class KMeansClustererPP extends KMeansClustererHW
The KMeansClustererPP class cluster several vectors/points using
the Hartigan-Wong algorithm.
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Instance Constructors
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new
KMeansClustererPP(x: MatriD, k: Int, flags: Array[Boolean] = Array (false, false))
- x
the vectors/points to be clustered stored as rows of a matrix
- k
the number of clusters to make
- flags
the flags used to adjust the algorithm
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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val
MAX_ITER: Int
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- KMeansClusterer
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final
def
asInstanceOf[T0]: T0
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def
assign(): Unit
Randomly assign each vector/point 'x(i)' to a random cluster.
Randomly assign each vector/point 'x(i)' to a random cluster. Primary technique for initiating the clustering.
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
calcCentroids(x: MatriD, to_c: Array[Int], sz: VectoI, cent: MatriD): Unit
Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.
Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.
- x
the data matrix holding the points {x_i = x(i)} in its rows
- to_c
the cluster assignment array
- sz
the sizes of the clusters (number of points)
- cent
the matrix holding the centroids in its rows
- Definition Classes
- Clusterer
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val
cent: MatrixD
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
centroids: MatriD
Return the centroids.
Return the centroids. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
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def
checkOpt(x: MatriD, to_c: Array[Int], opt: Double): Boolean
Check to see if the sum of squared errors is optimum.
Check to see if the sum of squared errors is optimum.
- x
the data matrix holding the points
- to_c
the cluster assignments
- opt
the known (from human/oracle) optimum
- Definition Classes
- Clusterer
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def
classify(z: VectoD): Int
Given a new point/vector 'z', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
Given a new point/vector 'z', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
- z
the vector to classify
- Definition Classes
- KMeansClusterer → Clusterer
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def
clone(): AnyRef
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
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def
cluster: Array[Int]
Return the cluster assignment vector.
Return the cluster assignment vector. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
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def
csize: VectoI
Return the sizes of the centroids.
Return the sizes of the centroids. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
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def
distance(u: VectoD, cn: MatriD, kc_: Int = -1): VectoD
Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'
Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'
- u
the given vector/point (u = x_i)
- cn
the matrix holding several centroids
- kc_
the number of centroids so far
- Definition Classes
- Clusterer
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def
distance2(u: VectoD, cent: MatriD, cc: Int): VectoD
Compute the adjusted distance to point 'u' according to the R2 value described in the Hartigan-Wong algorithm.
Compute the adjusted distance to point 'u' according to the R2 value described in the Hartigan-Wong algorithm.
- u
the point in question
- cent
the matrix holding the centroids
- cc
the current cluster for point u
- Definition Classes
- KMeansClustererHW
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
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def
fixEmptyClusters(): Unit
Fix all empty clusters by taking a point from the largest cluster.
Fix all empty clusters by taking a point from the largest cluster.
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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val
flags: Array[Boolean]
- Definition Classes
- KMeansClusterer
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
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final
def
getClass(): Class[_]
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- @native() @HotSpotIntrinsicCandidate()
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def
hashCode(): Int
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val
immediate: Boolean
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
initCentroids(): Boolean
Initialize the centroids according to the k-means++ technique.
Initialize the centroids according to the k-means++ technique.
- Definition Classes
- KMeansClustererPP → Clusterer
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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def
name(c: Int): String
Return the name of the 'c'-th cluster.
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def
name_(nm: Strings): Unit
Set the names for the clusters.
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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val
pmf: VectorD
- Attributes
- protected
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val
post: Boolean
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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var
raniv: PermutedVecI
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
reassign(): Boolean
Reassign each vector/point to the cluster with the closest centroid.
Reassign each vector/point to the cluster with the closest centroid. This one follows a version of the Hartigan-Wong algorithm. Indicate done, if no points changed clusters (for stopping rule). Note: randomized order for index 'i' tends to work better.
- Attributes
- protected
- Definition Classes
- KMeansClustererHW → KMeansClusterer
- See also
www.tqmp.org/RegularArticles/vol09-1/p015/p015.pdf
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def
setStream(s: Int): Unit
Set the random stream to 's'.
Set the random stream to 's'. Method must be called in implemeting classes before creating any random generators.
- s
the new value for the random number stream
- Definition Classes
- Clusterer
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def
show(l: Int): Unit
Show the state of the algorithm at iteration 'l'.
Show the state of the algorithm at iteration 'l'.
- l
the current iteration
- Definition Classes
- KMeansClusterer
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def
sse(x: MatriD, c: Int, to_c: Array[Int]): Double
Compute the sum of squared errors from the points in cluster 'c' to the cluster's centroid.
Compute the sum of squared errors from the points in cluster 'c' to the cluster's centroid.
- x
the data matrix holding the points
- c
the current cluster
- to_c
the cluster assignments
- Definition Classes
- Clusterer
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def
sse(x: MatriD, to_c: Array[Int]): Double
Compute the sum of squared errors within all clusters, where error is indicated by e.g., the distance from a point to its centroid.
Compute the sum of squared errors within all clusters, where error is indicated by e.g., the distance from a point to its centroid.
- x
the data matrix holding the points
- to_c
the cluster assignments
- Definition Classes
- Clusterer
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def
sst(x: MatriD): Double
Compute the sum of squares total for all the points from the mean.
Compute the sum of squares total for all the points from the mean.
- x
the data matrix holding the points
- Definition Classes
- Clusterer
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val
stream: Int
- Attributes
- protected
- Definition Classes
- Clusterer
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def
swap(): Unit
Try all pairwise swaps and make them if 'sse' improves.
Try all pairwise swaps and make them if 'sse' improves.
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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val
sz: VectorI
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
toString(): String
- Definition Classes
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val
to_c: Array[Int]
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
train(): KMeansClusterer
Iteratively recompute clusters until the assignment of points does not change.
Iteratively recompute clusters until the assignment of points does not change. Initialize by randomly assigning points to 'k' clusters.
- Definition Classes
- KMeansClusterer → Clusterer
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def
update_pmf(c: Int): Discrete
Update the probability mass function (pmf) used for picking the next centroid.
Update the probability mass function (pmf) used for picking the next centroid. The farther 'x_i' is from any existing centroid, the higher its probability. Return the corresponding distance-derived random variate generator.
- c
the current centroid index
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
wait(): Unit
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finalize(): Unit
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