Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

cache total word counts per category #4

Merged
merged 1 commit into from
Nov 9, 2014
Merged

cache total word counts per category #4

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 16 additions & 7 deletions lib/classifier-reborn/bayes.rb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ def initialize(*categories)
categories.each { |category| @categories[CategoryNamer.prepare_name(category)] = Hash.new }
@total_words = 0
@category_counts = Hash.new(0)
@category_word_count = Hash.new
end

# Provides a general training method for all categories specified in Bayes#new
Expand All @@ -24,10 +25,12 @@ def initialize(*categories)
# b.train "The other", "The other text"
def train(category, text)
category = CategoryNamer.prepare_name(category)
@category_counts[category] += 1
@category_word_count[category] ||= 0
@category_counts[category] += 1
Hasher.word_hash(text).each do |word, count|
@categories[category][word] ||= 0
@categories[category][word] += count
@category_word_count[category] += count
@total_words += count
end
end
Expand All @@ -41,16 +44,21 @@ def train(category, text)
# b.untrain :this, "This text"
def untrain(category, text)
category = CategoryNamer.prepare_name(category)
@category_word_count[category] ||= 0
@category_counts[category] -= 1
Hasher.word_hash(text).each do |word, count|
if @total_words >= 0
orig = @categories[category][word] || 0
@categories[category][word] ||= 0
@categories[category][word] -= count
@categories[category][word] ||= 0
@categories[category][word] -= count
if @categories[category][word] <= 0
@categories[category].delete(word)
count = orig
end

if @category_word_count[category] >= count
@category_word_count[category] -= count
end
@total_words -= count
end
end
Expand All @@ -62,13 +70,14 @@ def untrain(category, text)
# The largest of these scores (the one closest to 0) is the one picked out by #classify
def classifications(text)
score = Hash.new
training_count = @category_counts.values.reduce(0, :+).to_f
word_hash = Hasher.word_hash(text)
training_count = @category_counts.values.reduce(:+).to_f
@categories.each do |category, category_words|
score[category.to_s] = 0
total = category_words.values.reduce(0, :+)
Hasher.word_hash(text).each do |word, count|
total = (@category_word_count[category] || 1).to_f
word_hash.each do |word, count|
s = category_words.has_key?(word) ? category_words[word] : 0.1
score[category.to_s] += Math.log(s/total.to_f)
score[category.to_s] += Math.log(s/total)
end
# now add prior probability for the category
s = @category_counts.has_key?(category) ? @category_counts[category] : 0.1
Expand Down