mir.model.lda.hoffman

struct LdaHoffman(F) if (isFloatingPoint!F);

Batch variational Bayes for LDA with mini-batches.

this(size_t K, size_t W, size_t D, F alpha, F eta, F tau0, F kappa, F eps = 1e-05, TaskPool tp = taskPool());

Parameters:

size_t K	theme count
size_t W	dictionary size
size_t D	approximate total number of documents in a collection.
F alpha	Dirichlet document-topic prior (0.1)
F eta	Dirichlet word-topic prior (0.1)
F tau0	tau0 ≧ 0 slows down the early iterations of the algorithm.
F kappa	kappa belongs to (0.5, 1], controls the rate at which old values of lambda are forgotten. lambda = (1 - rho(tau)) lambda + rho lambda', rho(tau) = (tau0 + tau)^(-kappa). Use kappa = 0 for Batch variational Bayes LDA.
F eps	Stop iterations if ||lambda - lambda'||_l1 < s * eps, where s is a documents count in a batch.
TaskPool tp	task pool

void updateBeta();

@property Slice!(F*, 2) beta();

Posterior over the topics

@property Slice!(F*, 2) lambda();

Parameterized posterior over the topics.

const @property F tau();

@property void tau(F v);

Count of already seen documents. Slows down the iterations of the algorithm.

size_t putBatch(SliceKind kind, C, I, J)(Slice!(ChopIterator!(J*, Series!(I*, C*)), 1, kind) n, size_t maxIterations);

Accepts mini-batch and performs multiple E-step iterations for each document and single M-step.

This implementation is optimized for sparse documents, which contain much less unique words than a dictionary.

Parameters:

Slice!(ChopIterator!(J*, Series!(I*, C*)), 1, kind) n	mini-batch, a collection of compressed documents.
size_t maxIterations	maximal number of iterations for s This implementation is optimized for sparse documents, ingle document in a batch for E-step.