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<a href="gaussian__kernel_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_CORE_KERNELS_GAUSSIAN_KERNEL_HPP</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="preprocessor">#define MLPACK_CORE_KERNELS_GAUSSIAN_KERNEL_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;<span class="preprocessor">#include &lt;<a class="code" href="lmetric_8hpp.html">mlpack/core/metrics/lmetric.hpp</a>&gt;</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="kernel__traits_8hpp.html">mlpack/core/kernels/kernel_traits.hpp</a>&gt;</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacemlpack.html">mlpack</a> {</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="keyword">namespace </span>kernel {</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;</div><div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html"> 34</a></span>&#160;<span class="keyword">class </span><a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html">GaussianKernel</a></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;{</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160; <span class="keyword">public</span>:</div><div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a1982249e5fbb7e4b94ae88f1e0218303"> 40</a></span>&#160; <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a1982249e5fbb7e4b94ae88f1e0218303">GaussianKernel</a>() : <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>(1.0), <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a>(-0.5)</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; { }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab508d841b64919cd395fe5ad959c2fe5"> 48</a></span>&#160; <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab508d841b64919cd395fe5ad959c2fe5">GaussianKernel</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>) :</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; bandwidth(bandwidth),</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a>(-0.5 * pow(bandwidth, -2.0))</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; { }</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="keyword">template</span>&lt;<span class="keyword">typename</span> VecTypeA, <span class="keyword">typename</span> VecTypeB&gt;</div><div class="line"><a name="l00065"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a574ef2113ec8ac965df031eee219dd73"> 65</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a574ef2113ec8ac965df031eee219dd73">Evaluate</a>(<span class="keyword">const</span> VecTypeA&amp; a, <span class="keyword">const</span> VecTypeB&amp; b)<span class="keyword"> const</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="comment">// The precalculation of gamma saves us a little computation time.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="keywordflow">return</span> exp(<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a> * <a class="code" href="classmlpack_1_1metric_1_1LMetric.html#ac55458730b8e36f51d2f2a3741b77181">metric::SquaredEuclideanDistance::Evaluate</a>(a, b));</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5336c3dbf963c079757ed35fe685c39b"> 78</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5336c3dbf963c079757ed35fe685c39b">Evaluate</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> t)<span class="keyword"> const</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="comment">// The precalculation of gamma saves us a little computation time.</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; <span class="keywordflow">return</span> exp(<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a> * std::pow(t, 2.0));</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; }</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;</div><div class="line"><a name="l00092"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5faea1b64eff5c1e0705d560e742e2f7"> 92</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5faea1b64eff5c1e0705d560e742e2f7">Gradient</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> t)<span class="keyword"> const </span>{</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; <span class="keywordflow">return</span> 2 * t * <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a> * exp(<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a> * std::pow(t, 2.0));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; }</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;</div><div class="line"><a name="l00104"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab2bd0f1eb24fe8a7508d49fba1659942"> 104</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab2bd0f1eb24fe8a7508d49fba1659942">GradientForSquaredDistance</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> t)<span class="keyword"> const </span>{</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a> * exp(<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a> * t);</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="l00114"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a17bc9e87e69d5187a11fa12e2f9a3ba2"> 114</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a17bc9e87e69d5187a11fa12e2f9a3ba2">Normalizer</a>(<span class="keyword">const</span> <span class="keywordtype">size_t</span> dimension)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keywordflow">return</span> pow(sqrt(2.0 * <a class="code" href="prereqs_8hpp.html#ae71449b1cc6e6250b91f539153a7a0d3">M_PI</a>) * <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>, (<span class="keywordtype">double</span>) dimension);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keyword">template</span>&lt;<span class="keyword">typename</span> VecTypeA, <span class="keyword">typename</span> VecTypeB&gt;</div><div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a02b3cab5d575452053f69e9f69dfc47a"> 127</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a02b3cab5d575452053f69e9f69dfc47a">ConvolutionIntegral</a>(<span class="keyword">const</span> VecTypeA&amp; a, <span class="keyword">const</span> VecTypeB&amp; b)</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a574ef2113ec8ac965df031eee219dd73">Evaluate</a>(sqrt(<a class="code" href="classmlpack_1_1metric_1_1LMetric.html#ac55458730b8e36f51d2f2a3741b77181">metric::SquaredEuclideanDistance::Evaluate</a>(a, b) / 