LDAPrototype {ldaPrototype} | R Documentation |
Determine the Prototype LDA
Description
Performs multiple runs of LDA and computes the Prototype LDA of this set of LDAs.
Usage
LDAPrototype(
docs,
vocabLDA,
vocabMerge = vocabLDA,
n = 100,
seeds,
id = "LDARep",
pm.backend,
ncpus,
limit.rel,
limit.abs,
atLeast,
progress = TRUE,
keepTopics = FALSE,
keepSims = FALSE,
keepLDAs = FALSE,
...
)
Arguments
docs |
[ |
vocabLDA |
[ |
vocabMerge |
[ |
n |
[ |
seeds |
[ |
id |
[ |
pm.backend |
[ |
ncpus |
[ |
limit.rel |
[0,1] |
limit.abs |
[ |
atLeast |
[ |
progress |
[ |
keepTopics |
[ |
keepSims |
[ |
keepLDAs |
[ |
... |
additional arguments passed to |
Details
While LDAPrototype
marks the overall shortcut for performing
multiple LDA runs and choosing the Prototype of them, getPrototype
just hooks up at determining the Prototype. The generation of multiple LDAs
has to be done before use of getPrototype
.
To save memory a lot of interim calculations are discarded by default.
If you use parallel computation, no progress bar is shown.
For details see the details sections of the workflow functions at getPrototype
.
Value
[named list
] with entries
id
[
character(1)
] See above.protoid
[
character(1)
] Name (ID) of the determined Prototype LDA.lda
List of
LDA
objects of the determined Prototype LDA and - ifkeepLDAs
isTRUE
- all considered LDAs.jobs
[
data.table
] with parameter specifications for the LDAs.param
[
named list
] with parameter specifications forlimit.rel
[0,1],limit.abs
[integer(1)
] andatLeast
[integer(1)
]. See above for explanation.topics
[
named matrix
] with the count of vocabularies (row wise) in topics (column wise).sims
[
lower triangular named matrix
] with all pairwise jaccard similarities of the given topics.wordslimit
[
integer
] with counts of words determined as relevant based onlimit.rel
andlimit.abs
.wordsconsidered
[
integer
] with counts of considered words for similarity calculation. Could differ fromwordslimit
, ifatLeast
is greater than zero.sclop
[
symmetrical named matrix
] with all pairwise S-CLOP scores of the given LDA runs.
See Also
Other shortcut functions:
getPrototype()
Other PrototypeLDA functions:
getPrototype()
,
getSCLOP()
Other replication functions:
LDARep()
,
as.LDARep()
,
getJob()
,
mergeRepTopics()
Examples
res = LDAPrototype(docs = reuters_docs, vocabLDA = reuters_vocab,
n = 4, K = 10, num.iterations = 30)
res
getPrototype(res) # = getLDA(res)
getSCLOP(res)
res = LDAPrototype(docs = reuters_docs, vocabLDA = reuters_vocab,
n = 4, K = 10, num.iterations = 30, keepLDAs = TRUE)
res
getLDA(res, all = TRUE)
getPrototypeID(res)
getParam(res)