public void multilabelSVM() { var teacher = new MulticlassSupportVectorLearning <Gaussian>() { // Configure the learning algorithm to use SMO to train the // underlying SVMs in each of the binary class subproblems. Learner = (param) => new SequentialMinimalOptimization <Gaussian>() { // Estimate a suitable guess for the Gaussian kernel's parameters. // This estimate can serve as a starting point for a grid search. UseKernelEstimation = true } }; // Learn a machine var machine = teacher.Learn(inputs, outputs); // Create the multi-class learning algorithm for the machine var calibration = new MulticlassSupportVectorLearning <Gaussian>() { Model = machine, // We will start with an existing machine // Configure the learning algorithm to use Platt's calibration Learner = (param) => new ProbabilisticOutputCalibration <Gaussian>() { Model = param.Model // Start with an existing machine } }; // Configure parallel execution options calibration.ParallelOptions.MaxDegreeOfParallelism = 1; // Learn a machine calibration.Learn(inputs, outputs); // Obtain class predictions for each sample int[] predicted = machine.Decide(inputs); // Get class scores for each sample double[] scores = machine.Score(inputs); // Get log-likelihoods (should be same as scores) double[][] logl = machine.LogLikelihoods(inputs); // Get probability for each sample double[][] prob = machine.Probabilities(inputs); // Compute classification error double error = new ZeroOneLoss(outputs).Loss(predicted); double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob); message += "SVM Validacja\n"; message += "error " + error.ToString() + "\n"; message += "loss " + loss.ToString() + "\n\n"; }