/// <summary>
/// Initializes this instance.
/// </summary>
///
protected void Init(IFeatureDetector <TPoint, TFeature> detector, IClusteringAlgorithm <TFeature> algorithm)
{
this.NumberOfWords = algorithm.Clusters.Count;
this.Clustering = algorithm;
this.Detector = detector;
this.ParallelOptions = new ParallelOptions();
}
public Bitmap Determine(
Bitmap source,
int clustersNumber,
Region[] regions,
IClusteringAlgorithmFactory clusteringAlgorithmFactory)
{
LockableBitmap lockableBitmap = new LockableBitmap(source);
lockableBitmap.LockBits();
IClusteringAlgorithm <double> clusteringAlgorithm =
clusteringAlgorithmFactory.Create(clustersNumber, lockableBitmap);
try
{
Region[] clusteredRegions = clusteringAlgorithm.Distribute(regions);
foreach (Region region in clusteredRegions)
{
FillRegion(region, lockableBitmap);
}
}
finally
{
lockableBitmap.UnlockBits();
}
return(source);
}
private void button4_Click(object sender, EventArgs e)
{
Random r = new Random();
// grab random sample
Int32 sampleSize = (Int32)Math.Sqrt(coordinates.Length);
if (sampleSize < numericUpDown2.Value)
{
sampleSize = (int)numericUpDown2.Value;
}
//Coordinate[] sample = coordinates.OrderBy((Coordinate c) => { return r.Next(); }).Take(sampleSize).ToArray();
//Coordinate[] sample = coordinates.GetRandomSample(sampleSize);
Stopwatch sw = new Stopwatch();
sw.Start();
//processor = new KMedoidsProcessor<Coordinate>((int)numericUpDown2.Value, sample, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
//processor = new KMedoidsSampledProcessor<Coordinate>((int)numericUpDown2.Value, coordinates, sampleSize, 100, new RoutingAI.Algorithms.StraightDistanceAlgorithm(), 2);
processor = new CLARAClusteringAlgorithm <Coordinate>(coordinates, (int)numericUpDown2.Value, (int)numericUpDown6.Value, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
processor.Run();
sw.Stop();
lblTime.Text = String.Format("Time Elapsed: {0}ms", sw.ElapsedMilliseconds);
panel1.Invalidate();
}
public ClusterBMN(int clustersNumber, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure classificationMeasure, IClusteringAlgorithm clusteringAlgorithm, IClassificationAlgorithm classificationAlgorithm)
{
this._similarityMeasure = similarityMeasure;
this._clustersNumber = clustersNumber;
this._classificationMeasure = classificationMeasure;
this._clusteringAlgorithm = clusteringAlgorithm;
this._classificationAlgorithm = classificationAlgorithm;
}
public ClusterBMN(DataMining.Data.Dataset trainingset, int clustersNumber, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure classificationMeasure, IClusteringAlgorithm clusteringAlgorithm, IClassificationAlgorithm classificationAlgorithm)
{
this._trainingset = trainingset;
this._similarityMeasure = similarityMeasure;
this._clustersNumber = clustersNumber;
this._classificationMeasure = classificationMeasure;
this._clusteringAlgorithm = clusteringAlgorithm;
this._classificationAlgorithm = classificationAlgorithm;
}
private void button1_Click(object sender, EventArgs e)
{
processor = null;
Int32 count = (int)numericUpDown1.Value;
coordinates = new Coordinate[count];
Random r = new Random();
for (int i = 0; i < coordinates.Length; i++)
{
coordinates[i] = new Coordinate(r.Next(-10000, 10000) / 100.0f, r.Next(-10000, 10000) / 100.0f);
}
panel1.Invalidate();
}
private void button2_Click(object sender, EventArgs e)
{
Random r = new Random();
Stopwatch sw = new Stopwatch();
sw.Start();
//processor = new KMedoidsProcessor<Coordinate>((int)numericUpDown2.Value, coordinates, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
processor = new PAMClusteringAlgorithm <Coordinate>(coordinates, (int)numericUpDown2.Value, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
processor.Run();
sw.Stop();
lblTime.Text = String.Format("Time Elapsed: {0}ms", sw.ElapsedMilliseconds);
panel1.Invalidate();
}
private void button1_Click(object sender, EventArgs e)
{
processor = null;
Int32 count = (int)numericUpDown1.Value;
coordinates = new Coordinate[count];
Random r = new Random();
for (int i = 0; i < coordinates.Length; i++)
{
coordinates[i] = new Coordinate(r.Next(-10000, 10000) / 100.0f, r.Next(-10000, 10000) / 100.0f);
}
panel1.Invalidate();
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/>.
