public void new_method()
{
#region doc_learn
// Create some sample input data instances.
double[][] inputs =
{
// Class 0
new double[] { 4, 1 },
new double[] { 2, 4 },
new double[] { 2, 3 },
// Class 1
new double[] { 5, 5 },
new double[] { 5, 6 },
// Class 2
new double[] { 10, 8 }
};
int[] output =
{
0, 0, 0, // The first three are from class 0
1, 1, // The second two are from class 1
2 // The last is from class 2
};
// We will create a MMDC object for the data
var mmdc = new MinimumMeanDistanceClassifier();
// Compute the analysis and create a classifier
mmdc.Learn(inputs, output);
// Now we can project the data into mean distance space:
double[][] projection = mmdc.Scores(inputs);
// Or perform classification using:
int[] results = mmdc.Decide(inputs);
#endregion
Assert.IsTrue(results.IsEqual(output));
string str = projection.ToCSharp();
double[][] expected = new double[][]
{
new double[] { -4.55555555555556, -21.25, -85 },
new double[] { -2.22222222222222, -11.25, -80 },
new double[] { -0.555555555555555, -15.25, -89 },
new double[] { -10.8888888888889, -0.25, -34 },
new double[] { -16.5555555555556, -0.25, -29 },
new double[] { -82.2222222222222, -31.25, 0 }
};
Assert.IsTrue(expected.IsEqual(projection, 1e-6));
}
