public static void TestDataset()
{
Dataset <string, string> data = new Dataset <string, string>();
data.Add(new BasicDatum <string, string>(Arrays.AsList(new string[] { "fever", "cough", "congestion" }), "cold"));
data.Add(new BasicDatum <string, string>(Arrays.AsList(new string[] { "fever", "cough", "nausea" }), "flu"));
data.Add(new BasicDatum <string, string>(Arrays.AsList(new string[] { "cough", "congestion" }), "cold"));
// data.summaryStatistics();
NUnit.Framework.Assert.AreEqual(4, data.NumFeatures());
NUnit.Framework.Assert.AreEqual(4, data.NumFeatureTypes());
NUnit.Framework.Assert.AreEqual(2, data.NumClasses());
NUnit.Framework.Assert.AreEqual(8, data.NumFeatureTokens());
NUnit.Framework.Assert.AreEqual(3, data.Size());
data.ApplyFeatureCountThreshold(2);
NUnit.Framework.Assert.AreEqual(3, data.NumFeatures());
NUnit.Framework.Assert.AreEqual(3, data.NumFeatureTypes());
NUnit.Framework.Assert.AreEqual(2, data.NumClasses());
NUnit.Framework.Assert.AreEqual(7, data.NumFeatureTokens());
NUnit.Framework.Assert.AreEqual(3, data.Size());
//Dataset data = Dataset.readSVMLightFormat(args[0]);
//double[] scores = data.getInformationGains();
//System.out.println(ArrayMath.mean(scores));
//System.out.println(ArrayMath.variance(scores));
LinearClassifierFactory <string, string> factory = new LinearClassifierFactory <string, string>();
LinearClassifier <string, string> classifier = factory.TrainClassifier(data);
IDatum <string, string> d = new BasicDatum <string, string>(Arrays.AsList(new string[] { "cough", "fever" }));
NUnit.Framework.Assert.AreEqual("Classification incorrect", "flu", classifier.ClassOf(d));
ICounter <string> probs = classifier.ProbabilityOf(d);
NUnit.Framework.Assert.AreEqual("Returned probability incorrect", 0.4553, probs.GetCount("cold"), 0.0001);
NUnit.Framework.Assert.AreEqual("Returned probability incorrect", 0.5447, probs.GetCount("flu"), 0.0001);
System.Console.Out.WriteLine();
}
private double[] GetModelProbs(IDatum <L, F> datum)
{
double[] condDist = new double[labeledDataset.NumClasses()];
ICounter <L> probCounter = classifier.ProbabilityOf(datum);
foreach (L label in probCounter.KeySet())
{
int labelID = labeledDataset.labelIndex.IndexOf(label);
condDist[labelID] = probCounter.GetCount(label);
}
return(condDist);
}
protected internal virtual string ClassOf(IDatum <string, string> datum, ExtractionObject rel)
{
ICounter <string> probs = classifier.ProbabilityOf(datum);
IList <Pair <string, double> > sortedProbs = Counters.ToDescendingMagnitudeSortedListWithCounts(probs);
double nrProb = probs.GetCount(RelationMention.Unrelated);
foreach (Pair <string, double> choice in sortedProbs)
{
if (choice.first.Equals(RelationMention.Unrelated))
{
return(choice.first);
}
if (nrProb >= choice.second)
{
return(RelationMention.Unrelated);
}
// no prediction, all probs have the same value
if (CompatibleLabel(choice.first, rel))
{
return(choice.first);
}
}
return(RelationMention.Unrelated);
}