分类器SVM的C#源码

+-------------------------------------------------------------+ | | | SVM.NET 1.6 | | Copyright � 2009 by Matthew Johnson | | Adapted from libsvm 2.89 | | http://www.matthewajohnson.org/software/svm.html | | | +-------------------------------------------------------------+ Thank you for downloading this .NET version of the libsvm library. Full documentation of the library is found in the included help document. To get started quickly, however, here is a sample of how to perform learning on the sample data provided for download with this library in C# (for more data in the libsvm format, please go to http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. // First, read in the training and test data Problem train = Problem.Read("a1a.train"); Problem test = Problem.Read("a1a.test"); // For this example (and indeed, many scenarios), the default // parameters will suffice. Parameter parameters = new Parameter(); double C; double Gamma; // This will do a grid optimization to find the best parameters // and store them in C and Gamma, outputting the entire // search to params.txt. ParameterSelection.Grid( train, parameters, "params.txt", out C, out Gamma ); parameters.C = C; parameters.Gamma = Gamma; // Train the model using the optimal parameters. Model model = Training.Train(train, parameters); // Perform classification on the test data, putting the // results in results.txt. Prediction.Predict(test, "results.txt", model, false); ==================== Revision Information ==================== Version 1.6 (7/12/2009) ----------------------- * Fixed major bug in Kernel.cs with computeSquaredDistance() * Fixed major bug in svm_predict_probability where a two dimensional array was being intialized incorrectly. * Changed the way in which Scaling works. RangeTransform and GaussianTransform both have a Compute() method, which will compute the transform from a problem. They also have a Scale() method, which will scale a problem using that IRangeTransform object. Version 1.5 (6/7/2009) ---------------------- * Updated to libsvm 2.89 * Optimized Cache class and Kernel methods * Fixed bug with PerformanceEvaluator for multiple category problems Version 1.4 (9/3/2008) ---------------------- * Added PerformanceEvaluator and RankPair classes which enable easy evaluation using Precision/Recall and Receiver Operating Characteristic Curves * Added PrecomputedKernel class, which makes it easier to train SVMs using custom kernels. * Small changes to improve performance. See documentation for details. Version 1.3 (9/17/2007) ----------------------- * Updated to libsvm 2.84 Version 1.2 (3/27/2007) ----------------------- * Added the ability to predict class membership and probabilities for a single vector to the Prediction class. * Added the GaussianTransform class. * Fixed minor bugs and completed documentation. Version 1.1 (2/19/2007) ----------------------- * Fixed a bug when writing to file, where the existing file wouldn't be completely overwritten. * Updated documentation. Version 1.0 (2/16/2007) ----------------------- * First public release.