// end class class CRFBiasedClassifierOptimizer
/// <summary>Adjust the bias parameter to optimize some objective function.</summary>
/// <remarks>
/// Adjust the bias parameter to optimize some objective function.
/// Note that this function only tunes the bias parameter of one class
/// (class of index 0), and is thus only useful for binary classification
/// problems.
/// </remarks>
public virtual void AdjustBias(IList <IList <IN> > develData, IDoubleUnaryOperator evalFunction, double low, double high)
{
ILineSearcher ls = new GoldenSectionLineSearch(true, 1e-2, low, high);
CRFBiasedClassifier.CRFBiasedClassifierOptimizer optimizer = new CRFBiasedClassifier.CRFBiasedClassifierOptimizer(this, this, evalFunction);
double optVal = ls.Minimize(optimizer);
int bi = featureIndex.IndexOf(Bias);
log.Info("Class bias of " + weights[bi][0] + " reaches optimal value " + optVal);
}
private void TuneSigma(int[][] data, int[] labels)
{
IDoubleUnaryOperator CVSigmaToPerplexity = null;
//test if enough training data
//leave-one-out
//System.out.println("CV j: "+ j);
//System.out.println("test i: "+ i + " "+ new BasicDatum(featureIndex.objects(data[i])));
//System.err.printf("%d: %8g%n", j, score);
GoldenSectionLineSearch gsls = new GoldenSectionLineSearch(true);
sigma = gsls.Minimize(CVSigmaToPerplexity, 0.01, 0.0001, 2.0);
System.Console.Out.WriteLine("Sigma used: " + sigma);
}
