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<div class="title">mlpack Documentation </div> </div>
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<div class="textblock"><h1><a class="anchor" id="intro_sec"></a>
Introduction</h1>
<p>mlpack is an intuitive, fast, scalable C++ machine learning library, meant to be a machine learning analog to LAPACK. It aims to implement a wide array of machine learning methods and function as a "swiss army knife" for machine learning researchers. The mlpack development website can be found at <a href="http://mlpack.org">http://mlpack.org</a>.</p>
<p>mlpack uses the Armadillo C++ matrix library (<a href="http://arma.sourceforge.net">http://arma.sourceforge.net</a>) for general matrix, vector, and linear algebra support. mlpack also uses the program_options, math_c99, and unit_test_framework components of the Boost library, and optionally uses libbfd and libdl to give backtraces when compiled with debugging symbols on some platforms.</p>
<h1><a class="anchor" id="howto"></a>
How To Use This Documentation</h1>
<p>This documentation is API documentation similar to Javadoc. It isn't necessarily a tutorial, but it does provide detailed documentation on every namespace, method, and class.</p>
<p>Each mlpack namespace generally refers to one machine learning method, so browsing the list of namespaces provides some insight as to the breadth of the methods contained in the library.</p>
<p>To generate this documentation in your own local copy of mlpack, you can simply use Doxygen, from the root directory of the project:</p>
<div class="fragment"><div class="line">$ doxygen</div></div><!-- fragment --><h1><a class="anchor" id="executables"></a>
Executables</h1>
<p>mlpack provides several executables so that mlpack methods can be used without any need for knowledge of C++. These executables are all self-documented, and that documentation can be accessed by running the executables with the '-h' or '&ndash;help' flag.</p>
<p>A full list of executables is given below:</p>
<ul>
<li>mlpack_adaboost</li>
<li>mlpack_approx_kfn</li>
<li>mlpack_cf</li>
<li>mlpack_decision_stump</li>
<li>mlpack_decision_tree</li>
<li>mlpack_det</li>
<li>mlpack_emst</li>
<li>mlpack_fastmks</li>
<li>mlpack_gmm_train</li>
<li>mlpack_gmm_generate</li>
<li>mlpack_gmm_probability</li>
<li>mlpack_hmm_train</li>
<li>mlpack_hmm_loglik</li>
<li>mlpack_hmm_viterbi</li>
<li>mlpack_hmm_generate</li>
<li>mlpack_hoeffding_tree</li>
<li>mlpack_kernel_pca</li>
<li>mlpack_kfn</li>
<li>mlpack_kmeans</li>
<li>mlpack_knn</li>
<li>mlpack_krann</li>
<li>mlpack_lars</li>
<li>mlpack_linear_regression</li>
<li>mlpack_local_coordinate_coding</li>
<li>mlpack_logistic_regression</li>
<li>mlpack_lsh</li>
<li>mlpack_mean_shift</li>
<li>mlpack_nbc</li>
<li>mlpack_nca</li>
<li>mlpack_pca</li>
<li>mlpack_perceptron</li>
<li>mlpack_radical</li>
<li>mlpack_range_search</li>
<li>mlpack_softmax_regression</li>
<li>mlpack_sparse_coding</li>
</ul>
<h1><a class="anchor" id="tutorial"></a>
Tutorials</h1>
<p>A few short tutorials on how to use mlpack are given below.</p>
<ul>
<li><a class="el" href="build.html#build">Building mlpack</a></li>
<li><a class="el" href="matrices.html">Matrices in mlpack</a></li>
<li><a class="el" href="iodoc.html">mlpack Input and Output</a></li>
<li><a class="el" href="timer.html">mlpack Timers</a></li>
<li><a class="el" href="sample.html">Simple Sample mlpack Programs</a></li>
<li><a class="el" href="verinfo.html">mlpack version information</a></li>
</ul>
<p>Tutorials on specific methods are also available.</p>
<ul>
<li><a class="el" href="nstutorial.html">NeighborSearch tutorial (k-nearest-neighbors)</a></li>
<li><a class="el" href="lrtutorial.html">Linear/ridge regression tutorial (mlpack_linear_regression)</a></li>
<li><a class="el" href="rstutorial.html">RangeSearch tutorial (mlpack_range_search)</a></li>
<li><a class="el" href="dettutorial.html">Density Estimation Tree (DET) tutorial</a></li>
<li><a class="el" href="emst_tutorial.html">EMST Tutorial</a></li>
<li><a class="el" href="kmtutorial.html">K-Means tutorial (kmeans)</a></li>
<li><a class="el" href="fmkstutorial.html">Fast max-kernel search tutorial (fastmks)</a></li>
<li><a class="el" href="amftutorial.html">Alternating Matrix Factorization tutorial.</a></li>