2.0)) /</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; (<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a17bc9e87e69d5187a11fa12e2f9a3ba2">Normalizer</a>(a.n_rows) * pow(2.0, (<span class="keywordtype">double</span>) a.n_rows / 2.0));</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a80caca0e054169dbc73aeaed0c9ce2da"> 135</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a80caca0e054169dbc73aeaed0c9ce2da">Bandwidth</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>; }</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ac7723e82d94352245c203b9100485ae0"> 139</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ac7723e82d94352245c203b9100485ae0">Bandwidth</a>(<span class="keyword">const</span> <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>)</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; this-&gt;bandwidth = <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; this-&gt;<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a> = -0.5 * pow(bandwidth, -2.0);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7dd6194dab1f6d6562ac0be4fa00fcae"> 146</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7dd6194dab1f6d6562ac0be4fa00fcae">Gamma</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a>; }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; <span class="keyword">template</span>&lt;<span class="keyword">typename</span> Archive&gt;</div><div class="line"><a name="l00150"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ae459d2087dbad74c8aa57515db45123b"> 150</a></span>&#160; <span class="keywordtype">void</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ae459d2087dbad74c8aa57515db45123b">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="l00151"></a><span class="lineno"> 151</span>&#160; {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; ar &amp; <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">data::CreateNVP</a>(<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>, <span class="stringliteral">&quot;bandwidth&quot;</span>);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; ar &amp; <a class="code" href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">data::CreateNVP</a>(<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a>, <span class="stringliteral">&quot;gamma&quot;</span>);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; }</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; <span class="keyword">private</span>:</div><div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151"> 158</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a"> 162</a></span>&#160; <span class="keywordtype">double</span> <a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a>;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160;};</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160;<span class="keyword">template</span>&lt;&gt;</div><div class="line"><a name="l00167"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1KernelTraits_3_01GaussianKernel_01_4.html"> 167</a></span>&#160;<span class="keyword">class </span><a class="code" href="classmlpack_1_1kernel_1_1KernelTraits.html">KernelTraits</a>&lt;<a class="code" href="classmlpack_1_1kernel_1_1GaussianKernel.html">GaussianKernel</a>&gt;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160;{</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; <span class="keyword">public</span>:</div><div class="line"><a name="l00171"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1KernelTraits_3_01GaussianKernel_01_4.html#a05bdf2730803d7205420dc6d3fd35f87"> 171</a></span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> IsNormalized = <span class="keyword">true</span>;</div><div class="line"><a name="l00173"></a><span class="lineno"><a class="line" href="classmlpack_1_1kernel_1_1KernelTraits_3_01GaussianKernel_01_4.html#a860c5920304139c60a51fe7bb84f0a81"> 173</a></span>&#160; <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">bool</span> UsesSquaredDistance = <span class="keyword">true</span>;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160;};</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160;} <span class="comment">// namespace kernel</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160;} <span class="comment">// namespace mlpack</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160;<span class="preprocessor">#endif</span></div><div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a1982249e5fbb7e4b94ae88f1e0218303"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a1982249e5fbb7e4b94ae88f1e0218303">mlpack::kernel::GaussianKernel::GaussianKernel</a></div><div class="ttdeci">GaussianKernel()</div><div class="ttdoc">Default constructor; sets bandwidth to 1.0. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00040">gaussian_kernel.hpp:40</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a02b3cab5d575452053f69e9f69dfc47a"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a02b3cab5d575452053f69e9f69dfc47a">mlpack::kernel::GaussianKernel::ConvolutionIntegral</a></div><div class="ttdeci">double ConvolutionIntegral(const VecTypeA &amp;a, const VecTypeB &amp;b)</div><div class="ttdoc">Obtain a convolution integral of the Gaussian kernel. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00127">gaussian_kernel.hpp:127</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1KernelTraits_html"><div class="ttname"><a href="classmlpack_1_1kernel_1_1KernelTraits.html">mlpack::kernel::KernelTraits</a></div><div class="ttdoc">This is a template class that can provide information about various kernels. </div><div class="ttdef"><b>Definition:</b> <a href="kernel__traits_8hpp_source.html#l00027">kernel_traits.hpp:27</a></div></div>
