likelihood.jl

push!(LOAD_PATH, "./src/")

using AdaGram
using AdaGram.ArgParse
using Distributed

s = ArgParseSettings()

@add_arg_table s begin
  "model"
    help = "path to serialized model"
    arg_type = AbstractString
    required = true
  "text"
    help = "text to measure likelihood"
    arg_type = AbstractString
    required = true
  "--window"
    help = "window size"
    arg_type = Int
    default = 5
  "--workers"
    help = "number of workers"
    arg_type = Int
    default = 1
  "--minprob"
    help = "minimum probability of a prototype"
    arg_type = Float64
    default = 0.005
  "--batch"
    help = "size of buffer read into memory"
    arg_type = Int
    default = 16777216
  "--log"
    help = "save intermediate averages to the file"
    arg_type = AbstractString
end

args = parse_args(ARGS, s)

addprocs(args["workers"])
@everywhere using AdaGram

vm, dict = load_model(args["model"])
println(parallel_likelihood(vm, dict, args["text"], args["window"], args["minprob"];
  batch=args["batch"], log=args["log"]))