WIG Model

library(rwig) |> suppressPackageStartupMessages()

# create a small dataset
wigdf <- data.frame(
  ref_date = as.Date(c("2012-01-01", "2012-02-01")),
  docs = c("this is a sentence", "this is another sentence")
)

wigfit <- wig(wigdf, ref_date, docs, specs = wig_specs(
  wdl_control = list(num_topics = 2),
  word2vec_control = list(min_count = 1)
))
#> Preprocessing the data...
#> Running tokenizer on the sentences...
#> Running Word2Vec for the embeddings and distance matrix...
#> `method` is automatically switched to "log"
#> Running WDL in CUDA mode...
#> This might take a while depending on the problem size...
#> Initializing WDL model with 3 vocabs, 2 docs, and 2 topics...
#> Training WDL model with 2 epochs, 1 batches
#> Epoch 1 of 2, batch 1 of 1
#>   batch time: 0.00 sec
#> Epoch 2 of 2, batch 1 of 1
#>   batch time: 0.00 sec
#> Inference on the dataset
#>   Inference: 2 of 2 docs done
wigfit
#> WIG model from 2012-01-01 to 2012-02-01
#> WDL model topics:
#> 
#> Topic 1:
#> sentenc    </s>   anoth 
#>    0.44    0.41    0.15