fe40814ea52"> 36</a></span>  <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ab25ba64193df7f17b0106fe40814ea52">logDetCov</a>;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a9e14f26de8635cad1e1fd33ea63bd3b2"> 39</a></span>  <span class="keyword">static</span> <span class="keyword">const</span> constexpr <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a9e14f26de8635cad1e1fd33ea63bd3b2">log2pi</a> = 1.83787706640934533908193770912475883;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00045"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ac73762f8e0e8cfbde9c8646ef0c2a5c0"> 45</a></span>  <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ac73762f8e0e8cfbde9c8646ef0c2a5c0">GaussianDistribution</a>() { <span class="comment">/* nothing to do */</span> }</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00051"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a974ca20051328ce4388b1a3b973f55bd"> 51</a></span>  <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a974ca20051328ce4388b1a3b973f55bd">GaussianDistribution</a>(<span class="keyword">const</span> <span class="keywordtype">size_t</span> dimension) :</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  mean(arma::zeros<arma::vec>(dimension)),</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  covariance(arma::eye<arma::mat>(dimension, dimension)),</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  covLower(arma::eye<arma::mat>(dimension, dimension)),</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  invCov(arma::eye<arma::mat>(dimension, dimension)),</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  logDetCov(0)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  { <span class="comment">/* Nothing to do. */</span> }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ac73762f8e0e8cfbde9c8646ef0c2a5c0">GaussianDistribution</a>(<span class="keyword">const</span> arma::vec& mean, <span class="keyword">const</span> arma::mat& covariance);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// TODO(stephentu): do we want a (arma::vec&&, arma::mat&&) ctor?</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00069"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#af80519bc5659be9718842f68721c36d5"> 69</a></span>  <span class="keywordtype">size_t</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#af80519bc5659be9718842f68721c36d5">Dimensionality</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> mean.n_elem; }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#aa251b280010ecf47187a570e358a7983"> 74</a></span>  <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#aa251b280010ecf47187a570e358a7983">Probability</a>(<span class="keyword">const</span> arma::vec& observation)<span class="keyword"> const</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">return</span> exp(<a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ad3738d4082974e2da16624c24500d734">LogProbability</a>(observation));</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ad3738d4082974e2da16624c24500d734">LogProbability</a>(<span class="keyword">const</span> arma::vec& observation) <span class="keyword">const</span>;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a912e56bf26a1db38f8896ee25bfcc9ee"> 91</a></span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a912e56bf26a1db38f8896ee25bfcc9ee">Probability</a>(<span class="keyword">const</span> arma::mat& x, arma::vec& probabilities)<span class="keyword"> const</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  arma::vec logProbabilities;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ad3738d4082974e2da16624c24500d734">LogProbability</a>(x, logProbabilities);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  probabilities = arma::exp(logProbabilities);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ad3738d4082974e2da16624c24500d734">LogProbability</a>(<span class="keyword">const</span> arma::mat& x, arma::vec& logProbabilities) <span class="keyword">const</span>;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  arma::vec <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a40794ed450cd4dc6602bd4f5b76cb9ee">Random</a>() <span class="keyword">const</span>;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a0ae4ac53b3023f692e873c58d0515b94">Train</a>(<span class="keyword">const</span> arma::mat& observations);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a0ae4ac53b3023f692e873c58d0515b94">Train</a>(<span class="keyword">const</span> arma::mat& observations,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keyword">const</span> arma::vec& probabilities);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00126"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a2a64f63d99d78f778795a7e40cec6004"> 126</a></span>  <span class="keyword">const</span> arma::vec& <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a2a64f63d99d78f778795a7e40cec6004">Mean</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a792a82012b34799267f3840bbb980470">mean</a>; }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00131"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#acc79343195393c16d5fc57921e6cfeb2"> 131</a></span>  arma::vec& <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#acc79343195393c16d5fc57921e6cfeb2">Mean</a>() { <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a792a82012b34799267f3840bbb980470">mean</a>; }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00136"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a33054fff004ef83bd2db02866d836379"> 136</a></span>  <span class="keyword">const</span> arma::mat& <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a33054fff004ef83bd2db02866d836379">Covariance</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a996e66f349b41a80685c867af5337d8e">covariance</a>; }</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a33054fff004ef83bd2db02866d836379">Covariance</a>(<span class="keyword">const</span> arma::mat& covariance);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a33054fff004ef83bd2db02866d836379">Covariance</a>(arma::mat&& covariance);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keyword">template</span><<span class="keyword">typename</span> Archive></div><div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a40542531b890d22639727ca0d4bbea03"> 149</a></span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a40542531b890d22639727ca0d4bbea03">Serialize</a>(Archive& 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="l00150"></a><span class="lineno"> 150</span>  {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">using</span> <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">data::CreateNVP</a>;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="comment">// We just need to serialize each of the members.