public void yinyang_test()
{
// https://github.com/accord-net/framework/issues/451
double[][] observations = LeastSquaresLearningTest.yinyang.GetColumns(0, 1).ToJagged();
Accord.Math.Random.Generator.Seed = 0;
var kmeans = new BalancedKMeans(2)
{
// If a limit is not set, the following Learn call does not return....
MaxIterations = 1000
};
var clusters = kmeans.Learn(observations);
int[] labels = kmeans.Labels;
int[] expected = new int[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1 };
string str = labels.ToCSharp();
Assert.IsTrue(labels.IsEqual(expected));
int[] hist = Accord.Math.Vector.Histogram(labels);
for (int i = 0; i < hist.Length; i++)
{
Assert.AreEqual(hist[i], 50);
}
}
public void uniform_test()
{
// https://github.com/accord-net/framework/issues/451
int numClusters = 6;
double[][] observations =
{
new double[] { 10.8, 18.706148721743876 },
new double[] { -10.8, 18.706148721743876 },
new double[] { -21.6, 0.0 },
new double[] { -10.8, -18.706148721743876 },
new double[] { 10.8, -18.706148721743876 },
new double[] { 21.6, 0.0 },
new double[] { 32.4, 18.706148721743876 },
new double[] { 21.6, 37.412297443487752 },
new double[] { 0.0, 37.412297443487752 },
new double[] { -21.6, 37.412297443487752 },
new double[] { -32.4, 18.706148721743876 },
new double[] { -43.2, 0.0 },
new double[] { -32.4, -18.706148721743876 },
new double[] { -21.6, -37.412297443487752 },
new double[] { 0.0, -37.412297443487752 },
new double[] { 21.6, -37.412297443487752 },
new double[] { 32.4, -18.706148721743876 },
new double[] { 43.2, 0.0 }
};
Accord.Math.Random.Generator.Seed = 0;
var kmeans = new BalancedKMeans(numClusters)
{
// If a limit is not set, the following Learn call does not return....
MaxIterations = 1000
};
var clusters = kmeans.Learn(observations);
int[] labels = kmeans.Labels;
int[] expected = new[] { 2, 4, 4, 4, 0, 3, 3, 3, 2, 2, 5, 5, 5, 0, 0, 1, 1, 1 };
string str = labels.ToCSharp();
Assert.IsTrue(labels.IsEqual(expected));
int[] hist = Accord.Math.Vector.Histogram(labels);
for (int i = 0; i < hist.Length; i++)
{
Assert.AreEqual(hist[i], 3);
}
}
public void uniform_test_min_variance()
{
// https://github.com/accord-net/framework/issues/451
int numClusters = 6;
double[][] observations =
{
new double[] { 10.8, 18.706148721743876 },
new double[] { -10.8, 18.706148721743876 },
new double[] { -21.6, 0.0 },
new double[] { -10.8, -18.706148721743876 },
new double[] { 10.8, -18.706148721743876 },
new double[] { 21.6, 0.0 },
new double[] { 32.4, 18.706148721743876 },
new double[] { 21.6, 37.412297443487752 },
new double[] { 0.0, 37.412297443487752 },
new double[] { -21.6, 37.412297443487752 },
new double[] { -32.4, 18.706148721743876 },
new double[] { -43.2, 0.0 },
new double[] { -32.4, -18.706148721743876 },
new double[] { -21.6, -37.412297443487752 },
new double[] { 0.0, -37.412297443487752 },
new double[] { 21.6, -37.412297443487752 },
new double[] { 32.4, -18.706148721743876 },
new double[] { 43.2, 0.0 }
};
Accord.Math.Random.Generator.Seed = 0;
var kmeans = new BalancedKMeans(numClusters)
{
Distance = new SquareEuclidean(),
Centroids = new[]
{
// C = [ 0.00000 37.41230 ; -32.40000, -18.70615 ; 10.80000, 18.70615; -21.60000, 37.41230; -10.80000, -18.70615; 21.60000, -37.41230 ]
new[] { 0.00000, 37.41230 },
new[] { -32.40000, -18.70615 },
new[] { 10.80000, 18.70615 },
new[] { -21.60000, 37.41230 },
new[] { -10.80000, -18.70615 },
new[] { 21.60000, -37.41230 },
}
};
// If a limit is not set, the following Learn call does not return....
