public void TestPartialMatchGenomes()
{
var connGenes1 = new ConnectionGenes <double>(5);
connGenes1[0] = (0, 10, 1.0);
connGenes1[1] = (1, 11, 2.0);
connGenes1[2] = (2, 12, 3.0);
connGenes1[3] = (3, 13, 4.0);
connGenes1[4] = (4, 14, 5.0);
var connGenes2 = new ConnectionGenes <double>(5);
connGenes2[0] = (0, 10, 1.0);
connGenes2[1] = (3, 13, 4.0);
connGenes2[2] = (100, 11, 2.0);
connGenes2[3] = (200, 12, 3.0);
connGenes2[4] = (400, 14, 5.0);
var distanceMetric = new EuclideanDistanceMetric();
// GetDistance() tests.
Assert.Equal(Math.Sqrt(76), distanceMetric.CalcDistance(connGenes1, connGenes2));
Assert.Equal(Math.Sqrt(76), distanceMetric.CalcDistance(connGenes2, connGenes1));
// TestDistance() tests.
Assert.True(distanceMetric.TestDistance(connGenes1, connGenes2, Math.Sqrt(76) + 0.001));
Assert.False(distanceMetric.TestDistance(connGenes1, connGenes2, Math.Sqrt(76) - 0.001));
}
public void TestPythagoras()
{
var connGenes1 = new ConnectionGenes <double>(2);
connGenes1[0] = (0, 3, 0.0);
connGenes1[1] = (0, 4, 0.0);
var connGenes2 = new ConnectionGenes <double>(2);
connGenes2[0] = (0, 3, 3.0);
connGenes2[1] = (0, 4, 4.0);
var distanceMetric = new EuclideanDistanceMetric();
// GetDistance() tests.
Assert.Equal(5.0, distanceMetric.CalcDistance(connGenes1, connGenes2));
Assert.Equal(5.0, distanceMetric.CalcDistance(connGenes2, connGenes1));
// TestDistance() tests.
Assert.True(distanceMetric.TestDistance(connGenes1, connGenes2, 5.01));
Assert.False(distanceMetric.TestDistance(connGenes1, connGenes2, 5.0));
Assert.False(distanceMetric.TestDistance(connGenes1, connGenes2, 1.0));
Assert.False(distanceMetric.TestDistance(connGenes1, connGenes2, 0.0));
Assert.True(distanceMetric.TestDistance(connGenes2, connGenes1, 5.01));
Assert.False(distanceMetric.TestDistance(connGenes2, connGenes1, 5.0));
Assert.False(distanceMetric.TestDistance(connGenes2, connGenes1, 1.0));
Assert.False(distanceMetric.TestDistance(connGenes2, connGenes1, 0.0));
}
public void TestDatasets(string path)
{
Console.WriteLine("Test: " + path);
var features = GetClusterData(Path.Combine(TestPathSingleton.TestDirectory, path));
Assert.IsNotEmpty(features);
var cluster = new UMCClusterLight();
cluster.Id = features[0].Id;
features.ForEach(x => cluster.AddChildFeature(x));
var maps = new Dictionary <int, UMCClusterLight>();
// Map the features
var mapFeatures = new Dictionary <int, List <UMCLight> >();
foreach (var feature in features)
{
if (!mapFeatures.ContainsKey(feature.GroupId))
{
mapFeatures.Add(feature.GroupId, new List <UMCLight>());
}
mapFeatures[feature.GroupId].Add(feature);
}
Console.WriteLine("Cluster\tMass\tNET");
