public void KMeansClusteringWorksOnTwoDimensions()
{
var dataSet = new IdentifiableDataPointCollection();
var p1 = new IdentifiableDataPoint(0, 2);
var p2 = new IdentifiableDataPoint(1, 2);
var p3 = new IdentifiableDataPoint(2, 2);
var p4 = new IdentifiableDataPoint(3, 2);
p1.AddAttribute("Gender", 1);
p1.AddAttribute("Income", 1);
p2.AddAttribute("Gender", 0);
p2.AddAttribute("Income", 0.1429);
p3.AddAttribute("Gender", 1);
p3.AddAttribute("Income", 0.2858);
p4.AddAttribute("Gender", 1);
p4.AddAttribute("Income", 1);
dataSet.AddItem(p1);
dataSet.AddItem(p2);
dataSet.AddItem(p3);
dataSet.AddItem(p4);
var kmeans2 = new KMeans(dataSet, new [] { 0, 1, 2 }, new EuclideanMetric());
var result2 = kmeans2.Calculate();
Assert.AreEqual(2, result2.Clusters[0].Members.Count);
Assert.AreEqual(1, result2.Clusters[1].Members.Count);
Assert.AreEqual(1, result2.Clusters[2].Members.Count);
}
public void KMeansShouldCalculateCorrectCentroidsInOneDimension()
{
var dataSet = new IdentifiableDataPointCollection();
var p1 = new IdentifiableDataPoint(0, 1);
var p2 = new IdentifiableDataPoint(1, 1);
var p3 = new IdentifiableDataPoint(2, 1);
var p4 = new IdentifiableDataPoint(3, 1);
p1.AddAttribute("Gender", 1);
p2.AddAttribute("Gender", 0);
p3.AddAttribute("Gender", 1);
p4.AddAttribute("Gender", 1);
dataSet.AddItem(p1);
dataSet.AddItem(p2);
dataSet.AddItem(p3);
dataSet.AddItem(p4);
var kmeans2 = new KMeans(dataSet, new [] { 0, 1 }, new EuclideanMetric());
var result2 = kmeans2.Calculate();
double[] centroid1 = { 1 };
double[] centroid2 = { 0 };
Assert.AreEqual(centroid1, result2.Clusters[0].Centroid.Coordinates);
Assert.AreEqual(centroid2, result2.Clusters[1].Centroid.Coordinates);
}
public void KMeansShouldClusterDataPointsInOneDimensionCorrectly()
{
var dataSet = new IdentifiableDataPointCollection();
var p1 = new IdentifiableDataPoint(0, 1);
var p2 = new IdentifiableDataPoint(1, 1);
var p3 = new IdentifiableDataPoint(2, 1);
var p4 = new IdentifiableDataPoint(3, 1);
p1.AddAttribute("Gender", 1);
p2.AddAttribute("Gender", 0);
p3.AddAttribute("Gender", 1);
p4.AddAttribute("Gender", 1);
dataSet.AddItem(p1);
dataSet.AddItem(p2);
dataSet.AddItem(p3);
dataSet.AddItem(p4);
var kmeans2 = new KMeans(dataSet, new [] { 0, 1 }, new EuclideanMetric());
var result2 = kmeans2.Calculate();
var cluster1Members = result2.Clusters[0].Members.Select(e => e.Member).ToArray();
var cluster2Members = result2.Clusters[1].Members.Select(e => e.Member).ToArray();
Assert.AreEqual(3, cluster1Members.Length);
Assert.Contains(p1, cluster1Members);
Assert.Contains(p3, cluster1Members);
Assert.Contains(p4, cluster1Members);
Assert.AreEqual(1, cluster2Members.Length);
Assert.Contains(p2, cluster2Members);
}
public void KMeansCentroidsArePlacedRightInFiveDimensions()
{
var dataSet = new IdentifiableDataPointCollection();
