public void CountMeanOfNode_CountCorrectMean()
{
Params.inputDataDimension = 3;
Node node = new Node(1, 1, 0.0, 0.0, "");
// cluster 1
ClusterX newClusterX1 = new ClusterX(node);
newClusterX1.Items.Add(new Vector(new double[] { 1, 2, 3 }, new double[] { 1, -2, 3 }));
newClusterX1.Items.Add(new Vector(new double[] { 2, 3, 4 }, new double[] { 2, 3, 4 }));
newClusterX1.Items.Add(new Vector(new double[] { 3, 4, 5 }, new double[] { 3, 4, 5 }));
newClusterX1.Mean = new Vector(new double[] { 1, 2, 3 });
node.ClustersX.Add(newClusterX1);
ClusterPair clusterPair1 = new ClusterPair();
clusterPair1.X = newClusterX1;
newClusterX1.SetClusterPair(clusterPair1);
node.ClusterPairs.Add(clusterPair1);
node.CountMeanAndVarianceMDF();
node.CountOfSamples = 4;
node.UpdateMeanAndVarianceMdf(new Vector(new double[] { 4, 5, 6 }, new double[] { 4, 5, 6 }));
node.CountOfSamples = 5;
node.UpdateMeanAndVarianceMdf(new Vector(new double[] { 5, 6, -7 }, new double[] { 5, 6, -7 }));
Assert.AreEqual(node.VarianceMDF[0], 2.5);
Assert.AreEqual(node.VarianceMDF[1], 9.7);
Assert.AreEqual(node.VarianceMDF[2], 27.7);
}
public void ClusterXCreate_CreateCorrectClusterX()
{
// set params
Params.inputDataDimension = 3;
Sample sample = new Sample(new double[] { 1.0, 2.0, 3.0 }, 1.0, 0);
ClusterX clusterX = new ClusterX(sample, null);
Assert.IsTrue(clusterX.Mean.EqualsToVector(new Vector(new double[] { 1.0, 2.0, 3.0 })));
}
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 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 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 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 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 });
}
public void CountC_CountCCorrect()
{
Node node = new Node(0, 0, 0.0, 0.0, "");
Params.inputDataDimension = 3;
node.CountOfSamples = 6;
// cluster 1
ClusterX newClusterX1 = new ClusterX(node);
newClusterX1.Items.Add(new Vector(0, 0));
newClusterX1.Items.Add(new Vector(0, 0));
newClusterX1.Items.Add(new Vector(0, 0));
newClusterX1.Mean = new Vector(new double[] { 1, 2, 3 });
node.ClustersX.Add(newClusterX1);
ClusterPair clusterPair1 = new ClusterPair();
clusterPair1.X = newClusterX1;
newClusterX1.SetClusterPair(clusterPair1);
node.ClusterPairs.Add(clusterPair1);
// cluster 2
ClusterX newClusterX2 = new ClusterX(node);
newClusterX2.Items.Add(new Vector(0, 0));
newClusterX2.Items.Add(new Vector(0, 0));
newClusterX2.Mean = new Vector(new double[] { 3, 3, 4 });
node.ClustersX.Add(newClusterX2);
ClusterPair clusterPair2 = new ClusterPair();
clusterPair2.X = newClusterX2;
newClusterX2.SetClusterPair(clusterPair2);
node.ClusterPairs.Add(clusterPair2);
// cluster 3
ClusterX newClusterX3 = new ClusterX(node);
newClusterX3.Items.Add(new Vector(0, 0));
newClusterX3.Mean = new Vector(new double[] { 9, 6, 7 });
node.ClustersX.Add(newClusterX3);
ClusterPair clusterPair3 = new ClusterPair();
clusterPair3.X = newClusterX3;
newClusterX3.SetClusterPair(clusterPair3);
node.ClusterPairs.Add(clusterPair3);
Vector mean = node.GetCFromClustersX();
Assert.AreEqual(mean.Values[0], 3);
Assert.AreEqual(mean.Values[1], 3);
Assert.AreEqual(mean.Values[2], 1);
}
