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<div class="headertitle"><div class="title">kohonen_som_trace.cpp File Reference<div class="ingroups"><a class="el" href="../../d9/d66/group__machine__learning.html">Machine Learning Algorithms</a></div></div></div>
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<div class="contents">
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<p><a href="https://en.wikipedia.org/wiki/Self-organizing_map" target="_blank">Kohonen self organizing map</a> (data tracing)
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<a href="#details">More...</a></p>
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<div class="textblock"><code>#include <algorithm></code><br />
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<code>#include <array></code><br />
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<code>#include <cmath></code><br />
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<code>#include <cstdlib></code><br />
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<code>#include <ctime></code><br />
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<code>#include <fstream></code><br />
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<code>#include <iostream></code><br />
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<code>#include <valarray></code><br />
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<code>#include <vector></code><br />
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</div><div class="textblock"><div class="dynheader">
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Include dependency graph for kohonen_som_trace.cpp:</div>
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<div class="dyncontent">
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<div class="center"><div class="zoom"><iframe scrolling="no" loading="lazy" frameborder="0" src="../../d4/d60/kohonen__som__trace_8cpp__incl.svg" width="100%" height="394"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe></div></div>
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<p><a href="../../d9/d49/kohonen__som__trace_8cpp_source.html">Go to the source code of this file.</a></p>
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<table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 id="header-namespaces" class="groupheader"><a id="namespaces" name="namespaces"></a>
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Namespaces</h2></td></tr>
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<tr class="memitem:machine_5Flearning" id="r_machine_5Flearning"><td class="memItemLeft" align="right" valign="top">namespace  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d8/d77/namespacemachine__learning.html">machine_learning</a></td></tr>
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<tr class="memdesc:d8/d77/namespacemachine__learning"><td class="mdescLeft"> </td><td class="mdescRight"><a href="https://en.wikipedia.org/wiki/A*_search_algorithm" target="_blank">A* search algorithm</a> <br /></td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 id="header-func-members" class="groupheader"><a id="func-members" name="func-members"></a>
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Functions</h2></td></tr>
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<tr class="memitem:gaf5ce14f026d6d231bef29161bac2b485" id="r_gaf5ce14f026d6d231bef29161bac2b485"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a> (double a, double b)</td></tr>
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<tr class="memitem:gae0208548f8b393528e5db01717e88e67" id="r_gae0208548f8b393528e5db01717e88e67"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a> (const char *fname, const std::vector< std::valarray< double > > &X)</td></tr>
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<tr class="memitem:aa6aac06ccf128b0a9c55c9ee1a8e5631" id="r_aa6aac06ccf128b0a9c55c9ee1a8e5631"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d8/d77/namespacemachine__learning.html#aa6aac06ccf128b0a9c55c9ee1a8e5631">machine_learning::update_weights</a> (const std::valarray< double > &x, std::vector< std::valarray< double > > *W, std::valarray< double > *D, double alpha, int R)</td></tr>
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<tr class="memitem:a042f435bca0839e721fc1574a61e8da3" id="r_a042f435bca0839e721fc1574a61e8da3"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d8/d77/namespacemachine__learning.html#a042f435bca0839e721fc1574a61e8da3">machine_learning::kohonen_som_tracer</a> (const std::vector< std::valarray< double > > &X, std::vector< std::valarray< double > > *W, double alpha_min)</td></tr>
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<tr class="memitem:ae571600aa42a81bc14a4a602ea5ff00d" id="r_ae571600aa42a81bc14a4a602ea5ff00d"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#ae571600aa42a81bc14a4a602ea5ff00d">test_circle</a> (std::vector< std::valarray< double > > *<a class="el" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>)</td></tr>
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<tr class="memitem:a1440a7779ac56f47a3f355ce4a8c7da0" id="r_a1440a7779ac56f47a3f355ce4a8c7da0"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a1440a7779ac56f47a3f355ce4a8c7da0">test1</a> ()</td></tr>
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<tr class="memitem:a53082f2e5bacec40266499da4547309a" id="r_a53082f2e5bacec40266499da4547309a"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a53082f2e5bacec40266499da4547309a">test_lamniscate</a> (std::vector< std::valarray< double > > *<a class="el" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>)</td></tr>
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<tr class="memitem:a0283886819c7c140a023582b7269e2d0" id="r_a0283886819c7c140a023582b7269e2d0"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a0283886819c7c140a023582b7269e2d0">test2</a> ()</td></tr>
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<tr class="memitem:a7154fe319e6033485a8a6cd6f0d8932d" id="r_a7154fe319e6033485a8a6cd6f0d8932d"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a7154fe319e6033485a8a6cd6f0d8932d">test_3d_classes</a> (std::vector< std::valarray< double > > *<a class="el" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>)</td></tr>
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<tr class="memitem:a6d0455dd5c30adda100e95f0423c786e" id="r_a6d0455dd5c30adda100e95f0423c786e"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a6d0455dd5c30adda100e95f0423c786e">test3</a> ()</td></tr>
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<tr class="memitem:a2256c10b16edba377b64a44b6c656908" id="r_a2256c10b16edba377b64a44b6c656908"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a> (clock_t start_t, clock_t end_t)</td></tr>
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<tr class="memitem:ae66f6b31b5ad750f1fe042a706a4e3d4" id="r_ae66f6b31b5ad750f1fe042a706a4e3d4"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a> ()</td></tr>
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<tr class="memitem:a042f435bca0839e721fc1574a61e8da3" id="r_a042f435bca0839e721fc1574a61e8da3"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a> (const std::vector< std::valarray< double > > &X, std::vector< std::valarray< double > > *W, double alpha_min)</td></tr>
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</table>
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<a name="details" id="details"></a><h2 id="header-details" class="groupheader">Detailed Description</h2>
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<div class="textblock"><p><a href="https://en.wikipedia.org/wiki/Self-organizing_map" target="_blank">Kohonen self organizing map</a> (data tracing) </p>
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<p>This example implements a powerful self organizing map algorithm. The algorithm creates a connected network of weights that closely follows the given data points. This this creates a chain of nodes that resembles the given input shape.</p>
