private void FindStartPointViterbi(CRFGraph graph)
{
var request = new SolveInference(graph, NumberLabels);
request.RequestInDefaultContext();
var resultLabeling = request.Solution.Labeling;
foreach (var item in graph.Nodes)
{
item.Data.TempAssign = resultLabeling[item.GraphId];
}
}
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, fp = 0.001, fn = 0.001;
for (int i = 0; i < TrainingGraphs.Count; i++)
{
var graph = TrainingGraphs[i];
SetWeightsCRF(weights, graph);
//compute labeling with viterbi algorithm
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, null, 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;
}
}
}
var loss = LossFunctionIteration(labeling, graph.Data.ReferenceLabeling);
int[] countsPred = CountPred(graph, labeling);
int[] countsRef = CountPred(graph, graph.Data.ReferenceLabeling);
int[] countsRefMinusPred = new int[countsPred.Length];
for (int k = 0; k < countsPred.Length; k++)
{
countsRefMinusPred[k] = countsRef[k] - countsPred[k];
}
var weightedScore = 0.0;
for (int k = 0; k < weights.Length; k++)
{
weightedScore += weights[k] * countsRefMinusPred[k];
}
double l2normsq = (countsRefMinusPred.Sum(entry => entry * entry));
var deltaomegaFactor = (loss - weightedScore) / (l2normsq);
var deltaomega = new double[weights.Length];
for (int k = 0; k < weights.Length; k++)
{
if (l2normsq > 0)
{
deltaomega[k] = deltaomegaFactor * countsRefMinusPred[k];
}
weights[k] += deltaomega[k];
weightsSum[k] += weights[k];
}
Log.Post("loss: " + Math.Round(loss, 5));
Log.Post("weightedScore: " + Math.Round(weightedScore, 5));
Log.Post("l2normsquare: " + Math.Round(l2normsq, 5));
}
var mcc = (tp * tn + fp * fn) / Math.Sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
//mccMax = Math.Max(mccMax, mcc);
var weightChanges = new double[weights.Length];
for (int k = 0; k < weights.Length; k++)
{
weightChanges[k] = (weightsSum[k] / iteration) - weightCurrent[k];
}
weights = this.WeightObservationUnit.ApplyChangeVector(weightChanges, weightCurrent);
return(weights);
}
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];
lastWeights = weights.ToArray();
var totalChange = random.NextDouble();
//change weights
for (int i = 0; i < weights.Length; i++)
{
var localChange = random.NextDouble();
weights[i] += (random.NextDouble() * 0.2 - 0.1) * totalChange * localChange;
}
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));
if (mcc < mccMax)
{
weights = lastWeights;
Log.Post("Weight unchanged.");
}
else
{
Log.Post("Weight changed.");
}
mccMax = Math.Max(mccMax, mcc);
lastMCC = mcc;
return(weights);
}
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);
}
/*
* Die mit Herrn Waack besprochene Version des Projektzyklus zum Testen der verschiedenen Trainingsvarianten von OLM
*
*
*/
public void RunCycle(TrainingEvaluationCycleInputParameters inputParameters)
{
#region Schritt 0: Vorbereiten der Daten
// Zwischenspeichern von viel genutzten Variablen zur Übersichtlichkeit:
var inputGraph = inputParameters.Graph;
var graphList = new List <GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> >();
// Graphen erzeugen
for (int i = 0; i < inputParameters.NumberOfGraphInstances; i++)
{
var newGraph = inputGraph.Clone(nd => new CRFNodeData()
{
X = nd.Data.X, Y = nd.Data.Y, Z = nd.Data.Z
}, ed => new CRFEdgeData(), gd => new CRFGraphData());
graphList.Add(newGraph);
}
// Erzeugung der benötigten Objekte:
seedingMethodPatchCreation = new SeedingMethodPatchCreation(inputParameters.NumberOfSeedsForPatchCreation, inputParameters.MaximumTotalPatchSize);
#endregion
#region Schritt 1: Referenzlabelings erzeugen.