Console.WriteLine("{0}\t{1}\t{2}\t", cluster.Id, cluster.MassStandardDeviation, cluster.NetStandardDeviation);
Console.WriteLine();
var distance = new EuclideanDistanceMetric <FeatureLight>();
//features.ForEach(x => Console.WriteLine(distance.EuclideanDistance(x, cluster)));
}
public void TestPairwise(string path)
{
Console.WriteLine("Test: " + path);
var features = GetClusterData(Path.Combine(TestPathSingleton.TestDirectory, path));
Assert.IsNotEmpty(features);
var cluster = new UMCClusterLight();
cluster.Id = features[0].Id;
features.ForEach(x => cluster.AddChildFeature(x));
var distance = new EuclideanDistanceMetric <FeatureLight>();
for (var i = 0; i < features.Count; i++)
{
var featureX = features[i];
for (var j = 0; j < features.Count; j++)
{
if (i != j)
{
var featureY = features[j];
// Console.WriteLine(distance.EuclideanDistance(featureX, featureY));
}
}
}
}
public void TestMatchingGenomes()
{
var connGenes1 = new ConnectionGenes <double>(5);
connGenes1[0] = (0, 10, 1.0);
connGenes1[1] = (1, 11, 2.0);
connGenes1[2] = (2, 12, 3.0);
connGenes1[3] = (3, 13, 4.0);
connGenes1[4] = (4, 14, 5.0);
var connGenes2 = new ConnectionGenes <double>(5);
connGenes2[0] = (0, 10, 1.0);
connGenes2[1] = (1, 11, 2.0);
connGenes2[2] = (2, 12, 3.0);
connGenes2[3] = (3, 13, 4.0);
connGenes2[4] = (4, 14, 5.0);
var distanceMetric = new EuclideanDistanceMetric();;
// GetDistance() tests.
Assert.AreEqual(0.0, distanceMetric.CalcDistance(connGenes1, connGenes2));
Assert.AreEqual(0.0, distanceMetric.CalcDistance(connGenes2, connGenes1));
// TestDistance() tests.
Assert.IsTrue(distanceMetric.TestDistance(connGenes1, connGenes2, 1.0));
Assert.IsTrue(distanceMetric.TestDistance(connGenes1, connGenes2, 0.001));
Assert.IsFalse(distanceMetric.TestDistance(connGenes1, connGenes2, 0.0));
Assert.IsTrue(distanceMetric.TestDistance(connGenes2, connGenes1, 5.0));
Assert.IsTrue(distanceMetric.TestDistance(connGenes2, connGenes1, 0.001));
Assert.IsFalse(distanceMetric.TestDistance(connGenes2, connGenes1, 0.0));
}
public void TestMismatchGenomes()
{
var connGenes1 = new ConnectionGenes <double>(5);
connGenes1[0] = (0, 10, 1.0);
connGenes1[1] = (1, 11, 2.0);
connGenes1[2] = (2, 12, 3.0);
connGenes1[3] = (3, 13, 4.0);
connGenes1[4] = (4, 14, 5.0);
var connGenes2 = new ConnectionGenes <double>(5);
connGenes2[0] = (0, 100, 1.0);
connGenes2[1] = (1, 110, 2.0);
connGenes2[2] = (2, 120, 3.0);
connGenes2[3] = (3, 130, 4.0);
connGenes2[4] = (4, 140, 5.0);
var distanceMetric = new EuclideanDistanceMetric();;
// GetDistance() tests.
Assert.AreEqual(Math.Sqrt(110), distanceMetric.GetDistance(connGenes1, connGenes2));
Assert.AreEqual(Math.Sqrt(110), distanceMetric.GetDistance(connGenes2, connGenes1));
// TestDistance() tests.
