LdaHoffman

Batch variational Bayes for LDA with mini-batches.

struct LdaHoffman (

F

) if (

isFloatingPoint!F

) {

import std.parallelism;;

import mir.ndslice.iterator : FieldIterator;;

import mir.ndslice.topology : iota;;

import mir.ndslice.slice;;

import mir.ndslice.allocation : slice;;

import mir.math.common;;

import mir.sparse;;

this(size_t K, size_t W, size_t D, F alpha, F eta, F tau0, F kappa, F eps, TaskPool tp);

void updateBeta();

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

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

void tau(F v);

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

}

Constructors

this this(): Undocumented in source.
this this(size_t K, size_t W, size_t D, F alpha, F eta, F tau0, F kappa, F eps, TaskPool tp)

Postblit

this(this) this(this): Undocumented in source.

Members

Functions

beta Slice!(F*, 2) beta(): Posterior over the topics
lambda Slice!(F*, 2) lambda(): Parameterized posterior over the topics.
putBatch size_t putBatch(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.
tau F tau()
void tau(F v): Count of already seen documents. Slows down the iterations of the algorithm.
updateBeta void updateBeta()

Meta

Source

See Implementation

mir model lda hoffman structs

LdaHoffman