int[][] referenceLabelings = new int[inputParameters.NumberOfGraphInstances][];
for (int i = 0; i < inputParameters.NumberOfGraphInstances; i++)
{
seedingMethodPatchCreation.CreatePatchAndSetAsReferenceLabel(graphList[i]);
if (i == 0 && GraphVisalization == true)
{
var graph3D = graphList[i].Wrap3D(nd => new Node3DWrap <CRFNodeData>(nd.Data)
{
ReferenceLabel = nd.Data.ReferenceLabel, X = nd.Data.X, Y = nd.Data.Y, Z = nd.Data.Z
}, (ed) => new Edge3DWrap <CRFEdgeData>(ed.Data)
{
Weight = 1.0
});
new ShowGraph3D(graph3D).Request();
}
}
#endregion
#region Schritt 2: Beobachtungen erzeugen (und Scores)
var createObservationsUnit = new CreateObservationsUnit(inputParameters.TransitionProbabilities);
var isingModel = new IsingModel(inputParameters.IsingConformityParameter, inputParameters.IsingCorrelationParameter);
for (int i = 0; i < inputParameters.NumberOfGraphInstances; i++)
{
var graph = graphList[i];
createObservationsUnit.CreateObservation(graph);
//graph.Data.Observations = observation;
// zugehörige Scores erzeugen
isingModel.CreateCRFScore(graph);
if (i == 0)
{
var graph3D = graph.Wrap3D();
new ShowGraph3D(graph3D).Request();
}
}
#endregion
#region Schritt 3: Aufteilen der Daten in Evaluation und Training
// Verhaeltnis: 50 50
int separation = inputParameters.NumberOfGraphInstances / 2;
var testGraphs = new List <IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData> >
(new IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData> [separation]);
var evaluationGraphs = new List <GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> >
(new GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> [inputParameters.NumberOfGraphInstances - separation]);
for (int i = 0; i < separation; i++)
{
testGraphs[i] = graphList[i];
}
int k = 0;
for (int j = separation; j < inputParameters.NumberOfGraphInstances; j++)
{
evaluationGraphs[k++] = graphList[j];
}
#endregion
#region Schritt 4: Die verschiedenen Varianten von OLM trainieren und evaluieren
// object for evaluation
var evaluationResults = new Dictionary <OLMVariant, OLMEvaluationResult>();
foreach (var trainingVariant in inputParameters.TrainingVariantsToTest)
{
evaluationResults.Add(trainingVariant, new OLMEvaluationResult());
#region Schritt 4.1: Training der OLM-Variante
{
var request = new OLMRequest(trainingVariant, testGraphs);
request.BasisMerkmale.AddRange(new IsingMerkmalNode(), new IsingMerkmalEdge());
//TODO: loss function auslagern
request.LossFunctionValidation = (a, b) =>
{
var loss = 0.0;
for (int i = 0; i < a.Length; i++)
{
loss += a[i] != b[i] ? 1 : 0;
}
return(loss / a.Length);
};
request.Request();
var olmResult = request.Result;
// update Ising parameters in IsingModel
isingModel.ConformityParameter = olmResult.ResultingWeights[0];
isingModel.CorrelationParameter = olmResult.ResultingWeights[1];
// zugehörige Scores erzeugen für jeden Graphen (auch Evaluation)
foreach (var graph in graphList)
{
isingModel.CreateCRFScore(graph);
}
}
#endregion
#region Schritt 4.2: Evaluation der OLM-Variante
var keys = new ComputeKeys();
var results = new OLMEvaluationResult();
results.ConformityParameter = isingModel.ConformityParameter;
results.CorrelationParameter = isingModel.CorrelationParameter;
// 1) Viterbi-Heuristik starten (request: SolveInference) + zusätzliche Parameter hinzufügen
for (int graph = 0; graph < evaluationGraphs.Count; graph++)
{
var request2 = new SolveInference(evaluationGraphs[graph], inputParameters.NumberOfLabels,
inputParameters.BufferSizeViterbi);
request2.RequestInDefaultContext();
// 2) Ergebnis des request auswerten (request.Solution liefert ein Labeling)
int[] predictionLabeling = request2.Solution.Labeling;
// 3) Ergebnisse aller Evaluationsgraphen auswerten (TP, TN, FP, FN, MCC) und zwischenspeichern
// neues Objekt, damit in Schritt 5 darauf zugegriffen werden kann.
