/// <summary>
/// Makes the serialization.
/// </summary>
/// <param name="svm">The SVM.</param>
/// <param name="path">The path.</param>
public static void SerializeSVM(SupportVectorMachine <Gaussian> svm, string path)
{
SVMGaussianData data = new SVMGaussianData();
data.Initialize(
svm.NumberOfInputs,
svm.NumberOfOutputs,
svm.SupportVectors,
svm.Threshold,
svm.Weights,
svm.Kernel.Gamma,
svm.Kernel.Sigma,
svm.Kernel.SigmaSquared);
Serialize(data, path);
}
/// <summary>
/// Deserializes the SVM.
/// </summary>
/// <param name="path">The path.</param>
/// <returns></returns>
public static SupportVectorMachine <Gaussian> DeserializeSVM(string path)
{
var teacher = new SequentialMinimalOptimization <Gaussian>()
{
UseComplexityHeuristic = true,
UseKernelEstimation = true
};
double[][] inputs2 = new double[4][];
inputs2[0] = new[] { 1d, 1d };
inputs2[1] = new[] { 1d, 1d };
inputs2[2] = new[] { 1d, 1d };
inputs2[3] = new[] { 1d, 1d };
double[] outputs2 = { 1, 1, 0, 0 };
SupportVectorMachine <Gaussian> svmAfter = teacher.Learn(inputs2, outputs2);
SVMGaussianData dataAfter = Deserialize <SVMGaussianData>(path);
svmAfter.NumberOfInputs = dataAfter.NumberOfInputs;
svmAfter.NumberOfOutputs = dataAfter.NumberOfOutputs;
svmAfter.SupportVectors = dataAfter.SupportVectors;
svmAfter.Threshold = dataAfter.Threshold;
svmAfter.Weights = dataAfter.Weights;
Gaussian kernel = new Gaussian
{
Gamma = dataAfter.Gamma,
Sigma = dataAfter.Sigma,
SigmaSquared = dataAfter.SigmaSquared
};
svmAfter.Kernel = kernel;
return(svmAfter);
}