Assert.IsTrue(distanceMetric.TestDistance(connGenes1, connGenes2, Math.Sqrt(110) + 0.001));
Assert.IsFalse(distanceMetric.TestDistance(connGenes1, connGenes2, Math.Sqrt(110) - 0.001));
}
public void SpeciateAdd_Euclidean()
{
IRandomSource rng = RandomDefaults.CreateRandomSource(3);
var distanceMetric = new EuclideanDistanceMetric();
var speciationStrategy = new RegularizedGeneticKMeansSpeciationStrategy <double>(distanceMetric, 50, 0.1);
TestSpeciateAdd(100, 3, 2, 0.5, distanceMetric, speciationStrategy, rng, false);
TestSpeciateAdd(100, 10, 10, 0.2, distanceMetric, speciationStrategy, rng, false);
TestSpeciateAdd(100, 30, 10, 0.1, distanceMetric, speciationStrategy, rng, false);
}
public void TestSpeciateAll_Euclidean()
{
IRandomSource rng = RandomDefaults.CreateRandomSource(1);
var distanceMetric = new EuclideanDistanceMetric();
var speciationStrategy = new GeneticKMeansSpeciationStrategy <double>(distanceMetric, 50, RandomDefaults.CreateRandomSource());
TestSpeciateAll(100, 3, 2, 0.5, distanceMetric, speciationStrategy, rng);
TestSpeciateAll(100, 10, 10, 0.2, distanceMetric, speciationStrategy, rng);
TestSpeciateAll(100, 30, 10, 0.1, distanceMetric, speciationStrategy, rng);
}
public void TestDistanceChangeEuclidean()
{
var cluster = new UMCClusterLight();
cluster.MassMonoisotopic = 500;
cluster.Net = .5;
cluster.Net = .5;
cluster.DriftTime = 20;
var euclid = new EuclideanDistanceMetric <UMCClusterLight>();
DistanceFunction <UMCClusterLight> func = euclid.EuclideanDistance;
var deltaNet = .01;
double deltaMassPPM = 1;
double deltaDriftTime = 1;
Console.WriteLine("Mass Diff, Mass Dist, Net, Net Dist, Drift, Drift Dist");
for (var i = 0; i < 50; i++)
{
var clusterD = new UMCClusterLight();
var clusterN = new UMCClusterLight();
var clusterM = new UMCClusterLight();
clusterM.DriftTime = cluster.DriftTime + deltaDriftTime;
clusterM.Net = cluster.Net + deltaNet;
clusterM.Net = cluster.Net + deltaNet;
clusterM.MassMonoisotopic = FeatureLight.ComputeDaDifferenceFromPPM(cluster.MassMonoisotopic,
deltaMassPPM * i);
clusterN.DriftTime = cluster.DriftTime + deltaDriftTime;
clusterN.Net = cluster.Net + (deltaNet * i);
clusterN.Net = cluster.Net + (deltaNet * i);
clusterN.MassMonoisotopic = FeatureLight.ComputeDaDifferenceFromPPM(cluster.MassMonoisotopic,
deltaMassPPM);
clusterD.DriftTime = cluster.DriftTime + (deltaDriftTime * i);
clusterD.Net = cluster.Net + deltaNet;
clusterD.Net = cluster.Net + deltaNet;
clusterD.MassMonoisotopic = FeatureLight.ComputeDaDifferenceFromPPM(cluster.MassMonoisotopic,
deltaMassPPM);
var distM = func(cluster, clusterM);
var distN = func(cluster, clusterN);
var distD = func(cluster, clusterD);
var output = string.Format("{0},{1},{2},{3},{4},{5}", deltaMassPPM * i, distM, deltaNet * i, distN,
deltaDriftTime * i, distD);
Console.WriteLine(output);
}
}
public void TestTwoClusters(string path)
{
Console.WriteLine("Test: " + path);
var features = GetClusterData(Path.Combine(TestPathSingleton.TestDirectory, path));
Assert.IsNotEmpty(features);
var cluster = new UMCClusterLight();
cluster.Id = features[0].Id;
features.ForEach(x => cluster.AddChildFeature(x));
cluster.CalculateStatistics(ClusterCentroidRepresentation.Median);
Console.WriteLine("Cluster\tMass\tNET");
Console.WriteLine("{0}\t{1}\t{2}\t", cluster.Id, cluster.MassStandardDeviation, cluster.NetStandardDeviation);
Console.WriteLine();
var distance = new EuclideanDistanceMetric <FeatureLight>();
features.ForEach(x => Console.WriteLine(distance.EuclideanDistance(x, cluster)));
}
//[TestCase(@"ClusterData\clusterData-single-1500.txt")]
