static void runKMeans(DistanceObj[] groups)
{
int numGroups = groups.Length;
// Declaring and intializing array for K-Means
double[][] observations = new double[numGroups][];
for (int i = 0; i < observations.Length; i++)
{
observations[i] = new double[2];
observations[i][0] = groups[i].coords[0];
observations[i][1] = groups[i].coords[1];
}
KMeans km = new KMeans(7);
KMeansClusterCollection clusters = km.Learn(observations);
int[] labels = clusters.Decide(observations);
for (int i = 0; i < labels.Length; i++)
{
Console.WriteLine(groups[i].address + ": " + labels[i]);
}
}
public static void AddClusterInfo(ref SortedDictionary <double, RunOutput> dict)
{
if (dict.Count <= 3)
{
return;
}
Accord.Math.Random.Generator.Seed = 0;
double[][] metrics = new double[dict.Count][];
int i = 0;
foreach (RunOutput t in dict.Values)
{
metrics[i++] = t.metricDepths.ToArray();
}
// Create a new K-Means algorithm
KMeans kmeans = new KMeans(k: dict.Count / 3);
// Compute and retrieve the data centroids
KMeansClusterCollection clusters = kmeans.Learn(metrics);
// Use the centroids to parition all the data
int[] labels = clusters.Decide(metrics);
int j = 0;
foreach (RunOutput v in dict.Values)
{
v.cluster = labels[j++];
}
}
public void addPoint(int x, int y)
{
Double[][] d = { new Double[] { x, y } };
int[] cl = clusters.Decide(d);
Clusters[x][y] = cl[0] + 1;
Console.WriteLine("Новая точка кластера #" + cl[0] + 1);
}
public void Train(List <Person> trainingPeople, int skillSetSize)
{
double[][] inputs = _dataPointService.GenerateDataPointsFromPeople(trainingPeople, skillSetSize);
KMeans kMeans = new KMeans(2);
_clustersCollection = kMeans.Learn(inputs);
trainingPredictions = _clustersCollection.Decide(inputs);
}
private int[] clusterKMeans(double[][] observations)
{
Accord.Math.Random.Generator.Seed = 0;
KMeans kmeans = new KMeans(9);
KMeansClusterCollection clusters = kmeans.Learn(observations);
int[] labels = clusters.Decide(observations);
return(labels);
}
public bool isImageEmpty(Bitmap src)
{
bool ret = false;
Bitmap g = Grayscale.CommonAlgorithms.BT709.Apply(src);
ImageStatistics stat = new ImageStatistics(g);
double[][] ds = { new double[] { stat.Gray.Mean, stat.Gray.Median, stat.Gray.StdDev } };
Program.logIt(string.Format("{0},{1},{2}", ds[0][0], ds[0][1], ds[0][2]));
int[] res = clusters.Decide(ds);
ret = !output[res[0]];
return(ret);
}
private int[] CreateKClusters(int k, double[][] locations)
{
Accord.Math.Random.Generator.Seed = 0;
// Create a new K-Means algorithm with 3 clusters
KMeans kmeans = new KMeans(k);
KMeansClusterCollection clusters = kmeans.Learn(locations);
int[] labels = clusters.Decide(locations);
return(labels);
}
// Called by LandMap.GetZones (), returns number of subregions
public int ClusterLocationsAccordKMeans(MapPoint[,] points, TerrainVerticesDatabase vertDatabase)
{
// K-means cluster algorithm to separate locations in the regions
int regionId = 0;
for (int isleId = 0; isleId < regions.Count; isleId++)
{
MapRegion region = regions[isleId];
double[][] tileLocations = new double[region.turf.Count][];
for (int i = 0; i < tileLocations.Length; i++)
{
tileLocations[i] = new double[3];
TerrainVertData vertData = vertDatabase.GetVertDataFromRegionTile(region.turf[i], isleId);
//LoggerTool.Post ("Requesting " + region.turf[i].ToString ());
if (vertData != null)
{
tileLocations[i][0] = region.turf[i].x;
tileLocations[i][1] = vertData.inlandPosition;
tileLocations[i][2] = region.turf[i].y;
}
else
{
LoggerTool.Post("Null from VertDB for " + region.turf[i].ToString());
tileLocations[i][0] = 0;
tileLocations[i][1] = 0;
tileLocations[i][2] = 0;
}
}
int k = InitializeNumOfK(tileLocations.Length);
Debug.Log(k + " centroid(s)");
KMeans kmeans = new KMeans(k);
KMeansClusterCollection clusters = kmeans.Learn(tileLocations);
int[] labels = clusters.Decide(tileLocations);
Debug.Log("Number of labels (clusters) = " + labels.Length);
for (int i = 0; i < labels.Length; i++)
{
points[(int)tileLocations[i][0], (int)tileLocations[i][2]].areaValue = regionId + labels[i];
}
regionId += k;
}
return(regionId);
}
public void Engine(double[][] observations, int k, ref int[] labels)
{
Accord.Math.Random.Generator.Seed = 0;
KMeans kmeans = new KMeans(k);
kmeans.UseSeeding = Seeding.Uniform;
kmeans.MaxIterations = 0; // no limit
KMeansClusterCollection clusters = kmeans.Learn(observations);
double[][] centroids = kmeans.Centroids;
labels = clusters.Decide(observations);
double err = kmeans.Error;
}
public void binary_split_new_method()
{
#region doc_sample1
// Use a fixed seed for reproducibility
Accord.Math.Random.Generator.Seed = 0;
// Declare some data to be clustered
double[][] input =
{
new double[] { -5, -2, -1 },
new double[] { -5, -5, -6 },
new double[] { 2, 1, 1 },
new double[] { 1, 1, 2 },
new double[] { 1, 2, 2 },
new double[] { 3, 1, 2 },
new double[] { 11, 5, 4 },
new double[] { 15, 5, 6 },
new double[] { 10, 5, 6 },
};
// Create a new binary split with 3 clusters
BinarySplit binarySplit = new BinarySplit(3);
// Learn a data partitioning using the Binary Split algorithm
KMeansClusterCollection clustering = binarySplit.Learn(input);
// Predict group labels for each point
int[] output = clustering.Decide(input);
// As a result, the first two observations should belong to the
// same cluster (thus having the same label). The same should
// happen to the next four observations and to the last three.
