public void UpdateMatrix_CovarianceMatrixIsCorrect()
{
Params.inputDataDimension = 3;
Params.outputDataDimension = 3;
ClusterX clusterX = new ClusterX(new Sample(new double[] { 4.0, 2.0, 0.6 }, 1.0, 0), null);
clusterX.AddItem(new Vector(new double[] { 4.2, 2.1, 0.59 }), 0);
clusterX.AddItem(new Vector(new double[] { 3.9, 2.0, 0.58 }), 0);
clusterX.AddItem(new Vector(new double[] { 4.3, 2.1, 0.62 }), 0);
clusterX.AddItem(new Vector(new double[] { 4.1, 2.2, 0.63 }), 0);
}
public void CountLabelOfCluster_LabelIsCorrect()
{
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 }), 2.0);
clusterX.AddItem(new Vector(new double[] { 2, 3, 4 }), 3.0);
clusterX.AddItem(new Vector(new double[] { 2, 3, 4 }), 2.0);
clusterX.AddItem(new Vector(new double[] { 2, 3, 4 }), 1.0);
clusterX.AddItem(new Vector(new double[] { 2, 3, 4 }), 2.0);
clusterX.CountLabelOfCluter();
Assert.AreEqual(clusterX.Label, 2.0);
}
public void CountCovarianceMatrixMDF_CountCorrectCM()
{
Params.inputDataDimension = 3;
Params.outputDataDimension = 3;
ClusterX clusterX = new ClusterX(new Sample(new double[] { 4.0, 2.0, 0.6 }, 1.0, 0), null);
clusterX.AddItem(new Vector(new double[] { 4.2, 2.1, 0.59 }), 0);
clusterX.AddItem(new Vector(new double[] { 3.9, 2.0, 0.58 }), 0);
clusterX.AddItem(new Vector(new double[] { 4.3, 2.1, 0.62 }), 0);
clusterX.AddItem(new Vector(new double[] { 4.1, 2.2, 0.63 }), 0);
clusterX.Items[0].ValuesMDF = new double[] { 4.0, 2.0, 0.6 };
clusterX.Items[1].ValuesMDF = new double[] { 4.2, 2.1, 0.59 };
clusterX.Items[2].ValuesMDF = new double[] { 3.9, 2.0, 0.58 };
clusterX.Items[3].ValuesMDF = new double[] { 4.3, 2.1, 0.62 };
clusterX.Items[4].ValuesMDF = new double[] { 4.1, 2.2, 0.63 };
clusterX.CountCovarianceMatrixMDF();
}
public void CountCovarianceMatrix_CovarianceMatrixIsCorrect()
{
#warning not complet test, update of covariance matrix must be reimplemented
Params.inputDataDimension = 3;
Params.outputDataDimension = 3;
ClusterX clusterX = new ClusterX(new Sample(new double[] { 4.0, 2.0, 0.6 }, 1.0, 0), null);
clusterX.AddItem(new Vector(new double[] { 4.2, 2.1, 0.59 }), 0);
//clusterX.CountCovariacneMatrix();
clusterX.AddItem(new Vector(new double[] { 3.9, 2.0, 0.58 }), 0);
clusterX.AddItem(new Vector(new double[] { 4.3, 2.1, 0.62 }), 0);
//clusterX.CountCovariacneMatrix();
clusterX.AddItem(new Vector(new double[] { 4.1, 2.2, 0.63 }), 0);
//clusterX.CountCovariacneMatrix();
}
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 UpdateMean_UpdatingIsCorrect()
{
Params.inputDataDimension = 3;
Params.t1 = 10;
ClusterX clusterX = new ClusterX(new Sample(new double[] { 1.0, 2.0, 3.0 }, 1.0, 0), null);
clusterX.AddItem(new Vector(new double[] { 2.0, 3.0, 4.0 }), 0);
Assert.AreEqual(clusterX.Mean.Values[0], 1.5);
Assert.AreEqual(clusterX.Mean.Values[1], 2.5);
Assert.AreEqual(clusterX.Mean.Values[2], 3.5);
clusterX.AddItem(new Vector(new double[] { 3.0, 4.0, 5.0 }), 0);
Assert.AreEqual(clusterX.Mean.Values[0], 2.0);
Assert.AreEqual(clusterX.Mean.Values[1], 3.0);
}
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 });
}
