Omar Shrit
8e3e0e09f1
This change is related to the transition from boost serialization to CLI11 Signed-off-by: Omar Shrit <omar@shrit.me>
107 lines
3.4 KiB
C++
107 lines
3.4 KiB
C++
/*! @page sample Simple Sample mlpack Programs
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@section sampleintro Introduction
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On this page, several simple mlpack examples are contained, in increasing order
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of complexity. If you compile from the command-line, be sure that your compiler
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is in C++11 mode. With modern gcc and clang, this should already be the
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default.
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@note
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The command-line programs like @c knn_main.cpp and @c
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logistic_regression_main.cpp from the directory @c src/mlpack/methods/ cannot be
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compiled easily by hand (the same is true for the individual tests in @c
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src/mlpack/tests/); instead, those should be compiled with CMake, by running,
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e.g., @c make @c mlpack_knn or @c make @c mlpack_test; see @ref build. However,
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any program that uses mlpack (and is not a part of the library itself) can be
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compiled easily with g++ or clang from the command line.
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@section covariance Covariance Computation
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A simple program to compute the covariance of a data matrix ("data.csv"),
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assuming that the data is already centered, and save it to file.
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@code
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// Includes all relevant components of mlpack.
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#include <mlpack/core.hpp>
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// Convenience.
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using namespace mlpack;
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int main()
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{
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// First, load the data.
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arma::mat data;
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// Use data::Load() which transposes the matrix.
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data::Load("data.csv", data, true);
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// Now compute the covariance. We assume that the data is already centered.
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// Remember, because the matrix is column-major, the covariance operation is
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// transposed.
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arma::mat cov = data * trans(data) / data.n_cols;
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// Save the output.
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data::Save("cov.csv", cov, true);
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}
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@endcode
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@section nn Nearest Neighbor
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This simple program uses the mlpack::neighbor::NeighborSearch object to find the
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nearest neighbor of each point in a dataset using the L1 metric, and then print
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the index of the neighbor and the distance of it to stdout.
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@code
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#include <mlpack/core.hpp>
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#include <mlpack/methods/neighbor_search/neighbor_search.hpp>
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using namespace mlpack;
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using namespace mlpack::neighbor; // NeighborSearch and NearestNeighborSort
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using namespace mlpack::metric; // ManhattanDistance
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int main()
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{
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// Load the data from data.csv (hard-coded). Use IO for simple command-line
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// parameter handling.
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arma::mat data;
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data::Load("data.csv", data, true);
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// Use templates to specify that we want a NeighborSearch object which uses
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// the Manhattan distance.
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NeighborSearch<NearestNeighborSort, ManhattanDistance> nn(data);
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// Create the object we will store the nearest neighbors in.
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arma::Mat<size_t> neighbors;
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arma::mat distances; // We need to store the distance too.
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// Compute the neighbors.
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nn.Search(1, neighbors, distances);
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// Write each neighbor and distance using Log.
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for (size_t i = 0; i < neighbors.n_elem; ++i)
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{
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std::cout << "Nearest neighbor of point " << i << " is point "
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<< neighbors[i] << " and the distance is " << distances[i] << ".\n";
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}
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}
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@endcode
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@section other Other examples
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For more complex examples, it is useful to refer to the main executables, found
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in @c src/mlpack/methods/. A few are listed below.
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- methods/neighbor_search/knn_main.cpp
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- methods/neighbor_search/kfn_main.cpp
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- methods/emst/emst_main.cpp
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- methods/radical/radical_main.cpp
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- methods/nca/nca_main.cpp
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- methods/naive_bayes/nbc_main.cpp
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- methods/pca/pca_main.cpp
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- methods/lars/lars_main.cpp
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- methods/linear_regression/linear_regression_main.cpp
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- methods/gmm/gmm_main.cpp
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- methods/kmeans/kmeans_main.cpp
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*/
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