public override void Deploy(ClassifierSettings _setup)
{
setup = _setup;
// name = setup.kNN_k + "-NN";
switch (setup.distanceFunction)
{
case DistanceFunctionType.SquareEuclidean:
_distance = new SquareEuclidean();
break;
case DistanceFunctionType.Jaccard:
_distance = new Jaccard();
break;
case DistanceFunctionType.Hamming:
_distance = new Hamming();
break;
case DistanceFunctionType.Euclidean:
_distance = new Euclidean();
break;
case DistanceFunctionType.Dice:
_distance = new Dice();
break;
case DistanceFunctionType.Cosine:
_distance = new Cosine();
break;
}
kNearest = new KNearestNeighbors <Double[]>(k: setup.kNN_k, distance: _distance);
}
public override void Deploy(ClassifierSettings _setup)
{
setup = _setup;
model = setup.svmModel;
if (model == mSVMModels.linear)
{
// Create a one-vs-one multi-class SVM learning algorithm
teacher = new MulticlassSupportVectorLearning <Linear>()
{
// using LIBLINEAR's L2-loss SVC dual for each SVM
Learner = (p) => new LinearDualCoordinateDescent()
{
Loss = setup.lossFunctionForTraining
}
};
}
if (model == mSVMModels.gaussian)
{
// Create the multi-class learning algorithm for the machine
teacherGaussian = 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
}
};
}
//teacher.UseKernelEstimation = true;
// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
// teacher.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)
}
public abstract void Deploy(ClassifierSettings _setup);