var result = keys.computeEvalutionGraphResult(evaluationGraphs[graph], predictionLabeling);
// einfügen in Dictionary -> Liste
evaluationResults[trainingVariant].GraphResults.Add(result);
}
// Berechnen der Average-Werte
foreach (OLMVariant variant in evaluationResults.Keys)
{
results.ComputeValues(evaluationResults[trainingVariant]);
}
// debug output
Log.Post("Average Values");
Log.Post("Sensitivity: " + evaluationResults[trainingVariant].AverageSensitivity +
"\t Specificy: " + evaluationResults[trainingVariant].AverageSpecificity +
"\t MCC: " + evaluationResults[trainingVariant].AverageMCC +
//"\t Accuracy: " + evaluationResults[trainingVariant].AverageAccuracy +
"\t TotalTP: " + evaluationResults[trainingVariant].TotalTP + "\n");
#endregion
}
#endregion
#region Schritt 5: Ergebnisse präsentieren und speichern
// output of the keys
//outputKeys(evaluation, inputParameters, evaluationGraphs);
// output of the labels
//outputLabelingsScores(graphList, inputParameters);
// TODO: Marlon
// graphische Ausgabe
var olmPresentationRequest = new ShowOLMResult(evaluationResults.Values.ToList());
//foreach (var variant in evaluationResults.Keys)
//{
// //foreach (var graphresult in evaluationResults[variant].GraphResults)
// //{
// // //var graph = graphresult.Graph;
// //}
//}
olmPresentationRequest.Request();
#endregion
}
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;
if (AddRdmNode && globalIteration % 20 == 0)
{
for (int i = 0; i < weights.Length; i++)
{
weights[i] += 0.0 + 0.2 * random.NextDouble() - 0.1;
}
}
//double tp = 0.001, tn = CoreResidues, fp = 0.001, fn = 0.001;
tp = 0; tn = 0; fp = 0; fn = 0;
var newPoint = new MemoryPoint(weights, new int[weights.Length], 0.0);
MemoryPoints.Add(newPoint);
while (MemoryPoints.Count > MemoryPointsCount)
{
MemoryPoints.RemoveAt(0);
}
ReferencePoint = new MemoryPoint(weights, new int[weights.Length], 1.0);
for (int i = 0; i < TrainingGraphs.Count; i++)
{
var graph = TrainingGraphs[i];
SetWeightsCRF(weights, graph);
//compute labeling with viterbi algorithm
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, null, Labels, BufferSizeInference);
request.RequestInDefaultContext();
int[] labeling = request.Solution.Labeling;
//check nonequality
iteration++;
int[] countsPred = CountPred(graph, labeling);
int[] countsRef = CountPred(graph, graph.Data.ReferenceLabeling);
for (int k = 0; k < countsPred.Length; k++)
{
newPoint.Counts[k] += countsPred[k];
ReferencePoint.Counts[k] += countsRef[k];
}
TrackResults(labeling, graph.Data.ReferenceLabeling);
}
//WriterResults();
//var sensitivity = tp / (tp + fn);
//var specificity = tn / (tn + fp);
var mcc = (tp * tn - fp * fn) / Math.Sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
newPoint.Score = mcc;
//Log.Post("New MCC: " + Math.Round(mcc, 5) + " Sens: " + Math.Round(sensitivity, 5) + " Spec: " + Math.Round(specificity, 5));
//tn -= CoreResidues;
//var sensitivity2 = tp / (tp + fn);
//var specificity2 = tn / (tn + fp);
//var mcc2 = (tp * tn - fp * fn) / Math.Sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
//Log.Post("New MCC(2): " + Math.Round(mcc2, 5) + " Sens: " + Math.Round(sensitivity2, 5) + " Spec: " + Math.Round(specificity2, 5));
if (globalIteration == 1)
{
MemoryPoints.Add(ReferencePoint);
}
var deltaomega = new double[weights.Length];
for (int k = 0; k < MemoryPoints.Count - 1; k++)
{
var pointOne = MemoryPoints[k];
for (int l = k + 1; l < MemoryPoints.Count; l++)
{
var pointTwo = MemoryPoints[l];
int[] countsRefMinusPred = new int[weights.Length];
for (int m = 0; m < weights.Length; m++)
{
countsRefMinusPred[m] = (pointOne.Counts[m] - pointTwo.Counts[m]);
}
var weightedScore = 0.0;
for (int m = 0; m < weights.Length; m++)
{
