# 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);
```