private Dictionary <int, double> GetDistance(IDistance distance)
{
// userId with simularity
Dictionary <int, double> userDis = new Dictionary <int, double>();
// Getting other user with the same items rated
foreach (KeyValuePair <int, Dictionary <int, double> > item in _dict)
{
int curUser = item.Key;
List <double> p = new List <double>();
List <double> q = new List <double>();
foreach (KeyValuePair <int, double> curArticle in item.Value)
{
if (curUserItems.ContainsKey(curArticle.Key))
{
p.Add(curUserItems[curArticle.Key]);
q.Add(curArticle.Value);
}
}
// calculate the simularity
double dis = distance.Calculate(p.ToArray(), q.ToArray());
userDis.Add(item.Key, dis);
}
Console.WriteLine($"Interface = {distance.GetType()} Distance of items:");
Ranking(userDis);
return(userDis);
}
/// <summary>
/// Squared sum of errors of the points and their current mean. Uses the Euclidean distance.
/// </summary>
public float SSE()
{
byte[] mean = GetCurrentMeanPoint(); // TODO: could be Centroid instead if we would make sure that the Centroid is the current mean
float res = 0.0F;
foreach (uint ind in PointIndices)
{
res += _distance.Calculate(mean, _points[ind]);
}
return(res);
}
private void Affinity(List <Antibody> antibodies)
{
var antigenSize = _distance.SequenceSize();
for (int i = 0; i < antibodies.Count(); i++)
{
var sequence = antibodies[i].Sequence;
var length = sequence.Length;
var index = Constants.Random.Next(0, antigenSize - length);
antibodies[i].Affinity = _distance.Calculate(sequence, index, length);
}
}
protected Centroid ClosestCentroidTo(uint pointInd)
{
System.Diagnostics.Debug.Assert(ClusterNum > 0);
Centroid res = new Centroid(PointDim);
float minDist = float.MaxValue;
foreach (Cluster cluster in _clusters.Values)
{
Centroid centroid = cluster.Centroid;
float dist = _distance.Calculate(centroid.Values, _points[pointInd]);
if (dist < minDist)
{
minDist = dist;
res = centroid;
}
}
return(res);
}
