somhca (version 0.3.0)

finalSOM: Train Final SOM Model

Description

Re-trains the SOM using a specified optimal grid size and number of iterations.

Usage

finalSOM(data, dimension, iterations, chunk = 100)

Value

A trained SOM model object.

Arguments

data

A preprocessed data matrix containing the input data for SOM training.

dimension

An integer specifying the dimension of the square SOM grid (e.g., 5 results in a 5x5 grid).

iterations

An integer defining the number of iterations for training the SOM model. Use a large value, e.g., 500 or higher, for improved training (an error message could suggest that reducing the number of iterations might be necessary). For larger grids, more iterations may be required to ensure convergence. Reducing iterations may speed training but risk under-trained neurons.

chunk

An integer specifying the number of iterations per training block. The SOM will be trained in chunks of this many iterations, with a progress message printed after each block. This helps notify the user that the function is running and not frozen. Default is 100. Larger values reduce the frequency of messages; smaller values provide more frequent updates but may slightly slow execution.

Examples

# Create a toy matrix with 9 columns and 100 rows
data <- matrix(rnorm(900), ncol = 9, nrow = 100)  # 900 random numbers, 100 rows, 9 columns

# Run the finalSOM function with the mock data
myFinalSOM <- finalSOM(data, dimension = 6, iterations = 700)