public static object KCluster(double[,] Data, int NumClusters)
{
int nRows = Data.GetLength(0), nCols = Data.GetLength(1);
double[][] data = new double[nRows][];
for (int r = 0; r < nRows; ++r)
{
data[r] = new double[nCols];
for (int c = 0; c < nCols; ++c)
{
data[r][c] = Data[r, c];
}
}
// Unfortunately, you can't set the initial cluster centers manually here, which
// means that every time this function is called, it will return different clusters.
aml.KMeans km = new aml.KMeans(NumClusters);
aml.KMeansClusterCollection kcc = km.Learn(data);
int nClusters = kcc.Count;
double[,] ret = new double[nClusters, nCols];
// TODO - 19Apr20 - needs testing, this used to previously put the mean[c] into ret[x,c]?
int x = 0;
foreach (KMeansClusterCollection.KMeansCluster cc in kcc.Clusters.OrderBy(xx => xx.Centroid.Mean()))
{
for (int c = 0; c < nCols; ++c)
{
ret[x, c] = cc.Centroid.Mean();
}
x++;
}
return(ret);
}
private static Tuple <double[][], double[][]> split(double[][] cluster,
KMeans kmeans, double threshold)
{
kmeans.Randomize(cluster, useSeeding: false);
int[] idx = kmeans.Compute(cluster, threshold, false);
List <double[]> a = new List <double[]>();
List <double[]> b = new List <double[]>();
for (int i = 0; i < idx.Length; i++)
{
if (idx[i] == 0)
{
a.Add(cluster[i]);
}
else
{
b.Add(cluster[i]);
}
}
return(Tuple.Create(a.ToArray(), b.ToArray()));
}
/// <summary>
/// Initializes the model with initial values obtained
/// through a run of the K-Means clustering algorithm.
/// </summary>
///
public void Initialize(KMeans kmeans)
{
clusters.Initialize(kmeans);
}
/// <summary>
/// Divides the input data into K clusters.
/// </summary>
///
/// <param name="data">The data where to compute the algorithm.</param>
/// <param name="weights">The weight associated with each data point.</param>
///
public override int[] Compute(double[][] data, double[] weights)
{
// Initial argument checking
if (data == null)
{
throw new ArgumentNullException("data");
}
if (data.Length < K)
{
throw new ArgumentException("Not enough points. There should be more points than the number K of clusters.");
}
if (weights == null)
{
throw new ArgumentNullException("weights");
}
if (data.Length != weights.Length)
{
throw new ArgumentException("Data weights vector must be the same length as data samples.");
}
double weightSum = weights.Sum();
if (weightSum <= 0)
{
throw new ArgumentException("Not enough points. There should be more points than the number K of clusters.");
}
int cols = data[0].Length;
for (int i = 0; i < data.Length; i++)
{
if (data[0].Length != cols)
{
throw new DimensionMismatchException("data", "The points matrix should be rectangular. The vector at position {} has a different length than previous ones.");
}
}
int k = Clusters.Count;
KMeans kmeans = new KMeans(2)
{
Distance = (IDistance <double[]>)Clusters.Distance,
ComputeError = false,
ComputeCovariances = false,
UseSeeding = UseSeeding,
Tolerance = Tolerance,
MaxIterations = MaxIterations,
};
double[][] centroids = Clusters.Centroids;
double[][][] clusters = new double[k][][];
double[] distortions = new double[k];
// 1. Start with all data points in one cluster
clusters[0] = data;
// 2. Repeat steps 3 to 6 (k-1) times to obtain K centroids
for (int current = 1; current < k; current++)
{
// 3. Choose cluster with largest distortion
int choosen; distortions.Max(current, out choosen);
// 4. Split cluster into two sub-clusters
var splits = split(clusters[choosen], kmeans);
clusters[choosen] = splits.Item1;
clusters[current] = splits.Item2;
// 5. Replace chosen centroid and add a new one
centroids[choosen] = kmeans.Clusters.Centroids[0];
centroids[current] = kmeans.Clusters.Centroids[1];
// Recompute distortions for the updated clusters
distortions[choosen] = kmeans.Clusters[0].Distortion(clusters[choosen]);
distortions[current] = kmeans.Clusters[1].Distortion(clusters[current]);
// 6. Increment cluster count (current = current + 1)
}
return(Clusters.Nearest(data));
}
internal KMeansClusterCollection(KMeans owner, IList <KMeansCluster> list)
: base(list)
{
this.owner = owner;
}
internal KMeansCluster(KMeans owner, int index)
{
this.owner = owner;
this.index = index;
}
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);
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
public Tuple<double[,], Dictionary<string, int>> StartClustering()
{
convert_input(); // 1. build adjacency matrix.
diagonalMatrix(); // 2.1 build diganoal matrix.
buildLmat(); // 2.2 build L = D - adjacency_matrix.
calculate_eigendecomposition(); // 3. find k largest eigenvectors.