#endregion
Assert.AreEqual(output[0], output[1]);
Assert.AreEqual(output[2], output[3]);
Assert.AreEqual(output[2], output[4]);
Assert.AreEqual(output[2], output[5]);
Assert.AreEqual(output[6], output[7]);
Assert.AreEqual(output[6], output[8]);
Assert.AreNotEqual(output[0], output[2]);
Assert.AreNotEqual(output[2], output[6]);
Assert.AreNotEqual(output[0], output[6]);
int[] labels2 = binarySplit.Clusters.Nearest(input);
Assert.IsTrue(output.IsEqual(labels2));
}
/// <summary>
/// Initializes the Gaussian Mixture Models using K-Means
/// parameters as an initial parameter guess.
/// </summary>
///
private void btnInitialize_Click(object sender, EventArgs e)
{
// Creates and computes a new
// K-Means clustering algorithm:
kmeans = new KMeans(k);
KMeansClusterCollection clustering = kmeans.Learn(observations);
// Classify all instances in mixture data
int[] classifications = clustering.Decide(observations);
// Draw the classifications
updateGraph(classifications);
}
public void AccordKMeans()
{
Stopwatch stopwatch = Stopwatch.StartNew();
stopwatch.Start();
KMeans kmeans = new KMeans(50);
double[][] v = Input.ToRowArrays();
KMeansClusterCollection clusters = kmeans.Learn(v);
AssignedClusters = clusters.Decide(v).ToList();
BestClustering = AssignedClusters;
Centroids = clusters.Centroids.Select(x => CreateVector.Dense(x)).ToList();
stopwatch.Stop();
timer = stopwatch.ElapsedTicks;
}
private static double[][][] ClusterPoints(double[][] dataset, int k, KMeansClusterCollection clusters)
{
// points in each cluster
double[][][] clusterData = new double[k][][];
for (int i = 0; i < dataset.Length; i++)
{
int decision = clusters.Decide(dataset[i]);
if (clusterData[decision] == null)
{
clusterData[decision] = new double[][] { };
}
Array.Resize(ref clusterData[decision], clusterData[decision].Length + 1);
clusterData[decision][clusterData[decision].Length - 1] = dataset[i];
}
return(clusterData);
}
private static void splitCluster(int index)
{
//kmeans with k = 2 for clusters[index].members
//if kmeansArr == 1, add group to temp list for new cluster, delete from clusters[index]
//create new Cluster with the temp list, append to end of clusters (the for loop in findLargeClusters will adapt to it and check it later for >15
int numGroups = clusters[index].members.Count;
double[][] observations = new double[numGroups][];
for (int i = 0; i < observations.Length; i++)
{
observations[i] = new double[2];
observations[i][0] = clusters[index].members[i].destination.coords[0];
observations[i][1] = clusters[index].members[i].destination.coords[1];
}
KMeans km = new KMeans(2);
KMeansClusterCollection clust = km.Learn(observations);
int[] clustArr = clust.Decide(observations);
//if a group is in the second of the two clusters, we will put it in a new List and delete it from the old one
List <Group> forNewCluster = new List <Group>();
for (int i = clustArr.Length - 1; i >= 0; i--)
{
if (clustArr[i] == 1)
{
forNewCluster.Add(clusters[index].members[i]);
clusters[index].members.RemoveAt(i);
}
}
Cluster newCluster = new Cluster(forNewCluster);
//update the cluster attributes in each group for the new cluster
clusters.Add(newCluster);
foreach (Group g in clusters[clusters.Count() - 1].members)
{
g.cluster = clusters.Count() - 1;
}
}
void train()
{
clusters = kmeans.Learn(inputList.ToArray());
// make a empty picture
{
Bitmap mask = new Bitmap(300, 300);
using (Graphics g = Graphics.FromImage(mask))
{
g.FillRectangle(new SolidBrush(Color.Black), new Rectangle(0, 0, mask.Width, mask.Height));
}
//Bitmap g = Grayscale.CommonAlgorithms.BT709.Apply(mask);
ImageStatistics stat = new ImageStatistics(Grayscale.CommonAlgorithms.BT709.Apply(mask));
double[][] ds = { new double[] { stat.Gray.Mean, stat.Gray.Median, stat.Gray.StdDev } };
//Program.logIt(string.Format("{0},{1},{2}", ds[0][0], ds[0][1], ds[0][2]));
int[] res = clusters.Decide(ds);
output[0] = true;
output[1] = true;
output[res[0]] = false;
}
}
static void Main(string[] args)
{
// sample input
var sampleSet = new double[][]
{
new double[] { 1, 9 },
new double[] { 2, 8 },
new double[] { 3, 7 },
new double[] { 4, 6 },
new double[] { 5, 5 }
};
KMeans kmeans = new KMeans(2);
KMeansClusterCollection clusters = kmeans.Learn(sampleSet);
Console.WriteLine("\n\n* Clusters: {0}", String.Join(",", clusters.Decide(sampleSet)));
Console.WriteLine("\n\n\n\nDONE!!");
Console.ReadKey();
}
//sets the values in kmeansArr[]
public static void runKMeans(ref Group[] gs)
{
int numGroups = gs.Count();
// Declaring and intializing array for K-Means
double[][] observations = new double[numGroups][];
for (int i = 0; i < observations.Length; i++)
{
observations[i] = new double[2];
observations[i][0] = gs[i].destination.coords[0];
observations[i][1] = gs[i].destination.coords[1];
}
int numClusters = (gs.Count() / 6) + 1; //THIS WHAT WE WANT?
