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Refactor core.hpp and CONTRIBUTORS.md.
These work a little bit better for the new website (I think).
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@@ -21,13 +21,7 @@ | |
| * bindings to other languages. It is meant to be a machine learning analog to | ||
| * LAPACK, and 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 http://mlpack.org. | ||
| * | ||
| * mlpack uses the Armadillo C++ matrix library (http://arma.sourceforge.net) | ||
| * 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. | ||
| * mlpack website can be found at http://mlpack.org. | ||
| * | ||
| * @section howto How To Use This Documentation | ||
| * | ||
|
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@@ -39,58 +33,14 @@ | |
| * browsing the list of namespaces provides some insight as to the breadth of | ||
| * the methods contained in the library. | ||
| * | ||
| * To generate this documentation in your own local copy of mlpack, you can | ||
| * simply use Doxygen, from the root directory of the project: | ||
| * To generate this documentation in your own local copy of mlpack, you can use | ||
| * the 'doc' CMake target, which is available if CMake has found Doxygen, from | ||
| * the build directory: | ||
| * | ||
| * @code | ||
| * $ doxygen | ||
| * $ make doc | ||
| * @endcode | ||
| * | ||
| * @section executables Executables | ||
| * | ||
| * 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 '--help' flag. | ||
| * | ||
| * A full list of executables is given below: | ||
| * | ||
| * - mlpack_adaboost | ||
| * - mlpack_approx_kfn | ||
| * - mlpack_cf | ||
| * - mlpack_decision_stump | ||
| * - mlpack_decision_tree | ||
| * - mlpack_det | ||
| * - mlpack_emst | ||
| * - mlpack_fastmks | ||
| * - mlpack_gmm_train | ||
| * - mlpack_gmm_generate | ||
| * - mlpack_gmm_probability | ||
| * - mlpack_hmm_train | ||
| * - mlpack_hmm_loglik | ||
| * - mlpack_hmm_viterbi | ||
| * - mlpack_hmm_generate | ||
| * - mlpack_hoeffding_tree | ||
| * - mlpack_kernel_pca | ||
| * - mlpack_kfn | ||
| * - mlpack_kmeans | ||
| * - mlpack_knn | ||
| * - mlpack_krann | ||
| * - mlpack_lars | ||
| * - mlpack_linear_regression | ||
| * - mlpack_local_coordinate_coding | ||
| * - mlpack_logistic_regression | ||
| * - mlpack_lsh | ||
| * - mlpack_mean_shift | ||
| * - mlpack_nbc | ||
| * - mlpack_nca | ||
| * - mlpack_pca | ||
| * - mlpack_perceptron | ||
| * - mlpack_radical | ||
| * - mlpack_range_search | ||
| * - mlpack_softmax_regression | ||
| * - mlpack_sparse_coding | ||
| * | ||
| * @section tutorial Tutorials | ||
| * | ||
| * A few short tutorials on how to use mlpack are given below. | ||
|
|
@@ -106,159 +56,11 @@ | |
| * - @ref hpt | ||
| * - @ref verinfo | ||
| * | ||
| * Tutorials on specific methods are also available. | ||
| * | ||
| * - @ref nstutorial | ||
| * - @ref lrtutorial | ||
| * - @ref rstutorial | ||
| * - @ref dettutorial | ||
| * - @ref emst_tutorial | ||
| * - @ref kmtutorial | ||
| * - @ref fmkstutorial | ||
| * - @ref amftutorial | ||
| * | ||
| * @section methods Methods in mlpack | ||
| * | ||
| * The following methods are included in mlpack: | ||
| * | ||
| * - Density Estimation Trees - mlpack::det::DTree | ||
| * - Euclidean Minimum Spanning Trees - mlpack::emst::DualTreeBoruvka | ||