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<dl class="section author"><dt>Author</dt><dd><a href="https://github.com/kvedala" target="_blank">Krishna Vedala</a></dd></dl>
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<dl class="section note"><dt>Note</dt><dd>This C++ version of the program is considerable slower than its <a href="https://github.com/kvedala/C/blob/master/machine_learning/kohonen_som_trace.c" target="_blank">C counterpart</a> </dd>
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<dd>
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The compiled code is much slower when compiled with MS Visual C++ 2019 than with GCC on windows </dd></dl>
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<dl class="section see"><dt>See also</dt><dd><a class="el" href="../../d4/def/kohonen__som__topology_8cpp.html" title="Kohonen self organizing map (topological map)">kohonen_som_topology.cpp</a> </dd></dl>
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<p class="definition">Definition in file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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</div><a name="doc-func-members" id="doc-func-members"></a><h2 id="header-doc-func-members" class="groupheader">Function Documentation</h2>
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<a id="a2256c10b16edba377b64a44b6c656908" name="a2256c10b16edba377b64a44b6c656908"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a2256c10b16edba377b64a44b6c656908">◆ </a></span>get_clock_diff()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">double get_clock_diff </td>
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<td>(</td>
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<td class="paramtype">clock_t</td> <td class="paramname"><span class="paramname"><em>start_t</em></span>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">clock_t</td> <td class="paramname"><span class="paramname"><em>end_t</em></span> )</td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Convert clock cycle difference to time in seconds</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[in]</td><td class="paramname">start_t</td><td>start clock </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">end_t</td><td>end clock </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>time difference in seconds </dd></dl>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00452">452</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 452</span> {</div>
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<div class="line"><span class="lineno"> 453</span> <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(end_t - start_t) / CLOCKS_PER_SEC;</div>
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<div class="line"><span class="lineno"> 454</span>}</div>
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</div><!-- fragment -->
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</div>
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</div>
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<a id="a042f435bca0839e721fc1574a61e8da3" name="a042f435bca0839e721fc1574a61e8da3"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a042f435bca0839e721fc1574a61e8da3">◆ </a></span>kohonen_som_tracer()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">void machine_learning::kohonen_som_tracer </td>
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<td>(</td>
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<td class="paramtype">const std::vector< std::valarray< double > > &</td> <td class="paramname"><span class="paramname"><em>X</em></span>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">std::vector< std::valarray< double > > *</td> <td class="paramname"><span class="paramname"><em>W</em></span>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">double</td> <td class="paramname"><span class="paramname"><em>alpha_min</em></span> )</td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Apply incremental algorithm with updating neighborhood and learning rates on all samples in the given datset.</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[in]</td><td class="paramname">X</td><td>data set </td></tr>
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<tr><td class="paramdir">[in,out]</td><td class="paramname">W</td><td>weights matrix </td></tr>
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<tr><td class="paramdir">[in]</td><td class="paramname">alpha_min</td><td>terminal value of alpha </td></tr>
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</table>
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</dd>
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</dl>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00149">149</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 151</span> {</div>
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<div class="line"><span class="lineno"> 152</span> <span class="keywordtype">int</span> num_samples = X.size(); <span class="comment">// number of rows</span></div>
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<div class="line"><span class="lineno"> 153</span> <span class="comment">// int num_features = X[0].size(); // number of columns</span></div>
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<div class="line"><span class="lineno"> 154</span> <span class="keywordtype">int</span> num_out = W->size(); <span class="comment">// number of rows</span></div>
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<div class="line"><span class="lineno"> 155</span> <span class="keywordtype">int</span> R = num_out >> 2, iter = 0;</div>
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<div class="line"><span class="lineno"> 156</span> <span class="keywordtype">double</span> alpha = 1.f;</div>
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<div class="line"><span class="lineno"> 157</span> </div>
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<div class="line"><span class="lineno"> 158</span> std::valarray<double> D(num_out);</div>
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<div class="line"><span class="lineno"> 159</span> </div>
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<div class="line"><span class="lineno"> 160</span> <span class="comment">// Loop alpha from 1 to slpha_min</span></div>
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<div class="line"><span class="lineno"> 161</span> <span class="keywordflow">do</span> {</div>
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<div class="line"><span class="lineno"> 162</span> <span class="comment">// Loop for each sample pattern in the data set</span></div>
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<div class="line"><span class="lineno"> 163</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> sample = 0; sample < num_samples; sample++) {</div>
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<div class="line"><span class="lineno"> 164</span> <span class="comment">// update weights for the current input pattern sample</span></div>
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<div class="line"><span class="lineno"> 165</span> <a class="code hl_function" href="../../d8/d77/namespacemachine__learning.html#ae868ad43698a1d69ba46ea3827d7d2c3">update_weights</a>(X[sample], W, &D, alpha, R);</div>
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<div class="line"><span class="lineno"> 166</span> }</div>
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<div class="line"><span class="lineno"> 167</span> </div>
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<div class="line"><span class="lineno"> 168</span> <span class="comment">// every 10th iteration, reduce the neighborhood range</span></div>
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<div class="line"><span class="lineno"> 169</span> <span class="keywordflow">if</span> (iter % 10 == 0 && R > 1) {</div>
