diff --git a/src/TopicModels.jl b/src/TopicModels.jl
index 17ccde8..d1bd4e2 100644
--- a/src/TopicModels.jl
+++ b/src/TopicModels.jl
@@ -78,15 +78,17 @@ end
function initializeAssignments(model::Model)
for dd in 1:length(model.corpus)
- model.assignments[dd] = fill(0, length(model.corpus.documents[dd]))
- for ww in 1:length(model.corpus.documents[dd])
- word = model.corpus.documents[dd][ww]
+ @inbounds words = model.corpus.documents[dd]
+ @inbounds model.assignments[dd] = fill(0, length(words))
+ for ww in 1:length(words)
+ @inbounds word = words[ww]
topic = sampleMultinomial(model.alphaPrior)
- model.assignments[dd][ww] = topic
+ @inbounds model.assignments[dd][ww] = topic
updateSufficientStatistics(
word, topic, dd, model.corpus.weights[dd][ww], model)
end
end
+ return
end
function sampleMultinomial(p::Array{Float64,1})
@@ -109,11 +111,12 @@ function wordDistribution(word::Int,
out::Vector{Float64})
V = size(model.topics, 2)
for ii in 1:length(out)
- out[ii] = (model.documentSums[ii, document] + model.alphaPrior[ii]) *
- (model.topics[ii, word] + model.betaPrior) /
- (model.topicSums[ii] + V * model.betaPrior)
+ u = (model.documentSums[ii, document] + model.alphaPrior[ii]) *
+ (model.topics[ii, word] + model.betaPrior) /
+ (model.topicSums[ii] + V * model.betaPrior)
+ @inbounds out[ii] = u
end
- return out
+ return
end
function sampleWord(word::Int,
@@ -130,32 +133,37 @@ function updateSufficientStatistics(word::Int64,
document::Int64,
scale::Float64,
model::Model)
- model.documentSums[topic, document] += scale
- model.topicSums[topic] += scale * !model.frozen
- model.topics[topic, word] += scale * !model.frozen
+ fr = float64(!model.frozen)
+ @inbounds model.documentSums[topic, document] += scale
+ @inbounds model.topicSums[topic] += scale * fr
+ @inbounds model.topics[topic, word] += scale * fr
+ return
end
function sampleDocument(document::Int,
model::Model)
- words = model.corpus.documents[document]
+ @inbounds words = model.corpus.documents[document]
Nw = length(words)
- weights = model.corpus.weights[document]
+ @inbounds weights = model.corpus.weights[document]
K = length(model.alphaPrior)
p = Array(Float64, K)
+ @inbounds assignments = model.assignments[document]
for ii in 1:Nw
- word = words[ii]
- oldTopic = model.assignments[document][ii]
+ @inbounds word = words[ii]
+ @inbounds oldTopic = assignments[ii]
updateSufficientStatistics(word, oldTopic, document, -weights[ii], model)
- newTopic::Int64 = sampleWord(word, document, model, p)
- model.assignments[document][ii] = newTopic
+ newTopic = sampleWord(word, document, model, p)
+ @inbounds assignments[ii] = newTopic
updateSufficientStatistics(word, newTopic, document, weights[ii], model)
end
+ return
end
function sampleCorpus(model::Model)
for ii in 1:length(model.corpus)
sampleDocument(ii, model)
end
+ return
end
# Note, files are zero indexed, but we are 1-indexed.
@@ -171,6 +179,7 @@ function trainModel(model::Model,
println(string("Iteration ", ii, "..."))
sampleCorpus(model)
end
+ return
end
function topTopicWords(model::Model,
pFad - Phonifier reborn
Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.
Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.
Alternative Proxies:
Alternative Proxy
pFad Proxy
pFad v3 Proxy
pFad v4 Proxy