//findEigenvectors(); // 3. find k largest eigenvectors.
normalizeVectors(); // 4. normalize eigenvectors.
labels = new int[inputSize];
//////ClusterCollection clusters;
//////clusters = KMeans.ClusterDataSet(NumofClusters, Y);
//////int index = -1;
//////double[] temp = new double[NumofClusters];
//////for (int i = 0; i < clusters.Count; i++)
//////{
////// //System.Console.Out.Write(clusters.Count);
////// // System.Console.Out.Write(i + ") ( ");
////// for (int j = 0; j < clusters[i].Count; j++)
////// {
////// // System.Console.Out.Write(clusters[i].Count );
////// for (int k = 0; k < clusters[i][j].Length; k++) // one point (full row)
////// {
////// temp[k] = clusters[i][j][k];
////// }
////// index = findCluster(conArrayD(Y), temp);
////// if (index != -1) // cluster and row were matched
////// {
////// labels[index] = i;
////// index = -1; // reset index.
////// }
////// }
//////}
observations = new double[Y.GetLength(0)][];
for (int i = 0; i < Y.GetLength(0); i++)
{
observations[i] = new double[Y.GetLength(1)];
for (int j = 0; j < Y.GetLength(1); j++)
observations[i][j] = Y[i, j];
}
////////////////observations = new double[X.GetLength(0)][];
////////////////for (int i = 0; i < X.GetLength(0); i++)
////////////////{
//////////////// observations[i] = new double[X.GetLength(1)];
//////////////// for (int j = 0; j < X.GetLength(1); j++)
//////////////// observations[i][j] = X[i, j];
////////////////}
//K-Means
Accord.MachineLearning.KMeans kmeans = new Accord.MachineLearning.KMeans(k: NumofClusters);
var clusters = kmeans.Learn(observations);
labels = clusters.Decide(observations);
words_cluster = new Dictionary<string, int>();
for (int i = 0; i < labels.Length; i++)
words_cluster.Add(nodes[i], labels[i]);
var tuple = new Tuple<double[,], Dictionary<string, int>>(adjacency_matrix, words_cluster);
return tuple;
}// end StartClustering
}// end normalizeVectors()
/*
* Extract one vector from BoW.
* input: BoW matrix, number of Vector to extract.
* output: Extracted vector.
*/
private void convert_input()
{
string[] lines = input_file.ToArray<string>();
Dictionary<string, int> words = new Dictionary<string, int>();
List<string> val = new List<string>();
double[] vector = new double[lines.Length];
int k = 1;
for (int i = 0; i < lines.Length; i++)
{
string[] line = lines[i].Split('\t');
if (!words.ContainsKey(line[0]))
words.Add(line[0], k++);
if (!words.ContainsKey(line[1]))
words.Add(line[1], k++);
val.Add(line[2]);
vector[i] = double.Parse(line[2]);
}
double[][] observ = new double[vector.Length][];
for (int i = 0; i < vector.Length; i++)
observ[i] = new double[] { vector[i] };
Accord.MachineLearning.KMeans kmeans = new Accord.MachineLearning.KMeans(k:2);
var clusters = kmeans.Learn(observ);
int[] labels = clusters.Decide(observ);
////double min = 0, max = 0;
////Sigma(vector,ref min,ref max);
nodes = new List<string>();// { "Id" };
List<string> edges = new List<string>();// { "Source; Target" };
HashSet<string> tmpnodes = new HashSet<string>();
for (int i = 0; i < lines.Length; i++)
{
if (labels[i] == 2 || labels[i] == 1)//if (vector[i] < min || vector[i] > max)
continue;
string[] tmp = lines[i].Split('\t');
//newlines.Add(words[tmp[0]].ToString() + "," + words[tmp[1]].ToString());// + "\t" + val[i]);
edges.Add(tmp[0] + "\t" + tmp[1] + "\t" + val[i]);
//if (!nodes.Contains(tmp[0]))
tmpnodes.Add(tmp[0]);
//if (!nodes.Contains(tmp[1]))
tmpnodes.Add(tmp[1]);
}
nodes.AddRange(tmpnodes.ToList());
File.WriteAllLines("nodes.csv", nodes);
File.WriteAllLines("edges.csv", edges);
nodes = new List<string>();
foreach (var item in words)
{
nodes.Add(item.Key);
}
File.WriteAllLines("nodes.txt", nodes);
inputSize = nodes.Count;
adjacency_matrix = new double[inputSize, inputSize]; // init affinity matrix
