require(quanteda)
require(wordvector)
require(LSX)
require(testthat)

# original function
as.textmodel_lss0 <- function(x, seeds) {

  prob <- probability(x, names(seeds), "values")
  seeds <- seeds[names(seeds) %in% rownames(prob)]
  res <- list(model = prob,
              seeds = seeds,
              beta = rowMeans(prob %*% diag(seeds)),
              frequency = x$frequency)
  class(res) <- "textmodel_lss"
  return(res)
}

toks <- tokens(data_corpus_news2014) %>%
  tokens_remove(stopwords(), min_nchar = 2) %>%
  tokens_tolower()

seed <- as.seedwords(data_dictionary_sentiment)

wdv <- textmodel_word2vec(toks, dim = 100, type = "skip-gram", normalize = FALSE,
                          verbpse = TRUE)
lss_wdv0 <- as.textmodel_lss0(wdv, seed)
lss_wdv <- as.textmodel_lss(wdv, seed, spatial = FALSE)

expect_setequal(
  names(lss_wdv0$beta),
  names(lss_wdv$beta)
)

expect_equal(
  cor(coef(lss_wdv0), coef(lss_wdv)),
  1.0
)