weightedScore += weights[m] * (countsRefMinusPred[m]);
}
double l2normsq = (countsRefMinusPred.Sum(entry => entry * entry));
var loss = 100 * (pointOne.Score - pointTwo.Score);
var deltaomegaFactor = (loss - weightedScore) / (l2normsq);
for (int m = 0; m < weights.Length; m++)
{
if (l2normsq > 0)
{
deltaomega[m] += deltaomegaFactor * countsRefMinusPred[m];
}
}
}
}
//if (MemoryPoints.Count >= memoryPoints)
{
//normalize
var normFactor = MemoryPoints.Count * (MemoryPoints.Count - 1) / 2;
for (int m = 0; m < weights.Length; m++)
{
deltaomega[m] /= normFactor;
}
for (int k = 0; k < weights.Length; k++)
{
weights[k] += deltaomega[k];
}
}
//else
//{
// for (int k = 0; k < weights.Length; k++)
// {
// weights[k] = 0.02 * random.NextDouble() - 0.01;
// }
//}
//if (iteration == 1)
//MemoryPoints.Remove(ReferencePoint);
return(weights);
}
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, fp = 0.001, fn = 0.001;
int[] countsRefMinusPred = new int[weightCurrent.Length];
Log.Post("#Iteration: " + globalIteration);
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 weightedScore = 0.0;
for (int k = 0; k < weights.Length; k++)
{
weightedScore += weights[k] * countsRefMinusPred[k];
}
double l2normsq = (countsRefMinusPred.Sum(entry => entry * entry));
var loss = (fp + fn);
var deltaomegaFactor = (loss - weightedScore) / l2normsq;
var deltaomega = new double[weights.Length];
for (int k = 0; k < weights.Length; k++)
{
if (l2normsq > 0)
{
deltaomega[k] = deltaomegaFactor * countsRefMinusPred[k];
}
weights[k] += deltaomega[k];
weightsSum[k] = weights[k] + deltaomega[k];
}
return(weights);
}
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 mcc = 0.0, mcc2 = 0.0;
var newPoint = new Point(weights, new int[weights.Length], 0.0);
{
double tp = 0.001, tn = CoreResiduesTraining, fp = 0.001, fn = 0.001;
MemoryPoints.Add(newPoint);
if (MemoryPoints.Count > memoryPoints)
{
MemoryPoints.RemoveAt(0);
}
ReferencePoint = new Point(weights, new int[weights.Length], 1.0);
for (int i = 0; i < TrainingGraphs.Count; i++)
{
var graph = TrainingGraphs[i];
//set scores according to weights
#region set weights
foreach (var node in graph.Nodes)
{
var scores = new double[Labels];
node.Data.Scores = scores;
for (int label = 0; label < Labels; label++)
{
for (int k = 0; k < weights.Length; k++)
{
scores[label] += weights[k] * BasisMerkmale[k].Score(node, label);
}
}
}
foreach (var edge in graph.Edges)
{
var scores = new double[Labels, Labels];
edge.Data.Scores = scores;
for (int label1 = 0; label1 < Labels; label1++)
{
for (int label2 = 0; label2 < Labels; label2++)
{
for (int k = 0; k < weights.Length; k++)
{
scores[label1, label2] += weights[k] * BasisMerkmale[k].Score(edge, label1, label2);
}
}
}
}
#endregion
//compute labeling with viterbi algorithm
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, null, 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++)
{
newPoint.Counts[k] += countsPred[k];
ReferencePoint.Counts[k] += countsRef[k];
}
//TrackResults(labeling, graph.Data.ReferenceLabeling);
}
//WriterResults();
var sensitivity = tp / (tp + fn);
var specificity = tn / (tn + fp);
mcc = (tp * tn - fp * fn) / Math.Sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
newPoint.Score = mcc;
Log.Post("New MCC: " + Math.Round(mcc, 5) + " Sens: " + Math.Round(sensitivity, 5) + " Spec: " + Math.Round(specificity, 5));
}
//change Interval borders
{
var randomChanges = DivisorChange.RandomInstances(Intervals - 1);
foreach (var change in randomChanges)
{
var changesFor = random.Next(5);
if (changesFor == 0)
{
ValueMapRasa.ApplyChange(change);
}
else if (changesFor == 1)
{
ValueMapEMax01.ApplyChange(change);
}
else if (changesFor == 2)
{
ValueMapEMax11.ApplyChange(change);
}
else if (changesFor == 3)
