private List <IEmergingPattern> FilterPatterns(List <Instance> instances, List <List <IEmergingPattern> > patternClusterList)
{
var confidenceByPattern = new Dictionary <IEmergingPattern, double>(instances.Count);
for (int i = 0; i < ClusterCount; i++)
{
foreach (var pattern in patternClusterList[i])
{
confidenceByPattern.Add(pattern, pattern.Counts[i] / pattern.Counts.Sum());
}
}
EmergingPatternComparer epComparer = new EmergingPatternComparer(new ItemComparer());
var selectedPatterns = new List <IEmergingPattern>();
for (int i = 0; i < ClusterCount; i++)
{
patternClusterList[i].Sort(
(ep1, ep2) =>
confidenceByPattern[ep1] > confidenceByPattern[ep2]
? -1
: confidenceByPattern[ep1] < confidenceByPattern[ep2]
? 1
: ep1.Counts[i] > ep2.Counts[i]
? -1
: ep1.Counts[i] < ep2.Counts[i]
? 1
: ep1.Items.Count <ep2.Items.Count
? -1
: ep1.Items.Count> ep2.Items.Count ? 1 : 0);
var currentSelectedPatterns = new List <IEmergingPattern>();
for (int j = 0; j < patternClusterList[i].Count; j++)
{
var candidatePattern = patternClusterList[i][j];
bool generalPatternFound = false;
for (int k = 0; k < currentSelectedPatterns.Count && generalPatternFound == false; k++)
{
var patternRelation = epComparer.Compare(candidatePattern, currentSelectedPatterns[k]);
if (patternRelation == SubsetRelation.Subset || patternRelation == SubsetRelation.Equal)
{
generalPatternFound = true;
}
}
if (!generalPatternFound)
{
currentSelectedPatterns.Add(patternClusterList[i][j]);
selectedPatterns.Add(patternClusterList[i][j]);
}
}
}
return(selectedPatterns);
}
public IEnumerable <IEmergingPattern> Mine(InstanceModel model, IEnumerable <Instance> instances, Feature classFeature)
{
EmergingPatternCreator EpCreator = new EmergingPatternCreator();
EmergingPatternComparer epComparer = new EmergingPatternComparer(new ItemComparer());
IEmergingPatternSimplifier simplifier = new EmergingPatternSimplifier(new ItemComparer());
FilteredCollection <IEmergingPattern> minimal =
new FilteredCollection <IEmergingPattern>(epComparer.Compare, FilterRelation);
if (MinePatternsWhileBuildingTree)
{
DecisionTreeBuilder.OnSplitEvaluation =
delegate(IDecisionTreeNode node, ISplitIterator iterator, List <SelectorContext> currentContext)
{
IChildSelector currentSelector = null;
for (int i = 0; i < iterator.CurrentDistribution.Length; i++)
{
double[] distribution = iterator.CurrentDistribution[i];
if (EPTester.Test(distribution, model, classFeature))
{
if (currentSelector == null)
{
currentSelector = iterator.CreateCurrentChildSelector();
}
EmergingPattern ep = EpCreator.ExtractPattern(currentContext, model, classFeature,
currentSelector, i);
ep.Counts = (double[])distribution.Clone();
minimal.Add(simplifier.Simplify(ep));
}
}
};
DoMine(model, instances, classFeature, EpCreator, null);
}
else
{
DoMine(model, instances, classFeature, EpCreator, p =>
{
if (EPTester.Test(p.Counts, model, classFeature))
{
minimal.Add(simplifier.Simplify(p));
}
}
);
}
return(minimal.GetItems());
}
