clusterEntropy_fast: Entropy for Clusterings
In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series
Description
Usage
Arguments
Value
Description
Calculates the Shannon entropy for a cluster assignment vector. A value of
0 means that all elements are in one cluster, higher values indicate a more
even distribution of objects in the clusters. The value can be normalized
to [0,1] such that 1 means the same number of objects in each cluster.
Usage
1
clusterEntropy_fast(assignments, normalize = FALSE)
Arguments
assignments
Integer vector of cluster assignments containing only values
from 1 to k with k = number of clusters (code depends on this!).
normalize
Should the entropy be normalized to [0,1]?
Value
The entropy as double.
Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.
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Description Usage Arguments Value
Description
Calculates the Shannon entropy for a cluster assignment vector. A value of 0 means that all elements are in one cluster, higher values indicate a more even distribution of objects in the clusters. The value can be normalized to [0,1] such that 1 means the same number of objects in each cluster.
Usage
1 | clusterEntropy_fast(assignments, normalize = FALSE)
|
Arguments
assignments |
Integer vector of cluster assignments containing only values from 1 to k with k = number of clusters (code depends on this!). |
normalize |
Should the entropy be normalized to [0,1]? |
Value
The entropy as double.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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