GitHub - ccerhan/LibSVMsharp: C# wrapper of LibSVM (original) (raw)
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 webpage.
How to Install
To install LibSVMsharp, download the Nuget package 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 which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
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
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
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);