KMeans km = new KMeans(numClusters);
KMeansClusterCollection clust = km.Learn(observations);
kmeansArr = clust.Decide(observations);
}
// Called by LandMap.GetZones (), returns number of subregions
public int ClusterLocationsAccordKMeans(MapPoint[,] points)
{
// K-means cluster algorithm to separate locations in the regions
int regionId = 0;
for (int isleId = 0; isleId < regions.Count; isleId++)
{
MapRegion region = regions[isleId];
double[][] tileLocations = new double[region.turf.Count][];
for (int i = 0; i < tileLocations.Length; i++)
{
tileLocations[i] = new double[2];
tileLocations[i][0] = region.turf[i].x;
tileLocations[i][1] = region.turf[i].y;
}
int k = InitializeNumOfK(region.turf.Count);
Debug.Log(k + " centroid(s)");
KMeans kmeans = new KMeans(k);
KMeansClusterCollection clusters = kmeans.Learn(tileLocations);
int[] labels = clusters.Decide(tileLocations);
Debug.Log("Number of labels (clusters) = " + labels.Length);
for (int i = 0; i < labels.Length; i++)
{
points[(int)tileLocations[i][0], (int)tileLocations[i][1]].areaValue = regionId + labels[i];
}
regionId += k;
}
return(regionId);
}
/**
* Get related post for each post
* Use k-means cluster algorithm
*/
public void PostetsRelatedAi()
{
// Get all posts from db
var posts = (from p in db.Posts
where p.Content.Length > 50 // Avoid uneccesary outliers
select p).ToList();
// Create Array of all posts content
string[] documents = (from p in db.Posts
where p.Content.Length > 50 // Avoid uneccesary outliers
select p.Content).ToArray();
///Apply TF*IDF on the documents and get the resulting vectors.
double[][] inputs = TFIDFEX.TFIDF.Transform(documents);
inputs = TFIDFEX.TFIDF.Normalize(inputs);
// Create a new K-Means algorithm with Posts/2 clusters (create couples)
KMeans kmeans = new KMeans(Convert.ToInt32(posts.Count() / 2));
// Compute the algorithm, retrieving an integer array
// containing the labels for each of the observations
KMeansClusterCollection clusters = kmeans.Learn(inputs);
int[] labels = clusters.Decide(inputs);
// Create more handy list of clusters and their vectors
var clustersList = new List <List <int> >();
for (int j = 0; j < Convert.ToInt32(posts.Count() / 2); j++)
{
clustersList.Add(labels.Select((s, i) => new { i, s })
.Where(t => t.s == j)
.Select(t => t.i)
.ToList());
}
// Adjust all posts and thier related according to clustering results
foreach (var clusetr in clustersList)
{
// Handle clusters with 2 and more vectors
if (clusetr.Count() >= 2)
{
for (int i = 1; i < clusetr.Count(); i++)
{
// Attach each post in the cluster to it's neighbor
posts[clusetr[i - 1]].relatedPost = posts[clusetr[i]].Title;
}
// Attach the last post to the first one in the list
posts[clusetr.Last()].relatedPost = posts[clusetr.First()].Title;
}
// Handle clusters with only one vector
else if (clusetr.Count() > 0)
{
// In case matching not found
posts[clusetr.First()].relatedPost = null;
}
}
// Update Changes in DB
foreach (var p in posts)
{
db.Entry(p).State = EntityState.Modified;
}
db.SaveChanges();
}
public void dayWorkSchedule(DateTime date)
{
Func <WorkSchedule, bool> predicat = (w => w.workSchedule_date == date);
List <WorkSchedule> workSchedules = selectWorkSchedule(predicat);
List <Distributors> alldistributors = new List <Distributors>();
List <Distributors> ezerList = new List <Distributors>();
foreach (WorkSchedule item in workSchedules)
{
ezerList = selectDistributors(d => d.distributors_id == item.distributor_id);
if (ezerList.Count != 1)
{
throw new Exception("שגיאה בחיפוש אחרי מחלק זה");
}
alldistributors.Add(ezerList[0]);
}
List <Recipients> allrecipients = recipientsPackageByDay(date);
double[][] Coordinates = new double[allrecipients.Count][];
int i = 0;
foreach (Recipients item in allrecipients)
{
Coordinates[i] = getLatLongFromAddress(item.recipients_address);
i++;
}
// Create a new K-Means algorithm with 3 clusters
KMeans kmeans = new KMeans(3);
// Compute the algorithm, retrieving an integer array
// containing the labels for each of the observations
KMeansClusterCollection clusters = kmeans.Learn(Coordinates);
// As a result, the first two observations should belong to the
// same cluster (thus having the same label). The same should
// happen to the next four observations and to the last three.
