Public methods
Method new()
Creates GloVe model object
Usage
GloVe$new(
rank,
x_max,
learning_rate = 0.15,
alpha = 0.75,
lambda = 0,
shuffle = FALSE,
init = list(w_i = NULL, b_i = NULL, w_j = NULL, b_j = NULL)
)
Arguments
rank
desired dimension for the latent vectors
x_max
integer
maximum number of co-occurrences to use in the weighting function
learning_rate
numeric
learning rate for SGD. I do not recommend that you
modify this parameter, since AdaGrad will quickly adjust it to optimal
alpha
numeric = 0.75
the alpha in weighting function formula :
\(f(x) = 1 if x > x_max; else (x/x_max)^alpha\)
lambda
numeric = 0.0
regularization parameter
shuffle
see shuffle
field
init
list(w_i = NULL, b_i = NULL, w_j = NULL, b_j = NULL)
initialization for embeddings (w_i, w_j) and biases (b_i, b_j).
w_i, w_j
- numeric matrices, should have #rows = rank, #columns =
expected number of rows (w_i) / columns(w_j) in the input matrix.
b_i, b_j
= numeric vectors, should have length of
#expected number of rows(b_i) / columns(b_j) in input matrix
Method fit_transform()
fits model and returns embeddings
Usage
GloVe$fit_transform(
x,
n_iter = 10L,
convergence_tol = -1,
n_threads = getOption("rsparse_omp_threads", 1L),
...
)
Arguments
x
An input term co-occurence matrix. Preferably in dgTMatrix
format
n_iter
integer
number of SGD iterations
convergence_tol
numeric = -1
defines early stopping strategy. Stop fitting
when one of two following conditions will be satisfied: (a) passed
all iterations (b) cost_previous_iter / cost_current_iter - 1 <
convergence_tol
.
n_threads
number of threads to use
...
not used at the moment
Method get_history()
returns value of the loss function for each epoch
Usage
GloVe$get_history()
Method clone()
The objects of this class are cloneable with this method.
Usage
GloVe$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.