hdf5_apply: Apply a mathematical operation to multiple HDF5 datasets

Description

Standalone function that applies a predefined algebraic or statistical operation to a list of datasets stored in an HDF5 group, writing results to outgroup. No open HDF5Matrix object is required.

This is not equivalent to base::apply(). It dispatches built-in C++ operations (QR, cross-product, Cholesky, etc.) to a batch of named datasets in the file.

Usage

hdf5_apply(
  filename,
  group,
  datasets,
  func,
  outgroup,
  b_group = NULL,
  b_datasets = NULL,
  overwrite = FALSE,
  transp_dataset = FALSE,
  transp_bdataset = FALSE,
  fullMatrix = FALSE,
  byrows = FALSE,
  threads = NULL
)

Value

Invisibly NULL. Results written to outgroup. Open them with hdf5_matrix().

Arguments

filename: Path to the HDF5 file.
group: Group path containing datasets.
datasets: Character vector of dataset names to process.
func: Character. Operation to apply (see Details).
outgroup: Character. Output group path for results.
b_group: Character or NULL. Group of B datasets.
b_datasets: Character vector or NULL. Names of B datasets.
overwrite: Logical. Overwrite existing output datasets.
transp_dataset: Logical. Transpose A datasets before operation.
transp_bdataset: Logical. Transpose B datasets before operation.
fullMatrix: Logical. Return full matrix (not triangular).
byrows: Logical. Apply by rows (for normalize/sdmean).
threads: Integer or NULL. OpenMP threads.

Details

Use hdf5_apply() when you only have the file path and group name. If you already have an open HDF5Matrix, use apply_function(x, ...) instead — both produce the same result.

"CrossProd": Compute \(A^T A\) for each dataset.
"tCrossProd": Compute \(A A^T\) for each dataset.
"CrossProd_double": Double-precision cross-product.
"tCrossProd_double": Double-precision transposed cross-product.
"blockmult": Block-wise \(A \times B\) (requires b_datasets).
"QR": QR decomposition for each dataset.
"invChol": Inverse via Cholesky for each dataset.
"solve": Solve \(AX = B\) (requires b_datasets).
"normalize": Column-wise normalization.
"sdmean": Compute SD and mean.
"descChol": Cholesky decomposition.

Examples

Run this code

# \donttest{
tmp <- tempfile(fileext = ".h5")
A <- hdf5_create_matrix(tmp, "inp/A", data = matrix(rnorm(25), 5, 5))
B <- hdf5_create_matrix(tmp, "inp/B", data = matrix(rnorm(25), 5, 5))
hdf5_apply(tmp, group = "inp", datasets = c("A", "B"), 
func = "CrossProd", outgroup = "out")
res <- list_datasets(tmp)
res
res_A <- hdf5_matrix(tmp, res[3])
dim(res_A)   # 5 x 5
close(res_A)
hdf5_close_all()
unlink(tmp)
# }