protected override double[] DoIteration(List <IGWGraph <NodeData, EdgeData, GraphData> > TrainingGraphs, double[] weightCurrent, int globalIteration)
{
var weights = weightCurrent.ToArray();
int u = TrainingGraphs.Count;
var vit = new int[u][];
var mcmc = new int[u][];
double devges = 0.0;
// Anzahl Knoten
double mx = 0;
var refLabel = new int[u][];
double devgesT = 0;
// Summe aller Knoten aller Graphen
double mu = 0;
int[] countsMCMCMinusRef = new int[weightCurrent.Length];
int[] countsRefMinusMCMC = new int[weightCurrent.Length];
Log.Post("#Iteration: " + globalIteration);
for (int g = 0; g < TrainingGraphs.Count; g++)
{
var graph = TrainingGraphs[g];
mx = graph.Nodes.Count();
mu += mx;
// Labeling mit Viterbi (MAP)
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, Labels, BufferSizeInference);
request.RequestInDefaultContext();
int[] labelingVit = request.Solution.Labeling;
vit[g] = labelingVit;
// Labeling mit MCMC basierend auf MAP
var requestMCMC = new GiveProbableLabelings(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>)
{
StartingPoints = 1, PreRunLength = 100000, RunLength = 1
};
requestMCMC.RequestInDefaultContext();
var result = requestMCMC.Result;
int[] labelingMCMC = new int[labelingVit.Length];
foreach (var item in result)
{
labelingMCMC[item.Key.GraphId] = (int)item.Value;
}
mcmc[g] = labelingMCMC;
// reales labeling
int[] labeling = graph.Data.ReferenceLabeling;
refLabel[g] = labeling;
// Berechnung des typischen/mittleren Fehlers
devges += LossFunctionIteration(refLabel[g], mcmc[g]);
devgesT += LossFunctionIteration(refLabel[g], vit[g]);
// set scores according to weights
SetWeightsCRF(weights, graph);
if (debugOutputEnabled)
{
printLabelings(vit[g], mcmc[g], refLabel[g], g);
}
// calculate equation 6.13 and 6.14
int[] countsRef = CountPred(graph, refLabel[g]);
int[] countsMCMC = CountPred(graph, mcmc[g]);
for (int k = 0; k < countsRef.Length; k++)
{
countsMCMCMinusRef[k] += countsMCMC[k] - countsRef[k];
countsRefMinusMCMC[k] += countsRef[k] - countsMCMC[k];
}
}
// mittlerer (typischer) Fehler (Summen-Gibbs-Score)
middev = devges / u;
// realer Fehler fuer diese Runde (Summen-Trainings-Score)
realdev = devgesT / u;
var loss = (realdev - middev) * mu;
// Scores berechnen?? Im Skript so, aber nicht notwendig
double l2norm = (countsRefMinusMCMC.Sum(entry => entry * entry));
var deltaomegaFactor = 0.0;
var deltaomega = new double[weights.Length];
var weightedScore = 0.0;
for (int k = 0; k < weights.Length; k++)
{
if (l2norm > 0)
{
weightedScore += weights[k] * countsMCMCMinusRef[k];
deltaomegaFactor = (loss + weightedScore) / l2norm;
deltaomega[k] = deltaomegaFactor * countsRefMinusMCMC[k];
}
else
{
weightedScore += weights[k] * countsRefMinusMCMC[k];
deltaomegaFactor = (loss + weightedScore) / l2norm;
deltaomega[k] = deltaomegaFactor * countsMCMCMinusRef[k];
}
weights[k] += deltaomega[k];
}
// debug output
Log.Post("Loss: " + (int)loss + " Realdev: " + realdev + " Middev: " + middev);
return(weights);
}
protected override double[] DoIteration(List <IGWGraph <NodeData, EdgeData, GraphData> > TrainingGraphs, double[] weightCurrent, int globalIteration)
{
var weights = weightCurrent.ToArray();
int u = TrainingGraphs.Count;
var vit = new int[u][];
var samplesMCMC = new int[NumberOfSamples][];
double devges = 0.0;
// Anzahl Knoten
double mx = 0;
var refLabel = new int[u][];
double devgesT = 0;
// Summe aller Knoten aller Graphen
double mu = 0;
MCMCDeviations = 0.0;
refMCMCDeviations = 0.0;
int[] countsRefMinusPred = new int[weightCurrent.Length];
Log.Post("#Iteration: " + globalIteration);
for (int g = 0; g < TrainingGraphs.Count; g++)
{
var graph = TrainingGraphs[g];
mx = graph.Nodes.Count();
mu += mx;
// Labeling mit Viterbi (MAP)
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, Labels, BufferSizeInference);
request.RequestInDefaultContext();
int[] labelingVit = request.Solution.Labeling;
vit[g] = labelingVit;
// Labeling mit MCMC basierend auf MAP
// TODO generate not 1 but k labelings for each graph
for (int i = 0; i < NumberOfSamples; i++)
{
var requestMCMC = new GiveProbableLabelings(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>)
{
StartingPoints = 1, PreRunLength = 100000, RunLength = 1
};
requestMCMC.RequestInDefaultContext();
var result = requestMCMC.Result;
int[] labelingMCMC = new int[labelingVit.Length];
foreach (var item in result)
{
labelingMCMC[item.Key.GraphId] = (int)item.Value;
}
samplesMCMC[i] = labelingMCMC;
}
// TODO function to sum the deviations in mcmc labelings
CalculateMCMCDeviations(samplesMCMC);
// reales labeling
int[] labeling = graph.Data.ReferenceLabeling;
refLabel[g] = labeling;
// TODO function to sum deviations from reflabel to MCMC labelings
CalculateRefMCMCDeviations(samplesMCMC, labeling);
// Berechnung des realen Fehlers
devgesT += LossFunctionIteration(refLabel[g], vit[g]);
// set scores according to weights
SetWeightsCRF(weights, graph);
int[] countsRef = CountPred(graph, refLabel[g]);
int[] countsPred = CountPred(graph, vit[g]);
for (int k = 0; k < countsRef.Length; k++)
{
countsRefMinusPred[k] += countsRef[k] - countsPred[k];
}
}
// mittlerer (typischer) Fehler (Summen-Gibbs-Score)
middev = devges / u;
// realer Fehler fuer diese Runde (Summen-Trainings-Score)
realdev = devgesT / u;
MCMCDeviations /= u;
refMCMCDeviations /= u;
var loss = realdev * mu;
// Scores berechnen?? Im Skript so, aber nicht notwendig
double l2norm = (countsRefMinusPred.Sum(entry => entry * entry));
var deltaomega = new double[weights.Length];
var weightedScore = 0.0;
for (int k = 0; k < weights.Length; k++)
{
weightedScore += weights[k] * countsRefMinusPred[k];
}
var deltaomegaFactor = (loss - weightedScore) / l2norm;
for (int k = 0; k < weights.Length; k++)
{
if (l2norm > 0)
{
deltaomega[k] = deltaomegaFactor * countsRefMinusPred[k];
}
else
{
Log.Post("wiu wiu");
deltaomega[k] = 0;
}
weights[k] += deltaomega[k];
}
// debug output
Log.Post("Loss: " + (int)loss + " refMCMCDeviations: " + refMCMCDeviations + " MCMCDeviations: " + MCMCDeviations);
return(weights);
}
