/**

* @file core.hpp

*

* Include all of the base components required to write mlpack methods, and the

* main mlpack Doxygen documentation.

*

* mlpack is free software; you may redistribute it and/or modify it under the

* terms of the 3-clause BSD license. You should have received a copy of the

* 3-clause BSD license along with mlpack. If not, see

* http://www.opensource.org/licenses/BSD-3-Clause for more information.

*/

#ifndef MLPACK_CORE_HPP

#define MLPACK_CORE_HPP

/**

* @mainpage mlpack Documentation

*

* @section intro_sec Introduction

*

* mlpack is an intuitive, fast, and flexible C++ machine learning library with

* 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.

*

* @section howto How To Use This Documentation

*

* 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.

*

* 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.

*

* To generate this documentation in your own local copy of mlpack, you can

* simply use Doxygen, from the root directory of the project:

*

* @code

* $ doxygen

* @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.

*

* - @ref build

* - @ref build_windows

* - @ref matrices

* - @ref iodoc

* - @ref timer

* - @ref sample

* - @ref sample_ml_app

* - @ref cv

* - @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 <gth671b@mail.gatech.edu>

* - James Cline <james.cline@gatech.edu>

* - Neil Slagle <nslagle3@gatech.edu>

* - Matthew Amidon <mamidon@gatech.edu>

* - Vlad Grantcharov <vlad321@gatech.edu>

* - Ajinkya Kale <kaleajinkya@gmail.com>

* - Bill March <march@gatech.edu>

* - Dongryeol Lee <dongryel@cc.gatech.edu>

* - Nishant Mehta <niche@cc.gatech.edu>

* - Parikshit Ram <p.ram@gatech.edu>

* - Rajendran Mohan <rmohan88@gatech.edu>

* - Trironk Kiatkungwanglai <trironk@gmail.com>

* - Patrick Mason <patrick.s.mason@gmail.com>

* - Chip Mappus <cmappus@gatech.edu>

* - Hua Ouyang <houyang@gatech.edu>

* - Long Quoc Tran <tqlong@gmail.com>

* - Noah Kauffman <notoriousnoah@gmail.com>

* - Guillermo Colon <gcolon7@mail.gatech.edu>

* - Wei Guan <wguan@cc.gatech.edu>

* - Ryan Riegel <rriegel@cc.gatech.edu>

* - Nikolaos Vasiloglou <nvasil@ieee.org>

* - Garry Boyer <garryb@gmail.com>

* - Andreas Löf <andreas.lof@cs.waikato.ac.nz>

* - Marcus Edel <marcus.edel@fu-berlin.de>

* - Mudit Raj Gupta <mudit.raaj.gupta@gmail.com>

* - Sumedh Ghaisas <sumedhghaisas@gmail.com>

* - Michael Fox <michaelfox99@gmail.com>

* - Ryan Birmingham <birm@gatech.edu>

* - Siddharth Agrawal <siddharth.950@gmail.com>

* - Saheb Motiani <saheb210692@gmail.com>

* - Yash Vadalia <yashdv@gmail.com>

* - Abhishek Laddha <laddhaabhishek11@gmail.com>

* - Vahab Akbarzadeh <v.akbarzadeh@gmail.com>

* - Andrew Wells <andrewmw94@gmail.com>

* - Zhihao Lou <lzh1984@gmail.com>

* - Udit Saxena <saxena.udit@gmail.com>

* - Stephen Tu <tu.stephenl@gmail.com>

* - Jaskaran Singh <jaskaranvirdi@gmail.com>

* - Shangtong Zhang <zhangshangtong.cpp@icloud.com>

* - Hritik Jain <hritik.jain.cse13@itbhu.ac.in>

* - Vladimir Glazachev <glazachev.vladimir@gmail.com>

* - QiaoAn Chen <kazenoyumechen@gmail.com>

* - Janzen Brewer <jahabrewer@gmail.com>

* - Trung Dinh <dinhanhtrung@gmail.com>

* - Tham Ngap Wei <thamngapwei@gmail.com>

* - Grzegorz Krajewski <krajekg@gmail.com>

* - Joseph Mariadassou <joe.mariadassou@gmail.com>

* - Pavel Zhigulin <pashaworking@gmail.com>

* - Andy Fang <AndyFang.DZ@gmail.com>

* - Barak Pearlmutter <barak+git@pearlmutter.net>

* - Ivari Horm <ivari@risk.ee>

* - Dhawal Arora <d.p.arora1@gmail.com>

* - Alexander Leinoff <alexander-leinoff@uiowa.edu>

* - Palash Ahuja <abhor902@gmail.com>

* - Yannis Mentekidis <mentekid@gmail.com>

* - Ranjan Mondal <ranjan.rev@gmail.com>

* - Mikhail Lozhnikov <lozhnikovma@gmail.com>

* - Marcos Pividori <marcos.pividori@gmail.com>

* - Keon Kim <kwk236@gmail.com>

* - Nilay Jain <nilayjain13@gmail.com>

* - Peter Lehner <peter.lehner@dlr.de>

