An In-Depth Guide to Machine Learning Libraries in C++

Machine learning has become one of the hottest fields in software engineering, powering everything from recommendation systems to self-driving cars. While Python and R are the most popular languages for machine learning, C++ is a powerful alternative that offers several unique advantages.

As a fast, flexible, and highly efficient language, C++ is an excellent choice for machine learning applications that demand high performance. All of the major tech giants, including Google, Facebook, and Microsoft, use C++ extensively in their production systems.

In this article, we‘ll take a deep dive into the C++ machine learning ecosystem and some of the top libraries available for implementing ML models in C++. Whether you‘re a seasoned C++ programmer looking to get into ML or an experienced data scientist interested in boosting performance, this guide will walk you through everything you need to know to start doing machine learning in C++.

Why Use C++ for Machine Learning?

C++ offers several compelling advantages for machine learning:

Performance: C++ is well-known as a high-performance language. It compiles directly to machine code, allowing for fine-grained optimizations. The standard library and STL provide fast and efficient implementations of core data structures and algorithms. For machine learning workloads that require processing huge datasets or serving models at scale in production, C++ can provide a significant speed boost over interpreted languages like Python.

Flexibility: C++ is a multi-paradigm language that provides powerful features for writing all kinds of software. Its support for object-oriented programming, templates, and metaprogramming allow building very expressive and modular ML libraries. At the same time, it still allows low-level access to hardware when needed. This combination of high-level expressiveness and low-level control makes C++ well-suited for building large-scale ML systems.

Deployability: Getting ML models into production is a key challenge. With C++, you can compile your final program into a compact, self-contained binary without external dependencies. This makes deployment much simpler than with other languages that require a more complex runtime environment. For applications like robotics or mobile apps where resources are limited, C++‘s minimal footprint is a huge advantage.

Existing codebases: Many companies, especially large tech firms, have existing codebases in C++. The ability to integrate ML capabilities directly into these codebases can significantly speed up development time and simplify infrastructure.

Of course, C++ also has some drawbacks for ML. It‘s a complex language with a steeper learning curve than Python or R. It also lacks the extensive ecosystem of scientific computing and data analysis libraries found in those languages. However, as we‘ll see, there are a growing number of powerful machine learning libraries written in C++.

Overview of C++ Machine Learning Libraries

There are several excellent open source libraries for machine learning in C++. Some focus on specific domains like computer vision or natural language processing, while others aim to be comprehensive frameworks for all types of ML tasks. Here are some of the most popular libraries:

MLPACK: A fast, flexible machine learning library with an easy-to-use C++ API. Implements many standard supervised and unsupervised learning algorithms.

Shark: A modular library for the design and optimization of adaptive systems, including neural networks, linear and kernel methods, and evolutionary algorithms.

Dlib: A general-purpose cross-platform library with broad support for networking, threads, graphical interfaces, data structures, linear algebra, and machine learning.

Shogun: An open-source machine learning library that offers a wide range of efficient and unified machine learning methods.

DyNet: A neural network library developed by Carnegie Mellon and many others. Provides tools for implementing state-of-the-art deep learning models efficiently.

Caffe: A deep learning framework made with expression, speed, and modularity in mind. Originally developed by Berkeley AI Research.

tiny-dnn: A header-only deep learning framework in C++11 with focus on portability and low dependency.

Minerva: A fast and flexible system for deep learning. Minerva‘s core is built on C++ with CUDA and pthreads for parallelization.

In the next sections, we‘ll take a closer look at some of these libraries and walk through examples of using them for real machine learning tasks.

MLPACK

MLPACK is a comprehensive machine learning library written in C++ that aims to "provide fast, extensible implementations of cutting-edge machine learning algorithms." It provides a wide range of algorithms for supervised and unsupervised learning, from standard methods like linear regression and k-means clustering to powerful modern techniques like deep neural networks and gradient boosting.

