UniquePoints | R Documentation |
return only the unique points in Datapoints
UniquePoints(Datapoints, Cls, Eps=1e-10)
Datapoints |
[1:n,1:d] numeric matrix of Datapoints points of dimension d, the points are in the rows |
Cls |
[1:n] numeric vector of classes for each datapoint. |
Eps |
Optional,scalar above zero that defines minimum non-identical euclidean distance between two points |
Euclidean distance is computed and used within. Setting Eps
to a very small number results in the identification of unique data points. Setting epsilon to a higher number results in the definition of mesh points within an d-dimensional R-ball graph.
List with
Unique |
[1:k,1:d] Datapoints points without duplicate points |
UniqueInd |
[1:k] index vector such that Unique == Datapoints[UniqueInd,], it has k non-consecutive numbers or labels, each label defines a row number within Datapoints[1:n,1:d] of a unique data point |
Uniq2DatapointsInd |
[1:n] index vector. It has k unique index numbers
representing the arbitrary labels. Each labels is mapped uniquely to a point in
|
NewUniqueInd |
[1:k] index vector stating the index of the newly defined datastructure Unique. |
NewUniq2DataIdx |
[1:k] index vector such that Unique[NewUniq2DataIdx,] == Datapoints[Uniq2DatapointsInd,], it has n non-consecutive numbers or labels, each label defines a row number within Unique[1:k,1:d] of a unique data point |
IsDuplicate |
[1:n,1:n] matrix,for i!=j IsDuplicate[i,j]== 1 if Datapoints[i,] == Datapoints[j,] IsDuplicate[i,i]==0 |
Eps |
Numeric stating the neighborhood radius around unique points. |
Michael Thrun
Datapoints = rbind(c(0,0), c(1,1), c(2,2))
Datapoints2 = rbind(Datapoints, Datapoints+0.001)
Datapoints3 = rbind(Datapoints2, c(1,1)-0.001)
Datapoints = rbind(c(0,0), c(0,0.015), c(0,0.01), c(0,0.015))
V1 = UniquePoints(Datapoints = Datapoints, Eps = 0.01)
V2 = UniquePoints(Datapoints = Datapoints2, Eps = 0.01)
V3 = UniquePoints(Datapoints = Datapoints3, Eps = 0.01)
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