Gecko [ www.jdcpatala.org ]

Name	Size	Permission
bayes_cache_check.lua	542 B	-rw-r--r--
bayes_cache_learn.lua	1.74 KB	-rw-r--r--
bayes_classify.lua	1.21 KB	-rw-r--r--
bayes_learn.lua	1.46 KB	-rw-r--r--
bayes_stat.lua	554 B	-rw-r--r--
neural_maybe_invalidate.lua	754 B	-rw-r--r--
neural_maybe_lock.lua	567 B	-rw-r--r--
neural_save_unlock.lua	744 B	-rw-r--r--
neural_train_size.lua	594 B	-rw-r--r--
ratelimit_check.lua	3.26 KB	-rw-r--r--
ratelimit_cleanup_pending.lua	1.09 KB	-rw-r--r--
ratelimit_update.lua	3.54 KB	-rw-r--r--

Code Editor : bayes_classify.lua

-- Lua script to perform bayes classification
-- This script accepts the following parameters:
-- key1 - prefix for bayes tokens (e.g. for per-user classification)
-- key2 - set of tokens encoded in messagepack array of strings

local prefix = KEYS[1]
local output_spam = {}
local output_ham = {}

local learned_ham = tonumber(redis.call('HGET', prefix, 'learns_ham')) or 0
local learned_spam = tonumber(redis.call('HGET', prefix, 'learns_spam')) or 0

-- Output is a set of pairs (token_index, token_count), tokens that are not
-- found are not filled.
-- This optimisation will save a lot of space for sparse tokens, and in Bayes that assumption is normally held

if learned_ham > 0 and learned_spam > 0 then
  local input_tokens = cmsgpack.unpack(KEYS[2])
  for i, token in ipairs(input_tokens) do
    local token_data = redis.call('HMGET', token, 'H', 'S')

if token_data then
      local ham_count = token_data[1]
      local spam_count = token_data[2]

if ham_count then
        table.insert(output_ham, { i, tonumber(ham_count) })
      end

if spam_count then
        table.insert(output_spam, { i, tonumber(spam_count) })
      end
    end
  end
end

return { learned_ham, learned_spam, output_ham, output_spam }

${this.title}

Code Editor : bayes_classify.lua