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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Instance Constructors
- new KMeansClustering_F(x: MatrixD, t: VectorD, τ: VectorD, k: Int)
- 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
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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
- 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
- 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(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
- def classify(y: VectoD): Int
Given a new point/vector z, determine which cluster it belongs to.
Given a new point/vector z, determine which cluster it belongs to.
- Definition Classes
- KMeansClustering_F → 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]
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
- 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 'train'.
- Definition Classes
- KMeansClustering_F → 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
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- Clusterer
- final def eq(arg0: AnyRef): Boolean
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- def initCentroids(): Boolean
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- Clusterer
- final def isInstanceOf[T0]: Boolean
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- 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
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- final def notify(): Unit
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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
- 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
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- protected
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- Clusterer
- final def synchronized[T0](arg0: => T0): T0
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- def train(): Clusterer
Given a set of points/vectors, put them in clusters, returning the cluster assignments.
Given a set of points/vectors, put them in clusters, returning the cluster assignments. A basic goal is to minimize the sum of squared errors (sse) in terms of squared distances of points in the cluster to its centroid.
- Definition Classes
- KMeansClustering_F → Clusterer
- final def wait(arg0: Long, arg1: Int): Unit
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