zazabap / KernelMethod

Some Practice for use of Kernel Method, also serves as some base cases

C++ Kernel Methods for Support Vector Machines

This repository contains examples and utilities for implementing kernel methods, particularly for Support Vector Machines (SVMs) in C++ using the LightSVM library. The code demonstrates how to load datasets, preprocess data, train SVM models, and make predictions using various kernel functions.

Installation

1. Clone the repository:

   git clone https://github.com/your_username/kernel-methods-cpp.git
   cd kernel-methods-cpp

2. Install LightSVM:

   Download and install the LightSVM library from LightSVM website. Ensure that you have the necessary header files and library binaries.

3. Adjust CMakeLists.txt:

   Update the paths in the CMakeLists.txt file to point to the LightSVM library and header directories on your system.

4. Build the project:

   mkdir build
   cd build
   cmake ..
   make

Usage

Dataset Preparation

Prepare your dataset in a format compatible with LightSVM. Ensure your dataset is split into features and labels.

Training a Model

1. Modify main.cpp to load your dataset and convert it into the required format for LightSVM.

2. Choose the kernel type and set the SVM parameters according to your requirements.

3. Train the SVM model using the prepared dataset.

Making Predictions

Use the trained model to make predictions on new data points. Ensure the new data is processed in the same way as the training data.

Examples

The repository provides examples with simple datasets to illustrate how to use kernel methods with LightSVM. Check the examples/ directory for these demonstrations.

Contributing

Contributions are welcome! If you have ideas for improvement, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.