// gmmbin/gmm-post-to-gpost.cc
  
  // Copyright 2009-2011  Microsoft Corporation
  //                2014  Guoguo Chen
  
  // 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 "gmm/am-diag-gmm.h"
  #include "hmm/transition-model.h"
  #include "hmm/posterior.h"
  
  int main(int argc, char *argv[]) {
    using namespace kaldi;
    try {
      const char *usage =
          "Convert state-level posteriors to Gaussian-level posteriors
  "
          "Usage:  gmm-post-to-gpost [options] <model-in> <feature-rspecifier> <posteriors-rspecifier> "
          "<gpost-wspecifier>
  "
          "e.g.: 
  "
          " gmm-post-to-gpost 1.mdl scp:train.scp ark:1.post ark:1.gpost
  ";
  
      ParseOptions po(usage);
      bool binary = true;
      BaseFloat rand_prune = 0.0;
      po.Register("binary", &binary, "Write output in binary mode");
      po.Register("rand-prune", &rand_prune, "Randomized pruning of posteriors less than this");
      po.Read(argc, argv);
  
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_filename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          posteriors_rspecifier = po.GetArg(3),
          gpost_wspecifier = po.GetArg(4);
  
      using namespace kaldi;
      typedef kaldi::int32 int32;
  
      AmDiagGmm am_gmm;
      TransitionModel trans_model;
      {
        bool binary;
        Input ki(model_filename, &binary);
        trans_model.Read(ki.Stream(), binary);
        am_gmm.Read(ki.Stream(), binary);
      }
  
      double tot_like = 0.0;
      double tot_t = 0.0;
  
      SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
      RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
  
      GaussPostWriter gpost_writer(gpost_wspecifier);
  
      int32 num_done = 0, num_no_posterior = 0, num_other_error = 0;
      for (; !feature_reader.Done(); feature_reader.Next()) {
        std::string key = feature_reader.Key();
        if (!posteriors_reader.HasKey(key)) {
          num_no_posterior++;
        } else {
          const Matrix<BaseFloat> &mat = feature_reader.Value();
          const Posterior &posterior = posteriors_reader.Value(key);
          GaussPost gpost(posterior.size());
  
          if (posterior.size() != mat.NumRows()) {
            KALDI_WARN << "Posterior vector has wrong size "<< (posterior.size()) << " vs. "<< (mat.NumRows());
            num_other_error++;
            continue;
          }
  
          num_done++;
          BaseFloat tot_like_this_file = 0.0, tot_weight = 0.0;
  
          Posterior pdf_posterior;
          ConvertPosteriorToPdfs(trans_model, posterior, &pdf_posterior);
          for (size_t i = 0; i < posterior.size(); i++) {
            gpost[i].reserve(pdf_posterior[i].size());
            for (size_t j = 0; j < pdf_posterior[i].size(); j++) {
              int32 pdf_id = pdf_posterior[i][j].first;
              BaseFloat weight = pdf_posterior[i][j].second;
              const DiagGmm &gmm = am_gmm.GetPdf(pdf_id);
              Vector<BaseFloat> this_post_vec;
              BaseFloat like =
                  gmm.ComponentPosteriors(mat.Row(i), &this_post_vec);
              this_post_vec.Scale(weight);
              if (rand_prune > 0.0)
                for (int32 k = 0; k < this_post_vec.Dim(); k++)
                  this_post_vec(k) = RandPrune(this_post_vec(k),
                                               rand_prune);
              if (!this_post_vec.IsZero())
                gpost[i].push_back(std::make_pair(pdf_id, this_post_vec));
              tot_like_this_file += like * weight;
              tot_weight += weight;
            }
          }
          KALDI_VLOG(1) << "Average like for this file is "
                        << (tot_like_this_file/tot_weight) << " over "
                        << tot_weight <<" frames.";
          tot_like += tot_like_this_file;
          tot_t += tot_weight;
          gpost_writer.Write(key, gpost);
        }
      }
      KALDI_LOG << "Done " << num_done << " files, " << num_no_posterior
                << " with no posteriors, " << num_other_error
                << " with other errors.";
  
      KALDI_LOG << "Overall avg like per frame (Gaussian only) = "
                << (tot_like/tot_t) << " over " << tot_t << " frames.";
      
      KALDI_LOG << "Done converting post to gpost";
      if (num_done != 0) return 0;
      else return 1;
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }