bdDiag_scalar_hdf5

HDF5_ALGEBRA

1 Description

Performs optimized scalar operations on diagonal elements of matrices or vectors stored in HDF5 format. Automatically detects whether input is a matrix (extracts diagonal) or vector (direct operation) and applies the specified scalar operation.

2 Usage

bdDiag_scalar_hdf5(filename, group, dataset, scalar, operation, target = NULL, paral = NULL, threads = NULL, outgroup = NULL, outdataset = NULL, overwrite = NULL)

3 Arguments

Parameter	Description
`filename`	String. Path to the HDF5 file containing the dataset.
`group`	String. Group path containing the input dataset.
`dataset`	String. Name of the input dataset (matrix or vector).
`scalar`	Numeric. Scalar value for the operation.
`operation`	String. Operation to perform: “add”, “subtract”, “multiply”, “divide”.
`target`	Optional string. Where to write result: “input” or “new” (default: “new”).
`paral`	Optional logical. Whether to use parallel processing (default: FALSE).
`threads`	Optional integer. Number of threads for parallel processing.
`outgroup`	Optional string. Output group path (only used if target=“new”).
`outdataset`	Optional string. Output dataset name (only used if target=“new”).
`overwrite`	Optional logical. Whether to overwrite existing datasets (default: FALSE).

4 Value

List with components:

fn: Character string with the HDF5 filename
gr: Character string with the HDF5 group
ds: Character string with the full dataset path (group/dataset)

5 Details

This function provides flexible scalar operations on diagonals: - Supported operations: - “+”: diagonal[i] + scalar - “-”: diagonal[i] - scalar
- “”: diagonal[i] scalar -”/“: diagonal[i] / scalar -”pow”: diagonal[i] ^ scalar - Input types: - Matrix input: Extracts diagonal automatically - Vector input: Operates directly (most efficient) - Target options: - “input”: Modifies original dataset in-place - “new”: Creates new dataset with result

6 Examples

Code

library(BigDataStatMeth)

# Create test matrix
A <- matrix(rnorm(100), 10, 10)
bdCreate_hdf5_matrix("test.h5", A, "data", "matrix_A", overwriteFile = TRUE)

# Add scalar to diagonal (creates new dataset)
result <- bdDiag_scalar_hdf5("test.h5", "data", "matrix_A",
                            scalar = 5.0, operation = "+",
                            target = "new", outdataset = "diag_plus_5")

# Multiply diagonal in-place
result2 <- bdDiag_scalar_hdf5("test.h5", "data", "matrix_A", 
                             scalar = 2.0, operation = "*",
                             target = "input")

--- title: "bdDiag_scalar_hdf5" subtitle: "bdDiag_scalar_hdf5" --- <span class="category-badge hdf5_algebra">HDF5_ALGEBRA</span> ## Description Performs optimized scalar operations on diagonal elements of matrices or vectors stored in HDF5 format. Automatically detects whether input is a matrix (extracts diagonal) or vector (direct operation) and applies the specified scalar operation. ## Usage ```r bdDiag_scalar_hdf5(filename, group, dataset, scalar, operation, target = NULL, paral = NULL, threads = NULL, outgroup = NULL, outdataset = NULL, overwrite = NULL) ``` ## Arguments ::: {.param-table} | Parameter | Description | |-----------|-------------| | `filename` | String. Path to the HDF5 file containing the dataset. | | `group` | String. Group path containing the input dataset. | | `dataset` | String. Name of the input dataset (matrix or vector). | | `scalar` | Numeric. Scalar value for the operation. | | `operation` | String. Operation to perform: "add", "subtract", "multiply", "divide". | | `target` | Optional string. Where to write result: "input" or "new" (default: "new"). | | `paral` | Optional logical. Whether to use parallel processing (default: FALSE). | | `threads` | Optional integer. Number of threads for parallel processing. | | `outgroup` | Optional string. Output group path (only used if target="new"). | | `outdataset` | Optional string. Output dataset name (only used if target="new"). | | `overwrite` | Optional logical. Whether to overwrite existing datasets (default: FALSE). | ::: ## Value ::: {.return-value} List with components: - **`fn`**: Character string with the HDF5 filename - **`gr`**: Character string with the HDF5 group - **`ds`**: Character string with the full dataset path (group/dataset) ::: ## Details This function provides flexible scalar operations on diagonals: - Supported operations: - "+": diagonal\[i\] + scalar - "-": diagonal\[i\] - scalar - "*": diagonal\[i\] * scalar - "/": diagonal\[i\] / scalar - "pow": diagonal\[i\] ^ scalar - Input types: - Matrix input: Extracts diagonal automatically - Vector input: Operates directly (most efficient) - Target options: - "input": Modifies original dataset in-place - "new": Creates new dataset with result ## Examples ```{r} #| eval: false #| code-fold: show library(BigDataStatMeth) # Create test matrix A <- matrix(rnorm(100), 10, 10) bdCreate_hdf5_matrix("test.h5", A, "data", "matrix_A", overwriteFile = TRUE) # Add scalar to diagonal (creates new dataset) result <- bdDiag_scalar_hdf5("test.h5", "data", "matrix_A", scalar = 5.0, operation = "+", target = "new", outdataset = "diag_plus_5") # Multiply diagonal in-place result2 <- bdDiag_scalar_hdf5("test.h5", "data", "matrix_A", scalar = 2.0, operation = "*", target = "input") ```