//public SupportVectorMachine SVM
//{
// get { return svm; }
// private set { svm = value; }
//}
public override void TrainningModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] outputs = (int[])trainningData.ClassificationAttribute.Clone();
// Create output for SVM (-1 or 1)
for (int index = 0; index < outputs.Length; index++)
{
if (outputs[index] == 0)
{
outputs[index] = -1;
}
}
// Create a Support Vector Machine for the given inputs
//this.svm = new SupportVectorMachine(inputs[0].Length);
//// Create a Kernel Support Vector Machine for the given inputs
this.svm = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, outputs);
// Set up the learning algorithm
smo.Complexity = 1.0;
// Run the learning algorithm
double error = smo.Run();
}
//public SupportVectorMachine SVM
//{
// get { return svm; }
// private set { svm = value; }
//}
public override void TrainningModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] outputs = (int[])trainningData.ClassificationAttribute.Clone();
// Create output for SVM (-1 or 1)
for (int index = 0; index < outputs.Length; index++)
{
if (outputs[index] == 0)
{
outputs[index] = -1;
}
}
// Create a Support Vector Machine for the given inputs
//this.svm = new SupportVectorMachine(inputs[0].Length);
//// Create a Kernel Support Vector Machine for the given inputs
this.svm = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, outputs);
// Set up the learning algorithm
smo.Complexity = 1.0;
// Run the learning algorithm
double error = smo.Run();
}
public override List<ConfusionMatrix> TestModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] expected = trainningData.ClassificationAttribute;
int[] predicted = ComputeModel(inputs);
int positiveValue = trainningData.PositiveValue;
int negativeValue = trainningData.NegativeValue;
ConfusionMatrix confusionMatrix = new ConfusionMatrix(predicted, expected, positiveValue, negativeValue);
return new List<ConfusionMatrix> { confusionMatrix };
}
public override List <ConfusionMatrix> TestModel(TrainningData trainningData)
{
ContinuousDataTableAdapter continuousDataTableAdapter = new ContinuousDataTableAdapter();
DataTable continuousDataTable = continuousDataTableAdapter.GetData();
DataTable dataTable = continuousDataTable.DefaultView.ToTable(false, TableMetaData.TestingAttributes);
string[] columnNames;
double[][] inputs = dataTable.ToArray(out columnNames);
int[] expected = trainningData.ClassificationAttribute;
int[] predicted = ComputeModel(inputs);
int positiveValue = trainningData.PositiveValue;
int negativeValue = trainningData.NegativeValue;
ConfusionMatrix confusionMatrix = new ConfusionMatrix(predicted, expected, positiveValue, negativeValue);
return(new List <ConfusionMatrix> {
confusionMatrix
});
}