| * - Gaussian Mixture Models (GMMs) - mlpack::gmm::GMM | ||
| * - Hidden Markov Models (HMMs) - mlpack::hmm::HMM | ||
| * - Kernel PCA - mlpack::kpca::KernelPCA | ||
| * - K-Means Clustering - mlpack::kmeans::KMeans | ||
| * - Least-Angle Regression (LARS/LASSO) - mlpack::regression::LARS | ||
| * - Local Coordinate Coding - mlpack::lcc::LocalCoordinateCoding | ||
| * - Locality-Sensitive Hashing - mlpack::neighbor::LSHSearch | ||
| * - Naive Bayes Classifier - mlpack::naive_bayes::NaiveBayesClassifier | ||
| * - Neighborhood Components Analysis (NCA) - mlpack::nca::NCA | ||
| * - Principal Components Analysis (PCA) - mlpack::pca::PCA | ||
| * - RADICAL (ICA) - mlpack::radical::Radical | ||
| * - Simple Least-Squares Linear Regression - | ||
| * mlpack::regression::LinearRegression | ||
| * - Sparse Coding - mlpack::sparse_coding::SparseCoding | ||
| * - Tree-based neighbor search (KNN, KFN) - mlpack::neighbor::NeighborSearch | ||
| * - Tree-based range search - mlpack::range::RangeSearch | ||
| * | ||
| * @section remarks Final Remarks | ||
| * | ||
| * mlpack contributors include: | ||
| * | ||
| * - Ryan Curtin <[email protected]> | ||
| * - James Cline <[email protected]> | ||
| * - Neil Slagle <[email protected]> | ||
| * - Matthew Amidon <[email protected]> | ||
| * - Vlad Grantcharov <[email protected]> | ||
| * - Ajinkya Kale <[email protected]> | ||
| * - Bill March <[email protected]> | ||
| * - Dongryeol Lee <[email protected]> | ||
| * - Nishant Mehta <[email protected]> | ||
| * - Parikshit Ram <[email protected]> | ||
| * - Rajendran Mohan <[email protected]> | ||
| * - Trironk Kiatkungwanglai <[email protected]> | ||
| * - Patrick Mason <[email protected]> | ||
| * - Chip Mappus <[email protected]> | ||
| * - Hua Ouyang <[email protected]> | ||
| * - Long Quoc Tran <[email protected]> | ||
| * - Noah Kauffman <[email protected]> | ||
| * - Guillermo Colon <[email protected]> | ||
| * - Wei Guan <[email protected]> | ||
| * - Ryan Riegel <[email protected]> | ||
| * - Nikolaos Vasiloglou <[email protected]> | ||
| * - Garry Boyer <[email protected]> | ||
| * - Andreas Löf <[email protected]> | ||
| * - Marcus Edel <[email protected]> | ||
| * - Mudit Raj Gupta <[email protected]> | ||
| * - Sumedh Ghaisas <[email protected]> | ||
| * - Michael Fox <[email protected]> | ||
| * - Ryan Birmingham <[email protected]> | ||
| * - Siddharth Agrawal <[email protected]> | ||
| * - Saheb Motiani <[email protected]> | ||
| * - Yash Vadalia <[email protected]> | ||
| * - Abhishek Laddha <[email protected]> | ||
| * - Vahab Akbarzadeh <[email protected]> | ||
| * - Andrew Wells <[email protected]> | ||
| * - Zhihao Lou <[email protected]> | ||
| * - Udit Saxena <[email protected]> | ||
| * - Stephen Tu <[email protected]> | ||
| * - Jaskaran Singh <[email protected]> | ||
| * - Shangtong Zhang <[email protected]> | ||
| * - Hritik Jain <[email protected]> | ||
| * - Vladimir Glazachev <[email protected]> | ||
| * - QiaoAn Chen <[email protected]> | ||
| * - Janzen Brewer <[email protected]> | ||
| * - Trung Dinh <[email protected]> | ||
| * - Tham Ngap Wei <[email protected]> | ||
| * - Grzegorz Krajewski <[email protected]> | ||
| * - Joseph Mariadassou <[email protected]> | ||
| * - Pavel Zhigulin <[email protected]> | ||
| * - Andy Fang <[email protected]> | ||