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<div class="line"><span class="lineno"> 170</span> R--;</div>
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<div class="line"><span class="lineno"> 171</span> }</div>
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<div class="line"><span class="lineno"> 172</span> </div>
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<div class="line"><span class="lineno"> 173</span> alpha -= 0.01;</div>
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<div class="line"><span class="lineno"> 174</span> iter++;</div>
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<div class="line"><span class="lineno"> 175</span> } <span class="keywordflow">while</span> (alpha > alpha_min);</div>
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<div class="line"><span class="lineno"> 176</span>}</div>
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<div class="ttc" id="anamespacemachine__learning_html_ae868ad43698a1d69ba46ea3827d7d2c3"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#ae868ad43698a1d69ba46ea3827d7d2c3">machine_learning::update_weights</a></div><div class="ttdeci">double update_weights(const std::valarray< double > &X, std::vector< std::vector< std::valarray< double > > > *W, std::vector< std::valarray< double > > *D, double alpha, int R)</div><div class="ttdef"><b>Definition</b> <a href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00200">kohonen_som_topology.cpp:200</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ae66f6b31b5ad750f1fe042a706a4e3d4">◆ </a></span>main()</h2>
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<p>Main function </p>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00457">457</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 457</span> {</div>
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<div class="line"><span class="lineno"> 458</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 459</span> std::cout << <span class="stringliteral">"Using OpenMP based parallelization\n"</span>;</div>
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<div class="line"><span class="lineno"> 460</span><span class="preprocessor">#else</span></div>
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<div class="line"><span class="lineno"> 461</span> std::cout << <span class="stringliteral">"NOT using OpenMP based parallelization\n"</span>;</div>
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<div class="line"><span class="lineno"> 462</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 463</span> </div>
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<div class="line"><span class="lineno"> 464</span> std::srand(std::time(<span class="keyword">nullptr</span>));</div>
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<div class="line"><span class="lineno"> 465</span> </div>
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<div class="line"><span class="lineno"> 466</span> std::clock_t start_clk = std::clock();</div>
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<div class="line"><span class="lineno"> 467</span> <a class="code hl_function" href="#a1440a7779ac56f47a3f355ce4a8c7da0">test1</a>();</div>
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<div class="line"><span class="lineno"> 468</span> <span class="keyword">auto</span> end_clk = std::clock();</div>
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<div class="line"><span class="lineno"> 469</span> std::cout << <span class="stringliteral">"Test 1 completed in "</span> << <a class="code hl_function" href="#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a>(start_clk, end_clk)</div>
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<div class="line"><span class="lineno"> 470</span> << <span class="stringliteral">" sec\n"</span>;</div>
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<div class="line"><span class="lineno"> 471</span> </div>
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<div class="line"><span class="lineno"> 472</span> start_clk = std::clock();</div>
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<div class="line"><span class="lineno"> 473</span> <a class="code hl_function" href="#a0283886819c7c140a023582b7269e2d0">test2</a>();</div>
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<div class="line"><span class="lineno"> 474</span> end_clk = std::clock();</div>
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<div class="line"><span class="lineno"> 475</span> std::cout << <span class="stringliteral">"Test 2 completed in "</span> << <a class="code hl_function" href="#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a>(start_clk, end_clk)</div>
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<div class="line"><span class="lineno"> 476</span> << <span class="stringliteral">" sec\n"</span>;</div>
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<div class="line"><span class="lineno"> 477</span> </div>
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<div class="line"><span class="lineno"> 478</span> start_clk = std::clock();</div>
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<div class="line"><span class="lineno"> 479</span> <a class="code hl_function" href="#a6d0455dd5c30adda100e95f0423c786e">test3</a>();</div>
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<div class="line"><span class="lineno"> 480</span> end_clk = std::clock();</div>
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<div class="line"><span class="lineno"> 481</span> std::cout << <span class="stringliteral">"Test 3 completed in "</span> << <a class="code hl_function" href="#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a>(start_clk, end_clk)</div>
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<div class="line"><span class="lineno"> 482</span> << <span class="stringliteral">" sec\n"</span>;</div>
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<div class="line"><span class="lineno"> 483</span> </div>
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<div class="line"><span class="lineno"> 484</span> std::cout</div>
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<div class="line"><span class="lineno"> 485</span> << <span class="stringliteral">"(Note: Calculated times include: creating test sets, training "</span></div>
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<div class="line"><span class="lineno"> 486</span> <span class="stringliteral">"model and writing files to disk.)\n\n"</span>;</div>
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<div class="line"><span class="lineno"> 487</span> <span class="keywordflow">return</span> 0;</div>
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<div class="line"><span class="lineno"> 488</span>}</div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a0283886819c7c140a023582b7269e2d0"><div class="ttname"><a href="#a0283886819c7c140a023582b7269e2d0">test2</a></div><div class="ttdeci">void test2()</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00315">kohonen_som_trace.cpp:315</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a1440a7779ac56f47a3f355ce4a8c7da0"><div class="ttname"><a href="#a1440a7779ac56f47a3f355ce4a8c7da0">test1</a></div><div class="ttdeci">void test1()</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00233">kohonen_som_trace.cpp:233</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a2256c10b16edba377b64a44b6c656908"><div class="ttname"><a href="#a2256c10b16edba377b64a44b6c656908">get_clock_diff</a></div><div class="ttdeci">double get_clock_diff(clock_t start_t, clock_t end_t)</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00452">kohonen_som_trace.cpp:452</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a6d0455dd5c30adda100e95f0423c786e"><div class="ttname"><a href="#a6d0455dd5c30adda100e95f0423c786e">test3</a></div><div class="ttdeci">void test3()</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00414">kohonen_som_trace.cpp:414</a></div></div>
