# Matrices of Basic Probability Assignment Objects These functions enhance the functionality provided via `bpa` objects. They essentially provide for the storage of several bpa objects at once as a matrix. ```r bpamat(info = NULL, mat = NULL) ## S3 method for class 'bpamat' 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 - `x`: The bpamat object to be printed. - `...`: Additional arguments to print method. Not Used. ## Details The `ensemble` function returns objects of this type. ## Returns The `bpamat` function returns a list of functions which can be used to query and / or manipulate the create bpa object. - **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. ## Author(s) Mohit Dayal ## See Also bpa, combine.bpamat.bs, combine.bpamat.ds, combine.bpamat.list.bs, combine.bpamat.list.ds ## Examples ```r 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)) ```

bpamat function