/**
* <summary> The predict method takes an Instance as an input. First it gets the size of prior distribution and loops this size times.
* Then it gets the possible class labels and and calculates metric value. At the end, it returns the class which has the
* maximum value of metric.</summary>
*
* <param name="instance">{@link Instance} to predict.</param>
* <returns>The class which has the maximum value of metric.</returns>
*/
public override string Predict(Instance.Instance instance)
{
string predictedClass;
var maxMetric = double.MinValue;
int size;
if (instance is CompositeInstance compositeInstance)
{
predictedClass = compositeInstance.GetPossibleClassLabels()[0];
size = compositeInstance.GetPossibleClassLabels().Count;
}
else
{
predictedClass = priorDistribution.GetMaxItem();
size = priorDistribution.Count;
}
for (var i = 0; i < size; i++)
{
string ci;
if (instance is CompositeInstance compositeInstance1)
{
ci = compositeInstance1.GetPossibleClassLabels()[i];
}
else
{
ci = priorDistribution.GetItem(i);
}
if (priorDistribution.ContainsItem(ci))
{
var metric = CalculateMetric(instance, ci);
if (metric > maxMetric)
{
maxMetric = metric;
predictedClass = ci;
}
}
}
return(predictedClass);
}
public void TestAddItem2()
{
Random random = new Random();
DiscreteDistribution discreteDistribution = new DiscreteDistribution();
for (int i = 0; i < 1000; i++)
{
discreteDistribution.AddItem("" + random.Next(1000));
}
int count = 0;
for (int i = 0; i < 1000; i++)
{
if (discreteDistribution.ContainsItem("" + i))
{
count += discreteDistribution.GetCount("" + i);
}
}
Assert.AreEqual(1000, count);
}
public void TestContainsItem()
{
Assert.True(smallDistribution.ContainsItem("item1"));
Assert.False(smallDistribution.ContainsItem("item4"));
}