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<a id="a1440a7779ac56f47a3f355ce4a8c7da0" name="a1440a7779ac56f47a3f355ce4a8c7da0"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a1440a7779ac56f47a3f355ce4a8c7da0">◆ </a></span>test1()</h2>
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<p>Test that creates a random set of points distributed <em>near</em> the circumference of a circle and trains an SOM that finds that circular pattern. The following <a href="https://en.wikipedia.org/wiki/Comma-separated_values" target="_blank">CSV</a> files are created to validate the execution:</p><ul>
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<li><span class="tt">test1.csv</span>: random test samples points with a circular pattern</li>
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<li><span class="tt">w11.csv</span>: initial random map</li>
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<li><span class="tt">w12.csv</span>: trained SOM map</li>
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</ul>
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<p>The outputs can be readily plotted in <a href="https:://gnuplot.info" target="_blank">gnuplot</a> using the following snippet </p><div class="fragment"><div class="line">set datafile separator ','</div>
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<div class="line">plot "test1.csv" title "original", \</div>
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<div class="line"> "w11.csv" title "w1", \</div>
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<div class="line"> "w12.csv" title "w2"</div>
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</div><!-- fragment --><p> <img src="https://raw.githubusercontent.com/TheAlgorithms/C-Plus-Plus/docs/images/machine_learning/kohonen/test1.svg" alt="Sample execution
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output" style="pointer-events: none;" class="inline"/> </p>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00233">233</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 233</span> {</div>
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<div class="line"><span class="lineno"> 234</span> <span class="keywordtype">int</span> j = 0, N = 500;</div>
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<div class="line"><span class="lineno"> 235</span> <span class="keywordtype">int</span> features = 2;</div>
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<div class="line"><span class="lineno"> 236</span> <span class="keywordtype">int</span> num_out = 50;</div>
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<div class="line"><span class="lineno"> 237</span> std::vector<std::valarray<double>> X(N);</div>
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<div class="line"><span class="lineno"> 238</span> std::vector<std::valarray<double>> W(num_out);</div>
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<div class="line"><span class="lineno"> 239</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < std::max(num_out, N); i++) {</div>
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<div class="line"><span class="lineno"> 240</span> <span class="comment">// loop till max(N, num_out)</span></div>
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<div class="line"><span class="lineno"> 241</span> <span class="keywordflow">if</span> (i < N) { <span class="comment">// only add new arrays if i < N</span></div>
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<div class="line"><span class="lineno"> 242</span> X[i] = std::valarray<double>(features);</div>
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<div class="line"><span class="lineno"> 243</span> }</div>
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<div class="line"><span class="lineno"> 244</span> <span class="keywordflow">if</span> (i < num_out) { <span class="comment">// only add new arrays if i < num_out</span></div>
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<div class="line"><span class="lineno"> 245</span> W[i] = std::valarray<double>(features);</div>
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<div class="line"><span class="lineno"> 246</span> </div>
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<div class="line"><span class="lineno"> 247</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 248</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 249</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 250</span> <span class="keywordflow">for</span> (j = 0; j < features; j++) {</div>
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<div class="line"><span class="lineno"> 251</span> <span class="comment">// preallocate with random initial weights</span></div>
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<div class="line"><span class="lineno"> 252</span> W[i][j] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-1, 1);</div>
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<div class="line"><span class="lineno"> 253</span> }</div>
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<div class="line"><span class="lineno"> 254</span> }</div>
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<div class="line"><span class="lineno"> 255</span> }</div>
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<div class="line"><span class="lineno"> 256</span> </div>
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<div class="line"><span class="lineno"> 257</span> <a class="code hl_function" href="#ae571600aa42a81bc14a4a602ea5ff00d">test_circle</a>(&X); <span class="comment">// create test data around circumference of a circle</span></div>
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<div class="line"><span class="lineno"> 258</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"test1.csv"</span>, X); <span class="comment">// save test data points</span></div>
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<div class="line"><span class="lineno"> 259</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w11.csv"</span>, W); <span class="comment">// save initial random weights</span></div>
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<div class="line"><span class="lineno"> 260</span> <a class="code hl_function" href="#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a>(X, &W, 0.1); <span class="comment">// train the SOM</span></div>
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<div class="line"><span class="lineno"> 261</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w12.csv"</span>, W); <span class="comment">// save the resultant weights</span></div>
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<div class="line"><span class="lineno"> 262</span>}</div>
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<div class="ttc" id="agroup__machine__learning_html_gae0208548f8b393528e5db01717e88e67"><div class="ttname"><a href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a></div><div class="ttdeci">int save_nd_data(const char *fname, const std::vector< std::valarray< double > > &X)</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00058">kohonen_som_trace.cpp:58</a></div></div>
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<div class="ttc" id="agroup__machine__learning_html_gaf5ce14f026d6d231bef29161bac2b485"><div class="ttname"><a href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a></div><div class="ttdeci">double _random(double a, double b)</div><div class="ttdef"><b>Definition</b> <a href="../../d4/def/kohonen__som__topology_8cpp_source.html#l00053">kohonen_som_topology.cpp:53</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a042f435bca0839e721fc1574a61e8da3"><div class="ttname"><a href="#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a></div><div class="ttdeci">void kohonen_som_tracer(const std::vector< std::valarray< double > > &X, std::vector< std::valarray< double > > *W, double alpha_min)</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00149">kohonen_som_trace.cpp:149</a></div></div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_ae571600aa42a81bc14a4a602ea5ff00d"><div class="ttname"><a href="#ae571600aa42a81bc14a4a602ea5ff00d">test_circle</a></div><div class="ttdeci">void test_circle(std::vector< std::valarray< double > > *data)</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00196">kohonen_som_trace.cpp:196</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0283886819c7c140a023582b7269e2d0">◆ </a></span>test2()</h2>