int[] labels = clusters.Decide(Coordinates);
List <Recipients> group0 = new List <Recipients>();
List <Recipients> group1 = new List <Recipients>();
List <Recipients> group2 = new List <Recipients>();
for (int k = 0; k < labels.Length; k++)
{
if (labels[k] == 0)
{
group0.Add(allrecipients[k]);
}
if (labels[k] == 1)
{
group1.Add(allrecipients[k]);
}
if (labels[k] == 2)
{
group2.Add(allrecipients[k]);
}
}
double lat = 0; double lon = 0;
double[] d0 = new double[2];
double[] d1 = new double[2];
double[] d2 = new double[2];
foreach (var item in group0)
{
d0 = getLatLongFromAddress(item.recipients_address);
lat += d0[0];
lon += d0[1];
}
d0[0] = lat / group0.Count;
d0[1] = lon / group0.Count;
lat = 0; lon = 0;
foreach (var item in group1)
{
d1 = getLatLongFromAddress(item.recipients_address);
lat += d1[0];
lon += d1[1];
}
d1[0] = lat / group1.Count;
d1[1] = lon / group1.Count;
lat = 0; lon = 0;
foreach (var item in group2)
{
d2 = getLatLongFromAddress(item.recipients_address);
lat += d2[0];
lon += d2[1];
}
d2[0] = lat / group2.Count;
d2[1] = lon / group2.Count;
// double[] d0 = getLatLongFromAddress(group0[0].recipients_address);
// double[] d1 = getLatLongFromAddress(group1[0].recipients_address);
// double[] d2 = getLatLongFromAddress(group2[0].recipients_address);
double[,] distance = new double[3, 3];
for (i = 0; i < 3; i++)
{
double[] d3 = getLatLongFromAddress(alldistributors[i].distributors_address);
for (int j = 0; j < 3; j++)
{
if (j == 0)
{
distance[i, j] = dalImp.addressCalculations.calculateDistance(d0, d3);
}
if (j == 1)
{
distance[i, j] = dalImp.addressCalculations.calculateDistance(d1, d3);
}
if (j == 2)
{
distance[i, j] = dalImp.addressCalculations.calculateDistance(d2, d3);
}
}
}
int [,] minIndex = new int[3, 3];
for (i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
minIndex[i, j] = findMinDist(distance[i, 0], distance[i, 1], distance[i, 2], j);
}
}
if ((minIndex[0, 0] != minIndex[2, 0]) && (minIndex[0, 0] != minIndex[1, 0]) && (minIndex[2, 0] != minIndex[1, 0]))
{
if (minIndex[0, 0] == 0)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[0].distributors_id, date, group0));
if (minIndex[1, 0] == 1)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[1].distributors_id, date, group1));
UpdateWorkSchedule(new WorkSchedule(alldistributors[2].distributors_id, date, group2));
}
else if (minIndex[1, 0] == 2)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[1].distributors_id, date, group2));
UpdateWorkSchedule(new WorkSchedule(alldistributors[2].distributors_id, date, group1));
}
}
else if (minIndex[0, 0] == 1)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[0].distributors_id, date, group1));
if (minIndex[1, 0] == 0)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[1].distributors_id, date, group0));
UpdateWorkSchedule(new WorkSchedule(alldistributors[2].distributors_id, date, group2));
}
else if (minIndex[1, 0] == 2)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[1].distributors_id, date, group2));
UpdateWorkSchedule(new WorkSchedule(alldistributors[2].distributors_id, date, group0));
}
}
else if (minIndex[0, 0] == 2)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[0].distributors_id, date, group2));
if (minIndex[1, 0] == 0)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[1].distributors_id, date, group0));
UpdateWorkSchedule(new WorkSchedule(alldistributors[2].distributors_id, date, group1));
}
else if (minIndex[1, 0] == 1)
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[1].distributors_id, date, group1));
UpdateWorkSchedule(new WorkSchedule(alldistributors[2].distributors_id, date, group0));
}
}
}
else
{
if ((minIndex[0, 0] == minIndex[2, 0]) && (minIndex[0, 0] == minIndex[1, 0]) && (minIndex[2, 0] == minIndex[1, 0]))
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[0, 0]].distributors_id, date, group0));
if ((minIndex[1, 1] == minIndex[2, 1]))
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[1, 1]].distributors_id, date, group1));
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[2, 2]].distributors_id, date, group2));
}
else
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[1, 1]].distributors_id, date, group1));
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[2, 1]].distributors_id, date, group2));
}
}
else
{
int place;
if (minIndex[1, 0] == minIndex[2, 0])
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[0, 0]].distributors_id, date, group0));
place = minIndex[1, 1] + minIndex[1, 1] % 3;
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[1, 0]].distributors_id, date, group1));
UpdateWorkSchedule(new WorkSchedule(alldistributors[place].distributors_id, date, group2));
}
else
{
if (minIndex[0, 0] == minIndex[2, 0])
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[1, 0]].distributors_id, date, group1));
place = minIndex[0, 0] + minIndex[1, 0] % 3;
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[0, 0]].distributors_id, date, group1));
UpdateWorkSchedule(new WorkSchedule(alldistributors[place].distributors_id, date, group2));
}
else
{
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[2, 0]].distributors_id, date, group2));
place = minIndex[0, 0] + minIndex[2, 0] % 3;
UpdateWorkSchedule(new WorkSchedule(alldistributors[minIndex[1, 1]].distributors_id, date, group0));
UpdateWorkSchedule(new WorkSchedule(alldistributors[place].distributors_id, date, group1));
}
}
}
}
}
public void learn_test()
{
#region doc_learn
Accord.Math.Random.Generator.Seed = 0;
// Declare some observations
double[][] observations =
{
new double[] { -5, -2, -1 },
new double[] { -5, -5, -6 },
new double[] { 2, 1, 1 },
new double[] { 1, 1, 2 },
new double[] { 1, 2, 2 },
new double[] { 3, 1, 2 },
new double[] { 11, 5, 4 },
new double[] { 15, 5, 6 },
new double[] { 10, 5, 6 },
};
double[][] orig = observations.MemberwiseClone();
// Create a new K-Means algorithm with 3 clusters
BalancedKMeans kmeans = new BalancedKMeans(3)
{
// Note: in balanced k-means the chances of the algorithm oscillating
// between two solutions increases considerably. For this reason, we
// set a max-iterations limit to avoid iterating indefinitely.
