/// <summary>
/// Inserts a new feature tree into the mix...
/// </summary>
protected override void Insert(FeatureTree <T, U> node, T feature)
{
if (node == null)
{
node = new FeatureTree <T, U>(m_func);
}
node.Insert(feature);
}
protected override void Add(T feature)
{
m_features.Add(feature);
m_left = new FeatureTree <T, U>(m_func);
m_right = new FeatureTree <T, U>(m_func);
}
/// <summary>
/// Clusters features based on some specified values.
/// </summary>
/// <param name="features"></param>
/// <returns></returns>
public List <TParentFeature> Cluster(List <TChildFeature> features, IProgress <ProgressData> progress = null)
{
var progressData = new ProgressData(progress);
var clusters = new List <TParentFeature>();
var currentIndex = 0;
int i = 0;
//var n = features.Count();
// Sort the features based on m/z first
//var msFeatures = new List<TChild>();
//msFeatures.AddRange(features);
var msFeatures = features.ToList();
var n = msFeatures.Count;
OnProgress("Sorting features for partitioning step");
msFeatures.Sort(sortFunction);
OnProgress("Examining features within partitions");
// Iterate through all of the features
while (currentIndex < n)
{
var hasGap = false;
var lastFeature = msFeatures[currentIndex];
var lastIndex = currentIndex + 1;
var gapFeatures = new List <TChildFeature> {
lastFeature
};
while (!hasGap && lastIndex < n)
{
i++;
var currentFeature = msFeatures[lastIndex];
var massDiff = this.massDiffFunc(currentFeature, lastFeature);
// Time to quit
if (Math.Abs(massDiff) > massTolerance)
{
// Stop here...
hasGap = true;
}
else
{
// Increment and save this feature
lastIndex++;
gapFeatures.Add(currentFeature);
lastFeature = currentFeature;
}
}
currentIndex = lastIndex;
// Now that we have a gap...let's go a head and start building the features
// first we build a scan dictionary
// sorted by scans
var featureMap = new Dictionary <int, List <TChildFeature> >();
foreach (var feature in gapFeatures)
{
var scan = feature.Scan;
if (!featureMap.ContainsKey(scan))
{
featureMap.Add(scan, new List <TChildFeature>());
}
featureMap[scan].Add(feature);
}
// Now build the tree...where each node is a feature.
var scans = featureMap.Keys.OrderBy(x => x);
var tree = new FeatureTree <TChildFeature, TParentFeature>(comparison);
foreach (var feature in scans.SelectMany(scan => featureMap[scan]))
{
tree.Insert(feature);
}
var newFeatures = tree.Build();
clusters.AddRange(newFeatures);
progressData.Report(i, n);
}
return(clusters);
}
