static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
int k = 3;
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
DoubleMatrix trainlab = Load.load_labels("../data/label_train_multiclass.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
Labels labels = new Labels(trainlab);
KNN knn = new KNN(k, distance, labels);
knn.train();
DoubleMatrix out_labels = knn.apply(feats_test).get_labels();
Console.WriteLine(out_labels.ToString());
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
distance.set_disable_sqrt(true);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
foreach(double item in dm_train) {
Console.Write(item);
}
foreach(double item in dm_test) {
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
distance.set_disable_sqrt(true);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
foreach (double item in dm_train)
{
Console.Write(item);
}
foreach (double item in dm_test)
{
Console.Write(item);
}
modshogun.exit_shogun();
}
public virtual object run(IList para)
{
modshogun.init_shogun_with_defaults();
double tau_coef = (double)((double?)para[0]);
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
ExponentialKernel kernel = new ExponentialKernel(feats_train, feats_train, tau_coef, distance, 10);
kernel.init(feats_train, feats_train);
DoubleMatrix km_train =kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
DoubleMatrix km_test =kernel.get_kernel_matrix();
ArrayList result = new ArrayList();
result.Add(km_train);
result.Add(km_test);
result.Add(kernel);
modshogun.exit_shogun();
return (object)result;
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double degree = 1.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
PowerKernel kernel = new PowerKernel(feats_train, feats_test, degree, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach (double item in km_train)
Console.Write(item);
foreach (double item in km_test)
Console.Write(item);
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double degree = 2.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
WaveKernel kernel = new WaveKernel(feats_train, feats_test, degree, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach (double item in km_train)
{
Console.Write(item);
}
foreach (double item in km_test)
{
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
int k = 3;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
double[] trainlab = Load.load_labels("../data/label_train_multiclass.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
Labels labels = new Labels(trainlab);
KNN knn = new KNN(k, distance, labels);
knn.train();
double[] out_labels = knn.apply(feats_test).get_labels();
foreach(double item in out_labels) {
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int k = 3;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
double[] trainlab = Load.load_labels("../data/label_train_multiclass.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
MulticlassLabels labels = new MulticlassLabels(trainlab);
KNN knn = new KNN(k, distance, labels);
knn.train();
double[] out_labels = MulticlassLabels.obtain_from_generic(knn.apply(feats_test)).get_labels();
foreach (double item in out_labels)
{
Console.Write(item);
}
modshogun.exit_shogun();
}
public void ClusteringTest()
{
List <ClusteredData> datanew = new List <ClusteredData>();
EuclidianDistance distance = new EuclidianDistance();
K_Means k_means = new K_Means(2, 0.1, distance);
ClusterAnalysis clusterAnalysis = new ClusterAnalysis(k_means);
datanew.Add(new ClusteredData(0, 0));
datanew.Add(new ClusteredData(0, 20));
datanew.Add(new ClusteredData(20, 0));
datanew.Add(new ClusteredData(20, 20));
var list = clusterAnalysis.Clustering(datanew);
Assert.AreEqual(2, list.Count);
var flag = from value in k_means.FinishesCentroids.Values
where (value.X == 0 && value.Y == 0) ||
(value.X == 0 && value.Y == 20) ||
(value.X == 20 && value.Y == 20) ||
(value.X == 20 && value.Y == 0) ||
(value.X == 10 && value.Y == 20) ||
(value.X == 0 && value.Y == 10) ||
(value.X == 20 && value.Y == 10) ||
(value.X == 0 && value.Y == 10)
select value;
Assert.AreEqual(0.1, k_means.CoefficientTaboo);
