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<div class="title">Simple Sample mlpack Programs </div> </div>
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<div class="textblock"><h1><a class="anchor" id="sampleintro"></a>
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Introduction</h1>
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<p>On this page, several simple mlpack examples are contained, in increasing order of complexity. If you compile from the command-line, be sure that your compiler is in C++11 mode. With gcc and clang, this can be accomplished by adding the <code>-std=c++11</code> option.</p>
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<h1><a class="anchor" id="covariance"></a>
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Covariance Computation</h1>
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<p>A simple program to compute the covariance of a data matrix ("data.csv"), assuming that the data is already centered, and save it to file.</p>
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<div class="fragment"><div class="line"><span class="comment">// Includes all relevant components of mlpack.</span></div><div class="line"><span class="preprocessor">#include <<a class="code" href="core_8hpp.html">mlpack/core.hpp</a>></span></div><div class="line"></div><div class="line"><span class="comment">// Convenience.</span></div><div class="line"><span class="keyword">using namespace </span><a class="code" href="namespacemlpack.html">mlpack</a>;</div><div class="line"></div><div class="line"><span class="keywordtype">int</span> main()</div><div class="line">{</div><div class="line"> <span class="comment">// First, load the data.</span></div><div class="line"> arma::mat data;</div><div class="line"> <span class="comment">// Use data::Load() which transposes the matrix.</span></div><div class="line"> <a class="code" href="namespacemlpack_1_1data.html#a19805d6585ac8b0be7c4e4b7f081977c">data::Load</a>(<span class="stringliteral">"data.csv"</span>, data, <span class="keyword">true</span>);</div><div class="line"></div><div class="line"> <span class="comment">// Now compute the covariance. We assume that the data is already centered.</span></div><div class="line"> <span class="comment">// Remember, because the matrix is column-major, the covariance operation is</span></div><div class="line"> <span class="comment">// transposed.</span></div><div class="line"> arma::mat cov = data * trans(data) / data.n_cols;</div><div class="line"></div><div class="line"> <span class="comment">// Save the output.</span></div><div class="line"> <a class="code" href="namespacemlpack_1_1data.html#a16bc794b14db0ab126595b19a32a5bc0">data::Save</a>(<span class="stringliteral">"cov.csv"</span>, cov, <span class="keyword">true</span>);</div><div class="line">}</div></div><!-- fragment --><h1><a class="anchor" id="nn"></a>
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Nearest Neighbor</h1>
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<p>This simple program uses the <a class="el" href="classmlpack_1_1neighbor_1_1NeighborSearch.html" title="The NeighborSearch class is a template class for performing distance-based neighbor searches...">mlpack::neighbor::NeighborSearch</a> object to find the nearest neighbor of each point in a dataset using the L1 metric, and then print the index of the neighbor and the distance of it to stdout.</p>
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<div class="fragment"><div class="line"><span class="preprocessor">#include <<a class="code" href="core_8hpp.html">mlpack/core.hpp</a>></span></div><div class="line"><span class="preprocessor">#include <<a class="code" href="neighbor__search_8hpp.html">mlpack/methods/neighbor_search/neighbor_search.hpp</a>></span></div><div class="line"></div><div class="line"><span class="keyword">using namespace </span><a class="code" href="namespacemlpack.html">mlpack</a>;</div><div class="line"><span class="keyword">using namespace </span><a class="code" href="namespacemlpack_1_1neighbor.html">mlpack::neighbor</a>; <span class="comment">// NeighborSearch and NearestNeighborSort</span></div><div class="line"><span class="keyword">using namespace </span><a class="code" href="namespacemlpack_1_1metric.html">mlpack::metric</a>; <span class="comment">// ManhattanDistance</span></div><div class="line"></div><div class="line"><span class="keywordtype">int</span> main()</div><div class="line">{</div><div class="line"> <span class="comment">// Load the data from data.csv (hard-coded). Use CLI for simple command-line</span></div><div class="line"> <span class="comment">// parameter handling.</span></div><div class="line"> arma::mat data;</div><div class="line"> <a class="code" href="namespacemlpack_1_1data.html#a19805d6585ac8b0be7c4e4b7f081977c">data::Load</a>(<span class="stringliteral">"data.csv"</span>, data, <span class="keyword">true</span>);</div><div class="line"></div><div class="line"> <span class="comment">// Use templates to specify that we want a NeighborSearch object which uses</span></div><div class="line"> <span class="comment">// the Manhattan distance.</span></div><div class="line"> <a class="code" href="classmlpack_1_1neighbor_1_1NeighborSearch.html">NeighborSearch<NearestNeighborSort, ManhattanDistance></a> nn(data);</div><div class="line"></div><div class="line"> <span class="comment">// Create the object we will store the nearest neighbors in.</span></div><div class="line"> arma::Mat<size_t> neighbors;</div><div class="line"> arma::mat distances; <span class="comment">// We need to store the distance too.</span></div><div class="line"></div><div class="line"> <span class="comment">// Compute the neighbors.</span></div><div class="line"> nn.Search(1, neighbors, distances);</div><div class="line"></div><div class="line"> <span class="comment">// Write each neighbor and distance using Log.</span></div><div class="line"> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < neighbors.n_elem; ++i)</div><div class="line"> {</div><div class="line"> std::cout << <span class="stringliteral">"Nearest neighbor of point "</span> << i << <span class="stringliteral">" is point "</span></div><div class="line"> << neighbors[i] << <span class="stringliteral">" and the distance is "</span> << distances[i] << <span class="stringliteral">".\n"</span>;</div><div class="line"> }</div><div class="line">}</div></div><!-- fragment --><h1><a class="anchor" id="other"></a>
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Other examples</h1>
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<p>For more complex examples, it is useful to refer to the main executables:</p>
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<ul>
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<li>methods/neighbor_search/knn_main.cpp</li>
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<li>methods/neighbor_search/kfn_main.cpp</li>
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<li>methods/emst/emst_main.cpp</li>
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<li>methods/radical/radical_main.cpp</li>
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<li>methods/nca/nca_main.cpp</li>
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<li>methods/naive_bayes/nbc_main.cpp</li>
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<li>methods/pca/pca_main.cpp</li>
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<li>methods/lars/lars_main.cpp</li>
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<li>methods/linear_regression/linear_regression_main.cpp</li>
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<li>methods/gmm/gmm_main.cpp</li>
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<li>methods/kmeans/kmeans_main.cpp </li>
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