</ul>
<h1><a class="anchor" id="methods"></a>
Methods in mlpack</h1>
<p>The following methods are included in mlpack:</p>
<ul>
<li>Density Estimation Trees - <a class="el" href="classmlpack_1_1det_1_1DTree.html" title="A density estimation tree is similar to both a decision tree and a space partitioning tree (like a kd...">mlpack::det::DTree</a></li>
<li>Euclidean Minimum Spanning Trees - <a class="el" href="classmlpack_1_1emst_1_1DualTreeBoruvka.html" title="Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree...">mlpack::emst::DualTreeBoruvka</a></li>
<li>Gaussian Mixture Models (GMMs) - <a class="el" href="classmlpack_1_1gmm_1_1GMM.html" title="A Gaussian Mixture Model (GMM). ">mlpack::gmm::GMM</a></li>
<li>Hidden Markov Models (HMMs) - <a class="el" href="classmlpack_1_1hmm_1_1HMM.html" title="A class that represents a Hidden Markov Model with an arbitrary type of emission distribution. ">mlpack::hmm::HMM</a></li>
<li>Kernel PCA - <a class="el" href="classmlpack_1_1kpca_1_1KernelPCA.html" title="This class performs kernel principal components analysis (Kernel PCA), for a given kernel...">mlpack::kpca::KernelPCA</a></li>
<li>K-Means Clustering - <a class="el" href="classmlpack_1_1kmeans_1_1KMeans.html" title="This class implements K-Means clustering, using a variety of possible implementations of Lloyd&#39;s algo...">mlpack::kmeans::KMeans</a></li>
<li>Least-Angle Regression (LARS/LASSO) - <a class="el" href="classmlpack_1_1regression_1_1LARS.html" title="An implementation of LARS, a stage-wise homotopy-based algorithm for l1-regularized linear regression...">mlpack::regression::LARS</a></li>
<li>Local Coordinate Coding - <a class="el" href="classmlpack_1_1lcc_1_1LocalCoordinateCoding.html" title="An implementation of Local Coordinate Coding (LCC) that codes data which approximately lives on a man...">mlpack::lcc::LocalCoordinateCoding</a></li>
<li>Locality-Sensitive Hashing - <a class="el" href="classmlpack_1_1neighbor_1_1LSHSearch.html" title="The LSHSearch class; this class builds a hash on the reference set and uses this hash to compute the ...">mlpack::neighbor::LSHSearch</a></li>
<li>Naive Bayes Classifier - <a class="el" href="classmlpack_1_1naive__bayes_1_1NaiveBayesClassifier.html" title="The simple Naive Bayes classifier. ">mlpack::naive_bayes::NaiveBayesClassifier</a></li>
<li>Neighborhood Components Analysis (NCA) - <a class="el" href="classmlpack_1_1nca_1_1NCA.html" title="An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction techniq...">mlpack::nca::NCA</a></li>
<li>Principal Components Analysis (PCA) - <a class="el" href="namespacemlpack_1_1pca.html#a89b48ac6fa2787f4d1f69c20ce3d5c20" title="3.0.0 TODO: break reverse-compatibility by changing PCAType to PCA. ">mlpack::pca::PCA</a></li>
<li>RADICAL (ICA) - <a class="el" href="classmlpack_1_1radical_1_1Radical.html" title="An implementation of RADICAL, an algorithm for independent component analysis (ICA). ">mlpack::radical::Radical</a></li>
<li>Simple Least-Squares Linear Regression - <a class="el" href="classmlpack_1_1regression_1_1LinearRegression.html" title="A simple linear regression algorithm using ordinary least squares. ">mlpack::regression::LinearRegression</a></li>
<li>Sparse Coding - <a class="el" href="classmlpack_1_1sparse__coding_1_1SparseCoding.html" title="An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm reg...">mlpack::sparse_coding::SparseCoding</a></li>
<li>Tree-based neighbor search (KNN, KFN) - <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></li>
<li>Tree-based range search - <a class="el" href="classmlpack_1_1range_1_1RangeSearch.html" title="The RangeSearch class is a template class for performing range searches. ">mlpack::range::RangeSearch</a></li>
</ul>
<h1><a class="anchor" id="remarks"></a>
Final Remarks</h1>
<p>mlpack contributors include:</p>
<ul>
<li>Ryan Curtin <a href="#" onclick="location.href='mai'+'lto:'+'gth'+'67'+'1b@'+'ma'+'il.'+'ga'+'tec'+'h.'+'edu'; return false;">gth67<span style="display: none;">.nosp@m.</span>1b@m<span style="display: none;">.nosp@m.</span>ail.g<span style="display: none;">.nosp@m.</span>atec<span style="display: none;">.nosp@m.</span>h.edu</a></li>
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