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#ifndef LM_NGRAM_QUERY__
#define LM_NGRAM_QUERY__
#include "lm/enumerate_vocab.hh"
#include "lm/model.hh"
#include "util/usage.hh"
#include <cstdlib>
#include <iostream>
#include <ostream>
#include <istream>
#include <string>
#include <math.h>
namespace lm {
namespace ngram {
template <class Model> 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<unsigned int>(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 << "</s>=" << model.GetVocabulary().EndSentence() << ' ' << static_cast<unsigned int>(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<double>(corpus_tokens))) << std::endl;
}
template <class M> 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__
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