| public Learn ( double x, double weights = null ) : KMeansClusterCollection | ||
| x | double | The model inputs. |
| weights | double | The weight of importance for each input sample. |
| return | KMeansClusterCollection | |
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));
}
