public void CountMDFMean_CountCorrectMean()
{
Params.inputDataDimension = 3;
Params.outputDataDimension = 3;
ClusterX clusterX = new ClusterX(new Sample(new double[] { 1, 2, 3 }, 1.0, 0), null);
clusterX.AddItem(new Vector(new double[] { 2, 3, 4 }), 0);
clusterX.AddItem(new Vector(new double[] { 3, 4, 5 }), 0);
clusterX.Items[0].ValuesMDF = new double[] { 1, 2, 3 };
clusterX.Items[1].ValuesMDF = new double[] { 2, 3, 4 };
clusterX.Items[2].ValuesMDF = new double[] { 3, 4, 5 };
clusterX.CountMDFMean();
Assert.AreEqual(clusterX.MeanMDF[0], 2.0);
Assert.AreEqual(clusterX.MeanMDF[1], 3.0);
Assert.AreEqual(clusterX.MeanMDF[2], 4.0);
}
public void GetGaussianNLL_GetCorrectGausianNLL()
{
Params.inputDataDimension = 3;
Params.outputDataDimension = 3;
ClusterX clusterX = new ClusterX(new Sample(new double[] { 1, 2, 3 }, 1.0, 0), null);
clusterX.AddItem(new Vector(new double[] { 2, 3, 4 }), 0);
clusterX.AddItem(new Vector(new double[] { 3, 4, 5 }), 0);
clusterX.Items[0].ValuesMDF = new double[] { 1, 2, 3 };
clusterX.Items[1].ValuesMDF = new double[] { 2, 3, 4 };
clusterX.Items[2].ValuesMDF = new double[] { 3, 4, 5 };
clusterX.CountMDFMean();
clusterX.CovMatrixMDF = new double[3, 3] { { 1, 2, 3 }, { 2, 1, 2 }, { 3, 3, 1 } };
// clusterX.GetGaussianNLL(new double[] { 1, 2, 3 });
}