public void TestAverageLinkage(string path)
{
Console.WriteLine("Average Linkage Test: " + path);
var features = GetClusterData(Path.Combine(TestPaths.TestFilesDirectory, path));
Assert.IsNotEmpty(features);
var cluster = new UMCClusterLight();
cluster.Id = features[0].Id;
features.ForEach(x => cluster.AddChildFeature(x));
var maps = new Dictionary <int, UMCClusterLight>();
var average = new UMCAverageLinkageClusterer <UMCLight, UMCClusterLight>();
average.Parameters = new FeatureClusterParameters <UMCLight>();
average.Parameters.CentroidRepresentation = ClusterCentroidRepresentation.Median;
average.Parameters.Tolerances = new Algorithms.FeatureTolerances();
average.Parameters.Tolerances.Net = .02;
average.Parameters.Tolerances.Mass = 6;
average.Parameters.Tolerances.DriftTime = .3;
var distance = new WeightedEuclideanDistance <UMCLight>();
average.Parameters.DistanceFunction = distance.EuclideanDistance;
var euclid = new EuclideanDistanceMetric <UMCLight>();
average.Parameters.DistanceFunction = euclid.EuclideanDistance;
var clusters = average.Cluster(features);
Console.WriteLine("Clusters = {0}", clusters.Count);
var id = 1;
foreach (var testCluster in clusters)
{
testCluster.CalculateStatistics(ClusterCentroidRepresentation.Mean);
var distances = new List <double>();
// Show a sampling of 5 results
var threshold = (int)(testCluster.Features.Count / (double)5);
if (threshold < 1)
{
threshold = 1;
}
testCluster.Id = id++;
var featureID = 0;
foreach (var feature in testCluster.Features)
{
featureID++;
if (featureID % threshold == 0)
{
Console.WriteLine("{0},{1},{2},{3}",
feature.Net,
feature.MassMonoisotopicAligned,
feature.DriftTime,
testCluster.Id);
}
var newDistance = distance.EuclideanDistance(feature, testCluster);
distances.Add(newDistance);
}
//Console.WriteLine();
//Console.WriteLine("Distances");
//distances.ForEach(x => Console.WriteLine(x));
//Console.WriteLine();
}
}
public void TestDistanceChangeEuclidean()
{
var cluster = new UMCClusterLight();
cluster.MassMonoisotopic = 500;
cluster.Net = .5;
cluster.Net = .5;
cluster.DriftTime = 20;
var euclid = new EuclideanDistanceMetric<UMCClusterLight>();
DistanceFunction<UMCClusterLight> func = euclid.EuclideanDistance;
var deltaNet = .01;
double deltaMassPPM = 1;
double deltaDriftTime = 1;
Console.WriteLine("Mass Diff, Mass Dist, Net, Net Dist, Drift, Drift Dist");
for (var i = 0; i < 50; i++)
{
var clusterD = new UMCClusterLight();
var clusterN = new UMCClusterLight();
var clusterM = new UMCClusterLight();
clusterM.DriftTime = cluster.DriftTime + deltaDriftTime;
clusterM.Net = cluster.Net + deltaNet;
clusterM.Net = cluster.Net + deltaNet;
clusterM.MassMonoisotopic = FeatureLight.ComputeDaDifferenceFromPPM(cluster.MassMonoisotopic,
deltaMassPPM*i);
clusterN.DriftTime = cluster.DriftTime + deltaDriftTime;
clusterN.Net = cluster.Net + (deltaNet*i);
clusterN.Net = cluster.Net + (deltaNet*i);
clusterN.MassMonoisotopic = FeatureLight.ComputeDaDifferenceFromPPM(cluster.MassMonoisotopic,
deltaMassPPM);
clusterD.DriftTime = cluster.DriftTime + (deltaDriftTime*i);
clusterD.Net = cluster.Net + deltaNet;
clusterD.Net = cluster.Net + deltaNet;
clusterD.MassMonoisotopic = FeatureLight.ComputeDaDifferenceFromPPM(cluster.MassMonoisotopic,
deltaMassPPM);
var distM = func(cluster, clusterM);
var distN = func(cluster, clusterN);
var distD = func(cluster, clusterD);
var output = string.Format("{0},{1},{2},{3},{4},{5}", deltaMassPPM*i, distM, deltaNet*i, distN,
deltaDriftTime*i, distD);
Console.WriteLine(output);
}
}
