Machine Learning in C++

Most of us have C++ as our First Language but when it comes to something like Data Analysis and Machine Learning, Python becomes our go-to Language because of its simplicity and plenty of libraries of pre-written Modules.

But can C++ be used for Machine Learning too? and If yes, then how?

**Pre-requisites:**

**C++ Boost Library:-**It is a powerful C++ library used for various purposes like big Maths Operations, etc.

You can refer here for installation of this Library**ML pack C++ Library:-**This is a small and Scalable C++ Machine Learning Library.

You can refer here for the installation of this Library.**Note:**set USE_OPENMP=OFF when installing mlpack, don’t sweat, given link has guide on how to do that**Sample CSV Data File:-**As MLpack library does not have any inbuilt Sample Dataset so we have to use our own Sample Dataset.

### Our Model

The Code we are writing takes a simple dataset of vectors and finds the nearest neighbour for each data point.

The Training Part has been highlighted

Input :Our Input is a file named data.csv containing a dataset of vectors The File Contains the Following Data: 3, 3, 3, 3, 0 3, 4, 4, 3, 0 3, 4, 4, 3, 0 3, 3, 4, 3, 0 3, 6, 4, 3, 0 2, 4, 4, 3, 0 2, 4, 4, 1, 0 3, 3, 3, 2, 0 3, 4, 4, 2, 0 3, 4, 4, 2, 0 3, 3, 4, 2, 0 3, 6, 4, 2, 0 2, 4, 4, 2, 0

**Code:**

`#include <mlpack/core.hpp>` `#include <mlpack/methods/neighbor_search/neighbor_search.hpp>` ` ` `using` `namespace` `std;` `using` `namespace` `mlpack;` `// NeighborSearch and NearestNeighborSort` `using` `namespace` `mlpack::neighbor;` `// ManhattanDistance` `using` `namespace` `mlpack::metric;` ` ` `void` `mlModel()` `{` ` ` `// Armadillo is a C++ linear algebra library; ` ` ` `// mlpack uses its matrix data type.` ` ` `arma::mat data;` ` ` ` ` `/*` ` ` `data::Load is used to import data to the mlpack, ` ` ` `It takes 3 parameters,` ` ` `1. Filename = Name of the File to be used` ` ` `2. Matrix = Matrix to hold the Data in the File` ` ` `3. fatal = true if you want it to throw an exception` ` ` `if there is an issue` ` ` `*/` ` ` `data::Load(` `"data.csv"` `, data, ` `true` `);` ` ` ` ` `/*` ` ` `Create a NeighborSearch model. The parameters of the ` ` ` `model are specified with templates:` ` ` `1. Sorting method: "NearestNeighborSort" - This ` ` ` `class sorts by increasing distance.` ` ` `2. Distance metric: "ManhattanDistance" - The ` ` ` `L1 distance, the sum of absolute distances.` ` ` `3. Pass the reference dataset (the vectors to ` ` ` `be searched through) to the constructor.` ` ` `*/` ` ` `NeighborSearch<NearestNeighborSort, ManhattanDistance> nn(data);` ` ` `// in the above line we trained our model or ` ` ` `// fitted the data to the model` ` ` `// now we will predict` ` ` ` ` `arma::Mat<` `size_t` `> neighbors; ` `// Matrices to hold` ` ` `arma::mat distances; ` `// the results` ` ` ` ` `/*` ` ` `Find the nearest neighbors. Arguments are:-` ` ` `1. k = 1, Specify the number of neighbors to find` ` ` `2. Matrices to hold the result, in this case, ` ` ` `neighbors and distances` ` ` `*/` ` ` `nn.Search(1, neighbors, distances);` ` ` `// in the above line we find the nearest neighbor` ` ` ` ` `// Print out each neighbor and its distance.` ` ` `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"` `;` ` ` `}` `}` ` ` `int` `main()` `{` ` ` `mlModel();` ` ` `return` `0;` `}` |

Run the above code in Terminal/CMD using

g++ knn_example.cpp -o knn_example -std=c++11 -larmadillo -lmlpack -lboost_serialization

followed by

./knn_example

Output:Nearest neighbor of point 0 is point 7 and the distance is 1. Nearest neighbor of point 1 is point 2 and the distance is 0. Nearest neighbor of point 2 is point 1 and the distance is 0. Nearest neighbor of point 3 is point 10 and the distance is 1. Nearest neighbor of point 4 is point 11 and the distance is 1. Nearest neighbor of point 5 is point 12 and the distance is 1. Nearest neighbor of point 6 is point 12 and the distance is 1. Nearest neighbor of point 7 is point 10 and the distance is 1. Nearest neighbor of point 8 is point 9 and the distance is 0. Nearest neighbor of point 9 is point 8 and the distance is 0. Nearest neighbor of point 10 is point 9 and the distance is 1. Nearest neighbor of point 11 is point 4 and the distance is 1. Nearest neighbor of point 12 is point 9 and the distance is 1.

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