protected internal override double[] TrainWeights(int[][][][] data, int[][] labels, IEvaluator[] evaluators, int pruneFeatureItr, double[][][][] featureVals)
{
CRFLogConditionalObjectiveFloatFunction func = new CRFLogConditionalObjectiveFloatFunction(data, labels, windowSize, classIndex, labelIndices, map, flags.backgroundSymbol, flags.sigma);
cliquePotentialFunctionHelper = func;
QNMinimizer minimizer;
if (flags.interimOutputFreq != 0)
{
IFloatFunction monitor = new ResultStoringFloatMonitor(flags.interimOutputFreq, flags.serializeTo);
minimizer = new QNMinimizer(monitor);
}
else
{
minimizer = new QNMinimizer();
}
if (pruneFeatureItr == 0)
{
minimizer.SetM(flags.QNsize);
}
else
{
minimizer.SetM(flags.QNsize2);
}
float[] initialWeights;
if (flags.initialWeights == null)
{
initialWeights = func.Initial();
}
else
{
try
{
log.Info("Reading initial weights from file " + flags.initialWeights);
using (DataInputStream dis = new DataInputStream(new BufferedInputStream(new GZIPInputStream(new FileInputStream(flags.initialWeights)))))
{
initialWeights = ConvertByteArray.ReadFloatArr(dis);
}
}
catch (IOException)
{
throw new Exception("Could not read from float initial weight file " + flags.initialWeights);
}
}
log.Info("numWeights: " + initialWeights.Length);
float[] weightsArray = minimizer.Minimize(func, (float)flags.tolerance, initialWeights);
return(ArrayMath.FloatArrayToDoubleArray(weightsArray));
}