Assert.AreEqual(2, k_means.CountOfClusters);
Assert.IsNotNull(flag);
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double sigma = 1.0;
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
CauchyKernel kernel = new CauchyKernel(feats_train, feats_train, sigma, distance);
DoubleMatrix km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
DoubleMatrix km_test =kernel.get_kernel_matrix();
Console.WriteLine(km_train.ToString());
Console.WriteLine(km_test.ToString());
modshogun.exit_shogun();
}
public void GetValueOfDistanceTest()
{
EuclidianDistance euclidianDistance = new EuclidianDistance();
euclidianDistance.GetValueOfDistance(new ClusteredData(0, 0), new ClusteredData(2, 2));
Assert.AreEqual(2, 2);
}
static void Main(string[] args)
{
List<Double> seriesA = (new double[] { 4, 5, 6, 7, 8 }).ToList();
List<Double> seriesB = (new double[] { 4, 5, 6, 7, 8 }).ToList();
var distance = new EuclidianDistance();
var cost = DTW<Double>.getDistance(seriesA, seriesB, distance, 4);
Console.WriteLine("Cost: " + cost);
Console.ReadLine();
}
static void Main(string[] args)
{
List <Double> seriesA = (new double[] { 4, 5, 6, 7, 8 }).ToList();
List <Double> seriesB = (new double[] { 4, 5, 6, 7, 8 }).ToList();
var distance = new EuclidianDistance();
var cost = DTW <Double> .getDistance(seriesA, seriesB, distance, 4);
Console.WriteLine("Cost: " + cost);
Console.ReadLine();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double sigma = 1.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
CircularKernel kernel = new CircularKernel(feats_train, feats_train, sigma, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
// Parse and Display km_train
Console.Write("km_train:\n");
int numRows = km_train.GetLength(0);
int numCols = km_train.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(km_train[i, j] + " ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\nkm_test:\n");
numRows = km_test.GetLength(0);
numCols = km_test.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(km_test[i, j] + " ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double width = 1.7;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance();
DistanceKernel kernel = new DistanceKernel(feats_train, feats_test, width, distance);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
// Parse and Display km_train
Console.Write("dm_train:\n");
int numRows = dm_train.GetLength(0);
int numCols = dm_train.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(dm_train[i, j] + " ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\ndm_test:\n");
numRows = dm_test.GetLength(0);
numCols = dm_test.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(dm_test[i, j] + " ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
private static BasicClassifier GetTrainingClassifier()
{
var baseDirectory = @"C:\Users\pavel\Documents\Visual Studio 2017\Projects\DigitRecognizer\DigitRecognizer\";
var distance = new EuclidianDistance();
var classifier = new BasicClassifier(distance);
var trainingPath = $@"{baseDirectory}train.csv";
var training
= DataReader.ReadObservations(trainingPath);
classifier.Train(training);
return(classifier);
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double sigma = 1.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
CauchyKernel kernel = new CauchyKernel(feats_train, feats_train, sigma, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test=kernel.get_kernel_matrix();
// Parse and Display km_train
Console.Write("km_train:\n");
int numRows = km_train.GetLength(0);
int numCols = km_train.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(km_train[i,j] +" ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\nkm_test:\n");
numRows = km_test.GetLength(0);
numCols = km_test.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(km_test[i,j] +" ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
int merges = 3;
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
Hierarchical hierarchical = new Hierarchical(merges, distance);
hierarchical.train();
double[] out_distance = hierarchical.get_merge_distances();
