Machine Learning in C++: Overview of Libraries for Development

Machine Learning in C++: Power and Performance

Despite Python's dominance in Data Science, C++ remains an indispensable language for production systems where processing speed, low memory consumption, and operation under resource-constrained conditions (embedded systems, robotics, high-load servers) are critical. Libraries for machine learning in C++ provide ready-made algorithms optimized for modern processor architectures and allow integrating ML models directly into code without an interpreter layer.

In this article, we will review key C++ libraries for machine learning, their strengths, and provide practical code examples.

1. Shark — A Flexible Library for Neural Networks and Optimization

Shark is one of the oldest and most well-documented C++ libraries for machine learning. It includes implementations of:

• Multilayer perceptrons and convolutional neural networks (CNNs);
• Optimization methods (gradient descent, L-BFGS, CMA-ES);
• Clustering algorithms (k-means, DBSCAN);
• Linear models and decision trees.

Shark uses Eigen for linear algebra, ensuring high performance. Example of training linear regression:

#include <shark/Data/Dataset.h>#include <shark/Algorithms/Trainers/LinearRegression.h>

int main() {    // Create data: y = 2*x + 1 + noise    shark::RegressionDataset data = generateData(100);    shark::LinearRegression trainer;    shark::LinearModel<shark::RealVector> model;    trainer.train(model, data);

    std::cout << "Coefficients: " << model.parameterVector() << std::endl;    return 0;}

Pros: excellent documentation, GPU support (via OpenCL), active community.

2. Dlib — A Versatile Tool for Computer Vision and ML

Dlib is known primarily for face recognition and object detection algorithms, but its machine learning module is equally powerful. It includes:

• Support vector machines (SVM) with various kernels;
• Random forests and gradient boosting;
• Deep neural networks (with cuDNN support);
• Dimensionality reduction algorithms (t-SNE, PCA).

Example of training an SVM classifier:

#include <dlib/svm.h>

int main() {    typedef dlib::matrix<double, 2, 1> sample_type;    std::vector<sample_type> samples;    std::vector<double> labels;

    // Fill data (two classes)    samples.push_back({1.0, 2.0}); labels.push_back(1);    samples.push_back({3.0, 4.0}); labels.push_back(-1);

    dlib::svm_c_linear_trainer<dlib::linear_kernel<sample_type>> trainer;    trainer.set_c(10.0);    auto decision_func = trainer.train(samples, labels);

    std::cout << "Prediction for (0,0): " << decision_func({0.0, 0.0}) << std::endl;    return 0;}

Pros: unified API for all algorithms, excellent integration with OpenCV, cross-platform.

3. Shogun — A Scientific Library with a Focus on Kernel Methods

Shogun is a powerful library designed primarily for researchers. Its features include:

• Over 60 implementations of kernel functions (RBF, polynomial, string);
• Support for structured learning (HMM, CRF);
• Interfaces for Python, R, Java, Octave;
• Built-in visualization tools.

Example of using Gaussian process regression:

#include <shogun/regression/GaussianProcessRegression.h>#include <shogun/kernel/GaussianKernel.h>

int main() {    auto features = some_real_features(); // your data    auto labels = some_regression_labels();    auto kernel = std::make