##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);
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