Some key features of MLPACK include:

• Fast, efficient C++ implementations of standard ML algorithms
• Simple, consistent APIs for all algorithms
• Extensive documentation and tutorials
• Plenty of code examples for different ML tasks
• Multi-platform support (Linux, MacOS, Windows)
• OpenMP and Armadillo support for parallel processing and linear algebra

Here‘s an example of using MLPACK to train a simple linear regression model:

#include <mlpack/methods/linear_regression/linear_regression.hpp>

using namespace mlpack;
using namespace mlpack::regression;

int main()
{
 arma::mat data; 
 data::Load("data.csv", data, true);

 LinearRegression lr; 
 lr.Train(data);

 arma::vec parameters = lr.Parameters();
}

And here‘s an example of using MLPACK to perform k-means clustering:

#include <mlpack/methods/kmeans/kmeans.hpp>

using namespace mlpack;
using namespace mlpack::kmeans;

int main()
{
 arma::mat data;
 data::Load("data.csv", data, true);

 size_t clusters = 3;
 KMeans<> k;
 arma::Row<size_t> assignments;
 k.Cluster(data, clusters, assignments);  
}

As you can see, MLPACK provides a simple, consistent API for performing various machine learning tasks. It takes care of all the details of the underlying algorithms, allowing you to focus on preparing your data and interpreting the results.

One potential downside of MLPACK is the fact that its API relies heavily on the Armadillo library for data structures and linear algebra. This means you may need to spend some time learning Armadillo‘s syntax if you‘re not already familiar with it. However, Armadillo is a powerful library in its own right and is widely used in the C++ scientific computing community.

Overall, MLPACK is an excellent choice if you‘re looking for a comprehensive, high-quality machine learning library in C++. It‘s well-documented, actively maintained, and covers most of the algorithms and techniques you‘re likely to need for real-world ML projects.

Dlib

Dlib is a cross-platform C++ library that contains a broad range of components for developing software in C++ including machine learning, computer vision, numerical optimization, data mining, and more.

Some of the key features of Dlib include:

• Clean, readable code with extensive documentation
• All container classes are header-only which mean faster compile times
• Lots of pre-built data structures and utilities for faster development
• Comprehensive machine learning functionalties
• Well known for its powerful and efficient computer vision tools
• Highly portable with support for all major platforms

Here‘s a simple example showing how to use Dlib‘s support vector machine (SVM) classifier:

#include <dlib/svm_threaded.h>

using namespace std;
using namespace dlib;

int main()
{
  std::vector<sample_type> samples;
  std::vector<double> labels;

  // Populate samples and labels...

  svm_c_trainer<kernel_type> trainer;
  trainer.set_c(0.95);

  typedef decision_function<kernel_type> dec_funct_type;
  typedef normalized_function<dec_funct_type> funct_type;

  funct_type learned_function;
  learned_function.normalizer = normalizer;
  learned_function.function = trainer.train(samples, labels);

  // Use the learned_function to classify new samples...
}

Dlib also provides powerful tools for performing facial recognition using deep learning:

#include <dlib/dnn.h>
#include <dlib/image_io.h>

using namespace dlib;
using namespace std;

int main() 
{
 frontal_face_detector detector = get_frontal_face_detector();

 shape_predictor sp;
 deserialize("shape_predictor_5_face_landmarks.dat") >> sp;

 anet_type net;
 deserialize("dlib_face_recognition_resnet_model_v1.dat") >> net;

 matrix<rgb_pixel> img;
 load_image(img, "face.jpg");

 std::vector<matrix<rgb_pixel>> faces;

 for (auto face : detector(img))
 {
   auto shape = sp(img, face);
   matrix<rgb_pixel> face_chip;
   extract_image_chip(img, get_face_chip_details(shape,150,0.25), face_chip);
   faces.push_back(move(face_chip));
 }

 std::vector<matrix<float,0,1>> face_descriptors = net(faces);
}

Dlib is a powerful and flexible library that provides a wide range of machine learning and computer vision tools. Its clean, readable code and extensive documentation make it a great choice for C++ programmers who want to incorporate machine learning into their applications.

The main downside of Dlib is that it doesn‘t have quite as extensive a range of machine learning algorithms as some other libraries like MLPACK or Shogun. However, what it does provide is very high quality and tuned for performance.