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<td class="memname">void test2 </td>
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<p>Test that creates a random set of points distributed <em>near</em> the locus of the <a href="https://en.wikipedia.org/wiki/Lemniscate_of_Gerono" target="_blank">Lamniscate of Gerono</a> and trains an SOM that finds that circular pattern. The following <a href="https://en.wikipedia.org/wiki/Comma-separated_values" target="_blank">CSV</a> files are created to validate the execution:</p><ul>
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<li><span class="tt">test2.csv</span>: random test samples points with a lamniscate pattern</li>
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<li><span class="tt">w21.csv</span>: initial random map</li>
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<li><span class="tt">w22.csv</span>: trained SOM map</li>
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</ul>
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<p>The outputs can be readily plotted in <a href="https:://gnuplot.info" target="_blank">gnuplot</a> using the following snippet </p><div class="fragment"><div class="line">set datafile separator ','</div>
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<div class="line">plot "test2.csv" title "original", \</div>
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<div class="line"> "w21.csv" title "w1", \</div>
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<div class="line"> "w22.csv" title "w2"</div>
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</div><!-- fragment --><p> <img src="https://raw.githubusercontent.com/TheAlgorithms/C-Plus-Plus/docs/images/machine_learning/kohonen/test2.svg" alt="Sample execution
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output" style="pointer-events: none;" class="inline"/> </p>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00315">315</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 315</span> {</div>
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<div class="line"><span class="lineno"> 316</span> <span class="keywordtype">int</span> j = 0, N = 500;</div>
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<div class="line"><span class="lineno"> 317</span> <span class="keywordtype">int</span> features = 2;</div>
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<div class="line"><span class="lineno"> 318</span> <span class="keywordtype">int</span> num_out = 20;</div>
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<div class="line"><span class="lineno"> 319</span> std::vector<std::valarray<double>> X(N);</div>
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<div class="line"><span class="lineno"> 320</span> std::vector<std::valarray<double>> W(num_out);</div>
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<div class="line"><span class="lineno"> 321</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < std::max(num_out, N); i++) {</div>
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<div class="line"><span class="lineno"> 322</span> <span class="comment">// loop till max(N, num_out)</span></div>
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<div class="line"><span class="lineno"> 323</span> <span class="keywordflow">if</span> (i < N) { <span class="comment">// only add new arrays if i < N</span></div>
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<div class="line"><span class="lineno"> 324</span> X[i] = std::valarray<double>(features);</div>
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<div class="line"><span class="lineno"> 325</span> }</div>
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<div class="line"><span class="lineno"> 326</span> <span class="keywordflow">if</span> (i < num_out) { <span class="comment">// only add new arrays if i < num_out</span></div>
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<div class="line"><span class="lineno"> 327</span> W[i] = std::valarray<double>(features);</div>
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<div class="line"><span class="lineno"> 328</span> </div>
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<div class="line"><span class="lineno"> 329</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 330</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 331</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 332</span> <span class="keywordflow">for</span> (j = 0; j < features; j++) {</div>
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<div class="line"><span class="lineno"> 333</span> <span class="comment">// preallocate with random initial weights</span></div>
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<div class="line"><span class="lineno"> 334</span> W[i][j] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-1, 1);</div>
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<div class="line"><span class="lineno"> 335</span> }</div>
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<div class="line"><span class="lineno"> 336</span> }</div>
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<div class="line"><span class="lineno"> 337</span> }</div>
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<div class="line"><span class="lineno"> 338</span> </div>
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<div class="line"><span class="lineno"> 339</span> <a class="code hl_function" href="#a53082f2e5bacec40266499da4547309a">test_lamniscate</a>(&X); <span class="comment">// create test data around the lamniscate</span></div>
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<div class="line"><span class="lineno"> 340</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"test2.csv"</span>, X); <span class="comment">// save test data points</span></div>
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<div class="line"><span class="lineno"> 341</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w21.csv"</span>, W); <span class="comment">// save initial random weights</span></div>
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<div class="line"><span class="lineno"> 342</span> <a class="code hl_function" href="#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a>(X, &W, 0.01); <span class="comment">// train the SOM</span></div>
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<div class="line"><span class="lineno"> 343</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w22.csv"</span>, W); <span class="comment">// save the resultant weights</span></div>
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<div class="line"><span class="lineno"> 344</span>}</div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a53082f2e5bacec40266499da4547309a"><div class="ttname"><a href="#a53082f2e5bacec40266499da4547309a">test_lamniscate</a></div><div class="ttdeci">void test_lamniscate(std::vector< std::valarray< double > > *data)</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00277">kohonen_som_trace.cpp:277</a></div></div>
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<a id="a6d0455dd5c30adda100e95f0423c786e" name="a6d0455dd5c30adda100e95f0423c786e"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a6d0455dd5c30adda100e95f0423c786e">◆ </a></span>test3()</h2>
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<td class="memname">void test3 </td>
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<td>(</td>
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<td class="paramname"><span class="paramname"><em></em></span></td><td>)</td>
