cpp-ml-library is a C++ library implementing foundational machine learning algorithms inspired by the Udemy course "Machine Learning A-Z: AI, Python & R" taught by Kirill Eremenko and Hadelin de Ponteves. This project aims to reimplement key algorithms from the course in C++ to provide reusable implementations for educational purposes, experimentation, and practical applications in larger C++ projects.
cpp-ml-library/
├── ml_library_include/ # Header files
├── tests/ # Unit tests
├── examples/ # Example usage files
├── docs/ # Documentation files
└── CMakeLists.txt # CMake configuration file
To build the library with CMake:
mkdir build
cd build
cmake ..
make
After building, you can install the library system-wide:
make install
To use this library in your C++ project, include the master header file:
#include "ml_library_include/ml.h"
The following machine learning algorithms are planned, inspired by concepts and techniques taught in the Udemy course:
Regression
Classification
Clustering
Neural Networks
Association Rule Learning
Support Vector Machine
Algorithm Category | Algorithm | Implemented | Tests | Examples |
---|---|---|---|---|
Regression | Polynomial Regression (PolynomialRegression) | [x] | [ ] | [x] |
Logistic Regression (LogisticRegression) | [x] | [ ] | [x] | |
Multi-Linear Regression (MultilinearRegression) | [x] | [ ] | [x] | |
Decision Tree Regression (DecisionTreeRegressor) | [ ] | [ ] | [ ] | |
Random Forest Regression (RandomForestRegressor) | [ ] | [ ] | [ ] | |
K-Nearest Neighbors (KNNRegressor) | [x] | [ ] | [ ] | |
Classification | Decision Tree Classifier (DecisionTreeClassifier) | [ ] | [ ] | [ ] |
Random Forest Classifier (RandomForestClassifier) | [ ] | [ ] | [ ] | |
K-Nearest Neighbors (KNNClassifier) | [x] | [ ] | [ ] | |
Clustering | K-Means Clustering (KMeans) | [x] | [ ] | [ ] |
Hierarchical clustering (HierarchicalClustering) | [x] | [ ] | [ ] | |
Neural Networks | Neural Network (NeuralNetwork) | [x] | [x] | [x] |
Artificial Neural Network | [ ] | [ ] | [ ] | |
Convolutional Neural Network | [ ] | [ ] | [ ] | |
Association Rule Learning | Apriori | [x] | [x] | [x] |
Eclat | [x] | [x] | [x] | |
Support Vector Machine | Support Vector Regression (SupportVectorRegression) | [ ] | [ ] | [ ] |
Each algorithm will include example usage. Examples are located in the examples/
directory.
#include "ml/ml.h"
#include <vector>
#include <iostream>
int main() {
ml::tree::DecisionTreeClassifier dtc;
std::vector<std::vector<double>> data = { {5.1, 3.5, 1.4, 0.2}, {4.9, 3.0, 1.4, 0.2} };
std::vector<int> labels = { 0, 1 };
dtc.fit(data, labels);
int prediction = dtc.predict({5.1, 3.5, 1.4, 0.2});
std::cout << "Predicted class: " << prediction << std::endl;
return 0;
}
The documentation for this project is generated using Doxygen and is available online at GitHub Pages. The documentation provides details on each class, function, and algorithm.
Contributions are welcome! Please submit a pull request or open an issue if you'd like to discuss ideas or report bugs.
This project is licensed under the MIT License. See the LICENSE file for details.