//
// PROB OF TAG AT SINGLE POSITION CONDITIONED ON PREVIOUS AND FOLLOWING
// SEQUENCE OF LABELS
//
// public double condProbGivenPreviousAndNext(int position, int label, int[]
// prevLabels, int[] nextLabels) {
// }
//
// JOINT CONDITIONAL PROBS
//
/// <returns>a new CRFCliqueTree for the weights on the data</returns>
public static Edu.Stanford.Nlp.IE.Crf.CRFCliqueTree <E> GetCalibratedCliqueTree <E>(int[][][] data, IList <IIndex <CRFLabel> > labelIndices, int numClasses, IIndex <E> classIndex, E backgroundSymbol, ICliquePotentialFunction cliquePotentialFunc, double
[][][] featureVals)
{
FactorTable[] factorTables = new FactorTable[data.Length];
FactorTable[] messages = new FactorTable[data.Length - 1];
for (int i = 0; i < data.Length; i++)
{
double[][] featureValByCliqueSize = null;
if (featureVals != null)
{
featureValByCliqueSize = featureVals[i];
}
factorTables[i] = GetFactorTable(data[i], labelIndices, numClasses, cliquePotentialFunc, featureValByCliqueSize, i);
// log.info("before calibration,FT["+i+"] = " + factorTables[i].toProbString());
if (i > 0)
{
messages[i - 1] = factorTables[i - 1].SumOutFront();
// log.info("forward message, message["+(i-1)+"] = " + messages[i-1].toProbString());
factorTables[i].MultiplyInFront(messages[i - 1]);
}
}
// log.info("after forward calibration, FT["+i+"] = " + factorTables[i].toProbString());
for (int i_1 = factorTables.Length - 2; i_1 >= 0; i_1--)
{
FactorTable summedOut = factorTables[i_1 + 1].SumOutEnd();
summedOut.DivideBy(messages[i_1]);
// log.info("backward summedOut, summedOut= " + summedOut.toProbString());
factorTables[i_1].MultiplyInEnd(summedOut);
}
// log.info("after backward calibration, FT["+i+"] = " + factorTables[i].toProbString());
return(new Edu.Stanford.Nlp.IE.Crf.CRFCliqueTree <E>(factorTables, classIndex, backgroundSymbol));
}
// static FactorTable getFactorTable(double[][] weights, int[][] data, List<Index<CRFLabel>> labelIndices, int numClasses, int posInSent) {
// CliquePotentialFunction cliquePotentialFunc = new LinearCliquePotentialFunction(weights);
// return getFactorTable(data, labelIndices, numClasses, cliquePotentialFunc, null, posInSent);
// }
internal static FactorTable GetFactorTable(int[][] data, IList <IIndex <CRFLabel> > labelIndices, int numClasses, ICliquePotentialFunction cliquePotentialFunc, double[][] featureValByCliqueSize, int posInSent)
{
FactorTable factorTable = null;
for (int j = 0; j < sz; j++)
{
IIndex <CRFLabel> labelIndex = labelIndices[j];
FactorTable ft = new FactorTable(numClasses, j + 1);
double[] featureVal = null;
if (featureValByCliqueSize != null)
{
featureVal = featureValByCliqueSize[j];
}
// ... and each possible labeling for that clique
for (int k = 0; k < liSize; k++)
{
int[] label = labelIndex.Get(k).GetLabel();
double cliquePotential = cliquePotentialFunc.ComputeCliquePotential(j + 1, k, data[j], featureVal, posInSent);
// for (int m = 0; m < data[j].length; m++) {
// weight += weights[data[j][m]][k];
// }
// try{
ft.SetValue(label, cliquePotential);
}
// } catch (Exception e) {
// System.out.println("CRFCliqueTree::getFactorTable");
// System.out.println("NumClasses: " + numClasses + " j+1: " + (j+1));
// System.out.println("k: " + k+" label: " +label+" labelIndexSize: " +
// labelIndex.size());
// throw new RunTimeException(e.toString());
// }
if (j > 0)
{
ft.MultiplyInEnd(factorTable);
}
factorTable = ft;
}
return(factorTable);
}
public virtual void SetWeights(double[][] weights)
{
this.weights = weights;
cliquePotentialFunc = new LinearCliquePotentialFunction(weights);
}
