// fgmmbin/fgmm-global-gselect-to-post.cc
  
  // Copyright   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/full-gmm.h"
  #include "hmm/posterior.h"
  
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "Given features and Gaussian-selection (gselect) information for
  "
          "a full-covariance GMM, output per-frame posteriors for the selected
  "
          "indices.  Also supports pruning the posteriors if they are below
  "
          "a stated threshold, (and renormalizing the rest to sum to one)
  "
          "See also: gmm-gselect, fgmm-gselect, gmm-global-get-post,
  "
          " gmm-global-gselect-to-post
  "
          "
  "
          "Usage:  fgmm-global-gselect-to-post [options] <model-in> <feature-rspecifier> "
          "<gselect-rspecifier> <post-wspecifier>
  "
          "e.g.: fgmm-global-gselect-to-post 1.ubm ark:- 'ark:gunzip -c 1.gselect|' ark:-
  ";
          
      ParseOptions po(usage);
  
      BaseFloat min_post = 0.0;
      po.Register("min-post", &min_post, "If nonzero, posteriors below this "
                  "threshold will be pruned away and the rest will be renormalized "
                  "to sum to one.");
      
      po.Read(argc, argv);
  
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_rxfilename = po.GetArg(1),
          feature_rspecifier = po.GetArg(2),
          gselect_rspecifier = po.GetArg(3),
          post_wspecifier = po.GetArg(4);
      
      FullGmm fgmm;
      ReadKaldiObject(model_rxfilename, &fgmm);
      
      double tot_loglike = 0.0, tot_frames = 0.0;
      int64 tot_posts = 0;
  
      SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);
      RandomAccessInt32VectorVectorReader gselect_reader(gselect_rspecifier);
      PosteriorWriter post_writer(post_wspecifier);
      int32 num_done = 0, num_err = 0;
  
      for (; !feature_reader.Done(); feature_reader.Next()) {
        std::string utt = feature_reader.Key();
        const Matrix<BaseFloat> &mat = feature_reader.Value();
  
        int32 num_frames = mat.NumRows();
        // typedef std::vector<std::vector<std::pair<int32, BaseFloat> > > Posterior;
        Posterior post(num_frames);
        
        if (!gselect_reader.HasKey(utt)) {
          KALDI_WARN << "No gselect information for utterance " << utt;
          num_err++;
          continue;
        }
        const std::vector<std::vector<int32> > &gselect(gselect_reader.Value(utt));
        if (static_cast<int32>(gselect.size()) != num_frames) {
          KALDI_WARN << "gselect information for utterance " << utt
                     << " has wrong size " << gselect.size() << " vs. "
                     << num_frames;
          num_err++;
          continue;
        }
  
        double this_tot_loglike = 0;
        bool utt_ok = true;
        
        for (int32 t = 0; t < num_frames; t++) {
          SubVector<BaseFloat> frame(mat, t);
          const std::vector<int32> &this_gselect = gselect[t];
          KALDI_ASSERT(!gselect[t].empty());
          Vector<BaseFloat> loglikes;
          fgmm.LogLikelihoodsPreselect(frame, this_gselect, &loglikes);
          this_tot_loglike += loglikes.ApplySoftMax();
          // now "loglikes" contains posteriors.
          if (fabs(loglikes.Sum() - 1.0) > 0.01) {
            utt_ok = false;
          } else {
            if (min_post != 0.0) {
              int32 max_index = 0; // in case all pruned away...
              loglikes.Max(&max_index);
              for (int32 i = 0; i < loglikes.Dim(); i++)
                if (loglikes(i) < min_post)
                  loglikes(i) = 0.0;
              BaseFloat sum = loglikes.Sum();
              if (sum == 0.0) {
                loglikes(max_index) = 1.0;
              } else {
                loglikes.Scale(1.0 / sum);
              }
            }
            for (int32 i = 0; i < loglikes.Dim(); i++) {
              if (loglikes(i) != 0.0) {
                post[t].push_back(std::make_pair(this_gselect[i], loglikes(i)));
                tot_posts++;
              }
            }
            KALDI_ASSERT(!post[t].empty());
          }
        }
        if (!utt_ok) {
          KALDI_WARN << "Skipping utterance " << utt
                    << " because bad posterior-sum encountered (NaN?)";
          num_err++;
        } else {
          post_writer.Write(utt, post);
          num_done++;
          KALDI_VLOG(2) << "Like/frame for utt " << utt << " was "
                        << (this_tot_loglike/num_frames) << " per frame over "
                        << num_frames << " frames.";
          tot_loglike += this_tot_loglike;
          tot_frames += num_frames;
        }
      }
  
      KALDI_LOG << "Done " << num_done << " files; " << num_err << " had errors.";
      KALDI_LOG << "Overall loglike per frame is " << (tot_loglike / tot_frames)
                << " with " << (tot_posts / tot_frames) << " entries per frame, "
                << " over " << tot_frames << " frames";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }