bpamat: Matrices of Basic Probability Assignment Objects

Description

These functions enhance the functionality provided via bpa objects. They essentially provide for the storage of several bpa objects at once as a matrix.

Usage

bpamat(info = NULL, mat = NULL)
"print"(x, ...)

Arguments

info

A piece of auxiliary information that you want stored along with the matrix of bpa's. (Used internally to store the random seed, or the special character 'C' if the bpa object resulted from a combination.)

mat

The matrix of bpa's. Each column represents a point, and the rows represent classes. Should have column names set to the row names of the points, and row names set to the names of the classes

The bpamat object to be printed.

...

Additional arguments to print method. Not Used.

Value

set.info: Takes a single argument which is set as the auxiliary information you want stored with the matrix
get.info: Returns the auxiliary information stored with the matrix
assign.mat: Takes a single argument, which should be a matrix of bpa's, to be stored inside the bpamat object.
get.classify: Returns a vector as long as the number of points stored in the bpamat object. The elements are named after the points, and are the current classification of the point, based on the bpamat object.
get.point: Returns the bpa corresponding to a single point, whose name is passed as the argument.
get.mat: Returns the matrix of bpa's.
get.setlist: Returns the class names that occur in the current matrix
get.pointlist: Returns the names of all the points whose bpa's are stored in the current matrix.

Details

The ensemble function returns objects of this type.

Examples

Run this code

data(cancer)
table(cancer$V2)
colnames(cancer)[1:2] <- c('id', 'type')

cancer.d <- as.matrix(cancer[,3:32])
labs <- cancer$type
test_size <- floor(0.15*nrow(cancer.d))
train <- sample(1:nrow(cancer.d), size = nrow(cancer.d) - test_size)
test <- which(!(1:569 %in% train))
truelabs <- labs[test]

projectron <- function(A)
    cancer.d %*% A

kdebel <- kde_bel.builder(labs = labs[train], test = test, train =
train)

##A projection
seed1 <- .Random.seed
F1 <- projectron(basis_random(30))
x1 <- kdebel(F1)
y1 <- bpamat(info = seed1, mat = x1)
y1
predicted1 <- y1$get.classify()
table(truelabs, predicted1)

##Another projection
seed2 <- .Random.seed
F2 <- projectron(basis_random(30))
x2 <- kdebel(F2)
y2 <- bpamat(info = seed2, mat = x2)
y2
predicted2 <- y2$get.classify()
table(truelabs, predicted2)

z1 <- combine.bpamat.bs(y1, y2)
z2 <- combine.bpamat.ds(y1, y2)
table(truelabs, z1$get.classify())
table(truelabs, z2$get.classify())

##Same result
w1 <- combine.bpamat.list.bs(list(y1, y2))
w2 <- combine.bpamat.list.ds(list(y1, y2))