public OLM_II(double alpha, int labels, int bufferSizeInference, BasisMerkmal <NodeData, EdgeData, GraphData>[] basisMerkmale, Func <int[], int[], double> lossfunctionIteration, Func <int[], int[], double> lossfunctionValidation, double sensitivityFactor, string name)
{
Name = name;
Alpha = alpha;
Labels = labels;
BufferSizeInference = bufferSizeInference;
LossFunctionIteration = lossfunctionIteration;
LossFunctionValidation = lossfunctionValidation;
BasisMerkmale = basisMerkmale;
SensitivityFactor = sensitivityFactor;
WeightObservationUnit_II = new WeightObservationUnit_II(basisMerkmale.Length, 0.1);
}
protected override double[] DoIteration(List <IGWGraph <NodeData, EdgeData, GraphData> > TrainingGraphs, double[] weightCurrent, int globalIteration)
{
var weights = weightCurrent.ToArray();
var weightsSum = new double[weightCurrent.Length];
int iteration = 0;
double tp = 0.001, tn = 0.001 + 0, fp = 0.001, fn = 0.001;
int[] countsRefMinusPred = new int[weightCurrent.Length];
WeightObservationUnit_II.Update(weights);
for (int i = 0; i < TrainingGraphs.Count; i++)
{
var graph = TrainingGraphs[i];
//set scores according to weights
SetWeightsCRF(weights, graph);
//compute labeling with viterbi algorithm
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, Labels, BufferSizeInference);
request.RequestInDefaultContext();
int[] labeling = request.Solution.Labeling;
//check nonequality
iteration++;
for (int k = 0; k < labeling.Length; k++)
{
if (graph.Data.ReferenceLabeling[k] > 0)
{
if (labeling[k] > 0)
{
tp += 1;
}
else
{
fn += 1;
}
}
else
{
if (labeling[k] > 0)
{
fp += 1;
}
else
{
tn += 1;
}
}
}
int[] countsPred = CountPred(graph, labeling);
int[] countsRef = CountPred(graph, graph.Data.ReferenceLabeling);
for (int k = 0; k < countsPred.Length; k++)
{
countsRefMinusPred[k] += countsRef[k] - countsPred[k];
}
}
var mcc = (tp * tn + fp * fn) / Math.Sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
mccMax = Math.Max(mccMax, mcc);
return(weights);
}