hdf5_apply

AGGREGATIONS

1 Description

Applies one of several supported operations to a list of datasets stored in an HDF5 group. Delegates to bdapply_Function_hdf5().

2 Usage

hdf5_apply(...)

3 Arguments

Parameter	Description
`filename`	Path to the HDF5 file.
`group`	Group path containing .
`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 . Group of B datasets.
`b_datasets`	Character vector or . 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 . OpenMP threads.

4 Value

Invisibly . Results written to . Open them with .

5 Details

Supported values for func:

6 Examples

\donttest{
tmp <- tempfile(fileext = ".h5")
bdCreate_hdf5_matrix(tmp, matrix(rnorm(20), 4, 5), "inp", "A",
                     overwriteFile = FALSE)
bdCreate_hdf5_matrix(tmp, matrix(rnorm(20), 4, 5), "inp", "B",
                     overwriteFile = FALSE)
hdf5_apply(tmp, group = "inp", datasets = c("A", "B"),
           func = "CrossProd", outgroup = "out")
res_A <- hdf5_matrix(tmp, "out/A")
dim(res_A)   # 5 x 5
close(res_A)
unlink(tmp)
}

--- title: "hdf5_apply" subtitle: "hdf5_apply" --- <span class="category-badge aggregations">AGGREGATIONS</span> ## Description Applies one of several supported operations to a list of datasets stored in an HDF5 group. Delegates to `bdapply_Function_hdf5()`. ## Usage ```r hdf5_apply(...) ``` ## Arguments ::: {.param-table} | Parameter | Description | |-----------|-------------| | `filename` | Path to the HDF5 file. | | `group` | Group path containing \code{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 \code{NULL}. Group of B datasets. | | `b_datasets` | Character vector or \code{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 \code{NULL}. OpenMP threads. | ::: ## Value ::: {.return-value} Invisibly \code{NULL}. Results written to \code{outgroup}. Open them with \code{\link{hdf5_matrix}()}. ::: ## Details Supported values for `func`: \describe{ \item{`"CrossProd"`}{Compute \eqn{A^T A} for each dataset.} \item{`"tCrossProd"`}{Compute \eqn{A A^T} for each dataset.} \item{`"CrossProd_double"`}{Double-precision cross-product.} \item{`"tCrossProd_double"`}{Double-precision transposed cross-product.} \item{`"blockmult"`}{Block-wise \eqn{A \times B} (requires `b_datasets`).} \item{`"QR"`}{QR decomposition for each dataset.} \item{`"invChol"`}{Inverse via Cholesky for each dataset.} \item{`"solve"`}{Solve \eqn{AX = B} (requires `b_datasets`).} \item{`"normalize"`}{Column-wise normalization.} \item{`"sdmean"`}{Compute SD and mean.} \item{`"descChol"`}{Cholesky decomposition.} } ## Examples ```{r} #| eval: false #| warning: false \donttest{ tmp <- tempfile(fileext = ".h5") bdCreate_hdf5_matrix(tmp, matrix(rnorm(20), 4, 5), "inp", "A", overwriteFile = FALSE) bdCreate_hdf5_matrix(tmp, matrix(rnorm(20), 4, 5), "inp", "B", overwriteFile = FALSE) hdf5_apply(tmp, group = "inp", datasets = c("A", "B"), func = "CrossProd", outgroup = "out") res_A <- hdf5_matrix(tmp, "out/A") dim(res_A) # 5 x 5 close(res_A) unlink(tmp) } ``` ## See Also ::: {.see-also} [hdf5_reduce](hdf5_reduce.html), [crossprod.HDF5Matrix](../decompositions/crossprod.HDF5Matrix.html), [qr.HDF5Matrix](../decompositions/qr.HDF5Matrix.html) :::