{
ValueMapEDiff01.ApplyChange(change);
}
else if (changesFor == 4)
{
ValueMapEDiff11.ApplyChange(change);
}
}
double tp = 0.001, tn = CoreResiduesTraining, fp = 0.001, fn = 0.001;
//var newPoint = new Point(weights, new int[weights.Length], 0.0);
//MemoryPoints.Add(newPoint);
//if (MemoryPoints.Count > memoryPoints)
// MemoryPoints.RemoveAt(0);
//ReferencePoint = new Point(weights, new int[weights.Length], 1.0);
for (int i = 0; i < TrainingGraphs.Count; i++)
{
var graph = TrainingGraphs[i];
SetWeightsCRF(weights, graph);
//compute labeling with viterbi algorithm
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, null, Labels, BufferSizeInference);
request.RequestInDefaultContext();
int[] labeling = request.Solution.Labeling;
//check nonequality
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++)
//{
// newPoint.Counts[k] += countsPred[k];
// //ReferencePoint.Counts[k] += countsRef[k];
//}
//TrackResults(labeling, graph.Data.ReferenceLabeling);
}
//WriterResults();
var sensitivity = tp / (tp + fn);
var specificity = tn / (tn + fp);
mcc2 = (tp * tn - fp * fn) / Math.Sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
//newPoint.Score = mcc2;
Log.Post("New MCC(IChanged): " + Math.Round(mcc, 5) + " Sens: " + Math.Round(sensitivity, 5) + " Spec: " + Math.Round(specificity, 5));
}
if (mcc2 > mcc)
{
ValueMapRasa.lastChanges.Clear();
ValueMapEMax01.lastChanges.Clear();
ValueMapEMax11.lastChanges.Clear();
ValueMapEDiff01.lastChanges.Clear();
ValueMapEDiff11.lastChanges.Clear();
Log.Post("Intervals changed");
MemoryPoints.RemoveAt(MemoryPoints.Count - 1);
}
else
{
ValueMapRasa.UndoLastChanges();
ValueMapEMax01.UndoLastChanges();
ValueMapEMax11.UndoLastChanges();
ValueMapEDiff01.UndoLastChanges();
ValueMapEDiff11.UndoLastChanges();
if (MemoryPoints.Count == 1)
{
MemoryPoints.Add(ReferencePoint);
}
var deltaomega = new double[weights.Length];
for (int k = 0; k < MemoryPoints.Count - 1; k++)
{
var pointOne = MemoryPoints[k];
for (int l = k + 1; l < MemoryPoints.Count; l++)
{
var pointTwo = MemoryPoints[l];
int[] countsRefMinusPred = new int[weights.Length];
for (int m = 0; m < weights.Length; m++)
{
countsRefMinusPred[m] = (pointOne.Counts[m] - pointTwo.Counts[m]);
}
var weightedScore = 0.0;
for (int m = 0; m < weights.Length; m++)
{
weightedScore += weights[m] * (countsRefMinusPred[m]);
}
double l2normsq = (countsRefMinusPred.Sum(entry => entry * entry));
var loss = 100 * (pointOne.Score - pointTwo.Score);
var deltaomegaFactor = (loss - weightedScore) / (l2normsq);
for (int m = 0; m < weights.Length; m++)
{
if (l2normsq > 0)
{
deltaomega[m] += deltaomegaFactor * countsRefMinusPred[m];
}
}
}
}
//if (MemoryPoints.Count >= memoryPoints)
{
//normalize
var normFactor = MemoryPoints.Count * (MemoryPoints.Count - 1) / 2;
for (int m = 0; m < weights.Length; m++)
{
deltaomega[m] /= normFactor;
}
for (int k = 0; k < weights.Length; k++)
{
weights[k] += deltaomega[k];
}
}
//else
//{
// for (int k = 0; k < weights.Length; k++)
// {
// weights[k] = 0.02 * random.NextDouble() - 0.01;
// }
//}
//if (iteration == 1)
//MemoryPoints.Remove(ReferencePoint);
}
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 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();
var weightsSum = new double[weightCurrent.Length];
int iteration = 0;
double tp = 0.001, tn = 0.001, fp = 0.001, fn = 0.001;
int[] countsRefMinusPred = new int[weightCurrent.Length];
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
//BufferSizeInference = 2000;
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, null, 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;
}
}
}
//var loss = LossFunctionIteration(labeling, graph.Data.ReferenceLabeling);
