// gmmbin/gmm-acc-stats.cc
  
  // Copyright 2009-2012  Microsoft Corporation  Johns Hopkins University (Author: Daniel Povey)
  //                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 "gmm/mle-am-diag-gmm.h"
  #include "hmm/posterior.h"
  
  
  int main(int argc, char *argv[]) {
    using namespace kaldi;
    typedef kaldi::int32 int32;
    try {
      const char *usage =
          "Accumulate stats for GMM training (reading in posteriors).
  "
          "Usage:  gmm-acc-stats [options] <model-in> <feature-rspecifier>"
          "<posteriors-rspecifier> <stats-out>
  "
          "e.g.: 
  "
          " gmm-acc-stats 1.mdl scp:train.scp ark:1.post 1.acc
  ";
  
      ParseOptions po(usage);
      bool binary = true;
      std::string update_flags_str = "mvwt"; // note: t is ignored, we acc
      // transition stats regardless.
      po.Register("binary", &binary, "Write output in binary mode");
      po.Register("update-flags", &update_flags_str, "Which GMM parameters will be "
                  "updated: subset of mvwt.");
      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),
          accs_wxfilename = po.GetArg(4);
  
  
      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);
      }
  
      Vector<double> transition_accs;
      trans_model.InitStats(&transition_accs);
      AccumAmDiagGmm gmm_accs;
      gmm_accs.Init(am_gmm, StringToGmmFlags(update_flags_str));
  
      double tot_like = 0.0;
      double tot_t = 0.0;
  
      SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
      RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
  
      int32 num_done = 0, num_err = 0;
      for (; !feature_reader.Done(); feature_reader.Next()) {
        std::string key = feature_reader.Key();
        if (!posteriors_reader.HasKey(key)) {
          KALDI_WARN << "Could not find posteriors for utterance " << key;
          num_err++;
        } else {
          const Matrix<BaseFloat> &mat = feature_reader.Value();
          const Posterior &posterior = posteriors_reader.Value(key);
  
          if (static_cast<int32>(posterior.size()) != mat.NumRows()) {
            KALDI_WARN << "Posterior vector has wrong size " 
                       << (posterior.size()) << " vs. "
                       << (mat.NumRows());
            num_err++;
            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++) {
            // Accumulates for GMM.
            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;
              tot_like_this_file += gmm_accs.AccumulateForGmm(am_gmm, mat.Row(i), pdf_id, weight)
                  * weight;
              tot_weight += weight;
            }
  
            // Accumulates for transitions.
            for (size_t j = 0; j < posterior[i].size(); j++) {
              int32 tid = posterior[i][j].first;
              BaseFloat weight = posterior[i][j].second;
              trans_model.Accumulate(weight, tid, &transition_accs);
            }
          }
          if (num_done % 50 == 0) {
            KALDI_LOG << "Processed " << num_done << " utterances; for utterance "
                      << key << " avg. like is " << (tot_like_this_file/tot_weight)
                      << " over " << tot_weight <<" frames.";
          }
          tot_like += tot_like_this_file;
          tot_t += tot_weight;
        }
      }
  
      KALDI_LOG << "Done " << num_done << " files, " << num_err
                << " with errors.";
      
      KALDI_LOG << "Overall avg like per frame (Gaussian only) = "
                << (tot_like/tot_t) << " over " << tot_t << " frames.";
  
      {
        Output ko(accs_wxfilename, binary);
        transition_accs.Write(ko.Stream(), binary);
        gmm_accs.Write(ko.Stream(), binary);
      }
      KALDI_LOG << "Written accs.";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }