LibSVMsharp-master
所属分类：人工智能/神经网络/深度学习
开发工具：C#
文件大小：556KB
下载次数：50
上传日期：2016-06-16 20:39:38
上 传 者：
He Listen
说明： C#版SVM分类器源代码包，包含源代码，测试程序，测试数据，可完美运行。
(C# Version SVM classification source code ,include source code ,test procedure and test data ,it can be run perfectly.)
文件列表:
LibSVMsharp\bin\Debug\libsvm.dll (160256, 2015-12-17)
LibSVMsharp\bin\Debug\LibSVMsharp.dll (33792, 2016-06-16)
LibSVMsharp\bin\Debug\LibSVMsharp.pdb (85504, 2016-06-16)
LibSVMsharp\bin\Debug\LIBSVM_COPYRIGHT (1497, 2015-12-17)
LibSVMsharp\Core\libsvm.cs (5601, 2015-12-17)
LibSVMsharp\Core\svm_model.cs (760, 2015-12-17)
LibSVMsharp\Core\svm_node.cs (329, 2015-12-17)
LibSVMsharp\Core\svm_parameter.cs (735, 2015-12-17)
LibSVMsharp\Core\svm_problem.cs (370, 2015-12-17)
LibSVMsharp\Extensions\SVMModelExtensions.cs (1006, 2015-12-17)
LibSVMsharp\Extensions\SVMNodeExtensions.cs (1425, 2015-12-17)
LibSVMsharp\Extensions\SVMProblemExtensions.cs (3707, 2015-12-17)
LibSVMsharp\Helpers\SVMHelper.cs (3636, 2015-12-17)
LibSVMsharp\Helpers\SVMNodeHelper.cs (1383, 2015-12-17)
LibSVMsharp\Helpers\SVMProblemHelper.cs (5326, 2015-12-17)
LibSVMsharp\libsvm.dll (160256, 2015-12-17)
LibSVMsharp\LibSVMsharp.csproj (3505, 2015-12-17)
LibSVMsharp\LIBSVM_COPYRIGHT (1497, 2015-12-17)
LibSVMsharp\obj\Debug\DesignTimeResolveAssemblyReferencesInput.cache (6668, 2016-06-16)
LibSVMsharp\obj\Debug\LibSVMsharp.csproj.FileListAbsolute.txt (582, 2016-06-16)
LibSVMsharp\obj\Debug\LibSVMsharp.csprojResolveAssemblyReference.cache (1755, 2016-06-16)
LibSVMsharp\obj\Debug\LibSVMsharp.dll (33792, 2016-06-16)
LibSVMsharp\obj\Debug\LibSVMsharp.pdb (85504, 2016-06-16)
LibSVMsharp\obj\Debug\TemporaryGeneratedFile_036C0B5B-1481-4323-8D20-8F5ADCB23D92.cs (0, 2016-06-16)
LibSVMsharp\obj\Debug\TemporaryGeneratedFile_5937a670-0e60-4077-877b-f7221da3dda1.cs (0, 2016-06-16)
LibSVMsharp\obj\Debug\TemporaryGeneratedFile_E7A71F73-0F8D-4B9B-B56E-8E70B10BC5D3.cs (0, 2016-06-16)
LibSVMsharp\Properties\AssemblyInfo.cs (1398, 2015-12-17)
LibSVMsharp\SVM.cs (14017, 2015-12-17)
LibSVMsharp\SVMModel.cs (14129, 2015-12-17)
LibSVMsharp\SVMNode.cs (2601, 2015-12-17)
LibSVMsharp\SVMParameter.cs (7938, 2015-12-17)
LibSVMsharp\SVMProblem.cs (5009, 2015-12-17)
LibSVMsharp.Examples.Classification\App.config (182, 2015-12-17)
LibSVMsharp.Examples.Classification\bin\Debug\Dataset\wine.txt (28115, 2015-12-17)
LibSVMsharp.Examples.Classification\bin\Debug\libsvm.dll (160256, 2015-12-17)
LibSVMsharp.Examples.Classification\bin\Debug\LibSVMsharp.dll (33792, 2016-06-16)
LibSVMsharp.Examples.Classification\bin\Debug\LibSVMsharp.Examples.Classification.exe (7168, 2016-06-16)
LibSVMsharp.Examples.Classification\bin\Debug\LibSVMsharp.Examples.Classification.exe.config (182, 2015-12-17)
LibSVMsharp.Examples.Classification\bin\Debug\LibSVMsharp.Examples.Classification.pdb (13824, 2016-06-16)
... ...
##LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
It uses the latest LibSVM version 3.20 which released on 15th of November in 2014.
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);
```
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