/**
* <summary> The predict method takes an Instance as an input and returns the entry of distribution which has the maximum value.</summary>
*
* <param name="instance">Instance to make prediction.</param>
* <returns>The entry of distribution which has the maximum value.</returns>
*/
public override string Predict(Instance.Instance instance)
{
if (instance is CompositeInstance compositeInstance)
{
var possibleClassLabels = compositeInstance.GetPossibleClassLabels();
return(_distribution.GetMaxItem(possibleClassLabels));
}
return(_distribution.GetMaxItem());
}
/**
* <summary> The predict method takes an {@link Instance} as an input and loops through the {@link ArrayList} of {@link DecisionTree}s.
* Makes prediction for the items of that ArrayList and returns the maximum item of that ArrayList.</summary>
*
* <param name="instance">Instance to make prediction.</param>
* <returns>The maximum prediction of a given Instance.</returns>
*/
public override string Predict(Instance.Instance instance)
{
var distribution = new DiscreteDistribution();
foreach (var tree in _forest)
{
distribution.AddItem(tree.Predict(instance));
}
return(distribution.GetMaxItem());
}
/**
* <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 TestGetMaxItem1()
{
Assert.AreEqual("item1", smallDistribution.GetMaxItem());
}