</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  ar & <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">CreateNVP</a>(mean, <span class="stringliteral">"mean"</span>);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  ar & <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">CreateNVP</a>(covariance, <span class="stringliteral">"covariance"</span>);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  ar & <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">CreateNVP</a>(covLower, <span class="stringliteral">"covLower"</span>);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  ar & <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">CreateNVP</a>(invCov, <span class="stringliteral">"invCov"</span>);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  ar & <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">CreateNVP</a>(logDetCov, <span class="stringliteral">"logDetCov"</span>);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a09657acc92f4f54c598a648e71137f56">FactorCovariance</a>();</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> };</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00177"></a><span class="lineno"><a class="line" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ac770ef06e53a8507ae13beec6a2fa051"> 177</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ad3738d4082974e2da16624c24500d734">GaussianDistribution::LogProbability</a>(<span class="keyword">const</span> arma::mat& x,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  arma::vec& logProbabilities)<span class="keyword"> const</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="keyword"></span>{</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// Column i of 'diffs' is the difference between x.col(i) and the mean.</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  arma::mat diffs = x - (<a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a792a82012b34799267f3840bbb980470">mean</a> * arma::ones<arma::rowvec>(x.n_cols));</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// Now, we only want to calculate the diagonal elements of (diffs' * cov^-1 *</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="comment">// diffs). We just don't need any of the other elements. We can calculate</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="comment">// the right hand part of the equation (instead of the left side) so that</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// later we are referencing columns, not rows -- that is faster.</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> arma::mat rhs = -0.5 * <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a504a3081ccbd369ca8873a0473fd8e15">invCov</a> * diffs;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  arma::vec logExponents(diffs.n_cols); <span class="comment">// We will now fill this.</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < diffs.n_cols; i++)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  logExponents(i) = accu(diffs.unsafe_col(i) % rhs.unsafe_col(i));</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> k = x.n_rows;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  logProbabilities = -0.5 * k * <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a9e14f26de8635cad1e1fd33ea63bd3b2">log2pi</a> - 0.5 * <a class="code" href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ab25ba64193df7f17b0106fe40814ea52">logDetCov</a> + logExponents;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> } <span class="comment">// namespace distribution</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> } <span class="comment">// namespace mlpack</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="preprocessor">#endif</span></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>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a792a82012b34799267f3840bbb980470"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a792a82012b34799267f3840bbb980470">mlpack::distribution::GaussianDistribution::mean</a></div><div class="ttdeci">arma::vec mean</div><div class="ttdoc">Mean of the distribution. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00028">gaussian_distribution.hpp:28</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a2a64f63d99d78f778795a7e40cec6004"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a2a64f63d99d78f778795a7e40cec6004">mlpack::distribution::GaussianDistribution::Mean</a></div><div class="ttdeci">const arma::vec & Mean() const </div><div class="ttdoc">Return the mean. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00126">gaussian_distribution.hpp:126</a></div></div>
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<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="classmlpack_1_1distribution_1_1GaussianDistribution_html_a40794ed450cd4dc6602bd4f5b76cb9ee"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a40794ed450cd4dc6602bd4f5b76cb9ee">mlpack::distribution::GaussianDistribution::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>
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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="namespacemlpack_1_1data_html_a785ee98e0070286ef86eeeea85a74018"><div class="ttname"><a href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">mlpack::data::CreateNVP</a></div><div class="ttdeci">FirstShim< T > CreateNVP(T &t, const std::string &name, typename std::enable_if_t< HasSerialize< T >::value > *=0)</div><div class="ttdoc">Call this function to produce a name-value pair; this is similar to BOOST_SERIALIZATION_NVP(), but should be used for types that have a Serialize() function (or contain a type that has a Serialize() function) instead of a serialize() function. </div><div class="ttdef"><b>Definition:</b> <a href="serialization__shim_8hpp_source.html#l00094">serialization_shim.hpp:94</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a0ae4ac53b3023f692e873c58d0515b94"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a0ae4ac53b3023f692e873c58d0515b94">mlpack::distribution::GaussianDistribution::Train</a></div><div class="ttdeci">void Train(const arma::mat &observations)</div><div class="ttdoc">Estimate the Gaussian distribution directly from the given observations. </div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a9e14f26de8635cad1e1fd33ea63bd3b2"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a9e14f26de8635cad1e1fd33ea63bd3b2">mlpack::distribution::GaussianDistribution::log2pi</a></div><div class="ttdeci">static const constexpr