class KMeansClustererHW extends KMeansClusterer
The KMeansClustererHW class cluster several vectors/points using
the Hartigan-Wong algorithm.
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- new KMeansClustererHW(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
- final def !=(arg0: Any): Boolean
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- val MAX_ITER: Int
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- KMeansClusterer
- 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
- 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
- val cent: MatrixD
- Attributes
- protected
- Definition Classes
- KMeansClusterer
- def centroids: MatriD
Return the centroids.
Return the centroids. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
- 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
- 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
- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- def cluster: Array[Int]
Return the cluster assignment vector.
Return the cluster assignment vector. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
- 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
- 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
- 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
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
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- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- 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
- val flags: Array[Boolean]
- Definition Classes
- KMeansClusterer
- final def flaw(method: String, message: String): Unit
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- Error
- final def getClass(): Class[_ <: AnyRef]
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- @native() @HotSpotIntrinsicCandidate()
- def hashCode(): Int
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- @native() @HotSpotIntrinsicCandidate()
- val immediate: Boolean
- Attributes
- protected
- Definition Classes
- KMeansClusterer
- def initCentroids(): Boolean
- Definition Classes
- Clusterer
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def name(c: Int): String
Return the name of the 'c'-th cluster.
- def name_(nm: Strings): Unit
Set the names for the clusters.
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- val post: Boolean
- Attributes
- protected
- Definition Classes
- KMeansClusterer
- var raniv: PermutedVecI
- Attributes
- protected
- Definition Classes
- KMeansClusterer
- 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
- 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
- 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
- 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
- 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
- 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
- val stream: Int
- Attributes
- protected
- Definition Classes
- Clusterer
- 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
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- val sz: VectorI
- Attributes
- protected
- Definition Classes
- KMeansClusterer
- def toString(): String
- Definition Classes
- AnyRef → Any
- val to_c: Array[Int]
- Attributes
- protected
- Definition Classes
- KMeansClusterer
- 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
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- def finalize(): Unit
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