// gmmbin/gmm-acc-stats-twofeats.cc
  
  // Copyright 2009-2011  Microsoft Corporation
  //                2014  Guoguo Chen
  //                2014  Johns Hopkins University (author: Daniel Povey)
  
  // 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;
    try {
      const char *usage =
          "Accumulate stats for GMM training, computing posteriors with one set of features
  "        
          "but accumulating statistics with another.
  "
          "First features are used to get posteriors, second to accumulate stats
  "        
          "Usage:  gmm-acc-stats-twofeats [options] <model-in> <feature1-rspecifier> <feature2-rspecifier> <posteriors-rspecifier> <stats-out>
  "
          "e.g.: 
  "
          " gmm-acc-stats-twofeats 1.mdl 1.ali scp:train.scp scp:train_new.scp ark:1.ali 1.acc
  ";
  
      ParseOptions po(usage);
      bool binary = true;
      po.Register("binary", &binary, "Write output in binary mode");
      po.Read(argc, argv);
  
      if (po.NumArgs() != 5) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_filename = po.GetArg(1),
          feature1_rspecifier = po.GetArg(2),
          feature2_rspecifier = po.GetArg(3),
          posteriors_rspecifier = po.GetArg(4),
          accs_wxfilename = po.GetArg(5);
  
      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);
      }
  
      Vector<double> transition_accs;
      trans_model.InitStats(&transition_accs);
      int32 new_dim = 0;
      AccumAmDiagGmm gmm_accs;
      // will initialize once we know new_dim.
  
      double tot_like = 0.0;
      double tot_t = 0.0;
  
      SequentialBaseFloatMatrixReader feature1_reader(feature1_rspecifier);
      RandomAccessBaseFloatMatrixReader feature2_reader(feature2_rspecifier);
      RandomAccessPosteriorReader posteriors_reader(posteriors_rspecifier);
  
      int32 num_done = 0, num_no2ndfeats = 0, num_no_posterior = 0, num_other_error = 0;
      for (; !feature1_reader.Done(); feature1_reader.Next()) {
        std::string key = feature1_reader.Key();
        if (!feature2_reader.HasKey(key)) {
          KALDI_WARN << "For utterance " << key << ", second features not present.";
          num_no2ndfeats ++;
        } else if (!posteriors_reader.HasKey(key)) {
          num_no_posterior++;
        } else {
          const Matrix<BaseFloat> &mat1 = feature1_reader.Value();
          const Matrix<BaseFloat> &mat2 = feature2_reader.Value(key);
          KALDI_ASSERT(mat1.NumRows() == mat2.NumRows());
          if (new_dim == 0) {
            new_dim = mat2.NumCols();
            gmm_accs.Init(am_gmm, new_dim, kGmmAll);
          }
          const Posterior &posterior = posteriors_reader.Value(key);
  
          if (posterior.size() != mat1.NumRows()) {
            KALDI_WARN << "Posteriors has wrong size "<< (posterior.size()) << " vs. "<< (mat1.NumRows());
            num_other_error++;
            continue;
          }
          if (mat1.NumRows() != mat2.NumRows()) {
            KALDI_WARN << "Features have mismatched numbers of frames "
                       << mat1.NumRows() << " vs. " << mat2.NumRows();
            num_other_error++;
            continue;
          }
  
          num_done++;
          BaseFloat tot_like_this_file = 0.0,
              tot_weight_this_file = 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 += weight *
                  gmm_accs.AccumulateForGmmTwofeats(am_gmm,
                                                    mat1.Row(i),
                                                    mat2.Row(i),
                                                    pdf_id,
                                                    weight);
              tot_weight_this_file += 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);
            }
          }
          KALDI_LOG << "Average like for this file is "
                    << (tot_like_this_file/tot_weight_this_file) << " over "
                    << tot_weight_this_file <<" frames.";
          tot_like += tot_like_this_file;
          tot_t += tot_weight_this_file;
          if (num_done % 10 == 0)
            KALDI_LOG << "Avg like per frame so far is " << (tot_like/tot_t);
        }
      }
  
      KALDI_LOG << "Done " << num_done << " files, " << num_no_posterior
                << " with no posteriors, " << num_no2ndfeats
                << " with no second features, " << num_other_error
                << " with other 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.";
      if (num_done != 0) return 0;
      else return 1;
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }