Initialize LDA/STM state from a (d, v, c) sparse count matrix.

Given a document-term matrix in triplet form (d, v, c) using 0-based indices, this function initializes the LDA state: - samples initial topic assignments z, - constructs document-topic counts nd, - constructs topic-word counts nw, - computes ndsum, nwsum, and normalized topic proportions X.

Usage

init_mod_from_count(count, K = NULL, phi = NULL, seed = NULL)

Arguments

count

Integer matrix with 3 columns representing triples (d, v, c), where d and v are 0-based indices.

K

Integer, number of topics. Required if `phi` is NULL. If `phi` is provided, K is inferred from ncol(phi).

phi

Optional numeric matrix of size V x K specifying per-word topic probabilities used only during initialization.

seed

Optional integer random seed.

Value

A list with components:

z

Integer vector (length NZ) of sampled topics, 0-based.

nd

DxK document-topic count matrix.

nw

KxV topic-word count matrix.

ndsum

Integer vector (length D) with row sums of nd.

nwsum

Integer vector (length K) with row sums of nw.

X

DxK matrix of normalized topic proportions nd / ndsum.

D

Number of documents.

V

Vocabulary size.

K

Number of topics.

NZ

Number of non-zero entries (rows in count).

Details

If a topic-word probability matrix `phi` is provided (V x K), initial topics are sampled according to phi[v+1, ]. Otherwise, topics are sampled uniformly from K topics.