Shogun

Shogun is an open-source machine learning library that offers a wide range of unified and efficient machine learning methods. It was initially developed for bioinformatics at the Max Planck Society but has since evolved into a comprehensive ML toolkit.

Some of the key features of Shogun include:

• Supports vector machines, dimensionality reduction, clustering, regression, and more
• Implements numerous standard machine learning algorithms efficiently
• Supports multi-class and structured prediction tasks
• Extensive documentation and active developer community
• Interfaces available for many languages including C++, Python, Octave, R, Java, Lua, C#, etc
• Portable architecture with support for major platforms

Here‘s a simple example of using Shogun‘s C++ interface for binary classification with a support vector machine:

#include <shogun/base/init.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/classifier/svm/LibSVM.h>

using namespace shogun;

int main(int argc, char** argv)
{
 init_shogun_with_defaults();

 SGMatrix<float64_t> matrix(2,3);
 for (int32_t i=0; i<6; i++)
   matrix.matrix[i]=i;

 auto features=some<CDenseFeatures<float64_t>>(matrix);

 SGVector<float64_t> lab(3);
 lab[0]=1; lab[1]=-1; lab[2]=1;
 auto labels=some<CBinaryLabels>(lab);

 auto svm = new CLibSVM();
 svm->set_C(1.0);
 svm->set_kernel(new CGaussianKernel());
 svm->train(features,labels);

 auto result = svm->apply(features);
 result->get_labels().display_vector("predictions");

 exit_shogun();
 return 0;
}

Shogun provides a unified interface for working with many different kinds of data, including dense and sparse vectors, strings, graphs, and more. It also provides efficient implementations of many standard ML algorithms like SVMs, PCA, k-means, and linear and logistic regression.

One of the nicest aspects of Shogun is its cross-language support. While the core of Shogun is implemented in C++, it provides automatically generated wrappers for many other popular programming languages including Python, R, Octave, and more. This makes it easy to integrate Shogun into existing codebases or data science workflows.

The main weakness of Shogun is its limited support for deep learning compared to some other modern machine learning libraries. While it does provide some basic neural network functionality, it lacks the extensive customizability and pre-trained models of dedicated deep learning frameworks.

Choosing a C++ Machine Learning Library

With so many different machine learning libraries available for C++, which one should you choose for your project? The answer depends on your specific needs and constraints.

If you‘re looking for a comprehensive, general-purpose machine learning library with a wide selection of algorithms, MLPACK or Shogun are probably your best bets. Both of these libraries cover most of the techniques and use cases you‘re likely to encounter in real-world ML projects.

If you‘re working on computer vision or image processing applications, Dlib is definitely worth checking out. Its facial recognition and object detection capabilities are among the best available in any open source library.

If you need the absolute fastest performance, you may want to consider lower-level libraries like Eigen or Armadillo for linear algebra, and XGBoost or LightGBM for gradient boosting. However, be prepared to write a lot more code, as these libraries typically don‘t provide as much abstraction as the higher-level ML frameworks.

It‘s also worth considering ease of integration with the rest of your application. If you have an existing C++ codebase, you may want to choose a library that integrates well with your current build system, package manager, etc. If you need to deploy to resource-constrained devices like mobile phones or embedded systems, lean toward more lightweight libraries with minimal dependencies.

Of course, you don‘t have to limit yourself to using just one library. Many of the C++ ML libraries are modular and interoperable, so you can mix and match components from different libraries as needed. Feel free to experiment and find the combination that works best for your project!

Conclusion

C++ is a powerful and increasingly popular language for machine learning, offering a compelling combination of performance, flexibility, and deployability. While the C++ ML ecosystem is not as extensive as that of Python or R, there are still many excellent libraries available that cover a wide range of algorithms and use cases.

In this article, we‘ve taken a deep dive into some of the most popular C++ machine learning libraries, including MLPACK, Shogun, and Dlib. We‘ve discussed the key features and benefits of each library, and walked through code examples of using them for various ML tasks.

Whether you‘re a C++ programmer looking to add machine learning to your skillset, or an experienced data scientist interested in boosting performance and simplifying deployment, I encourage you to check out these libraries and start experimenting. With the power and flexibility of C++, you can take your machine learning projects to new heights!