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 weightedScore = 0.0;
for (int k = 0; k < weights.Length; k++)
{
weightedScore += weights[k] * countsRefMinusPred[k];
}
double l2normsq = (countsRefMinusPred.Sum(entry => entry * entry));
//var loss = (mccMax - mcc) * lossFunctionFactorMCC;
var loss = (fp + fn);
var deltaomegaFactor = (loss - weightedScore) / l2normsq;
var deltaomega = new double[weights.Length];
for (int k = 0; k < weights.Length; k++)
{
if (l2normsq > 0)
{
deltaomega[k] = deltaomegaFactor * countsRefMinusPred[k];
}
weights[k] += deltaomega[k];
//weights[k] = 2 * (random.NextDouble() - 0.5);
weightsSum[k] = weights[k] + deltaomega[k];
}
//var weightChanges = new double[weights.Length];
//for (int k = 0; k < weights.Length; k++)
//{
// weightChanges[k] = ((weights[k]) - weightCurrent[k]);
//}
//weights = this.WeightObservationUnit.ApplyChangeVector(weightChanges, weightCurrent);
return(weights);
}
public void Do(int weights, IEnumerable <IGWGraph <NodeData, EdgeData, GraphData> > graphs, int maxIterations, OLMRequest olmrequest)
{
Weights = weights;
int validationQuantils = 2;
double quantilratio = 1.0 / validationQuantils;
var quantiledGraphs = new List <IGWGraph <NodeData, EdgeData, GraphData> > [validationQuantils];
for (int i = 0; i < validationQuantils; i++)
{
quantiledGraphs[i] = new List <IGWGraph <NodeData, EdgeData, GraphData> >();
}
//divide graphs in training / validation
foreach (var graph in graphs)
{
var quantil = random.Next(validationQuantils);
quantiledGraphs[quantil].Add(graph);
}
for (int quantilIteration = 0; quantilIteration < validationQuantils; quantilIteration++)
{
TrainingGraphs = new List <IGWGraph <NodeData, EdgeData, GraphData> >();
ValidationGraphs = new List <IGWGraph <NodeData, EdgeData, GraphData> >();
//CoreResidues = 0;
for (int quantil = 0; quantil < validationQuantils; quantil++)
{
if (quantil == quantilIteration)
{
ValidationGraphs.AddRange(quantiledGraphs[quantil]);
}
else
{
TrainingGraphs.AddRange(quantiledGraphs[quantil]);
}
}
//foreach (var graph in ValidationGraphs)
//{
// CoreResidues += graph.Data.CoreResidues;
//}
Iteration = 0;
MaxIterations = maxIterations;
SetStartingWeights();
this.WeightObservationUnit.Init(weightCurrent);
var lossOpt = double.MaxValue;
var lossOptOld = 0.0;
var lossCurrent = 0.0;
OLMTracker = new OLMTracking(weights, new int[] { 1, 3, 5, 8, 12, 20, 50 }, weightCurrent, Name + "_q" + quantilIteration + "_OLMTracking.txt");
var interfaceValid = 0;
var noninterfaceValid = 0;
foreach (var graph in ValidationGraphs)
{
interfaceValid += graph.Data.ReferenceLabeling.Sum();
noninterfaceValid += graph.Nodes.Count() - graph.Data.ReferenceLabeling.Sum();
}
var sitesValid = interfaceValid + noninterfaceValid;
while (!CheckCancelCriteria())
{
Iteration++;
var oldWVector = weightCurrent.ToArray();
weightCurrent = DoIteration(TrainingGraphs, weightCurrent, Iteration);
ResultingWeights = weightCurrent;
//for (int i = 1; i < 20; i++)
//{
// for (int k = 1; k < 20; k++)
// {
// weightCurrent[0] = Math.Pow(-1.0, i) * ((int)(i / 2)) * 0.1;
// weightCurrent[1] = Math.Pow(-1.0, k) * ((int)(k / 2)) * 0.1;
tp = 0; tn = 0; fp = 0; fn = 0;
lossCurrent = 0.0;
foreach (var graph in ValidationGraphs)
{
SetWeightsCRF(weightCurrent, graph);
var request = new SolveInference(graph as IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData>, null, Labels, BufferSizeInference);
request.RequestInDefaultContext();
var prediction = request.Solution.Labeling;
lossCurrent += LossFunctionValidation(graph.Data.ReferenceLabeling, prediction);
TrackResults(graph.Data.ReferenceLabeling, prediction);
}
WriterResults();
lossCurrent /= sitesValid;
if (lossCurrent < lossOpt)
{
lossOptOld = lossOpt;
lossOpt = lossCurrent;
