// ivectorbin/logistic-regression-eval.cc
  
  // Copyright 2014  David Snyder
  
  // See ../../COPYING for clarification regarding multiple authors
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //  http://www.apache.org/licenses/LICENSE-2.0
  //
  // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  // MERCHANTABLITY OR NON-INFRINGEMENT.
  // See the Apache 2 License for the specific language governing permissions and
  // limitations under the License.
  
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "ivector/logistic-regression.h"
  
  using namespace kaldi;
  
  int ComputeLogPosteriors(ParseOptions &po,
    const LogisticRegressionConfig &config,
    bool apply_log) {
    std::string model = po.GetArg(1),
        vector_rspecifier = po.GetArg(2),
        log_posteriors_wspecifier = po.GetArg(3);
  
    LogisticRegression classifier;
    ReadKaldiObject(model, &classifier);
  
    std::vector<Vector<BaseFloat> > vectors;
    SequentialBaseFloatVectorReader vector_reader(vector_rspecifier);
    BaseFloatVectorWriter posterior_writer(log_posteriors_wspecifier);
    std::vector<std::string> utt_list;
    int32 num_utt_done = 0;
  
    for (; !vector_reader.Done(); vector_reader.Next()) {
      std::string utt = vector_reader.Key();
      const Vector<BaseFloat> &vector = vector_reader.Value();
      Vector<BaseFloat> log_posteriors;
      classifier.GetLogPosteriors(vector, &log_posteriors);
      if (!apply_log)
        log_posteriors.ApplyExp();
      posterior_writer.Write(utt, log_posteriors);
      num_utt_done++;
    }
    KALDI_LOG << "Calculated log posteriors for " << num_utt_done << " vectors.";
    return (num_utt_done == 0 ? 1 : 0);
  }
  
  int32 ComputeScores(ParseOptions &po, const LogisticRegressionConfig &config,
                      bool apply_log) {
    std::string model_rspecifier = po.GetArg(1),
        trials_rspecifier = po.GetArg(2),
        vector_rspecifier = po.GetArg(3),
        scores_out = po.GetArg(4);
  
    SequentialInt32Reader class_reader(trials_rspecifier);
    LogisticRegression classifier = LogisticRegression();
    ReadKaldiObject(model_rspecifier, &classifier);
  
    std::vector<Vector<BaseFloat> > vectors;
    std::vector<int32> ys;
    std::vector<std::string> utt_list;
    int32 num_utt_done = 0, num_utt_err = 0;
  
    RandomAccessBaseFloatVectorReader vector_reader(vector_rspecifier);
    for (; !class_reader.Done(); class_reader.Next()) {
      std::string utt = class_reader.Key();
      int32 class_label = class_reader.Value();
      if (!vector_reader.HasKey(utt)) {
        KALDI_WARN << "No vector for utterance " << utt;
        num_utt_err++;
      } else {
        utt_list.push_back(utt);
        ys.push_back(class_label);
        const Vector<BaseFloat> &vector = vector_reader.Value(utt);
        vectors.push_back(vector);
        num_utt_done++;
      }
    }
  
    if (vectors.empty()) {
      KALDI_WARN << "Read no input";
      return 1;
    }
  
    Matrix<BaseFloat> xs(vectors.size(), vectors[0].Dim());
    for (int i = 0; i < vectors.size(); i++) {
      xs.Row(i).CopyFromVec(vectors[i]);
    }
  
    Matrix<BaseFloat> log_posteriors;
    classifier.GetLogPosteriors(xs, &log_posteriors);
  
    bool binary = false;
    Output ko(scores_out.c_str(), binary);
  
    if (!apply_log)
      log_posteriors.ApplyExp();
  
    for (int i = 0; i < ys.size(); i++) {
      ko.Stream() << utt_list[i] << " " << ys[i] << " " << log_posteriors(i, ys[i]) << std::endl;
    }
    KALDI_LOG << "Calculated scores for " << num_utt_done
              << " vectors with "
              << num_utt_err << " missing. ";
    return (num_utt_done == 0 ? 1 : 0);
  }
  
  int main(int argc, char *argv[]) {
    using namespace kaldi;
    typedef kaldi::int32 int32;
    try {
      const char *usage =
          "Evaluates a model on input vectors and outputs either
  "
          "log posterior probabilities or scores.
  "
          "Usage1: logistic-regression-eval <model> <input-vectors-rspecifier>
  "
          "                                <output-log-posteriors-wspecifier>
  "
          "Usage2: logistic-regression-eval <model> <trials-file> <input-vectors-rspecifier>
  "
          "                                <output-scores-file>
  ";
  
    ParseOptions po(usage);
  
    bool apply_log = true;
    po.Register("apply-log", &apply_log,
                "If false, apply Exp to the log posteriors output. This is "
                "helpful when combining posteriors from multiple logistic "
                "regression models.");
    LogisticRegressionConfig config;
    config.Register(&po);
    po.Read(argc, argv);
  
    if (po.NumArgs() != 3 && po.NumArgs() != 4) {
      po.PrintUsage();
      exit(1);
    }
  
    if (po.NumArgs() == 4) {
      return ComputeScores(po, config, apply_log);
    } else {
      return ComputeLogPosteriors(po, config, apply_log);
    }
  
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }