// 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);
}