D = new double[inputSize, inputSize]; // init diagonal matrix
Y = new double[inputSize, NumofClusters]; D = new double[inputSize, inputSize];
L = new double[inputSize, inputSize];
SortedDictionary<int, List<string>> lst = new SortedDictionary<int, List<string>>();
List<string> all_temp = new List<string>();
string[] file = edges.ToArray();
for (int i = 0; i < file.Length; i++)
{
string[] row = file[i].Split('\t');
int feat = 0;
if (!lst.ContainsKey(feat))
lst.Add(feat, new List<string>());
lst[feat].Add(row[0] + "\t" + row[1] + "\t" + row[2]);
}
for (int i = 0; i < lst.Count; i++)
normalize(lst[i], i.ToString());
List<string> all_results = new List<string>();
for (int i = 0; i < lst.Count; i++)
{
string[] files = all_res[i].ToArray<string>();
for (int j = 0; j < files.Length; j++)
all_results.Add(files[j]);
}
build_adjmat(all_results);
}
/// <summary>
/// Learns a model that can map the given inputs to the desired outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="weights">The weight of importance for each input sample.</param>
/// <returns>A model that has learned how to produce suitable outputs
/// given the input data <paramref name="x" />.</returns>
public override KMeansClusterCollection Learn(double[][] x, double[] weights = null)
{
// Initial argument checking
if (x == null)
{
throw new ArgumentNullException("x");
}
if (x.Length < K)
{
throw new ArgumentException("Not enough points. There should be more points than the number K of clusters.");
}
if (weights == null)
{
weights = Vector.Ones(x.Length);
}
if (x.Length != weights.Length)
{
throw new ArgumentException("Data weights vector must be the same length as data samples.");
}
double weightSum = weights.Sum();
if (weightSum <= 0)
{
throw new ArgumentException("Not enough points. There should be more points than the number K of clusters.");
}
int cols = x.Columns();
for (int i = 0; i < x.Length; i++)
{
if (x[i].Length != cols)
{
throw new DimensionMismatchException("data", "The points matrix should be rectangular. The vector at position {} has a different length than previous ones.");
}
}
int k = Clusters.Count;
KMeans kmeans = new KMeans(2)
{
Distance = (IDistance <double[]>)Clusters.Distance,
ComputeError = false,
ComputeCovariances = false,
UseSeeding = UseSeeding,
Tolerance = Tolerance,
MaxIterations = MaxIterations,
};
var centroids = Clusters.Centroids;
var clusters = new double[k][][];
var distortions = new double[k];
// 1. Start with all data points in one cluster
clusters[0] = x;
// 2. Repeat steps 3 to 6 (k-1) times to obtain K centroids
for (int current = 1; current < k; current++)
{
// 3. Choose cluster with largest distortion
int choosen; distortions.Max(current, out choosen);
// 4. Split cluster into two sub-clusters
var splits = split(clusters[choosen], kmeans);
clusters[choosen] = splits.Item1;
clusters[current] = splits.Item2;
// 5. Replace chosen centroid and add a new one
centroids[choosen] = kmeans.Clusters.Centroids[0];
centroids[current] = kmeans.Clusters.Centroids[1];
// Recompute distortions for the updated clusters
distortions[choosen] = kmeans.Clusters[0].Distortion(clusters[choosen]);
distortions[current] = kmeans.Clusters[1].Distortion(clusters[current]);
// 6. Increment cluster count (current = current + 1)
}
Clusters.NumberOfInputs = cols;
Accord.Diagnostics.Debug.Assert(Clusters.NumberOfClasses == K);
Accord.Diagnostics.Debug.Assert(Clusters.NumberOfOutputs == K);
Accord.Diagnostics.Debug.Assert(Clusters.NumberOfInputs == x[0].Length);
if (ComputeProportions)
{
int[] y = Clusters.Decide(x);
int[] counts = y.Histogram();
counts.Divide(y.Length, result: Clusters.Proportions);
ComputeInformation(x, y);
}
else
{
ComputeInformation(x);
}
return(Clusters);
}
