library(text2vec)
library(Rtsne)

text8_file = "~/text8"
if (!file.exists(text8_file)) {
  download.file("http://mattmahoney.net/dc/text8.zip", "~/text8.zip")
  unzip ("~/text8.zip", files = "text8", exdir = "~/")
}
wiki = readLines(text8_file, n = 1, warn = FALSE)

# Create iterator over tokens
tokens <- space_tokenizer(wiki)
# Create vocabulary. Terms will be unigrams (simple words).
it = itoken(tokens, progressbar = FALSE)
vocab <- create_vocabulary(it)
# At least 5 appearances
vocab <- prune_vocabulary(vocab, term_count_min = 5L)
# Use our filtered vocabulary
vectorizer <- vocab_vectorizer(vocab)
# Use window of 5 for context words
tcm <- create_tcm(it, vectorizer, skip_grams_window = 5L)

glove = GlobalVectors$new(word_vectors_size = 50, vocabulary = vocab, x_max = 10)
wv_main = glove$fit_transform(tcm, n_iter = 10, convergence_tol = 0.01)
dim(wv_main)
wv_context = glove$components
dim(wv_context)
# Word vectors add together
word_vectors = wv_main + t(wv_context)

cos_sim = sim2(x = word_vectors, y =   word_vectors["data", , drop = FALSE], method = "cosine", norm = "l2")
head(sort(cos_sim[,1], decreasing = TRUE), 10)