kmeans.MaxIterations = 1;
double[,] expectedCost =
{
{ 3.2659e+003, 1.8662e+003, 4.6656e+002, 4.6656e+002, 1.8662e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 4.1990e+003, 3.2659e+003, 1.3997e+003, 4.6656e+002, 1.6339e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 6.0653e+003 },
{ 1.6339e-012, 4.6656e+002, 1.3997e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 4.6656e+002, 4.6656e+002, 4.6656e+002, 1.3997e+003, 1.8662e+003, 3.2659e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003 },
{ 1.3997e+003, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.8662e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 7.4650e+003, 6.0653e+003, 5.5987e+003 },
{ 1.8662e+003, 1.3997e+003, 4.6656e+002, 1.6339e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 4.6656e+002, 4.6656e+002, 1.3997e+003, 1.8662e+003, 3.2659e+003 },
{ 3.2659e+003, 4.1990e+003, 3.2659e+003, 1.3997e+003, 4.6656e+002, 1.3997e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 7.4650e+003, 6.0653e+003, 5.5987e+003, 3.2659e+003, 1.8662e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.8662e+003 },
{ 4.6656e+002, 4.6656e+002, 1.8662e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 6.0653e+003, 5.5987e+003, 6.0653e+003, 4.1990e+003, 3.2659e+003 },
{ 3.2659e+003, 1.8662e+003, 4.6656e+002, 4.6656e+002, 1.8662e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 4.1990e+003, 3.2659e+003, 1.3997e+003, 4.6656e+002, 1.6339e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 6.0653e+003 },
{ 1.6339e-012, 4.6656e+002, 1.3997e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 4.6656e+002, 4.6656e+002, 4.6656e+002, 1.3997e+003, 1.8662e+003, 3.2659e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003 },
{ 1.3997e+003, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.8662e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 7.4650e+003, 6.0653e+003, 5.5987e+003 },
{ 1.8662e+003, 1.3997e+003, 4.6656e+002, 1.6339e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 4.6656e+002, 4.6656e+002, 1.3997e+003, 1.8662e+003, 3.2659e+003 },
{ 3.2659e+003, 4.1990e+003, 3.2659e+003, 1.3997e+003, 4.6656e+002, 1.3997e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 7.4650e+003, 6.0653e+003, 5.5987e+003, 3.2659e+003, 1.8662e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.8662e+003 },
{ 4.6656e+002, 4.6656e+002, 1.8662e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 6.0653e+003, 5.5987e+003, 6.0653e+003, 4.1990e+003, 3.2659e+003 },
{ 3.2659e+003, 1.8662e+003, 4.6656e+002, 4.6656e+002, 1.8662e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 4.1990e+003, 3.2659e+003, 1.3997e+003, 4.6656e+002, 1.6339e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 6.0653e+003 },
{ 1.6339e-012, 4.6656e+002, 1.3997e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 4.6656e+002, 4.6656e+002, 4.6656e+002, 1.3997e+003, 1.8662e+003, 3.2659e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003 },
{ 1.3997e+003, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.8662e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 7.4650e+003, 6.0653e+003, 5.5987e+003 },
{ 1.8662e+003, 1.3997e+003, 4.6656e+002, 1.6339e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 4.6656e+002, 4.6656e+002, 1.3997e+003, 1.8662e+003, 3.2659e+003 },
{ 3.2659e+003, 4.1990e+003, 3.2659e+003, 1.3997e+003, 4.6656e+002, 1.3997e+003, 3.2659e+003, 5.5987e+003, 6.0653e+003, 7.4650e+003, 6.0653e+003, 5.5987e+003, 3.2659e+003, 1.8662e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.8662e+003 },
{ 4.6656e+002, 4.6656e+002, 1.8662e+003, 3.2659e+003, 3.2659e+003, 1.8662e+003, 1.3997e+003, 4.6656e+002, 6.5358e-012, 4.6656e+002, 1.3997e+003, 3.2659e+003, 4.1990e+003, 6.0653e+003, 5.5987e+003, 6.0653e+003, 4.1990e+003, 3.2659e+003 }
};
Assert.AreEqual(0, kmeans.Iterations);
var clusters = kmeans.Learn(observations);
Assert.AreEqual(1, kmeans.Iterations);
double[][] actualCost = kmeans.munkres.CostMatrix;
for (int i = 0; i < actualCost.Length; i++)
{
for (int j = 0; j < actualCost[i].Length; j++)
{
double a = actualCost[i][j];
double e = expectedCost[i, j];
Assert.AreEqual(e, a, 0.1);
}
}
int[] labels = kmeans.Labels;
int[] hist = Accord.Math.Vector.Histogram(labels);
for (int i = 0; i < hist.Length; i++)
{
Assert.AreEqual(hist[i], 3);
}
}
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]);
}
}