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Machine Learning in C++

  • Difficulty Level : Easy
  • Last Updated : 30 Jun, 2019

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:

  1. 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
  2. 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
  3. 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.

Attention reader! Don’t stop learning now. Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready.




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