# LibSVMsharp **Repository Path**: ice_elegant/LibSVMsharp ## Basic Information - **Project Name**: LibSVMsharp - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-02-22 - **Last Updated**: 2024-02-22 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## LibSVMsharp LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines. This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018. For more information visit the official [libsvm](http://www.csie.ntu.edu.tw/~cjlin/libsvm/) webpage. ## How to Install To install LibSVMsharp, download the [Nuget package](https://www.nuget.org/packages/LibSVMsharp) or run the following command in the Package Manager Console: `PM> Install-Package LibSVMsharp` ## License LibSVMsharp is released under the MIT License and libsvm is released under the [modified BSD Lisence](http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f204) which is compatible with many free software licenses such as GPL. ## Example Codes #### Simple Classification ```C# SVMProblem problem = SVMProblemHelper.Load(@"dataset_path.txt"); SVMProblem testProblem = SVMProblemHelper.Load(@"test_dataset_path.txt"); SVMParameter parameter = new SVMParameter(); parameter.Type = SVMType.C_SVC; parameter.Kernel = SVMKernelType.RBF; parameter.C = 1; parameter.Gamma = 1; SVMModel model = SVM.Train(problem, parameter); double[] target = new double[testProblem.Length]; for (int i = 0; i < testProblem.Length; i++) target[i] = SVM.Predict(model, testProblem.X[i]); double accuracy = SVMHelper.EvaluateClassificationProblem(testProblem, target); ``` #### Simple Classification with Extension Methods ```C# SVMProblem problem = SVMProblemHelper.Load(@"dataset_path.txt"); SVMProblem testProblem = SVMProblemHelper.Load(@"test_dataset_path.txt"); SVMParameter parameter = new SVMParameter(); SVMModel model = problem.Train(parameter); double[] target = testProblem.Predict(model); double accuracy = testProblem.EvaluateClassificationProblem(target); ``` #### Simple Regression ```C# SVMProblem problem = SVMProblemHelper.Load(@"dataset_path.txt"); SVMProblem testProblem = SVMProblemHelper.Load(@"test_dataset_path.txt"); SVMParameter parameter = new SVMParameter(); SVMModel model = problem.Train(parameter); double[] target = testProblem.Predict(model); double correlationCoeff; double meanSquaredErr = testProblem.EvaluateRegressionProblem(target, out correlationCoeff); ```