##LibSVMsharp

LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines. This library uses LibSVM version 3.20 which released on 15th of November in 2014.

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);