/// </summary>
///
/// <param name="detector">The feature detector to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IFeatureDetector <TPoint> detector, IClusteringAlgorithm <double[]> algorithm)
: base(detector, algorithm)
{
}
public static BayesianClusterMultinetClassifier CreateClusteringBMNClassifier(int seed, int clusterNumber, Dataset dataset, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure accuracy, IClusteringAlgorithm algorithm, IClassificationAlgorithm naive, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
if (fireEvents)
{
if (algorithm is ACOClustering_IB)
{
((ACOClustering_IB)algorithm).OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
}
if (algorithm is ACOClustering_MB)
{
((ACOClustering_MB)algorithm).OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
}
}
ClusterBMN cBMN = new ClusterBMN(dataset, clusterNumber, similarityMeasure, accuracy, algorithm, naive);
return(cBMN.CreateClassifier() as BayesianClusterMultinetClassifier);
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/> using a
/// <see cref="SpeededUpRobustFeaturesDetector">surf</see>
/// feature detector to identify features.
/// </summary>
///
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IClusteringAlgorithm <double[]> algorithm)
: base(new SpeededUpRobustFeaturesDetector(), algorithm)
{
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/>.
/// </summary>
///
/// <param name="detector">The feature detector to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IFeatureDetector <TPoint, TFeature> detector, IClusteringAlgorithm <TFeature> algorithm)
{
this.NumberOfWords = algorithm.Clusters.Count;
this.Clustering = algorithm;
this.Detector = detector;
}
private void button2_Click(object sender, EventArgs e)
{
Random r = new Random();
Stopwatch sw = new Stopwatch();
sw.Start();
//processor = new KMedoidsProcessor<Coordinate>((int)numericUpDown2.Value, coordinates, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
processor = new PAMClusteringAlgorithm<Coordinate>(coordinates, (int)numericUpDown2.Value, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
processor.Run();
sw.Stop();
lblTime.Text = String.Format("Time Elapsed: {0}ms", sw.ElapsedMilliseconds);
panel1.Invalidate();
}
private void button4_Click(object sender, EventArgs e)
{
Random r = new Random();
// grab random sample
Int32 sampleSize = (Int32)Math.Sqrt(coordinates.Length);
if (sampleSize < numericUpDown2.Value) sampleSize = (int)numericUpDown2.Value;
//Coordinate[] sample = coordinates.OrderBy((Coordinate c) => { return r.Next(); }).Take(sampleSize).ToArray();
//Coordinate[] sample = coordinates.GetRandomSample(sampleSize);
Stopwatch sw = new Stopwatch();
sw.Start();
//processor = new KMedoidsProcessor<Coordinate>((int)numericUpDown2.Value, sample, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
//processor = new KMedoidsSampledProcessor<Coordinate>((int)numericUpDown2.Value, coordinates, sampleSize, 100, new RoutingAI.Algorithms.StraightDistanceAlgorithm(), 2);
processor = new CLARAClusteringAlgorithm<Coordinate>(coordinates, (int)numericUpDown2.Value, (int)numericUpDown6.Value, new RoutingAI.Algorithms.StraightDistanceAlgorithm());
processor.Run();
sw.Stop();
lblTime.Text = String.Format("Time Elapsed: {0}ms", sw.ElapsedMilliseconds);
panel1.Invalidate();
}
/// <summary>
/// Constructs a new <see cref="BagOfVisualWords"/>.
/// </summary>
///
/// <param name="detector">The feature detector to use.</param>
/// <param name="algorithm">The clustering algorithm to use.</param>
///
public BagOfVisualWords(IFeatureDetector <TPoint, TFeature> detector, IClusteringAlgorithm <TFeature> algorithm)
{
Init(detector, algorithm);
}
