handwriter (version 3.1.1)

make_clustering_template: Make Clustering Template

Description

make_clustering_template() applies a K-means clustering algorithm to the input handwriting samples pre-processed with process_batch_dir() and saved in the input folder main_dir > data > template_graphs. The K-means algorithm sorts the graphs in the input handwriting samples into groups, or clusters, of similar graphs.

Usage

make_clustering_template(
  main_dir,
  template_docs,
  writer_indices,
  centers_seed,
  K = 40,
  num_dist_cores = 1,
  max_iters = 25
)

Value

List containing the cluster template

Arguments

main_dir

Main directory that will store template files

template_docs

A directory containing template training images

writer_indices

A vector of the starting and ending location of the writer ID in the file name.

centers_seed

Integer seed for the random number generator when selecting starting cluster centers.

K

Integer number of clusters

num_dist_cores

Integer number of cores to use for the distance calculations in the K-means algorithm. Each iteration of the K-means algorithm calculates the distance between each input graph and each cluster center.

max_iters

Maximum number of iterations to allow the K-means algorithm to run

Examples

if (FALSE) {
main_dir <- "path/to/folder"
template_docs <- "path/to/template_training_docs"
template_list <- make_clustering_template(
  main_dir = main_dir,
  template_docs = template_docs,
  writer_indices = c(2, 5),
  K = 10,
  num_dist_cores = 2,
  max_iters = 25,
  centers_seed = 100,
)
}