internal void GetBestTagSequence(IList <FeatureVector> words, out double[] prob_c_w, out int[] c_w)
{
c_w = new int[words.Count];
prob_c_w = new double[words.Count];
double[][] probs_v_c = new double[words.Count][];
for (int v_i = 0; v_i < words.Count; v_i++)
{
double[] details;
int sysClass = classifier.Classify(words[v_i], out details);
probs_v_c[v_i] = details;
string prevT_name = v_i - 1 < 0 ? "BOS" : classToClassId[c_w[v_i - 1]];
string prevT_featureName = string.Format("prevT={0}", prevT_name);
string prevTwoTags_name = v_i - 2 < 0 ? "BOS" : classToClassId[c_w[v_i - 2]];
string prevTwoTags_featureName = string.Format("prevTwoTags={0}+{1}", prevTwoTags_name, prevT_name);
var prevT_f = featureToFeatureId[prevT_featureName];
var prevTwoTags_f = featureToFeatureId[prevTwoTags_featureName];
for (int c_i = 0; c_i < classToClassId.Count; c_i++)
{
double logProb = Math.Log(probs_v_c[v_i][c_i], Math.E);
logProb += classifier.CalculateLogProb_c_f(c_i, prevT_f);
logProb += classifier.CalculateLogProb_c_f(c_i, prevTwoTags_f);
probs_v_c[v_i][c_i] = Math.Pow(Math.E, logProb);
}
NormalizationHelper.Normalize(probs_v_c[v_i]);
int bestClass = StatisticsHelper.ArgMax(probs_v_c[v_i]);
c_w[v_i] = bestClass;
prob_c_w[v_i] = probs_v_c[v_i][bestClass];
}
}
