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));
}
private static void binarySplit(double[][] inputs)
{
// Create a binary-split algorithm
var binarySplit = new BinarySplit(k: 3)
{
Distance = new SquareEuclidean(),
MaxIterations = 1000
};
// Use it to learn a data model
var model = binarySplit.Learn(inputs);
// Use the model to group new instances
int[] prediction = model.Decide(inputs);
// Plot the results
ScatterplotBox.Show("Binary Split's answer", inputs, prediction).Hold();
}
public void binary_split_information_test()
{
// 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)
{
ComputeProportions = true,
ComputeCovariances = true,
ComputeError = true,
};
// Learn a data partitioning using the Binary Split algorithm
KMeansClusterCollection clustering = binarySplit.Learn(input);
string str = clustering.Proportions.ToCSharp();
double[] expectedProportions = new double[] { 0.333333333333333, 0.444444444444444, 0.222222222222222 };
Assert.IsTrue(expectedProportions.IsEqual(clustering.Proportions, 1e-10));
var strs = clustering.Covariances.Apply(x => x.ToCSharp());
double[][][] expectedCovar =
{
new double[][]
{
new double[] { 7, 0, 1 },
new double[] { 0, 0, 0 },
new double[] { 1, 0, 1.33333333333333 }
},
new double[][]
{
new double[] { 0.916666666666667, -0.25, -0.0833333333333333 },
new double[] { -0.25, 0.25, 0.0833333333333333 },
new double[] { -0.0833333333333333, 0.0833333333333333, 0.25 }
},
new double[][]
{
new double[] { 0, 0, 0 },
new double[] { 0, 4.5, 7.5 },
new double[] { 0, 7.5, 12.5 }
}
};
Assert.IsTrue(expectedCovar.IsEqual(clustering.Covariances, 1e-10));
}