weightOpt = weightCurrent;
}
OLMTracker.Track(weightCurrent, lossCurrent);
var iterationResult = new OLMIterationResult(weightCurrent.ToArray(), lossCurrent);
olmrequest.Result.ResultsHistory.IterationResultHistory.Add(iterationResult);
// }
//}
}
}
OLMTracker.WriteWeights();
//return weightOpt;
}
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);
}
/*
* Die mit Herrn Waack besprochene Version des Projektzyklus zum Testen der verschiedenen Trainingsvarianten von OLM
*
*
*/
public void RunCycle(TrainingEvaluationCycleInputParameters inputParameters)
{
#region Schritt 1: Vorbereiten der Daten
var graphList = inputParameters.Graphs;
int numberOfLabels = inputParameters.NumberOfLabels;
int numberOfIntervals = inputParameters.NumberOfIntervals;
#endregion
#region Schritt 2: Beobachtungen erzeugen (und Scores)
// var createObservationsUnit = new CreateObservationsUnit(inputParameters.Threshold);
var createObservationsUnit = new CreateObservationsUnit(inputParameters.TransitionProbabilities);
if (UseIsingModel)
{
Log.Post("Ising-Model");
}
else
{
Log.Post("Potts-Model with " + inputParameters.NumberOfIntervals + " Intervals");
}
var isingModel = new IsingModel(inputParameters.IsingConformityParameter, inputParameters.IsingCorrelationParameter);
//var pottsModel = new PottsModel(inputParameters.PottsConformityParameters, inputParameters.IsingCorrelationParameter,
// inputParameters.AmplifierControlParameter, inputParameters.NumberOfLabels);
var pottsModel = new PottsModelComplex(inputParameters.PottsConformityParameters, inputParameters.PottsCorrelationParameters,
inputParameters.AmplifierControlParameter, inputParameters.NumberOfLabels);
for (int i = 0; i < inputParameters.NumberOfGraphInstances; i++)
{
var graph = graphList[i];
createObservationsUnit.CreateObservation(graph);
//createObservationsUnit.CreateObservationThresholding(graph);
// zugehörige Scores erzeugen
if (UseIsingModel)
{
isingModel.CreateCRFScore(graph);
}
else
{
pottsModel.InitCRFScore(graph);
}
if (i == 0 && GraphVisualization == true)
{
var graph3D = graph.Wrap3D();
new ShowGraph3D(graph3D).Request();
}
}
#endregion
#region Schritt 3: Aufteilen der Daten in Evaluation und Training
// Verhaeltnis: 80 20
int separation = inputParameters.NumberOfGraphInstances - inputParameters.NumberOfGraphInstances / 5;
// Verhältnis Leave-one-out
//int separation = inputParameters.NumberOfGraphInstances - 1;
var trainingGraphs = new List <IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData> >
(new IGWGraph <ICRFNodeData, ICRFEdgeData, ICRFGraphData> [separation]);
var evaluationGraphs = new List <GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> >
(new GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> [inputParameters.NumberOfGraphInstances - separation]);
var randomizedGraphList = graphList.RandomizeOrder().ToList();
for (int i = 0; i < separation; i++)
{
trainingGraphs[i] = randomizedGraphList[i];
//trainingGraphs[i] = graphList[i];
}
int k = 0;
for (int j = separation; j < inputParameters.NumberOfGraphInstances; j++, k++)
{
evaluationGraphs[k] = randomizedGraphList[j];
//evaluationGraphs[i] = graphList[i];
}
Log.Post("Evaluation Graph ID: " + evaluationGraphs[0].Id);
#endregion
#region Schritt 4: Die verschiedenen Varianten von OLM trainieren und evaluieren
// object for evaluation
var evaluationResults = new Dictionary <OLMVariant, OLMEvaluationResult>();
foreach (var trainingVariant in inputParameters.TrainingVariantsToTest)
{
evaluationResults.Add(trainingVariant, new OLMEvaluationResult());
#region Schritt 4.1: Training der OLM-Variante
{
var request = new OLMRequest(trainingVariant, trainingGraphs);
if (UseIsingModel)
{
request.BasisMerkmale.AddRange(new IsingMerkmalNode(), new IsingMerkmalEdge());
