BigDataStatMeth (version 1.0.3)

bdWriteOppsiteTriangularMatrix_hdf5: Write Upper/Lower Triangular Matrix

Description

Creates a symmetric matrix by mirroring values from one triangular part to the other in an HDF5-stored matrix. This function modifies the matrix in-place, either copying the upper triangular values to the lower triangular part or vice versa.

Usage

bdWriteOppsiteTriangularMatrix_hdf5(
  filename,
  group,
  dataset,
  copytolower = NULL,
  elementsBlock = 1000000L
)

Value

List with components. If an error occurs, all string values are returned as empty strings (""):

fn

Character string with the HDF5 filename

ds

Character string with the full dataset path to the modified matrix. The opposite triangular part is written to the same input dataset, completing the symmetric matrix (group/dataset)

Arguments

filename

Character string specifying the path to an existing HDF5 file

group

Character string indicating the input group containing the dataset

dataset

Character string specifying the dataset to be modified

copytolower

Logical. If TRUE, copies upper triangular to lower triangular. If FALSE (default), copies lower triangular to upper triangular.

elementsBlock

Integer defining the maximum number of elements to process in each block. Default is 1,000,000. For matrices larger than 5000x5000, automatically adjusted to number of rows or columns * 2.

Details

This function provides an efficient way to create symmetric matrices from triangular data. It operates directly on HDF5 datasets using block processing for memory efficiency. The function:

• Validates that the input matrix is square

• Processes the matrix in blocks for memory efficiency

• Performs in-place modification of the dataset

• Preserves the original values in the source triangular part

• Supports both upper-to-lower and lower-to-upper mirroring

The implementation uses block processing to handle large matrices efficiently, making it suitable for big data applications. The block size can be adjusted based on available memory and performance requirements.

References

• Golub, G. H., & Van Loan, C. F. (2013). Matrix Computations, 4th Edition. Johns Hopkins University Press.

• The HDF Group. (2000-2010). HDF5 User's Guide.

See Also

• bdCreate_hdf5_matrix for creating HDF5 matrices

Examples

library(BigDataStatMeth)

# Create a matrix with upper triangular values
X <- matrix(rnorm(100), 10, 10)
X.1 <- X
X[lower.tri(X)] <- 0

# Save to HDF5
bdCreate_hdf5_matrix("test_file.hdf5", X, "data", "X", 
                     overwriteFile = TRUE, 
                     overwriteDataset = FALSE, 
                     unlimited = FALSE)
                     
# Mirror upper triangular to lower
bdWriteOppsiteTriangularMatrix_hdf5(
  filename = "test_file.hdf5", 
  group = "data",
  dataset = "X",
  copytolower = TRUE,
  elementsBlock = 10
)

# Create a matrix with lower triangular values
X <- X.1
X[upper.tri(X)] <- 0

# Add to HDF5 file
bdCreate_hdf5_matrix("test_file.hdf5", X, "data", "Y", 
                     overwriteFile = FALSE, 
                     overwriteDataset = FALSE, 
                     unlimited = FALSE)
                     
# Mirror lower triangular to upper
bdWriteOppsiteTriangularMatrix_hdf5(
  filename = "test_file.hdf5", 
  group = "data",
  dataset = "Y",
  copytolower = FALSE,
  elementsBlock = 10
)

# Cleanup
if (file.exists("test_file.hdf5")) {
  file.remove("test_file.hdf5")
}