C++中的机器学习

我们大多数人都将C++作为第一语言，但是当涉及到诸如数据分析和机器学习之类的东西时， Python成为了我们的首选语言，因为它的简单性和大量预编写的模块库。
但是C++也可以用于机器学习吗?如果是，那怎么办?

先决条件：

1. C++ Boost库：-这是一个功能强大的C++库，可用于各种目的，例如大型数学运算等。
   您可以在这里参考此库的安装
2. ML pack C++库：-这是一个小型且可扩展的C++机器学习库。
   您可以在这里参考此库的安装。
   注意：安装mlpack时将USE_OPENMP = OFF设置为OFF，不要出汗，因为给出了有关如何执行该操作的指南
3. 样本CSV数据文件：-由于MLpack库没有任何内置的样本数据集，因此我们必须使用自己的样本数据集。

我们的模型

我们正在编写的代码采用简单的向量数据集，并为每个数据点找到最近的邻居。

培训部分已突出显示

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

代码：

#include 
#include 
  
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 nn(data);
    // in the above line we trained our model or 
    // fitted the data to the model
    // now we will predict
  
    arma::Mat 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;
}

使用以下命令在终端/ CMD中运行以上代码

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

其次是

./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.