#ifndef LM_NGRAM_QUERY__ #define LM_NGRAM_QUERY__ #include "lm/enumerate_vocab.hh" #include "lm/model.hh" #include "util/usage.hh" #include #include #include #include #include #include namespace lm { namespace ngram { template void Query(const Model &model, bool sentence_context, std::istream &in_stream, std::ostream &out_stream) { typename Model::State state, out; lm::FullScoreReturn ret; std::string word; double corpus_total = 0.0; uint64_t corpus_oov = 0; uint64_t corpus_tokens = 0; while (in_stream) { state = sentence_context ? model.BeginSentenceState() : model.NullContextState(); float total = 0.0; bool got = false; uint64_t oov = 0; while (in_stream >> word) { got = true; lm::WordIndex vocab = model.GetVocabulary().Index(word); if (vocab == 0) ++oov; ret = model.FullScore(state, vocab, out); total += ret.prob; out_stream << word << '=' << vocab << ' ' << static_cast(ret.ngram_length) << ' ' << ret.prob << '\t'; ++corpus_tokens; state = out; char c; while (true) { c = in_stream.get(); if (!in_stream) break; if (c == '\n') break; if (!isspace(c)) { in_stream.unget(); break; } } if (c == '\n') break; } if (!got && !in_stream) break; if (sentence_context) { ret = model.FullScore(state, model.GetVocabulary().EndSentence(), out); total += ret.prob; ++corpus_tokens; out_stream << "=" << model.GetVocabulary().EndSentence() << ' ' << static_cast(ret.ngram_length) << ' ' << ret.prob << '\t'; } out_stream << "Total: " << total << " OOV: " << oov << '\n'; corpus_total += total; corpus_oov += oov; } out_stream << "Perplexity " << pow(10.0, -(corpus_total / static_cast(corpus_tokens))) << std::endl; } template void Query(const char *file, bool sentence_context, std::istream &in_stream, std::ostream &out_stream) { Config config; M model(file, config); Query(model, sentence_context, in_stream, out_stream); } } // namespace ngram } // namespace lm #endif // LM_NGRAM_QUERY__