// gmmbin/gmm-gselect.cc
  
  // Copyright 2009-2011   Saarland University;  Microsoft Corporation
  //                2013   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/diag-gmm.h"
  #include "hmm/transition-model.h"
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using std::vector;
      typedef kaldi::int32 int32;
      const char *usage =
          "Precompute Gaussian indices for pruning
  "
          " (e.g. in training UBMs, SGMMs, tied-mixture systems)
  "
          " For each frame, gives a list of the n best Gaussian indices,
  "
          " sorted from best to worst.
  "
          "See also: gmm-global-get-post, fgmm-global-gselect-to-post,
  "
          "copy-gselect, fgmm-gselect
  "
          "Usage: gmm-gselect [options] <model-in> <feature-rspecifier> <gselect-wspecifier>
  "
          "The --gselect option (which takes an rspecifier) limits selection to a subset
  "
          "of indices:
  "
          "e.g.: gmm-gselect \"--gselect=ark:gunzip -c bigger.gselect.gz|\" --n=20 1.gmm \"ark:feature-command |\" \"ark,t:|gzip -c >gselect.1.gz\"
  ";
      
      ParseOptions po(usage);
      int32 num_gselect = 50;
      std::string gselect_rspecifier;
      std::string likelihood_wspecifier;
      po.Register("n", &num_gselect, "Number of Gaussians to keep per frame
  ");
      po.Register("write-likes", &likelihood_wspecifier, "rspecifier for likelihoods per "
                  "utterance");
      po.Register("gselect", &gselect_rspecifier, "rspecifier for gselect objects "
                  "to limit the search to");
      po.Read(argc, argv);
  
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_filename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          gselect_wspecifier = po.GetArg(3);
  
      DiagGmm gmm;
      ReadKaldiObject(model_filename, &gmm);
      KALDI_ASSERT(num_gselect > 0);
      int32 num_gauss = gmm.NumGauss();
      if (num_gselect > num_gauss) {
        KALDI_WARN << "You asked for " << num_gselect << " Gaussians but GMM "
                   << "only has " << num_gauss << ", returning this many. "
                   << "Note: this means the Gaussian selection is pointless.";
        num_gselect = num_gauss;
      }
      
      double tot_like = 0.0;
      kaldi::int64 tot_t = 0;
      
      SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
      Int32VectorVectorWriter gselect_writer(gselect_wspecifier);
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier); // may be ""
      BaseFloatWriter likelihood_writer(likelihood_wspecifier); // may be ""
  
      int32 num_done = 0, num_err = 0;
      for (; !feature_reader.Done(); feature_reader.Next()) {
        int32 tot_t_this_file = 0; double tot_like_this_file = 0;
        std::string utt = feature_reader.Key();
        const Matrix<BaseFloat> &mat = feature_reader.Value();
        vector<vector<int32> > gselect(mat.NumRows());
        tot_t_this_file += mat.NumRows();
        if(gselect_rspecifier != "") { // Limit Gaussians to preselected group...
          if (!gselect_reader.HasKey(utt)) {
            KALDI_WARN << "No gselect information for utterance " << utt;
            num_err++;
            continue;
          }
          const vector<vector<int32> > &preselect = gselect_reader.Value(utt);
          if (preselect.size() != static_cast<size_t>(mat.NumRows())) {
            KALDI_WARN << "Input gselect for utterance " << utt << " has wrong size "
                       << preselect.size() << " vs. " << mat.NumRows();
            num_err++;
            continue;
          }
          for (int32 i = 0; i < mat.NumRows(); i++)
            tot_like_this_file +=
                gmm.GaussianSelectionPreselect(mat.Row(i), preselect[i],
                                               num_gselect, &(gselect[i]));
        } else { // No "preselect" [i.e. no existing gselect]: simple case.
          tot_like_this_file =
              gmm.GaussianSelection(mat, num_gselect, &gselect);
        }
        
        gselect_writer.Write(utt, gselect);
        if (num_done % 10 == 0)
          KALDI_LOG << "For " << num_done << "'th file, average UBM likelihood over "
                    << tot_t_this_file << " frames is "
                    << (tot_like_this_file/tot_t_this_file);
        tot_t += tot_t_this_file;
        tot_like += tot_like_this_file;
        
        if(likelihood_wspecifier != "")
          likelihood_writer.Write(utt, tot_like_this_file);
        num_done++;
      }
  
      KALDI_LOG << "Done " << num_done << " files, " << num_err
                << " with errors, average UBM log-likelihood is "
                << (tot_like/tot_t) << " over " << tot_t << " frames.";
      
      if (num_done != 0) return 0;
      else return 1;
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }