public SemiSupervisedLogConditionalObjectiveFunction(AbstractCachingDiffFunction objFunc, AbstractCachingDiffFunction biasedObjFunc, LogPrior prior, double convexComboFrac)
{
this.objFunc = objFunc;
this.biasedObjFunc = biasedObjFunc;
this.prior = prior;
this.convexComboFrac = convexComboFrac;
if (convexComboFrac < 0 || convexComboFrac > 1.0)
{
throw new Exception("convexComboFrac has to lie between 0 and 1 (both inclusive).");
}
}
private double[] TrainWeightsUsingNonLinearCRF(AbstractCachingDiffFunction func, IEvaluator[] evaluators)
{
IMinimizer <IDiffFunction> minimizer = GetMinimizer(0, evaluators);
double[] initialWeights;
if (flags.initialWeights == null)
{
initialWeights = func.Initial();
}
else
{
log.Info("Reading initial weights from file " + flags.initialWeights);
try
{
using (DataInputStream dis = new DataInputStream(new BufferedInputStream(new GZIPInputStream(new FileInputStream(flags.initialWeights)))))
{
initialWeights = ConvertByteArray.ReadDoubleArr(dis);
}
}
catch (IOException)
{
throw new Exception("Could not read from double initial weight file " + flags.initialWeights);
}
}
log.Info("numWeights: " + initialWeights.Length);
if (flags.testObjFunction)
{
StochasticDiffFunctionTester tester = new StochasticDiffFunctionTester(func);
if (tester.TestSumOfBatches(initialWeights, 1e-4))
{
log.Info("Testing complete... exiting");
System.Environment.Exit(1);
}
else
{
log.Info("Testing failed....exiting");
System.Environment.Exit(1);
}
}
//check gradient
if (flags.checkGradient)
{
if (func.GradientCheck())
{
log.Info("gradient check passed");
}
else
{
throw new Exception("gradient check failed");
}
}
return(minimizer.Minimize(func, flags.tolerance, initialWeights));
}
public SemiSupervisedLogConditionalObjectiveFunction(AbstractCachingDiffFunction objFunc, AbstractCachingDiffFunction biasedObjFunc, LogPrior prior)
: this(objFunc, biasedObjFunc, prior, 0.5)
{
}
