/// <summary>Generates a clustering.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="linker">The linker.</param>
/// <param name="data">(Optional) the data.</param>
/// <returns>The clustering.</returns>
private Cluster GenerateClustering(Matrix X, ILinker linker, object[] data = null)
{
// Initialize
Linker = linker;
var clusters = new List <Cluster>();
var distances = new Dictionary <Tuple <int, int>, double>();
// Create a new cluster for each data point
for (var i = 0; i < X.Rows; i++)
{
clusters.Add(
new Cluster
{
Id = i,
Points = new[] { X.Row(i) },
Members = data != null ? new[] { data[i] } : new object[] { X.Row(i) }
});
}
var clusterId = X.Rows;
while (clusters.Count > 1)
{
var closestClusters = new Tuple <int, int>(0, 1);
var smallestDistance = Linker.Distance(clusters[0].Points, clusters[1].Points);
// this needs to be parallelized....
// Loop through each of the clusters looking for the two closest
for (var i = 0; i < clusters.Count; i++)
{
for (var j = i + 1; j < clusters.Count; j++)
{
// Set the current closest distance/pair to the first pair of clusters
var key = new Tuple <int, int>(clusters[i].Id, clusters[j].Id);
// Cache the distance if it hasn't been calculated yet
if (!distances.ContainsKey(key))
{
distances.Add(key, Linker.Distance(clusters[i].Points, clusters[j].Points));
}
// Update closest clusters and distance if necessary
var distance = distances[key];
if (!(distance < smallestDistance))
{
continue;
}
smallestDistance = distance;
closestClusters = new Tuple <int, int>(i, j);
}
}
// order clusters by distance
var min = System.Math.Min(closestClusters.Item1, closestClusters.Item2);
var max = System.Math.Max(closestClusters.Item1, closestClusters.Item2);
var newCluster = new Cluster(clusterId, clusters[min], clusters[max]);
// Remove the merged clusters
clusters.RemoveAt(min);
clusters.RemoveAt(max - 1);
// Add new cluster
clusters.Add(newCluster);
clusterId++;
}
return(clusters.Single());
}
/// <summary>Generates a clustering.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="linker">The linker.</param>
/// <param name="data">(Optional) the data.</param>
/// <returns>The clustering.</returns>
private Cluster GenerateClustering(Matrix X, ILinker linker, object[] data = null)
{
// Initialize
Linker = linker;
var clusters = new List<Cluster>();
var distances = new Dictionary<Tuple<int, int>, double>();
// Create a new cluster for each data point
for (int i = 0; i < X.Rows; i++)
clusters.Add(new Cluster
{
Id = i,
Points = new Vector[] { (Vector)X.Row(i) },
Members = data != null ? new object[] { data[i] } : new object[] { X.Row(i) }
});
// Set the current closest distance/pair to the first pair of clusters
var key = new Tuple<int, int>(0, 0);
var distance = 0.0;
var clusterId = X.Rows;
while (clusters.Count > 1)
{
var closestClusters = new Tuple<int, int>(0, 1);
var smallestDistance = Linker.Distance(clusters[0].Points, clusters[1].Points);
// this needs to be parallelized....
// Loop through each of the clusters looking for the two closest
for (int i = 0; i < clusters.Count; i++)
{
for (int j = i + 1; j < clusters.Count; j++)
{
key = new Tuple<int, int>(clusters[i].Id, clusters[j].Id);
// Cache the distance if it hasn't been calculated yet
if (!distances.ContainsKey(key))
distances.Add(key, Linker.Distance(clusters[i].Points, clusters[j].Points));
// Update closest clusters and distance if necessary
distance = distances[key];
if (distance < smallestDistance)
{
smallestDistance = distance;
closestClusters = new Tuple<int, int>(i, j);
}
}
}
// order clusters by distance
var min = System.Math.Min(closestClusters.Item1, closestClusters.Item2);
var max = System.Math.Max(closestClusters.Item1, closestClusters.Item2);
var newCluster = new Cluster(clusterId, clusters[min], clusters[max]);
// Remove the merged clusters
clusters.RemoveAt(min);
clusters.RemoveAt(max - 1);
// Add new cluster
clusters.Add(newCluster);
clusterId++;
}
return clusters.Single();
}
public HCluster Generate(IEnumerable <T> examples, ILinker linker)
{
// Initialize
Linker = linker;
var clusters = new List <HCluster>();
var distances = new Dictionary <Tuple <int, int>, double>();
// Load data
if (Description == null)
{
Description = Converter.GetDescription(typeof(T)).BuildDictionaries <T>(examples);
}
Matrix X = Converter.Convert <T>(examples, Description.Features);
// Create a new cluster for each data point
for (int i = 0; i < X.Rows; i++)
{
clusters.Add(new HCluster {
Id = i, Points = new Vector[] { X[i, VectorType.Row] }
});
}
// Set the current closest distance/pair to the first pair of clusters
var key = new Tuple <int, int>(0, 0);
var distance = 0.0;
var clusterId = -1;
while (clusters.Count > 1)
{
var closestClusters = new Tuple <int, int>(0, 1);
var smallestDistance = Linker.Distance(clusters[0].Points, clusters[1].Points);
// Loop through each of the clusters looking for the two closest
for (int i = 0; i < clusters.Count; i++)
{
for (int j = i + 1; j < clusters.Count; j++)
{
key = new Tuple <int, int>(clusters[i].Id, clusters[j].Id);
// Cache the distance if it hasn't been calculated yet
if (!distances.ContainsKey(key))
{
distances.Add(key, Linker.Distance(clusters[i].Points, clusters[j].Points));
}
// Update closest clusters and distance if necessary
distance = distances[key];
if (distance < smallestDistance)
{
smallestDistance = distance;
closestClusters = new Tuple <int, int>(i, j);
}
}
}
var min = System.Math.Min(closestClusters.Item1, closestClusters.Item2);
var max = System.Math.Max(closestClusters.Item1, closestClusters.Item2);
var newCluster = new HCluster(clusterId, clusters[min],
clusters[max]);
// Remove the merged clusters
clusters.RemoveAt(min);
clusters.RemoveAt(max - 1);
// Add new cluster
clusters.Add(newCluster);
clusterId += 1;
}
return(clusters.Single());
}