int[,] out_cluster = hierarchical.get_cluster_pairs();
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double width = 1.7;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance();
DistanceKernel kernel = new DistanceKernel(feats_train, feats_test, width, distance);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
// Parse and Display km_train
Console.Write("dm_train:\n");
int numRows = dm_train.GetLength(0);
int numCols = dm_train.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(dm_train[i,j] +" ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\ndm_test:\n");
numRows = dm_test.GetLength(0);
numCols = dm_test.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(dm_test[i,j] +" ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
int k = 3;
// already tried init_random(17)
Math.init_random(17);
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
KMeans kmeans = new KMeans(k, distance);
kmeans.train();
double[,] out_centers = kmeans.get_cluster_centers();
kmeans.get_radiuses();
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int merges = 3;
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
Hierarchical hierarchical = new Hierarchical(merges, distance);
hierarchical.train();
double[] out_distance = hierarchical.get_merge_distances();
int[,] out_cluster = hierarchical.get_cluster_pairs();
modshogun.exit_shogun();
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
DoubleMatrix dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
DoubleMatrix dm_test = distance.get_distance_matrix();
Console.WriteLine(dm_train.ToString());
Console.WriteLine(dm_test.ToString());
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int k = 3;
// already tried init_random(17)
Math.init_random(17);
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
KMeans kmeans = new KMeans(k, distance);
kmeans.train();
double[,] out_centers = kmeans.get_cluster_centers();
kmeans.get_radiuses();
modshogun.exit_shogun();
}
internal static ArrayList run(int para)
{
modshogun.init_shogun_with_defaults();
int merges = para;
DoubleMatrix fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
Hierarchical hierarchical = new Hierarchical(merges, distance);
hierarchical.train();
DoubleMatrix out_distance = hierarchical.get_merge_distances();
DoubleMatrix out_cluster = hierarchical.get_cluster_pairs();
ArrayList result = new ArrayList();
result.Add(hierarchical);
result.Add(out_distance);
result.Add(out_cluster);
modshogun.exit_shogun();
return result;
}
internal static ArrayList run(int para)
{
modshogun.init_shogun_with_defaults();
int k = para;
init_random(17);
DoubleMatrix fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclidianDistance distance = new EuclidianDistance(feats_train, feats_train);
KMeans kmeans = new KMeans(k, distance);
kmeans.train();
DoubleMatrix out_centers = kmeans.get_cluster_centers();
kmeans.get_radiuses();
ArrayList result = new ArrayList();
result.Add(kmeans);
result.Add(out_centers);
modshogun.exit_shogun();
return result;
}
internal static HandleRef getCPtr(EuclidianDistance obj) {
return (obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr;
}
public int[] Generate(IEnumerable <T> examples, int k, IDistance metric = null)
{
#region Setup
if (examples == null)
{
throw new InvalidOperationException("Cannot generate a model will no data!");
}
if (k < 2)
{
throw new InvalidOperationException("Can only cluter with k > 1");
}
if (Description == null)
{
Description = Converter.GetDescription(typeof(T)).BuildDictionaries <T>(examples);
}
Matrix X = Converter.Convert <T>(examples, Description.Features);
// clear out zeros (if any...)
//var indices = X.Indices(v => !v.All(d => d == 0), VectorType.Row);
//X = X.Slice(indices, VectorType.Row);
//X.Normalize(VectorType.Row);
if (k >= X.Rows)
{
throw new InvalidOperationException(
string.Format("Cannot cluster {0} items {1} different ways!", X.Rows, k));
}
var means = InitializeRandom(X, k);
var diff = double.MaxValue;
int[] assignments = new int[X.Rows];
#endregion
if (metric == null)
{
metric = new EuclidianDistance();
}
for (int i = 0; i < 100; i++)
{
// Assignment step
