mlpack
3.0.0

Simple Sample mlpack Programs
Introduction
On this page, several simple mlpack examples are contained, in increasing order of complexity. If you compile from the commandline, be sure that your compiler is in C++11 mode. With modern gcc and clang, this should already be the default.
 Note
 The commandline programs like
knn_main.cpp
andlogistic_regression_main.cpp
from the directorysrc/mlpack/methods/
cannot be compiled easily by hand (the same is true for the individual tests insrc/mlpack/tests/
); instead, those should be compiled with CMake, by running, e.g.,make
mlpack_knn
ormake
mlpack_test
; see Building mlpack From Source. However, any program that uses mlpack (and is not a part of the library itself) can be compiled easily with g++ or clang from the command line.
Covariance Computation
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.
// Includes all relevant components of mlpack.
#include <mlpack/core.hpp>
// Convenience.
using namespace mlpack;
int main()
{
// First, load the data.
arma::mat data;
// Use data::Load() which transposes the matrix.
// Now compute the covariance. We assume that the data is already centered.
// Remember, because the matrix is columnmajor, the covariance operation is
// transposed.
arma::mat cov = data * trans(data) / data.n_cols;
// Save the output.
}
Nearest Neighbor
This simple program uses the mlpack::neighbor::NeighborSearch 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.
#include <mlpack/core.hpp>
using namespace mlpack;
int main()
{
// Load the data from data.csv (hardcoded). Use CLI for simple commandline
// parameter handling.
arma::mat data;
// Use templates to specify that we want a NeighborSearch object which uses
// the Manhattan distance.
// Create the object we will store the nearest neighbors in.
arma::Mat<size_t> neighbors;
arma::mat distances; // We need to store the distance too.
// Compute the neighbors.
nn.Search(1, neighbors, distances);
// Write each neighbor and distance using Log.
for (size_t i = 0; i < neighbors.n_elem; ++i)
{
std::cout << "Nearest neighbor of point " << i << " is point "
<< neighbors[i] << " and the distance is " << distances[i] << ".\n";
}
}
Other examples
For more complex examples, it is useful to refer to the main executables, found in src/mlpack/methods/
. A few are listed below.
 methods/neighbor_search/knn_main.cpp
 methods/neighbor_search/kfn_main.cpp
 methods/emst/emst_main.cpp
 methods/radical/radical_main.cpp
 methods/nca/nca_main.cpp
 methods/naive_bayes/nbc_main.cpp
 methods/pca/pca_main.cpp
 methods/lars/lars_main.cpp
 methods/linear_regression/linear_regression_main.cpp
 methods/gmm/gmm_main.cpp
 methods/kmeans/kmeans_main.cpp
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