bdapply_Function_hdf5

bdapply_Function_hdf5

HDF5_IO_MANAGEMENT

1 Usage

bdapply_Function_hdf5(filename, group, datasets, outgroup, func, b_group = NULL, b_datasets = NULL, overwrite = FALSE, transp_dataset = FALSE, transp_bdataset = FALSE, fullMatrix = FALSE, byrows = FALSE, threads = 2L)

2 Arguments

Parameter	Description
filename	Character array, indicating the name of the file to create
group	Character array, indicating the input group where the data set to be imputed is
datasets	Character array, indicating the input datasets to be used
outgroup	Character array, indicating group where the data set will be saved after imputation. If NULL, output dataset is stored in the same input group
func	Character array, function to be applied: - “QR”: QR decomposition via bdQR() - “CrossProd”: Cross product via bdCrossprod() - “tCrossProd”: Transposed cross product via bdtCrossprod() - “invChol”: Inverse via Cholesky decomposition - “blockmult”: Matrix multiplication - “CrossProd_double”: Cross product with two matrices - “tCrossProd_double”: Transposed cross product with two matrices - “solve”: Matrix equation solving - “sdmean”: Standard deviation and mean computation
b_group	Optional character array indicating the input group for secondary datasets (used in two-matrix operations)
b_datasets	Optional character array indicating the secondary datasets for two-matrix operations
overwrite	Optional boolean. If true, overwrites existing results
transp_dataset	Optional boolean. If true, transposes first dataset
transp_bdataset	Optional boolean. If true, transposes second dataset
fullMatrix	Optional boolean for Cholesky operations. If true, stores complete matrix; if false, stores only lower triangular
byrows	Optional boolean for statistical operations. If true, computes by rows; if false, by columns
threads	Optional integer specifying number of threads for parallel processing

3 Value

Modifies the HDF5 file in place, adding computed results

4 Details

//’ For matrix multiplication operations (blockmult, CrossProd_double, tCrossProd_double), the datasets and b_datasets vectors must have the same length. Each operation is performed element-wise between the corresponding pairs of datasets. Specifically, the b_datasets vector defines the second operand for each matrix multiplication. For example, if datasets = {"A1", "A2", "A3"} and b_datasets = {"B1", "B2", "B3"}, the operations executed are: A1 %*% B1, A2 %*% B2, and A3 %*% B3.

Example: If datasets = {"A1", "A2", "A3"} and b_datasets = {"B1", "B2", "B3"}, the function computes: A1 %*% B1, A2 %*% B2, and A3 %*% B3

5 Examples

\donttest{
fn <- tempfile(fileext = ".h5")
Y <- matrix(rnorm(100), 10, 10)
X <- matrix(rnorm(100), 10, 10)
Z <- matrix(rnorm(100), 10, 10)

hdf5_create_matrix(fn, "data/Y", data = Y)
hdf5_create_matrix(fn, "data/X", data = X)
hdf5_create_matrix(fn, "data/Z", data = Z)

dsets <- list_datasets(fn, group = "data")

bdapply_Function_hdf5(filename = fn,
                      group = "data", datasets = dsets,
                      outgroup = "QR", func = "QR",
                      overwrite = TRUE)
hdf5_close_all()
unlink(fn)
}