Parallel.For(0, X.Rows, j =>
{
var min_index = -1;
var min = double.MaxValue;
// current example
var x = X[j, VectorType.Row];
for (int m = 0; m < means.Rows; m++)
{
var d = means[m, VectorType.Row];
var distance = metric.Compute(x, d);
if (distance < min)
{
min = distance;
min_index = m;
}
assignments[j] = min_index;
}
});
// Update Step
// new means has k rows and X.Cols columns
Matrix new_means = Matrix.Zeros(k, X.Cols);
Vector sum = Vector.Zeros(k);
// Part 1: Sum up assignments
for (int j = 0; j < X.Rows; j++)
{
new_means[assignments[j], VectorType.Row] += X[j, VectorType.Row];
sum[assignments[j]]++;
}
// Part 2: Divide by counts
for (int j = 0; j < new_means.Rows; j++)
{
new_means[j, VectorType.Row] /= sum[j];
}
// find sum of normdiff's of means
diff = means.GetRows()
.Zip(new_means.GetRows(), (v1, v2) => new { V1 = v1, V2 = v2 })
.Sum(a => Vector.NormDiff(a.V1, a.V2));
// small diff? return
if (diff < .00001)
{
Centers = means;
return(assignments);
}
means = new_means;
}
Centers = means;
return(assignments);
}
public SelectorResult Compute(float Percent, int maxrank)
{
if (Percent > 100 || Percent < 0)
{
throw new ArgumentException("");
}
int countA = (int)(Percent * this._x.ColumnCount / 100);
var GroupAIndex = new List <int>();
var GroupBIndex = new List <int>();
if (this._x == null)
{
return(null);
}
var resid = this._x * this._x.Transpose();
var j = this._x.ColumnCount;
var p2 = Math.Round(j * Percent / 100);
int k = 0;
this._x = (Matrix)this._x.Transpose();
var i = maxrank;
var p = new DenseMatrix(i, this._x.ColumnCount);
var told = new DenseMatrix(j, i);
var t = new DenseMatrix(j, i, 1);
var w = new DenseMatrix(i, this._x.ColumnCount);
var l = new DenseVector(i);
for (int h = 0; h < i; h++)
{
double sumt = 0;
for (int ddd = 0; ddd < j; ddd++)
{
sumt += Math.Abs(told[ddd, h] - t[ddd, h]);
}
while ((sumt > 1E-15 && k < 100))
{
told.SetColumn(h, t.Column(h));
k++;
var wt = t.Column(h) * this._x / t.Column(h).DotProduct(t.Column(h));//更新更改
wt = wt / Math.Sqrt(wt.DotProduct(wt));
w.SetRow(h, wt);
t.SetColumn(h, this._x * w.Row(h) / w.Row(h).DotProduct(w.Row(h)));
}
l[h] = k;
k = 0;
var pt = t.Column(h) * this._x / t.Column(h).DotProduct(t.Column(h));
pt = pt / Math.Sqrt(pt.DotProduct(pt));
var tempt = Math.Sqrt(pt.DotProduct(pt));
p.SetRow(h, pt);
t.SetColumn(h, t.Column(h) / tempt);
w.SetRow(h, w.Row(h) / tempt);
this._x = (Matrix)(this._x - t.Column(h).ToColumnMatrix() * p.Row(h).ToRowMatrix());
}
var G = t.Transpose();
var dist = new EuclidianDistance();
var MM = Matlab.PDist((Matrix)G, dist, VectorType.Column);
var M = Matlab.Triu(MM);
double[] maxVal;
int[] maxIdx;
Matlab.Max(M, out maxVal, out maxIdx);
var ttt = new DenseVector(maxVal);
int idx = ttt.MaximumIndex();
double temp = ttt.Maximum();
GroupAIndex.AddRange(new int[] { idx, maxIdx[idx] });
for (int tr = 2; tr < p2; tr++)
{
var tm = new DenseMatrix(GroupAIndex.Count, MM.ColumnCount);
for (int kkk = 0; kkk < GroupAIndex.Count; kkk++)
{
tm.SetRow(kkk, MM.Column(GroupAIndex[kkk]));
foreach (var r in GroupAIndex)
{
tm[kkk, r] = double.NaN;
}
}
int[] midx;
double[] minval;
Matlab.Min(tm, out minval, out midx);
var minttt = new DenseVector(minval);
temp = minttt.Maximum();
idx = minttt.MaximumIndex();
GroupAIndex.Add(idx);
}
GroupAIndex.Sort();
for (int r = 0; r < j; r++)
{
if (!GroupAIndex.Contains(r))
{
GroupBIndex.Add(r);
}
}
return(new SelectorResult()
{
GroupAIndex = GroupAIndex.ToArray(), GroupBIndex = GroupBIndex.ToArray()
});
}
/// <summary>Generates.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="k">The int to process.</param>
/// <param name="metric">the metric.</param>
/// <returns>An int[].</returns>
public int[] Generate(Matrix x, int k, IDistance metric)
{
if (metric == null)
{
metric = new EuclidianDistance();
}
X = x;
var means = InitializeRandom(X, k);
var assignments = new int[X.Rows];
for (var i = 0; i < 100; i++)
{
// Assignment step
for (var j = 0; j < X.Rows; j++)
{
var min_index = -1;
var min = double.MaxValue;
for (var m = 0; m < means.Rows; m++)
{
var d = metric.Compute(X[j], means[m]);
if (d < min)
{
min = d;
min_index = m;
}
}
// bounds?
