trait Clusterer extends AnyRef
The Clusterer trait provides a common framework for several clustering
algorithms.
- See also
package.scalafor 'distance' function
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Abstract Value Members
- abstract def centroids: MatriD
Return the centroids (a centroid is the mean of points in a cluster).
Return the centroids (a centroid is the mean of points in a cluster). Should only be called after 'train'.
- abstract def classify(z: 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.
- z
the vector to classify
- abstract def cluster: Array[Int]
Return the cluster assignments.
Return the cluster assignments. Should only be called after 'train'.
- abstract def csize: VectoI
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'.
- abstract 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.
Concrete Value Members
- 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
- 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
- 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
- def initCentroids(): Boolean
- def name(c: Int): String
Return the name of the 'c'-th cluster.
Return the name of the 'c'-th cluster.
- c
the c-th cluster
- def name_(nm: Strings): Unit
Set the names for the clusters.
Set the names for the clusters.
- nm
the array of names
- 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
- 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
- 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
- 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