<div class="ttc" id="lmetric_8hpp_html"><div class="ttname"><a href="lmetric_8hpp.html">lmetric.hpp</a></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="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="namespacemlpack_1_1data_html_a785ee98e0070286ef86eeeea85a74018"><div class="ttname"><a href="namespacemlpack_1_1data.html#a785ee98e0070286ef86eeeea85a74018">mlpack::data::CreateNVP</a></div><div class="ttdeci">FirstShim&lt; T &gt; CreateNVP(T &amp;t, const std::string &amp;name, typename std::enable_if_t&lt; HasSerialize&lt; T &gt;::value &gt; *=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>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_ac7723e82d94352245c203b9100485ae0"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#ac7723e82d94352245c203b9100485ae0">mlpack::kernel::GaussianKernel::Bandwidth</a></div><div class="ttdeci">void Bandwidth(const double bandwidth)</div><div class="ttdoc">Modify the bandwidth. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00139">gaussian_kernel.hpp:139</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a5336c3dbf963c079757ed35fe685c39b"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5336c3dbf963c079757ed35fe685c39b">mlpack::kernel::GaussianKernel::Evaluate</a></div><div class="ttdeci">double Evaluate(const double t) const </div><div class="ttdoc">Evaluation of the Gaussian kernel given the distance between two points. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00078">gaussian_kernel.hpp:78</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a7f019a726e3dca20edb7721ad994fe6a"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">mlpack::kernel::GaussianKernel::gamma</a></div><div class="ttdeci">double gamma</div><div class="ttdoc">Precalculated constant depending on the bandwidth; . </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00162">gaussian_kernel.hpp:162</a></div></div>
<div class="ttc" id="prereqs_8hpp_html_ae71449b1cc6e6250b91f539153a7a0d3"><div class="ttname"><a href="prereqs_8hpp.html#ae71449b1cc6e6250b91f539153a7a0d3">M_PI</a></div><div class="ttdeci">#define M_PI</div><div class="ttdef"><b>Definition:</b> <a href="prereqs_8hpp_source.html#l00040">prereqs.hpp:40</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_ab2bd0f1eb24fe8a7508d49fba1659942"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab2bd0f1eb24fe8a7508d49fba1659942">mlpack::kernel::GaussianKernel::GradientForSquaredDistance</a></div><div class="ttdeci">double GradientForSquaredDistance(const double t) const </div><div class="ttdoc">Evaluation of the gradient of Gaussian kernel given the squared distance between two points...</div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00104">gaussian_kernel.hpp:104</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a5faea1b64eff5c1e0705d560e742e2f7"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5faea1b64eff5c1e0705d560e742e2f7">mlpack::kernel::GaussianKernel::Gradient</a></div><div class="ttdeci">double Gradient(const double t) const </div><div class="ttdoc">Evaluation of the gradient of Gaussian kernel given the distance between two points. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00092">gaussian_kernel.hpp:92</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_ae459d2087dbad74c8aa57515db45123b"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#ae459d2087dbad74c8aa57515db45123b">mlpack::kernel::GaussianKernel::Serialize</a></div><div class="ttdeci">void Serialize(Archive &amp;ar, const unsigned int)</div><div class="ttdoc">Serialize the kernel. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00150">gaussian_kernel.hpp:150</a></div></div>
<div class="ttc" id="classmlpack_1_1metric_1_1LMetric_html_ac55458730b8e36f51d2f2a3741b77181"><div class="ttname"><a href="classmlpack_1_1metric_1_1LMetric.html#ac55458730b8e36f51d2f2a3741b77181">mlpack::metric::LMetric::Evaluate</a></div><div class="ttdeci">static VecTypeA::elem_type Evaluate(const VecTypeA &amp;a, const VecTypeB &amp;b)</div><div class="ttdoc">Computes the distance between two points. </div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_ab508d841b64919cd395fe5ad959c2fe5"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab508d841b64919cd395fe5ad959c2fe5">mlpack::kernel::GaussianKernel::GaussianKernel</a></div><div class="ttdeci">GaussianKernel(const double bandwidth)</div><div class="ttdoc">Construct the Gaussian kernel with a custom bandwidth. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00048">gaussian_kernel.hpp:48</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a17bc9e87e69d5187a11fa12e2f9a3ba2"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a17bc9e87e69d5187a11fa12e2f9a3ba2">mlpack::kernel::GaussianKernel::Normalizer</a></div><div class="ttdeci">double Normalizer(const size_t dimension)</div><div class="ttdoc">Obtain the normalization constant of the Gaussian kernel. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00114">gaussian_kernel.hpp:114</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a80caca0e054169dbc73aeaed0c9ce2da"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a80caca0e054169dbc73aeaed0c9ce2da">mlpack::kernel::GaussianKernel::Bandwidth</a></div><div class="ttdeci">double Bandwidth() const </div><div class="ttdoc">Get the bandwidth. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00135">gaussian_kernel.hpp:135</a></div></div>
<div class="ttc" id="kernel__traits_8hpp_html"><div class="ttname"><a href="kernel__traits_8hpp.html">kernel_traits.hpp</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html">mlpack::kernel::GaussianKernel</a></div><div class="ttdoc">The standard Gaussian kernel. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00034">gaussian_kernel.hpp:34</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a574ef2113ec8ac965df031eee219dd73"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a574ef2113ec8ac965df031eee219dd73">mlpack::kernel::GaussianKernel::Evaluate</a></div><div class="ttdeci">double Evaluate(const VecTypeA &amp;a, const VecTypeB &amp;b) const </div><div class="ttdoc">Evaluation of the Gaussian kernel. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00065">gaussian_kernel.hpp:65</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_a7dd6194dab1f6d6562ac0be4fa00fcae"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7dd6194dab1f6d6562ac0be4fa00fcae">mlpack::kernel::GaussianKernel::Gamma</a></div><div class="ttdeci">double Gamma() const </div><div class="ttdoc">Get the precalculated constant. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00146">gaussian_kernel.hpp:146</a></div></div>
<div class="ttc" id="classmlpack_1_1kernel_1_1GaussianKernel_html_abda0129ea431b857678f9c4ec9fab151"><div class="ttname"><a href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">mlpack::kernel::GaussianKernel::bandwidth</a></div><div class="ttdeci">double bandwidth</div><div class="ttdoc">Kernel bandwidth. </div><div class="ttdef"><b>Definition:</b> <a href="gaussian__kernel_8hpp_source.html#l00158">gaussian_kernel.hpp:158</a></div></div>
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