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<p>Test that creates a random set of points distributed in six clusters in 3D space. The following <a href="https://en.wikipedia.org/wiki/Comma-separated_values" target="_blank">CSV</a> files are created to validate the execution:</p><ul>
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<li><span class="tt">test3.csv</span>: random test samples points with a circular pattern</li>
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<li><span class="tt">w31.csv</span>: initial random map</li>
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<li><span class="tt">w32.csv</span>: trained SOM map</li>
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</ul>
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<p>The outputs can be readily plotted in <a href="https:://gnuplot.info" target="_blank">gnuplot</a> using the following snippet </p><div class="fragment"><div class="line">set datafile separator ','</div>
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<div class="line">plot "test3.csv" title "original", \</div>
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<div class="line"> "w31.csv" title "w1", \</div>
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<div class="line"> "w32.csv" title "w2"</div>
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</div><!-- fragment --><p> <img src="https://raw.githubusercontent.com/TheAlgorithms/C-Plus-Plus/docs/images/machine_learning/kohonen/test3.svg" alt="Sample execution
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output" style="pointer-events: none;" class="inline"/> </p>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00414">414</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 414</span> {</div>
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<div class="line"><span class="lineno"> 415</span> <span class="keywordtype">int</span> j = 0, N = 200;</div>
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<div class="line"><span class="lineno"> 416</span> <span class="keywordtype">int</span> features = 3;</div>
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<div class="line"><span class="lineno"> 417</span> <span class="keywordtype">int</span> num_out = 20;</div>
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<div class="line"><span class="lineno"> 418</span> std::vector<std::valarray<double>> X(N);</div>
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<div class="line"><span class="lineno"> 419</span> std::vector<std::valarray<double>> W(num_out);</div>
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<div class="line"><span class="lineno"> 420</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < std::max(num_out, N); i++) {</div>
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<div class="line"><span class="lineno"> 421</span> <span class="comment">// loop till max(N, num_out)</span></div>
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<div class="line"><span class="lineno"> 422</span> <span class="keywordflow">if</span> (i < N) { <span class="comment">// only add new arrays if i < N</span></div>
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<div class="line"><span class="lineno"> 423</span> X[i] = std::valarray<double>(features);</div>
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<div class="line"><span class="lineno"> 424</span> }</div>
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<div class="line"><span class="lineno"> 425</span> <span class="keywordflow">if</span> (i < num_out) { <span class="comment">// only add new arrays if i < num_out</span></div>
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<div class="line"><span class="lineno"> 426</span> W[i] = std::valarray<double>(features);</div>
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<div class="line"><span class="lineno"> 427</span> </div>
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<div class="line"><span class="lineno"> 428</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 429</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 430</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 431</span> <span class="keywordflow">for</span> (j = 0; j < features; j++) {</div>
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<div class="line"><span class="lineno"> 432</span> <span class="comment">// preallocate with random initial weights</span></div>
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<div class="line"><span class="lineno"> 433</span> W[i][j] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-1, 1);</div>
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<div class="line"><span class="lineno"> 434</span> }</div>
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<div class="line"><span class="lineno"> 435</span> }</div>
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<div class="line"><span class="lineno"> 436</span> }</div>
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<div class="line"><span class="lineno"> 437</span> </div>
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<div class="line"><span class="lineno"> 438</span> <a class="code hl_function" href="#a7154fe319e6033485a8a6cd6f0d8932d">test_3d_classes</a>(&X); <span class="comment">// create test data around the lamniscate</span></div>
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<div class="line"><span class="lineno"> 439</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"test3.csv"</span>, X); <span class="comment">// save test data points</span></div>
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<div class="line"><span class="lineno"> 440</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w31.csv"</span>, W); <span class="comment">// save initial random weights</span></div>
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<div class="line"><span class="lineno"> 441</span> <a class="code hl_function" href="#a042f435bca0839e721fc1574a61e8da3">kohonen_som_tracer</a>(X, &W, 0.01); <span class="comment">// train the SOM</span></div>
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<div class="line"><span class="lineno"> 442</span> <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gae0208548f8b393528e5db01717e88e67">save_nd_data</a>(<span class="stringliteral">"w32.csv"</span>, W); <span class="comment">// save the resultant weights</span></div>
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<div class="line"><span class="lineno"> 443</span>}</div>
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<div class="ttc" id="akohonen__som__trace_8cpp_html_a7154fe319e6033485a8a6cd6f0d8932d"><div class="ttname"><a href="#a7154fe319e6033485a8a6cd6f0d8932d">test_3d_classes</a></div><div class="ttdeci">void test_3d_classes(std::vector< std::valarray< double > > *data)</div><div class="ttdef"><b>Definition</b> <a href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00359">kohonen_som_trace.cpp:359</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a7154fe319e6033485a8a6cd6f0d8932d">◆ </a></span>test_3d_classes()</h2>
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<td class="memname">void test_3d_classes </td>
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<td>(</td>
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<td class="paramtype">std::vector< std::valarray< double > > *</td> <td class="paramname"><span class="paramname"><em>data</em></span></td><td>)</td>
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<p>Creates a random set of points distributed in six clusters in 3D space with centroids at the points</p><ul>
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<li>\({0.5, 0.5, 0.5}\)</li>
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<li>\({0.5, 0.5, -0.5}\)</li>
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<li>\({0.5, -0.5, 0.5}\)</li>
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<li>\({0.5, -0.5, -0.5}\)</li>
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<li>\({-0.5, 0.5, 0.5}\)</li>