MaxIterations = 100
};
// Compute the algorithm, retrieving an integer array
// containing the labels for each of the observations
KMeansClusterCollection clusters = kmeans.Learn(observations);
// As a result, the first two observations should belong to the
// same cluster (thus having the same label). The same should
// happen to the next four observations and to the last three.
int[] labels = clusters.Decide(observations);
#endregion
Assert.AreEqual(labels[0], labels[1]);
Assert.AreEqual(labels[2], labels[3]);
Assert.AreEqual(labels[2], labels[4]);
Assert.AreEqual(labels[2], labels[5]);
Assert.AreEqual(labels[6], labels[7]);
Assert.AreEqual(labels[6], labels[8]);
Assert.AreNotEqual(labels[0], labels[2]);
Assert.AreNotEqual(labels[2], labels[6]);
Assert.AreNotEqual(labels[0], labels[6]);
int[] labels2 = kmeans.Clusters.Decide(observations);
Assert.IsTrue(labels.IsEqual(labels2));
// the data must not have changed!
Assert.IsTrue(orig.IsEqual(observations));
var c = new KMeansClusterCollection.KMeansCluster[clusters.Count];
int i = 0;
foreach (var cluster in clusters)
{
c[i++] = cluster;
}
for (i = 0; i < c.Length; i++)
{
Assert.AreSame(c[i], clusters[i]);
}
}
public void Test(List <Person> testingPeople, int skillSetSize)
{
var inputs = _dataPointService.GenerateDataPointsFromPeople(testingPeople, skillSetSize);
testPredictions = _clustersCollection.Decide(inputs);
}
/// <summary>
/// Computes the silhouette width for the given set of clusters and observations.
/// </summary>
/// <param name="clusters">The clusters in the dataset.</param>
/// <param name="observations">The observation vectors.</param>
/// <param name="isTwoDimensionalObservations">
/// Indicates whether or not the observation vector consists of flattened,
/// two-dimensioal observations (which is how agent trajectories are stored), prompting special consideration for
/// euclidean distasnce calculation.
/// </param>
/// <returns>The silhouette width for the given set of clusters and observations.</returns>
private static double ComputeSilhouetteWidth(KMeansClusterCollection clusters, double[][] observations,
bool isTwoDimensionalObservations)
{
var lockObj = new object();
double totalSilhouetteWidth = 0;
// Get cluster assignments for all of the observations
var clusterAssignments = clusters.Decide(observations);
Parallel.For(0, observations.Length, observationIdx =>
{
double obsIntraclusterDissimilarity = 0;
// Get the cluster assignment of the current observation
var curObsClusterAssignment = clusterAssignments[observationIdx];
// Only add observation silhouette width if it is NOT the sole member of its assigned cluster
if (clusterAssignments.Count(ca => ca == curObsClusterAssignment) > 1)
{
// Setup list to hold average distance from current observation to every other neighboring cluster
var neighboringClusterDistances = new List <double>(clusters.Count);
for (var clusterIdx = 0; clusterIdx < clusters.Count; clusterIdx++)
{
// Handle the case where the current cluster is the cluster of which the observation is a member
if (clusterIdx == curObsClusterAssignment)
{
// Sum the distance between current observation and every other observation in the same cluster
for (var caIdx = 0; caIdx < clusterAssignments.Length; caIdx++)
{
if (curObsClusterAssignment == clusterAssignments[caIdx])
{
obsIntraclusterDissimilarity +=
isTwoDimensionalObservations
? ComputeEuclideanTrajectoryDifference(observations[observationIdx],
observations[caIdx])
: ComputeEuclideanObservationDifference(observations[observationIdx],
observations[caIdx]);
}
}
}
// Otherwise, handle the case where we're on a neighboring cluster
else
{
// Create new variable to hold sum of dissimilarities between observation and
// neighboring cluster observations
double curObsNeighboringClusterDissimilarity = 0;
// Sum the distance between current observation and cluster centroids to which
// the current observation is NOT assigned
for (var caIdx = 0; caIdx < clusterAssignments.Length; caIdx++)
{
if (curObsClusterAssignment != clusterAssignments[caIdx])
{
curObsNeighboringClusterDissimilarity +=
isTwoDimensionalObservations
? ComputeEuclideanTrajectoryDifference(observations[observationIdx],
observations[caIdx])
: ComputeEuclideanObservationDifference(observations[observationIdx],
observations[caIdx]);
}
}
// Compute the average intercluster dissimilarity for the current neighboring
// cluster and add to the list of average neighboring cluster distances
