// IKernel kernel; public double BuildTheModel(double[][] inputs, int[] outputs, int ClassNum, ConfigurationFieldClassifier config) { Reset(); IKernel kernal = null; switch (config.AccordConfiguration.Kernel) { case KernelTypes.Gaussian: kernal = new Gaussian(config.AccordConfiguration.GaussianKernel.Sigma); break; case KernelTypes.Polynomial: kernal = new Polynomial(config.AccordConfiguration.PolynominalKernel.Degree, config.AccordConfiguration.PolynominalKernel.Constant); break; case KernelTypes.ChiSquare: kernal = new ChiSquare(); break; case KernelTypes.HistogramIntersction: kernal = new HistogramIntersection(); break; default: break; } Tuple <double, double> estimatedComplexity = SequentialMinimalOptimization.EstimateComplexity(kernal, inputs, outputs); machine = new MulticlassSupportVectorMachine(inputs[0].Length, kernal, ClassNum); teacher = new MulticlassSupportVectorLearning(machine, inputs, outputs); // Configure the learning algorithm teacher.Algorithm = (svm, classInputs, classOutputs, i, j) => { var smo = new SequentialMinimalOptimization(svm, classInputs, classOutputs); smo.Complexity = config.AccordConfiguration.Complexity; smo.Tolerance = config.AccordConfiguration.Tolerance; smo.CacheSize = config.AccordConfiguration.CacheSize; smo.Strategy = (Accord.MachineLearning.VectorMachines.Learning.SelectionStrategy)((int)(config.AccordConfiguration.SelectionStrategy)); // smo.UseComplexityHeuristic = true; // smo.PositiveWeight = 1; // smo.NegativeWeight = 1; smo.Run(); var probabilisticOutputLearning = new ProbabilisticOutputLearning(svm, classInputs, classOutputs); return(probabilisticOutputLearning); // return smo; }; // Train the machines. It should take a while. // Thread.Sleep(10000); //#if temp double error = teacher.Run(); //#endif // return 0; return(error); }
// IKernel kernel; public double BuildTheModel(double[][] inputs, int[] outputs, int ClassNum, ConfigurationFieldClassifier config) { cpuCounter.CategoryName = "Processor"; cpuCounter.CounterName = "% Processor Time"; cpuCounter.InstanceName = "_Total"; Reset(); _usenongoldenclass = config.FeatureExtraction.UseNonGoldenClass; // scalers = scalresin; IKernel kernal = null; switch (config.AccordConfiguration.Kernel) { case KernelTypes.Gaussian: kernal = new Gaussian(config.AccordConfiguration.GaussianKernel.Sigma); break; case KernelTypes.Polynomial: kernal = new Polynomial(config.AccordConfiguration.PolynominalKernel.Degree, config.AccordConfiguration.PolynominalKernel.Constant); break; case KernelTypes.ChiSquare: kernal = new ChiSquare(); break; case KernelTypes.HistogramIntersction: kernal = new HistogramIntersection(); break; default: break; } if (ClassNum > 2) { m_machine = new MulticlassSupportVectorMachine(inputs[0].Length, kernal, ClassNum); m_teacher = (new MulticlassSupportVectorLearning((MulticlassSupportVectorMachine)m_machine, inputs, outputs)); (m_teacher as MulticlassSupportVectorLearning).Algorithm = (svm, classInputs, classOutputs, i, j) => { var smo = new SequentialMinimalOptimization(svm, classInputs, classOutputs); smo.Complexity = config.AccordConfiguration.Complexity; smo.Tolerance = config.AccordConfiguration.Tolerance; smo.CacheSize = config.AccordConfiguration.CacheSize; smo.Strategy = (Accord.MachineLearning.VectorMachines.Learning.SelectionStrategy)((int)(config.AccordConfiguration.SelectionStrategy)); // smo.UseComplexityHeuristic = true; // smo.PositiveWeight = 1; int k = 0; while (cpuCounter.NextValue() > 50) { Thread.Sleep(50); k++; if (k > 30000) { break; } } // smo.NegativeWeight = 1; smo.Run(); var probabilisticOutputLearning = new ProbabilisticOutputLearning(svm, classInputs, classOutputs); return(probabilisticOutputLearning); // return smo; }; } else { // FIX TO BASE TYPES THAN RUN THAN MAKE OTHER 2 CHANGES from LATEST - line ... and CLUSTER AND RUNTEST.. and check again... m_machine = new SupportVectorMachine(inputs[0].Length); m_teacher = new SequentialMinimalOptimization((SupportVectorMachine)m_machine, inputs, outputs); (m_teacher as SequentialMinimalOptimization).Complexity = config.AccordConfiguration.Complexity;; (m_teacher as SequentialMinimalOptimization).Tolerance = config.AccordConfiguration.Tolerance; (m_teacher as SequentialMinimalOptimization).CacheSize = config.AccordConfiguration.CacheSize; (m_teacher as SequentialMinimalOptimization).Strategy = (Accord.MachineLearning.VectorMachines.Learning.SelectionStrategy)((int)(config.AccordConfiguration.SelectionStrategy)); (m_teacher as SequentialMinimalOptimization).Complexity = config.AccordConfiguration.Complexity;; } // Configure the learning algorithm // Train the machines. It should take a while. // Thread.Sleep(10000); //#if temp double error = m_teacher.Run(); //#endif // return 0; return(error); }