double log2pi</div><div class="ttdoc">log(2pi) </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00039">gaussian_distribution.hpp:39</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a09657acc92f4f54c598a648e71137f56"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a09657acc92f4f54c598a648e71137f56">mlpack::distribution::GaussianDistribution::FactorCovariance</a></div><div class="ttdeci">void FactorCovariance()</div><div class="ttdoc">This factors the covariance using arma::chol(). </div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_ab25ba64193df7f17b0106fe40814ea52"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ab25ba64193df7f17b0106fe40814ea52">mlpack::distribution::GaussianDistribution::logDetCov</a></div><div class="ttdeci">double logDetCov</div><div class="ttdoc">Cached logdet(cov). </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00036">gaussian_distribution.hpp:36</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a996e66f349b41a80685c867af5337d8e"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a996e66f349b41a80685c867af5337d8e">mlpack::distribution::GaussianDistribution::covariance</a></div><div class="ttdeci">arma::mat covariance</div><div class="ttdoc">Positive definite covariance of the distribution. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00030">gaussian_distribution.hpp:30</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a40542531b890d22639727ca0d4bbea03"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a40542531b890d22639727ca0d4bbea03">mlpack::distribution::GaussianDistribution::Serialize</a></div><div class="ttdeci">void Serialize(Archive &ar, const unsigned int)</div><div class="ttdoc">Serialize the distribution. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00149">gaussian_distribution.hpp:149</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a6939db7f1ae0111a399c00cee480f407"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a6939db7f1ae0111a399c00cee480f407">mlpack::distribution::GaussianDistribution::covLower</a></div><div class="ttdeci">arma::mat covLower</div><div class="ttdoc">Lower triangular factor of cov (e.g. cov = LL^T). </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00032">gaussian_distribution.hpp:32</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_aa251b280010ecf47187a570e358a7983"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#aa251b280010ecf47187a570e358a7983">mlpack::distribution::GaussianDistribution::Probability</a></div><div class="ttdeci">double Probability(const arma::vec &observation) const </div><div class="ttdoc">Return the probability of the given observation. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00074">gaussian_distribution.hpp:74</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_af80519bc5659be9718842f68721c36d5"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#af80519bc5659be9718842f68721c36d5">mlpack::distribution::GaussianDistribution::Dimensionality</a></div><div class="ttdeci">size_t Dimensionality() const </div><div class="ttdoc">Return the dimensionality of this distribution. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00069">gaussian_distribution.hpp:69</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a974ca20051328ce4388b1a3b973f55bd"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a974ca20051328ce4388b1a3b973f55bd">mlpack::distribution::GaussianDistribution::GaussianDistribution</a></div><div class="ttdeci">GaussianDistribution(const size_t dimension)</div><div class="ttdoc">Create a Gaussian distribution with zero mean and identity covariance with the given dimensionality...</div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00051">gaussian_distribution.hpp:51</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a33054fff004ef83bd2db02866d836379"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a33054fff004ef83bd2db02866d836379">mlpack::distribution::GaussianDistribution::Covariance</a></div><div class="ttdeci">const arma::mat & Covariance() const </div><div class="ttdoc">Return the covariance matrix. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00136">gaussian_distribution.hpp:136</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_ad3738d4082974e2da16624c24500d734"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ad3738d4082974e2da16624c24500d734">mlpack::distribution::GaussianDistribution::LogProbability</a></div><div class="ttdeci">double LogProbability(const arma::vec &observation) const </div><div class="ttdoc">Return the log probability of the given observation. </div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a504a3081ccbd369ca8873a0473fd8e15"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a504a3081ccbd369ca8873a0473fd8e15">mlpack::distribution::GaussianDistribution::invCov</a></div><div class="ttdeci">arma::mat invCov</div><div class="ttdoc">Cached inverse of covariance. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00034">gaussian_distribution.hpp:34</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_ac73762f8e0e8cfbde9c8646ef0c2a5c0"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#ac73762f8e0e8cfbde9c8646ef0c2a5c0">mlpack::distribution::GaussianDistribution::GaussianDistribution</a></div><div class="ttdeci">GaussianDistribution()</div><div class="ttdoc">Default constructor, which creates a Gaussian with zero dimension. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00045">gaussian_distribution.hpp:45</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_a912e56bf26a1db38f8896ee25bfcc9ee"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#a912e56bf26a1db38f8896ee25bfcc9ee">mlpack::distribution::GaussianDistribution::Probability</a></div><div class="ttdeci">void Probability(const arma::mat &x, arma::vec &probabilities) const </div><div class="ttdoc">Calculates the multivariate Gaussian probability density function for each data point (column) in the...</div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00091">gaussian_distribution.hpp:91</a></div></div>
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<div class="ttc" id="classmlpack_1_1distribution_1_1GaussianDistribution_html_acc79343195393c16d5fc57921e6cfeb2"><div class="ttname"><a href="classmlpack_1_1distribution_1_1GaussianDistribution.html#acc79343195393c16d5fc57921e6cfeb2">mlpack::distribution::GaussianDistribution::Mean</a></div><div class="ttdeci">arma::vec & Mean()</div><div class="ttdoc">Return a modifiable copy of the mean. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__distribution_8hpp_source.html#l00131">gaussian_distribution.hpp:131</a></div></div>
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