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<li>\({-0.5, 0.5, -0.5}\)</li>
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<li>\({-0.5, -0.5, 0.5}\)</li>
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<li>\({-0.5, -0.5, -0.5}\)</li>
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</ul>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[out]</td><td class="paramname">data</td><td>matrix to store data in </td></tr>
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</table>
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</dd>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00359">359</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 359</span> {</div>
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<div class="line"><span class="lineno"> 360</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>->size();</div>
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<div class="line"><span class="lineno"> 361</span> <span class="keyword">const</span> <span class="keywordtype">double</span> R = 0.1; <span class="comment">// radius of cluster</span></div>
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<div class="line"><span class="lineno"> 362</span> <span class="keywordtype">int</span> i = 0;</div>
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<div class="line"><span class="lineno"> 363</span> <span class="keyword">const</span> <span class="keywordtype">int</span> num_classes = 8;</div>
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<div class="line"><span class="lineno"> 364</span> <span class="keyword">const</span> std::array<const std::array<double, 3>, num_classes> centres = {</div>
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<div class="line"><span class="lineno"> 365</span> <span class="comment">// centres of each class cluster</span></div>
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<div class="line"><span class="lineno"> 366</span> std::array<double, 3>({.5, .5, .5}), <span class="comment">// centre of class 0</span></div>
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<div class="line"><span class="lineno"> 367</span> std::array<double, 3>({.5, .5, -.5}), <span class="comment">// centre of class 1</span></div>
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<div class="line"><span class="lineno"> 368</span> std::array<double, 3>({.5, -.5, .5}), <span class="comment">// centre of class 2</span></div>
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<div class="line"><span class="lineno"> 369</span> std::array<double, 3>({.5, -.5, -.5}), <span class="comment">// centre of class 3</span></div>
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<div class="line"><span class="lineno"> 370</span> std::array<double, 3>({-.5, .5, .5}), <span class="comment">// centre of class 4</span></div>
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<div class="line"><span class="lineno"> 371</span> std::array<double, 3>({-.5, .5, -.5}), <span class="comment">// centre of class 5</span></div>
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<div class="line"><span class="lineno"> 372</span> std::array<double, 3>({-.5, -.5, .5}), <span class="comment">// centre of class 6</span></div>
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<div class="line"><span class="lineno"> 373</span> std::array<double, 3>({-.5, -.5, -.5}) <span class="comment">// centre of class 7</span></div>
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<div class="line"><span class="lineno"> 374</span> };</div>
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<div class="line"><span class="lineno"> 375</span> </div>
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<div class="line"><span class="lineno"> 376</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 377</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 378</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 379</span> <span class="keywordflow">for</span> (i = 0; i < N; i++) {</div>
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<div class="line"><span class="lineno"> 380</span> <span class="keywordtype">int</span> cls =</div>
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<div class="line"><span class="lineno"> 381</span> std::rand() % num_classes; <span class="comment">// select a random class for the point</span></div>
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<div class="line"><span class="lineno"> 382</span> </div>
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<div class="line"><span class="lineno"> 383</span> <span class="comment">// create random coordinates (x,y,z) around the centre of the class</span></div>
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<div class="line"><span class="lineno"> 384</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][0] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(centres[cls][0] - R, centres[cls][0] + R);</div>
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<div class="line"><span class="lineno"> 385</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][1] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(centres[cls][1] - R, centres[cls][1] + R);</div>
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<div class="line"><span class="lineno"> 386</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][2] = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(centres[cls][2] - R, centres[cls][2] + R);</div>
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<div class="line"><span class="lineno"> 387</span> </div>
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<div class="line"><span class="lineno"> 388</span> <span class="comment">/* The follosing can also be used</span></div>
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<div class="line"><span class="lineno"> 389</span><span class="comment"> for (int j = 0; j < 3; j++)</span></div>
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<div class="line"><span class="lineno"> 390</span><span class="comment"> data[0][i][j] = _random(centres[cls][j] - R, centres[cls][j] + R);</span></div>
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<div class="line"><span class="lineno"> 391</span><span class="comment"> */</span></div>
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<div class="line"><span class="lineno"> 392</span> }</div>
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<div class="line"><span class="lineno"> 393</span>}</div>
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<div class="ttc" id="ahash__search_8cpp_html_a6e1a77282bc65ad359d753d25df23243"><div class="ttname"><a href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a></div><div class="ttdeci">int data[MAX]</div><div class="ttdoc">test data</div><div class="ttdef"><b>Definition</b> <a href="../../d1/df3/hash__search_8cpp_source.html#l00024">hash_search.cpp:24</a></div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ae571600aa42a81bc14a4a602ea5ff00d">◆ </a></span>test_circle()</h2>
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<p>Creates a random set of points distributed <em>near</em> the circumference of a circle and trains an SOM that finds that circular pattern. The generating function is </p><p class="formulaDsp">
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\begin{eqnarray*}r &\in& [1-\delta r, 1+\delta r)\\
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\theta &\in& [0, 2\pi)\\
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x &=& r\cos\theta\\
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y &=& r\sin\theta
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\end{eqnarray*}
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</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<tr><td class="paramdir">[out]</td><td class="paramname">data</td><td>matrix to store data in </td></tr>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00196">196</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 196</span> {</div>