* - Anuraj Kanodia <akanuraj200@gmail.com>

* - Ivan Georgiev <ivan@jonan.info>

* - Shikhar Bhardwaj <shikharbhardwaj68@gmail.com>

* - Yashu Seth <yashuseth2503@gmail.com>

* - Mike Izbicki <mike@izbicki.me>

* - Sudhanshu Ranjan <sranjan.sud@gmail.com>

* - Piyush Jaiswal <piyush.jaiswal@st.niituniversity.in>

* - Dinesh Raj <dinu.iota@gmail.com>

* - Prasanna Patil <prasannapatil08@gmail.com>

* - Lakshya Agrawal <zeeshan.lakshya@gmail.com>

* - Vivek Pal <vivekpal.dtu@gmail.com>

* - Praveen Ch <chvsp972911@gmail.com>

* - Kirill Mishchenko <ki.mishchenko@gmail.com>

* - Abhinav Moudgil <abhinavmoudgil95@gmail.com>

* - Thyrix Yang <thyrixyang@gmail.com>

* - Sagar B Hathwar <sagarbhathwar@gmail.com>

* - Nishanth Hegde <hegde.nishanth@gmail.com>

* - Parminder Singh <parmsingh101@gmail.com>

* - CodeAi (deep learning bug detector) <benjamin.bales@assrc.us>

* - Franciszek Stokowacki <franek.stokowacki@gmail.com>

* - Samikshya Chand <samikshya289@gmail.com>

* - N Rajiv Vaidyanathan <rajivvaidyanathan4@gmail.com>

* - Kartik Nighania <kartiknighania@gmail.com>

* - Eugene Freyman <evg.freyman@gmail.com>

* - Manish Kumar <manish887kr@gmail.com>

* - Haritha Sreedharan Nair <haritha1313@gmail.com>

* - Sourabh Varshney <sourabhvarshney111@gmail.com>

* - Projyal Dev <projyal@gmail.com>

* - Nikhil Goel <nikhilgoel199797@gmail.com>

* - Shikhar Jaiswal <jaiswalshikhar87@gmail.com>

* - B Kartheek Reddy <bkartheekreddy@gmail.com>

* - Atharva Khandait <akhandait45@gmail.com>

* - Wenhao Huang <wenhao.huang.work@gmail.com>

* - Roberto Hueso <robertohueso96@gmail.com>

* - Prabhat Sharma <prabhatsharma7298@gmail.com>

* - Tan Jun An <yamidarkxxx@gmail.com>

* - Moksh Jain <mokshjn00@gmail.com>

* - Manthan-R-Sheth <manthanrsheth96@gmail.com>

* - Namrata Mukhija <namratamukhija@gmail.com>

* - Rohan Raj <rajrohan1108@gmail.com>

* - Conrad Sanderson

* - Thanasis Mattas <mattasa@auth.gr>

* - Shashank Shekhar <contactshashankshekhar@gmail.com>

* - Yasmine Dumouchel <yasmine.dumouchel@gmail.com>

* - German Lancioni

*/

// First, include all of the prerequisites.

#include <mlpack/prereqs.hpp>

// Now the core mlpack classes.

#include <mlpack/core/util/arma_traits.hpp>

#include <mlpack/core/util/log.hpp>

#include <mlpack/core/util/cli.hpp>

#include <mlpack/core/util/deprecated.hpp>

#include <mlpack/core/data/load.hpp>

#include <mlpack/core/data/save.hpp>

#include <mlpack/core/data/normalize_labels.hpp>

#include <mlpack/core/math/clamp.hpp>

#include <mlpack/core/math/random.hpp>

#include <mlpack/core/math/random_basis.hpp>

#include <mlpack/core/math/lin_alg.hpp>

#include <mlpack/core/math/range.hpp>

#include <mlpack/core/math/round.hpp>

#include <mlpack/core/math/shuffle_data.hpp>

#include <mlpack/core/math/make_alias.hpp>

#include <mlpack/core/dists/discrete_distribution.hpp>

#include <mlpack/core/dists/gaussian_distribution.hpp>

#include <mlpack/core/dists/laplace_distribution.hpp>

#include <mlpack/core/dists/gamma_distribution.hpp>

// mlpack::backtrace only for linux

#ifdef HAS_BFD_DL

#include <mlpack/core/util/backtrace.hpp>

#endif

// Include kernel traits.

#include <mlpack/core/kernels/kernel_traits.hpp>

#include <mlpack/core/kernels/linear_kernel.hpp>

#include <mlpack/core/kernels/polynomial_kernel.hpp>

#include <mlpack/core/kernels/cosine_distance.hpp>

#include <mlpack/core/kernels/gaussian_kernel.hpp>

#include <mlpack/core/kernels/epanechnikov_kernel.hpp>

#include <mlpack/core/kernels/hyperbolic_tangent_kernel.hpp>

#include <mlpack/core/kernels/laplacian_kernel.hpp>

#include <mlpack/core/kernels/pspectrum_string_kernel.hpp>

#include <mlpack/core/kernels/spherical_kernel.hpp>

#include <mlpack/core/kernels/triangular_kernel.hpp>

// Use OpenMP if compiled with -DHAS_OPENMP.

#ifdef HAS_OPENMP

#include <omp.h>

#endif

// Use Armadillo's C++ version detection.

#ifdef ARMA_USE_CXX11

#define MLPACK_USE_CX11

#endif

#endif