| * - Barak Pearlmutter <[email protected]> | ||
| * - Ivari Horm <[email protected]> | ||
| * - Dhawal Arora <[email protected]> | ||
| * - Alexander Leinoff <[email protected]> | ||
| * - Palash Ahuja <[email protected]> | ||
| * - Yannis Mentekidis <[email protected]> | ||
| * - Ranjan Mondal <[email protected]> | ||
| * - Mikhail Lozhnikov <[email protected]> | ||
| * - Marcos Pividori <[email protected]> | ||
| * - Keon Kim <[email protected]> | ||
| * - Nilay Jain <[email protected]> | ||
| * - Peter Lehner <[email protected]> | ||
| * - Anuraj Kanodia <[email protected]> | ||
| * - Ivan Georgiev <[email protected]> | ||
| * - Shikhar Bhardwaj <[email protected]> | ||
| * - Yashu Seth <[email protected]> | ||
| * - Mike Izbicki <[email protected]> | ||
| * - Sudhanshu Ranjan <[email protected]> | ||
| * - Piyush Jaiswal <[email protected]> | ||
| * - Dinesh Raj <[email protected]> | ||
| * - Prasanna Patil <[email protected]> | ||
| * - Lakshya Agrawal <[email protected]> | ||
| * - Vivek Pal <[email protected]> | ||
| * - Praveen Ch <[email protected]> | ||
| * - Kirill Mishchenko <[email protected]> | ||
| * - Abhinav Moudgil <[email protected]> | ||
| * - Thyrix Yang <[email protected]> | ||
| * - Sagar B Hathwar <[email protected]> | ||
| * - Nishanth Hegde <[email protected]> | ||
| * - Parminder Singh <[email protected]> | ||
| * - CodeAi (deep learning bug detector) <[email protected]> | ||
| * - Franciszek Stokowacki <[email protected]> | ||
| * - Samikshya Chand <[email protected]> | ||
| * - N Rajiv Vaidyanathan <[email protected]> | ||
| * - Kartik Nighania <[email protected]> | ||
| * - Eugene Freyman <[email protected]> | ||
| * - Manish Kumar <[email protected]> | ||
| * - Haritha Sreedharan Nair <[email protected]> | ||
| * - Sourabh Varshney <[email protected]> | ||
| * - Projyal Dev <[email protected]> | ||
| * - Nikhil Goel <[email protected]> | ||
| * - Shikhar Jaiswal <[email protected]> | ||
| * - B Kartheek Reddy <[email protected]> | ||
| * - Atharva Khandait <[email protected]> | ||
| * - Wenhao Huang <[email protected]> | ||
| * - Roberto Hueso <[email protected]> | ||
| * - Prabhat Sharma <[email protected]> | ||
| * - Tan Jun An <[email protected]> | ||
| * - Moksh Jain <[email protected]> | ||
| * - Manthan-R-Sheth <[email protected]> | ||
| * - Namrata Mukhija <[email protected]> | ||
| * - Rohan Raj <[email protected]> | ||
| * - Conrad Sanderson | ||
| * - Thanasis Mattas <[email protected]> | ||
| * - Shashank Shekhar <[email protected]> | ||
| * - Yasmine Dumouchel <[email protected]> | ||
| * - German Lancioni | ||
| * - Arash Abghari <[email protected]> | ||
| * - Ayush Chamoli | ||
| * - Tommi Laivamaa <[email protected]> | ||
| * - Kim SangYeon <[email protected]> | ||
| * - Niteya Shah <[email protected]> | ||
| * - Bhavya Bahl <[email protected]> | ||
| * - Toshal Agrawal <[email protected]> | ||
| * - Dan Timson | ||
| * - Miguel Canteras <[email protected]> | ||
| * For the list of contributors to mlpack, see | ||
| * http://www.mlpack.org/contributors.html. This library would not be possible | ||
| * without everyone's hard work and contributions! | ||
| */ | ||
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| // First, include all of the prerequisites. | ||
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