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<div class="line"><span class="lineno"> 197</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>->size();</div>
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<div class="line"><span class="lineno"> 198</span> <span class="keyword">const</span> <span class="keywordtype">double</span> R = 0.75, dr = 0.3;</div>
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<div class="line"><span class="lineno"> 199</span> <span class="keywordtype">double</span> a_t = 0., b_t = 2.f * M_PI; <span class="comment">// theta random between 0 and 2*pi</span></div>
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<div class="line"><span class="lineno"> 200</span> <span class="keywordtype">double</span> a_r = R - dr, b_r = R + dr; <span class="comment">// radius random between R-dr and R+dr</span></div>
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<div class="line"><span class="lineno"> 201</span> <span class="keywordtype">int</span> i = 0;</div>
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<div class="line"><span class="lineno"> 202</span> </div>
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<div class="line"><span class="lineno"> 203</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 204</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 205</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 206</span> <span class="keywordflow">for</span> (i = 0; i < N; i++) {</div>
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<div class="line"><span class="lineno"> 207</span> <span class="keywordtype">double</span> r = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(a_r, b_r); <span class="comment">// random radius</span></div>
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<div class="line"><span class="lineno"> 208</span> <span class="keywordtype">double</span> theta = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(a_t, b_t); <span class="comment">// random theta</span></div>
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<div class="line"><span class="lineno"> 209</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][0] = r * cos(theta); <span class="comment">// convert from polar to cartesian</span></div>
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<div class="line"><span class="lineno"> 210</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][1] = r * sin(theta);</div>
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<div class="line"><span class="lineno"> 211</span> }</div>
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<div class="line"><span class="lineno"> 212</span>}</div>
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<a id="a53082f2e5bacec40266499da4547309a" name="a53082f2e5bacec40266499da4547309a"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a53082f2e5bacec40266499da4547309a">◆ </a></span>test_lamniscate()</h2>
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<td class="memname">void test_lamniscate </td>
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<td>(</td>
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<td class="paramtype">std::vector< std::valarray< double > > *</td> <td class="paramname"><span class="paramname"><em>data</em></span></td><td>)</td>
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<td></td>
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<p>Creates a random set of points distributed <em>near</em> the locus of the <a href="https://en.wikipedia.org/wiki/Lemniscate_of_Gerono" target="_blank">Lamniscate of Gerono</a>. </p><p class="formulaDsp">
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\begin{eqnarray*}\delta r &=& 0.2\\
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\delta x &\in& [-\delta r, \delta r)\\
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\delta y &\in& [-\delta r, \delta r)\\
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\theta &\in& [0, \pi)\\
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x &=& \delta x + \cos\theta\\
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y &=& \delta y + \frac{\sin(2\theta)}{2}
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\end{eqnarray*}
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</p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramdir">[out]</td><td class="paramname">data</td><td>matrix to store data in </td></tr>
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</table>
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</dd>
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</dl>
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<p class="definition">Definition at line <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html#l00277">277</a> of file <a class="el" href="../../d9/d49/kohonen__som__trace_8cpp_source.html">kohonen_som_trace.cpp</a>.</p>
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<div class="fragment"><div class="line"><span class="lineno"> 277</span> {</div>
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<div class="line"><span class="lineno"> 278</span> <span class="keyword">const</span> <span class="keywordtype">int</span> N = <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>->size();</div>
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<div class="line"><span class="lineno"> 279</span> <span class="keyword">const</span> <span class="keywordtype">double</span> dr = 0.2;</div>
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<div class="line"><span class="lineno"> 280</span> <span class="keywordtype">int</span> i = 0;</div>
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<div class="line"><span class="lineno"> 281</span> </div>
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<div class="line"><span class="lineno"> 282</span><span class="preprocessor">#ifdef _OPENMP</span></div>
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<div class="line"><span class="lineno"> 283</span><span class="preprocessor">#pragma omp for</span></div>
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<div class="line"><span class="lineno"> 284</span><span class="preprocessor">#endif</span></div>
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<div class="line"><span class="lineno"> 285</span> <span class="keywordflow">for</span> (i = 0; i < N; i++) {</div>
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<div class="line"><span class="lineno"> 286</span> <span class="keywordtype">double</span> dx = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-dr, dr); <span class="comment">// random change in x</span></div>
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<div class="line"><span class="lineno"> 287</span> <span class="keywordtype">double</span> dy = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(-dr, dr); <span class="comment">// random change in y</span></div>
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<div class="line"><span class="lineno"> 288</span> <span class="keywordtype">double</span> theta = <a class="code hl_function" href="../../d9/d66/group__machine__learning.html#gaf5ce14f026d6d231bef29161bac2b485">_random</a>(0, M_PI); <span class="comment">// random theta</span></div>
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<div class="line"><span class="lineno"> 289</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][0] = dx + cos(theta); <span class="comment">// convert from polar to cartesian</span></div>
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<div class="line"><span class="lineno"> 290</span> <a class="code hl_variable" href="../../d1/df3/hash__search_8cpp.html#a6e1a77282bc65ad359d753d25df23243">data</a>[0][i][1] = dy + sin(2. * theta) / 2.f;</div>
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<div class="line"><span class="lineno"> 291</span> }</div>
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<div class="line"><span class="lineno"> 292</span>}</div>
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