neighboringClusterDistances.Add(curObsNeighboringClusterDissimilarity /
clusterAssignments.Count(ca => ca == clusterIdx));
}
}
// Compute the average intracluster dissimilarity (local variance)
obsIntraclusterDissimilarity = obsIntraclusterDissimilarity /
clusterAssignments.Count(ca => ca == curObsClusterAssignment);
// Get the minimum intercluster dissimilarity (0 if there are no centroid differences)
var obsInterClusterDissimilarity = neighboringClusterDistances.Any()
? neighboringClusterDistances.Min()
: 0;
// Compute the silhouette width for the current observation
// If its the only point in the cluster, then the silhouette width is 0
var curSilhouetteWidth = Math.Abs(obsIntraclusterDissimilarity) < 0.0000001
? 0
: (obsInterClusterDissimilarity -
obsIntraclusterDissimilarity) /
Math.Max(obsIntraclusterDissimilarity,
obsInterClusterDissimilarity);
lock (lockObj)
{
// Add the silhoutte width for the current observation
totalSilhouetteWidth += curSilhouetteWidth;
}
}
});
// Return the silhoutte width
return(totalSilhouetteWidth / observations.Length);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
int i, j, k;
bool IsPointData = false;
GH_Structure <IGH_Goo> data = new GH_Structure <IGH_Goo>();
GH_Structure <IGH_GeometricGoo> geo = new GH_Structure <IGH_GeometricGoo>();
List <int> numCluster = new List <int>();
if (!DA.GetDataTree(0, out data))
{
return;
}
if (!DA.GetDataTree(1, out geo))
{
return;
}
if (!DA.GetDataList(2, numCluster))
{
return;
}
data.Simplify(GH_SimplificationMode.CollapseAllOverlaps);
DataTree <IGH_Goo> outputData = new DataTree <IGH_Goo>();
DataTree <IGH_GeometricGoo> outputGeo = new DataTree <IGH_GeometricGoo>();
DataTree <Point3d> outputCentroids = new DataTree <Point3d>();
for (i = 0; i < data.Branches.Count; i++)
{
double[] x = new double[data.Branches[i].Count];
double[] y = new double[data.Branches[i].Count];
double[] z = new double[data.Branches[i].Count];
for (j = 0; j < data.Branches[i].Count; j++)
{
if (data.Branches[i][j] is GH_Point)
{
IsPointData = true;
GH_Point target = new GH_Point();
if (GH_Convert.ToGHPoint(data.Branches[i][j], GH_Conversion.Both, ref target))
{
x[j] = target.Value.X;
y[j] = target.Value.Y;
z[j] = target.Value.Z;
}
}
else
{
break;
}
}
if (IsPointData)
{
List <double[]> datalist = new List <double[]>
{
x,
y,
z
};
double[][] _data = ArrayConvert.To2DArray(datalist);
KMeans m = new KMeans(numCluster[i]);
KMeansClusterCollection cluster = m.Learn(_data);
int[] labels = cluster.Decide(_data);
double[][] centroids = m.Centroids;
for (j = 0; j < data.Branches[i].Count; j++)
{
GH_Path path = new GH_Path(i, labels[j]);
outputData.Add(data.Branches[i][j], path);
outputGeo.Add(geo.Branches[i][j], path);
}
for (k = 0; k < centroids.Length; k++)
{
outputCentroids.Add(new Point3d(centroids.ElementAt(k).ElementAt(0), centroids.ElementAt(k).ElementAt(1), centroids.ElementAt(k).ElementAt(2)), new GH_Path(k));
}
}
else
{
break;
}
}
if (!IsPointData)
{
GH_Path oldPath = new GH_Path();
GH_Path newPath = new GH_Path();
int DataGroupCount = 0;
for (i = 0; i < data.PathCount; i++)
{
if (data.Paths[i].Indices.Length == 1)
{
DataGroupCount = 1;
break;
}
else
{
int[] pp = new int[data.Paths[i].Indices.Length - 1];
for (j = 0; j < data.Paths[i].Indices.Length - 1; j++)
{
pp[j] = data.Paths[i].Indices[j];
}
newPath.Indices = pp;
if (newPath != oldPath)
{
DataGroupCount++;
oldPath = newPath;
}
newPath = new GH_Path();
}
}
for (i = 0; i < DataGroupCount; i++)
{
List <double[]> datalist = new List <double[]>();
for (j = 0; j < data.Branches.Count / DataGroupCount; j++)
{
double[] values = new double[data.Branches[DataGroupCount * i + j].Count];
for (k = 0; k < data.Branches[DataGroupCount * i + j].Count; k++)
{
if (data.Branches[DataGroupCount * i + j][k] is GH_Number)
{
if (GH_Convert.ToDouble(data.Branches[DataGroupCount * i + j][k], out double value, GH_Conversion.Both))
{
values[k] = value;
}
}
else
{
break;
}
}
datalist.Add(values);
}
double[][] _data = ArrayConvert.ToDoubleArray(datalist);
KMeans m = new KMeans(numCluster[0]);
KMeansClusterCollection cluster = m.Learn(_data);
int[] labels = cluster.Decide(_data);
for (j = 0; j < labels.Length; j++)
{
List <IGH_Goo> numbers = new List <IGH_Goo>();
List <IGH_GeometricGoo> geos = new List <IGH_GeometricGoo>();
for (k = 0; k < data.Branches[DataGroupCount * i + j].Count; k++)
{
numbers.Add(data.Branches[DataGroupCount * i + j][k]);
geos.Add(geo.Branches[DataGroupCount * i + j][k]);
