public Dictionary <CustomSocketDomain.SocketDomain, List <FuzzyFact> > GetFuzzyFacts(List <FuzzyDomain> domains,
List <CustomSocket> sockets)
{
var socketType = typeof(CustomSocket);
var fuzzyFacts = new Dictionary <CustomSocketDomain.SocketDomain, List <FuzzyFact> >();
foreach (var domain in domains)
{
var type = CustomSocketDomain.SocketDomainType[domain.Domain];
var domainValues = sockets
.Select(p => socketType.GetProperty(domain.Domain.ToString()).GetValue(p))
.Where(p => !CustomSocketDomain.SocketDefaultValue[type].Equals(p))
.Distinct()
.Select(p => Convert.ToDouble(p))
.ToList();
var factList = new List <FuzzyFact>();
foreach (var value in ClusteringService.CMeans(domain.Clusters.Count(), domainValues))
{
factList.Add(new FuzzyFact(domain, value.Value, value.ClusterDegree));
}
fuzzyFacts.Add(domain.Domain, SortClusters(factList));
}
return(fuzzyFacts);
}
public List <FuzzyFact> GetAmperageCircuitFact(List <FuzzyFuncStatement> statements, CustomSocket socket)
{
var values = new List <double>();
foreach (var statement in statements)
{
values.Add(statement.Result(socket));
}
var domain = new FuzzyDomain(CustomSocketDomain.SocketDomain.AmperageCircuit, new FuzzyDomainOption
{
ClusterCount =
FuzzyCustomSocket.GetFuzzySocketDomains()[CustomSocketDomain.SocketDomain.AmperageCircuit],
Min = values.Min(),
Max = values.Max()
});
var facts = new List <FuzzyFact>();
foreach (var value in ClusteringService.CMeans(domain.Clusters.Count, values))
{
facts.Add(new FuzzyFact(domain, value.Value, value.ClusterDegree));
}
return(facts);
}
public void CMeans_isSeedCapable()
{
var values = new List <double>
{
1,
1,
2,
3,
3,
4,
5,
5
};
var fuzzyValues = ClusteringService.CMeans(3, values, randSeed: 0).ToList();
}
public void CMeans_isPlainCorrect()
{
var plainClusters = new List <double> {
1, 1, 1, 5, 5, 5, 9, 9, 9
};
const int plainClustersExpectedCount = 3;
const int elements = 3;
var result = ClusteringService.CMeans(plainClustersExpectedCount, plainClusters).ToList();
for (var k = 0; k < plainClustersExpectedCount; k++)
{
var clusterDegree = result[k * elements].GetMostProbableCluster();
for (var j = k * elements; j < elements; j++)
{
Assert.Equal(clusterDegree.Key, result[j].GetMostProbableCluster().Key);
}
}
}
public void CMeans_isComplexCorrect()
{
var complexClusters = new List <double>
{
2,
2,
3,
3,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
5,
5,
5,
5,
5,
5,
6,
6,
7,
7,
7,
8,
8,
8,
8,
8,
8,
8,
8,
8,
10,
10,
10,
10,
10,
10,
12,
12,
12,
14,
15,
15,
16,
16,
19,
19,
19,
19,
20,
22,
22,
26,
28,
28,
29,
30,
37,
37,
38,
41,
55,
55,
55,
55,
63,
64,
70,
120,
120,
120,
120,
120,
120,
184
};
const int complexClustersExpectedCount = 3;
var result = ClusteringService.CMeans(complexClustersExpectedCount, complexClusters).ToList();
}
