class KMeansClustering_F extends Clusterer
The KMeansClustering_F class provides a simple form of k-means clustering
that simply smoothes the data and then appliers KMeansClustering.
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new
KMeansClustering_F(x: MatrixD, t: VectorD, τ: VectorD, k: Int, s: Int = 0)
- x
the vectors/points to be clustered stored as rows of a matrix
- t
the time points
- τ
the time points for knots
- k
the number of clusters to make
- s
the random number stream (to vary the clusters made)
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def
centroids(): MatrixD
As seen from class KMeansClustering_F, the missing signatures are as follows.
As seen from class KMeansClustering_F, the missing signatures are as follows. For convenience, these are usable as stub implementations.
- Definition Classes
- KMeansClustering_F → Clusterer
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def
classify(y: VectorD): Int
Given a new point/vector 'y', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
Given a new point/vector 'y', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
- y
the vector to classify
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- KMeansClustering_F → Clusterer
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def
clone(): AnyRef
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def
cluster(): Array[Int]
Create 'k' clusters consisting of points/rows that are closest to each other.
Create 'k' clusters consisting of points/rows that are closest to each other.
- Definition Classes
- KMeansClustering_F → Clusterer
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val
clustered: Boolean
Flag indicating whether the points have already been clusterer
Flag indicating whether the points have already been clusterer
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def
csize(): VectorI
Return the sizes (number of points within) of the clusters.
Return the sizes (number of points within) of the clusters. Should only be called after 'cluster ()'.
- Definition Classes
- KMeansClustering_F → Clusterer
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def
distance(u: VectorD, v: VectorD): Double
Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'.
Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'. Override this methods to use a different metric, e.g., 'norm' - the Euclidean distance, 2-norm 'norm1' - the Manhattan distance, 1-norm
- u
the first vector/point
- v
the second vector/point
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getClass(): Class[_]
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def
getName(i: Int): String
Get the name of the i-th cluster.
Get the name of the i-th cluster.
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hashCode(): Int
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def
name_(n: Array[String]): Unit
Set the names for the clusters.
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def
sse(x: MatrixD): Double
Compute the sum of squared errors within the clusters, where error is indicated by e.g., the distance from a point to its centroid.
Compute the sum of squared errors within the clusters, where error is indicated by e.g., the distance from a point to its centroid.
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