<jupyter_start><jupyter_text>e)<jupyter_code>qgraph(1/distances, layout='spring', vsize=3, theme='Hollywood')
# Obtenemos las tarjetas más relacionadas entre sí
min_distance <- min(distances)
cat("Min distance between cards:", min_distance, "\n")

cat("Cards with higher similarity:\n")
min_indexes <- which(as.matrix(distances)==min_distance, arr.ind=TRUE)

names = colnames(data)
correlated_pairs <- data.frame(Item1=character(), Item2=character()) 
for (row in 1:nrow(min_indexes)) {
    new_row <- data.frame(names[min_indexes[row, "row"]],
                          names[min_indexes[row, "col"]])
    names(new_row) <- c("Item1", "Item2")
    
    correlated_pairs <- rbind(correlated_pairs, new_row)
}
correlated_pairs<jupyter_output>Min distance between cards: 1.414214 
Cards with higher similarity:
{'fraction_non_alphanumeric': 0.14356435643564355, 'fraction_numerical': 0.017326732673267328, 'mean_word_length': 5.420634920634921, 'pattern_counts': {'":': 0, '<': 10, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '492964', 'license': 'MIT', 'n_tokens_mistral': 301, 'n_tokens_neox': 285, 'n_words': 55, 'path': 'P3/p3.ipynb', 'repo_name': 'fjsaezm/mcd-gd', 'size': '808'}