}
else
{
request.BasisMerkmale.AddRange(pottsModel.AddNodeFeatures(graphList, numberOfIntervals));
//request.BasisMerkmale.Add(new IsingMerkmalEdge());
request.BasisMerkmale.AddRange(pottsModel.AddEdgeFeatures(graphList, numberOfIntervals));
}
// loss function
request.LossFunctionIteration = OLM.OLM.LossRatio;
request.LossFunctionValidation = OLM.OLM.LossRatio;
// execute training methods by calling OLMManager -> OLMBase
request.Request();
var olmResult = request.Result;
// update parameters in PottsModel
if (UseIsingModel)
{
isingModel.ConformityParameter = olmResult.ResultingWeights[0];
isingModel.CorrelationParameter = olmResult.ResultingWeights[1];
}
else
{
int i = 0;
for (i = 0; i < pottsModel.ConformityParameter.Length; i++)
{
pottsModel.ConformityParameter[i] = olmResult.ResultingWeights[i];
}
//pottsModel.CorrelationParameter = olmResult.ResultingWeights[numberOfIntervals * 2];
for (int j = 0; j < pottsModel.CorrelationParameter.Length; j++)
{
pottsModel.CorrelationParameter[j] = olmResult.ResultingWeights[i++];
}
}
// zugehörige Scores erzeugen für jeden Graphen (auch Evaluation)
foreach (var graph in graphList)
{
if (UseIsingModel)
{
isingModel.CreateCRFScore(graph);
}
else
{
pottsModel.CreateCRFScore(graph, request.BasisMerkmale);
}
}
}
#endregion
#region Schritt 4.2: Evaluation der OLM-Variante
var keys = new ComputeKeys();
var results = new OLMEvaluationResult();
if (UseIsingModel)
{
results = new OLMEvaluationResult
{
ConformityParameter = isingModel.ConformityParameter,
CorrelationParameter = isingModel.CorrelationParameter
};
}
else
{
results = new OLMEvaluationResult
{
ConformityParameters = pottsModel.ConformityParameter,
// CorrelationParameter = pottsModel.CorrelationParameter
CorrelationParameters = pottsModel.CorrelationParameter
};
}
if (UseIsingModel)
{
Log.Post("Conformity: " + results.ConformityParameter + "\t Correlation: " + results.CorrelationParameter);
}
else
{
for (int i = 0; i < results.ConformityParameters.Length; i++)
{
Log.Post("Conformity " + i + ": " + results.ConformityParameters[i] + "\t");
}
Log.Post("Correlation: " + results.CorrelationParameter);
}
// 1) Viterbi-Heuristik starten (request: SolveInference) + zusätzliche Parameter hinzufügen
for (int graph = 0; graph < evaluationGraphs.Count; graph++)
{
var request2 = new SolveInference(evaluationGraphs[graph], inputParameters.NumberOfLabels,
inputParameters.BufferSizeViterbi);
request2.RequestInDefaultContext();
// 2) Ergebnis des request auswerten (request.Solution liefert ein Labeling)
int[] predictionLabeling = request2.Solution.Labeling;
// 3) Ergebnisse aller Evaluationsgraphen auswerten (TP, TN, FP, FN, MCC) und zwischenspeichern
// neues Objekt, damit in Schritt 5 darauf zugegriffen werden kann.
var result = keys.computeEvalutionGraphResult(evaluationGraphs[graph], predictionLabeling);
// einfügen in Dictionary -> Liste
evaluationResults[trainingVariant].GraphResults.Add(result);
}
// Berechnen der Average-Werte
foreach (OLMVariant variant in evaluationResults.Keys)
{
results.ComputeValues(evaluationResults[trainingVariant]);
}
// debug output
Log.Post("Average Values");
Log.Post("Sensitivity: " + evaluationResults[trainingVariant].AverageSensitivity +
"\t Specificy: " + evaluationResults[trainingVariant].AverageSpecificity +
"\t MCC: " + evaluationResults[trainingVariant].AverageMCC +
//"\t Accuracy: " + evaluationResults[trainingVariant].AverageAccuracy +
"\t TotalTP: " + evaluationResults[trainingVariant].TotalTP +
"\t TotalFP: " + evaluationResults[trainingVariant].TotalFP +
"\t TotalTN: " + evaluationResults[trainingVariant].TotalTN +
"\t TotalFN: " + evaluationResults[trainingVariant].TotalFN);
#endregion
}
#endregion
}