}
GH_Path path = new GH_Path(i, j, labels[j]);
outputData.AddRange(numbers, path);
outputGeo.AddRange(geos, path);
}
}
}
DA.SetDataTree(0, outputData);
DA.SetDataTree(1, outputGeo);
DA.SetDataTree(2, outputCentroids);
}
public void objectClustering()
{
int applyClusterNum = clusterNum;
if (applyClusterNum > objectidList.Count)
{
applyClusterNum = objectidList.Count;
}
var kmeans = new KMeans(k: applyClusterNum);
double[][] points = new double[objectidList.Count][];
for (int i = 0; i < objectidList.Count; i++)
{
int id = objectidList[i];
points[i] = ObjList[idxbyObjid[id]].getStartingPoint();
}
KMeansClusterCollection clusters = kmeans.Learn(points);
int[] output = clusters.Decide(points);
setStartingGroup();
int maxClusterLength = objectidList.Count / applyClusterNum;
for (int i = 0; i < objectidList.Count; i++)
{
int id = objectidList[i];
//startingGroup[output[i]].Add(id);
if (startingGroup[output[i]].idList.Count < maxClusterLength)
{
startingGroup[output[i]].Add(id);
}
else
{
for (int j = output[i]; j < applyClusterNum + output[i]; j++)
{
if (startingGroup[(j + 1) % applyClusterNum].idList.Count < maxClusterLength)
{
startingGroup[(j + 1) % applyClusterNum].Add(id);
break;
}
}
}
//^
}
//sort
int maxFrameLength = 0;
for (int k = 0; k < startingGroup.Count; k++)
{
startingGroup[k].sort(ref ObjList, ref idxbyObjid);
int curFrameLength = startingGroup[k].getFrameLength();
if (maxFrameLength < curFrameLength)
{
maxFrameLength = curFrameLength;
}
}
//set maxFrmae
//MessageBox.Show(overlayFrameNum.ToString());
outputFrameNum = trackingBar.Maximum = maxFrameLength - 1;
if (videoInfo3Flag == true)
{
strVideoInfo3 += "\nresult Frame: " + outputFrameNum;
}
videoInfo3Flag = false;
labelVideoInfo3.Text = strVideoInfo3;
labelProgress.Text = "Detection 100%\nTracking 100%\nOverlay 100%";
int min = outputFrameNum / analysisFPS / 60;
int sec = outputFrameNum / analysisFPS % 60;
labelEndTime.Text = min.ToString("00") + ":" + sec.ToString("00");
}
/// <summary>
/// Computes the silhouette width for the given set of clusters and observations.
/// </summary>
/// <param name="clusters">The clusters in the dataset.</param>
/// <param name="observations">The observation vectors.</param>
/// <returns>The silhouette width for the given set of clusters and observations.</returns>
private static double ComputeSilhouetteWidth(KMeansClusterCollection clusters, double[][] observations)
{
Object lockObj = new object();
double totalSilhouetteWidth = 0;
// Get cluster assignments for all of the observations
int[] clusterAssignments = clusters.Decide(observations);
Parallel.For(0, observations.Length, observationIdx =>
{
double obsIntraclusterDissimilarity = 0;
double obsInterClusterDissimilarity = 0;
// Sum the distance between current observation and every other observation in the same cluster
for (int caIdx = 0; caIdx < clusterAssignments.Length; caIdx++)
{
if (clusterAssignments[caIdx] == clusterAssignments[observationIdx])
{
obsIntraclusterDissimilarity +=
ComputeEuclideanTrajectoryDifference(observations[observationIdx], observations[caIdx]);
}
}
// Compute the average intracluster dissimilarity (local variance)
obsIntraclusterDissimilarity = obsIntraclusterDissimilarity/
clusterAssignments.Where(ca => ca == clusterAssignments[observationIdx])
.Count();
// Setup list to hold distance from current observation to every other cluster centroid
List<double> centroidDistances = new List<double>(clusters.Count);
// Sum the distance between current observation and cluster centroids to which the current
// observation is NOT assigned
for (int idx = 0; idx < clusters.Count; idx++)
{
// Only compute distance when observation is not assigned to the current cluster
if (idx != clusterAssignments[observationIdx])
{
centroidDistances.Add(ComputeEuclideanTrajectoryDifference(observations[observationIdx],
clusters[idx].Centroid));
}
}
// Get the minimum intercluster dissimilarity (0 if there are no centroid differences)
obsInterClusterDissimilarity = centroidDistances.Any() ? centroidDistances.Min() : 0;
// Add the silhoutte width for the current observation
var curSilhouetteWidth = (Math.Abs(obsIntraclusterDissimilarity) < 0.0000001 &&
Math.Abs(obsInterClusterDissimilarity) < 0.0000001)
? 0
: (obsInterClusterDissimilarity -
obsIntraclusterDissimilarity)/
Math.Max(obsIntraclusterDissimilarity,
obsInterClusterDissimilarity);
lock (lockObj)
{
totalSilhouetteWidth += curSilhouetteWidth;
