public virtual int classify(int k, int[] unknown, double distanceThreshold)
{
FeatureVectorAsObject key = new FeatureVectorAsObject(unknown, FEATURES);
int?catI = null;
classifyCache.TryGetValue(key, out catI);
nClassifyCalls++;
if (catI != null)
{
nClassifyCacheHits++;
return(catI.Value);
}
Neighbor[] kNN = this.kNN(unknown, k, distanceThreshold);
IDictionary <int, MutableDouble> similarities = getCategoryToSimilarityMap(kNN, k, Y);
int cat = getCategoryWithMaxValue(similarities);
if (cat == -1)
{
// try with less strict match threshold to get some indication of alignment
kNN = this.kNN(unknown, k, org.antlr.codebuff.Trainer.MAX_CONTEXT_DIFF_THRESHOLD2);
similarities = getCategoryToSimilarityMap(kNN, k, Y);
cat = getCategoryWithMaxValue(similarities);
}
classifyCache[key] = cat;
return(cat);
}
public virtual string getPredictionAnalysis(InputDocument doc, int k, int[] unknown, IList <int> Y, double distanceThreshold)
{
FeatureVectorAsObject key = new FeatureVectorAsObject(unknown, FEATURES);
Neighbor[] kNN = null;
neighborCache.TryGetValue(key, out kNN);
nNNCalls++;
if (kNN == null)
{
kNN = this.kNN(unknown, k, distanceThreshold);
neighborCache[key] = kNN;
}
else
{
nNNCacheHits++;
}
IDictionary <int, MutableDouble> similarities = getCategoryToSimilarityMap(kNN, k, Y);
int cat = getCategoryWithMaxValue(similarities);
if (cat == -1)
{
// try with less strict match threshold to get some indication of alignment
kNN = this.kNN(unknown, k, org.antlr.codebuff.Trainer.MAX_CONTEXT_DIFF_THRESHOLD2);
similarities = getCategoryToSimilarityMap(kNN, k, Y);
cat = getCategoryWithMaxValue(similarities);
}
string displayCat;
int c = cat & 0xFF;
if (c == org.antlr.codebuff.Trainer.CAT_INJECT_NL || c == org.antlr.codebuff.Trainer.CAT_INJECT_WS)
{
displayCat = Formatter.getWSCategoryStr(cat);
}
else
{
displayCat = Formatter.getHPosCategoryStr(cat);
}
displayCat = !string.ReferenceEquals(displayCat, null) ? displayCat : "none";
StringBuilder buf = new StringBuilder();
buf.Append(Trainer.featureNameHeader(FEATURES));
buf.Append(Trainer._toString(FEATURES, doc, unknown) + "->" + similarities + " predicts " + displayCat);
buf.Append("\n");
if (kNN.Length > 0)
{
kNN = kNN.Take(Math.Min(k, kNN.Length)).ToArray();
foreach (Neighbor n in kNN)
{
buf.Append(n.ToString(FEATURES, Y));
buf.Append("\n");
}
}
return(buf.ToString());
}