if (min_index == -1)
{
min_index = 0;
}
assignments[j] = min_index;
}
// Update Step
var new_means = Matrix.Zeros(k, X.Cols);
var sum = Vector.Zeros(k);
// Part 1: Sum up assignments
for (var j = 0; j < X.Rows; j++)
{
var a = assignments[j];
new_means[a] += X[j, VectorType.Row];
sum[a]++;
}
// Part 2: Divide by counts
for (var j = 0; j < new_means.Rows; j++)
{
new_means[j] /= sum[j];
}
// Part 3: Check for convergence
// find norm of the difference
if ((means - new_means).Norm() < .00001)
{
break;
}
means = new_means;
}
Centers = means;
return(assignments);
}
internal static HandleRef getCPtr(EuclidianDistance obj)
{
return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr);
}
static void Main(string[] args)
{
List <ClusteredData> datanew = new List <ClusteredData>();
EuclidianDistance distance = new EuclidianDistance();
K_Means k_means = new K_Means(2, 0.1, distance);
ClusterAnalysis clusterAnalysis = new ClusterAnalysis(k_means);
datanew.Add(new ClusteredData(0, 0));
datanew.Add(new ClusteredData(0, 20));
datanew.Add(new ClusteredData(20, 0));
datanew.Add(new ClusteredData(20, 20));
var list = clusterAnalysis.Clustering(datanew);
/*
* List<ClusteredData> datanew = new List<ClusteredData>();
* List<K_Means> k_meansList = new List<K_Means>();
* List<List<Cluster>> clustersList = new List<List<Cluster>>();
* System.Diagnostics.Stopwatch myStopwatch = new System.Diagnostics.Stopwatch();
* EuclidianDistance distance = new EuclidianDistance();
*
* datanew.Add(new ClusteredData(0,0));
* datanew.Add(new ClusteredData(0, 20));
* datanew.Add(new ClusteredData(20, 0));
* datanew.Add(new ClusteredData(20, 20));
*
* var ewrt = datanew.Select(Er => Er.X);
* var ety = ewrt.Average();
*
*
*
* var e = datanew.Average(er => er.X);
*
*
* myStopwatch.Start(); //запуск
* for (var i = 0; i < 1000; i++)
* {
* K_Means k_means = new K_Means(2, 0.1, distance);
* clustersList.Add(k_means.Clustering(datanew));
* k_meansList.Add(k_means);
* Thread.Sleep(10);
*
* var flag = k_means.FinishesCentroids.ContainsValue(new Centroid(0, 0));
* var flag1 = k_means.FinishesCentroids.ContainsValue(new Centroid(0, 20));
* var flag2 = k_means.FinishesCentroids.ContainsValue(new Centroid(20, 20));
* var flag3 = k_means.FinishesCentroids.ContainsValue(new Centroid(20, 0));
* }
* myStopwatch.Stop();
*
* var fulltime = myStopwatch.ElapsedMilliseconds;
* double Onetime = myStopwatch.ElapsedMilliseconds/1000.0;
*
* int a = 0;
* int b = 0;
* int c = 0;
* int d = 0;
*
* foreach (var list in clustersList)
* {
* a += list.Count(q => q.Data.Count == 3);
* b += list.Count(q => q.Data.Count == 2);
* c += list.Count(q => q.Data.Count == 1);
* d += list.Count(q => q.Data.Count == 0);
* }
* b = b / 2;
* var ads = new Cluster(1, new Centroid(1, 10));
* //ads.IntraClusterDistance();
*
* //// Поиск объектов класса Cluster в списке optimumClusters с максимальным внутрикластерным расcтоянием
* //var nonOptimumClusters = from list in clustersList // определяем каждый объект из clusters как cluster
* // where
* // select cluster.IntraClusterDistance();// выбираем объект
*
*/
}
/// <summary>
/// Initializes a new Collaborative Filtering recommender model.
/// </summary>
public CofiRecommenderModel()
{
RelatedDistanceFunction = new EuclidianDistance();
}