}
});
// Return the silhoutte width
return totalSilhouetteWidth/observations.Length;
}
protected override void SolveInstance(IGH_DataAccess DA)
{
int n = 0;
DA.GetData(0, ref n);
List <List <double> > data = new List <List <double> >();
for (int i = 2; i < Params.Input.Count; i++)
{
List <double> d = new List <double>();
DA.GetDataList(i, d);
if (d.Count > 0)
{
data.Add(d);
}
}
// Declare some observations
double[][] observations = new double[data[0].Count][];
for (int i = 0; i < data[0].Count; i++)
{
List <double> num = new List <double>();
for (int j = 0; j < data.Count; j++)
{
num.Add(data[j][i]);
}
observations[i] = num.ToArray();
}
//Get Weights
List <double> weights = new List <double>();
DA.GetDataList(1, weights);
if (weights.Count != data[0].Count)
{
weights = Enumerable.Repeat(1.0, data[0].Count).ToList();
}
//Seed
Accord.Math.Random.Generator.Seed = 0;
// Create a new K-Means algorithm with n clusters
Accord.MachineLearning.KMeans kmeans = new Accord.MachineLearning.KMeans(n);
KMeansClusterCollection clusters = kmeans.Learn(observations, weights.ToArray());
int[] labels = clusters.Decide(observations);
//Message
base.Message = "Weights " + weights.Count.ToString() + "\r\n" + "Dimensions " + observations.Length.ToString() + " of length " + observations[0].Length.ToString();
//Output
DA.SetDataList(0, labels.ToList());
DataTree <int> dataTree = new DataTree <int>();
for (int i = 0; i < labels.Length; i++)
{
dataTree.Add(i, new GH_Path(labels[i]));
}
DA.SetDataTree(1, dataTree);
}
static void Main(string[] args)
{
Console.SetWindowSize(100, 50);
// Read in the Online Retail feature dataset
// TODO: change the path to point to your data directory
string dataDirPath = @"\\Mac\Home\Documents\c-sharp-machine-learning\ch.6\input-data";
// Load the data into a data frame
string dataPath = Path.Combine(dataDirPath, "features.csv");
Console.WriteLine("Loading {0}\n\n", dataPath);
var ecommerceDF = Frame.ReadCsv(
dataPath,
hasHeaders: true,
inferTypes: true
);
Console.WriteLine("* Shape: {0}, {1}", ecommerceDF.RowCount, ecommerceDF.ColumnCount);
string[] features = new string[] { "NetRevenuePercentile", "AvgUnitPricePercentile", "AvgQuantityPercentile" };
Console.WriteLine("* Features: {0}\n\n", String.Join(", ", features));
var normalizedDf = Frame.CreateEmpty <int, string>();
var average = ecommerceDF.Columns[features].Sum() / ecommerceDF.RowCount;
foreach (string feature in features)
{
normalizedDf.AddColumn(feature, (ecommerceDF[feature] - average[feature]) / ecommerceDF[feature].StdDev());
}
double[][] sampleSet = BuildJaggedArray(
normalizedDf.Columns[features].ToArray2D <double>(),
normalizedDf.RowCount,
features.Length
);
// Create a new K-Means algorithm with n clusters
Accord.Math.Random.Generator.Seed = 0;
int[] numClusters = new int[] { 4, 5, 6, 7, 8 };
List <string> clusterNames = new List <string>();
List <double> silhouetteScores = new List <double>();
for (int i = 0; i < numClusters.Length; i++)
{
KMeans kmeans = new KMeans(numClusters[i]);
KMeansClusterCollection clusters = kmeans.Learn(sampleSet);
int[] labels = clusters.Decide(sampleSet);
string colname = String.Format("Cluster-{0}", numClusters[i]);
clusterNames.Add(colname);
normalizedDf.AddColumn(colname, labels);
ecommerceDF.AddColumn(colname, labels);
Console.WriteLine("\n\n\n##################### {0} ###########################", colname);
Console.WriteLine("\n\n* Centroids for {0} clusters:", numClusters[i]);
PrintCentroidsInfo(clusters.Centroids, features);
Console.WriteLine("\n");
VisualizeClusters(normalizedDf, colname, "NetRevenuePercentile", "AvgUnitPricePercentile");
VisualizeClusters(normalizedDf, colname, "AvgUnitPricePercentile", "AvgQuantityPercentile");
VisualizeClusters(normalizedDf, colname, "NetRevenuePercentile", "AvgQuantityPercentile");
for (int j = 0; j < numClusters[i]; j++)
{
GetTopNItemsPerCluster(ecommerceDF, j, colname);
}
double silhouetteScore = CalculateSilhouetteScore(normalizedDf, features, numClusters[i], colname);
Console.WriteLine("\n\n* Silhouette Score: {0}", silhouetteScore.ToString("0.0000"));
silhouetteScores.Add(silhouetteScore);
Console.WriteLine("\n\n##############################################################\n\n\n");
}
for (int i = 0; i < clusterNames.Count; i++)
{
Console.WriteLine("- Silhouette Score for {0}: {1}", clusterNames[i], silhouetteScores[i].ToString("0.0000"));
}
Console.WriteLine("\n\n\nDONE!!");
Console.ReadKey();
}
