public void ClusterWithOneBlogsGivesOneCluster()
{
// Arrange
var blogs = new List <Blog>
{
new Blog
{
Name = "Blog1",
Words = new Dictionary <string, double>
{
{ "word1", 5 },
{ "word2", 0 },
{ "word3", 5 },
},
},
};
var sut = new HierarchicalClustering(new PearsonCorrelationSimilarityAlgorithm(), false);
// Act
var result = sut.Cluster(blogs);
// Assert
Assert.Equal("Blog1", result.Blog.Name);
Assert.Null(result.Left);
Assert.Null(result.Right);
}
public ActionResult <Cluster> Get(bool useCache)
{
var blogs = blogDataRepository.GetBlogData();
var pearson = new PearsonCorrelationSimilarityAlgorithm();
var hierarchicalClustering = new HierarchicalClustering(pearson, useCache);
return(hierarchicalClustering.Cluster(blogs));
}
public void Test()
{
var releaseInfos = GetTestData(@"TestData\standard.json");
var clusterAnalyser = new HierarchicalClustering <SizeInfo>((x, y) => Math.Abs(x.Size - y.Size),
Math.Max);
var clusters = clusterAnalyser.Cluster(releaseInfos);
var instances = clusters.GetClusteredInstances(d => d > 200 * 1024 * 1024).OrderByDescending(c => c.Count).ToArray();
}
public IEnumerator <Clustering <TElement> > GetEnumerator()
{
var hca = new HierarchicalClustering <TElement>(_distanceFunction, _linkageFunction);
var results = hca.Cluster(_source);
var clusters = results.GetClusteredInstances(_clusterDistanceCutPoint);
return(clusters
.Select(cluster => new Clustering <TElement>(cluster, _clusterValueFunc))
.GetEnumerator());
}
public void ClusterWithZeroBlogsGivesZeroClusters()
{
// Arrange
var sut = new HierarchicalClustering(new PearsonCorrelationSimilarityAlgorithm(), false);
// Act
var result = sut.Cluster(new List <Blog>());
// Assert
Assert.Null(result);
}
public void Clusters(List <TestClusterCandidate> candidates, double cutPoint,
Func <double, double, double> linkageFunction, Func <TestClusterCandidate, TestClusterCandidate, double> distanceFunction,
ISet <ISet <TestClusterCandidate> > expectedClusterResults)
{
var subject = new HierarchicalClustering <TestClusterCandidate>(distanceFunction, linkageFunction);
var results = subject.Cluster(candidates);
var clustered = results.GetClusteredInstances(d => d > cutPoint);
clustered.Count.ShouldBe(expectedClusterResults.Count);
var count = expectedClusterResults
.Sum(expectedClusterResult => clustered.Count(cluster => cluster.SetEquals(expectedClusterResult)));
count.ShouldBe(expectedClusterResults.Count);
}
public void ShouldClusterValueTypes()
{
var source = new List <int> {
101, 204, 203, 105, 103
};
var expectedSets = new HashSet <HashSet <int> > {
new HashSet <int> {
204, 105
}, new HashSet <int> {
101, 203, 103
}
};
double DistanceValueSelector(int c) => c % 100;
double DistanceFunction(int a, int b) => Math.Abs(DistanceValueSelector(a) - DistanceValueSelector(b));
var subject = new HierarchicalClustering <int>(DistanceFunction, TestClusterCandidate.CompleteLinkage);
var results = subject.Cluster(source);
var clusters = results.GetClusteredInstances(2);
clusters.Count.ShouldBe(2);
clusters.ShouldBe(expectedSets);
}
public void NullListShouldReturnEmptyCluster()
{
_subject.Cluster(null).Contents.ShouldBeEmpty();
}
