MLPack Learning
The project provides an example of working with machine learning algorithms using the MLPack library. The project demonstrates available functionality and its usage, including neural networks, collaborative filtering, and decision trees.
Build status:
- example -
- dev -
Table of Contents
- Compatibility
- Build features
- Branch info
- Install and running
- Screenshots
- Use cases
- Project Structure
- Terms of Use and Participation in Development
Compatibility
The project is compatible with all current versions of the Aurora OS.
Build features
The project is built using CMake build system. The project uses Conan package manager to obtain dependencies including MLPack library and its dependencies.
Branch info
Application versions conform to the branch naming convention
Install and running
Installation and build are performed according to the Build example instruction.
Screenshots
Use cases
The application demonstrates various machine learning algorithms and use cases:
Neural Networks
- Digit Recognition CNN: Trains a convolutional neural network for digit classification
- Custom Neural Networks: Ability to train neural networks with custom data
- Prediction capabilities: Real-time prediction using trained models
Collaborative Filtering
- Recommendation System: Implements collaborative filtering for movie recommendations
- User-based filtering: Generates personalized recommendations for users
- RMSE calculation: Evaluates model performance using Root Mean Square Error
Decision Trees
- Classification: Trains decision tree classifiers for categorical data
- Prediction: Classifies new data points using trained decision trees
Utility Functions
- Sample Data Generation: Creates synthetic datasets for testing
- Model Accuracy Tracking: Monitors training and validation accuracy
- Asynchronous Processing: Non-blocking machine learning operations
Project Structure
The project has a standard structure of an application based on C++ and QML for Aurora OS with machine learning capabilities.
- CMakeLists.txt file describes the project structure for the CMake build system and MLPack dependencies.
- icons directory contains the application icons for different screen resolutions.
-
qml directory contains the QML source code and the UI resources.
- cover directory contains the application cover implementations.
- icons directory contains the additional custom UI icons.
- pages directory contains the application pages.
- MLPackLearning.qml file provides the application window implementation.
-
rpm directory contains the rpm-package build settings.
- ru.auroraos.MLPackLearning.spec file is used by rpmbuild tool.
-
src directory contains the C++ source code with MLPack integration.
- main.cpp file is the application entry point.
- utilityservice.h header file defines the machine learning service interface.
- utilityservice.cpp implementation of machine learning algorithms using MLPack.
- translations directory contains the UI translation files.
- data directory contains datasets and training data for machine learning models.
- ru.auroraos.MLPackLearning.desktop file defines the display and parameters for launching the application.
Terms of Use and Participation in Development
The source code of the project is provided under license, which allows to use it in third-party applications.
To participate in the development of the project, please read the member agreement. If you plan to submit your own source code for inclusion in the project, you will need to accept the CLA terms and conditions.
Participant information is listed in the AUTHORS file.
The Code of Conduct is the current set of rules of the Open Mobile Platform Company, which informs about the expectations for interaction between community members when communicating and working on projects.