var p1 = new IdentifiableDataPoint(0, 5);
var p2 = new IdentifiableDataPoint(1, 5);
var p3 = new IdentifiableDataPoint(2, 5);
var p4 = new IdentifiableDataPoint(3, 5);
p1.AddAttribute("Gender", 1);
p1.AddAttribute("Income", 1);
p1.AddAttribute("Age", 0.16);
p1.AddAttribute("Purchase", 0.5);
p1.AddAttribute("Control", 1);
p2.AddAttribute("Gender", 0);
p2.AddAttribute("Income", 0.1429);
p2.AddAttribute("Age", 0.16);
p2.AddAttribute("Purchase", 1);
p2.AddAttribute("Control", 0);
p3.AddAttribute("Gender", 1);
p3.AddAttribute("Income", 0.2858);
p3.AddAttribute("Age", 0.16);
p3.AddAttribute("Purchase", 1);
p3.AddAttribute("Control", 1);
p4.AddAttribute("Gender", 1);
p4.AddAttribute("Income", 1);
p4.AddAttribute("Age", 0.16);
p4.AddAttribute("Purchase", 1);
p4.AddAttribute("Control", 0.5);
dataSet.AddItem(p1);
dataSet.AddItem(p2);
dataSet.AddItem(p3);
dataSet.AddItem(p4);
var kmeans2 = new KMeans(dataSet, new [] { 0, 1, 2 }, new EuclideanMetric());
var result2 = kmeans2.Calculate();
double[] _centroid1 = { 1, 0.2858, 0.16, 1, 1 };
double[] _centroid2 = { 1, 1, 0.16, 0.75, 0.75 };
double[] _centroid3 = { 0, 0.1429, 0.16, 1, 0 };
Assert.AreEqual(_centroid2, result2.Clusters[0].Centroid.Coordinates);
Assert.AreEqual(_centroid3, result2.Clusters[1].Centroid.Coordinates);
Assert.AreEqual(_centroid1, result2.Clusters[2].Centroid.Coordinates);
}
public void CentroidsHaveDistinctValues()
{
var dataSet = new IdentifiableDataPointCollection();
var p1 = new IdentifiableDataPoint(0, 2);
var p2 = new IdentifiableDataPoint(1, 2);
var p3 = new IdentifiableDataPoint(2, 2);
p1.AddAttribute("Gender", 1);
p1.AddAttribute("Income", 1);
p2.AddAttribute("Gender", 1);
p2.AddAttribute("Income", 1);
p3.AddAttribute("Gender", 0);
p3.AddAttribute("Income", 0);
dataSet.AddItem(p1);
dataSet.AddItem(p2);
dataSet.AddItem(p3);
Assert.Throws <InvalidOperationException>(() => new KMeans(dataSet, new [] { 0, 1 }, new EuclideanMetric()));
}
public void CalculateWithZeroClusters()
{
var dataSet = new IdentifiableDataPointCollection();
var p1 = new IdentifiableDataPoint(0, 1);
var p2 = new IdentifiableDataPoint(1, 1);
var p3 = new IdentifiableDataPoint(2, 1);
var p4 = new IdentifiableDataPoint(3, 1);
p1.AddAttribute("Gender", 1);
p2.AddAttribute("Gender", 0);
p3.AddAttribute("Gender", 1);
p4.AddAttribute("Gender", 1);
dataSet.AddItem(p1);
dataSet.AddItem(p2);
dataSet.AddItem(p3);
dataSet.AddItem(p4);
Assert.Throws <ArgumentException>(() => new KMeans(dataSet, 0, new EuclideanMetric()));
}
public IdentifiableDataPointCollection Run()
{
var dataSet = new IdentifiableDataPointCollection();
var csv = new CsvReader(reader);
csv.Configuration.Delimiter = FieldDelimiter;
var idCounter = 0;
while (csv.Read())
{
IdentifiableDataPoint dataPoint = ParseRow(csv, idCounter++);
dataSet.AddItem(dataPoint);
}
return(dataSet);
}