// nnet3bin/nnet3-am-train-transitions.cc
  
  // Copyright 2012-2015  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 "hmm/transition-model.h"
  #include "nnet3/am-nnet-simple.h"
  #include "tree/context-dep.h"
  
  namespace kaldi {
  namespace nnet3 {
  void SetPriors(const TransitionModel &tmodel,
                 const Vector<double> &transition_accs,
                 double prior_floor,
                 AmNnetSimple *am_nnet) {
    KALDI_ASSERT(tmodel.NumPdfs() == am_nnet->NumPdfs());
    Vector<BaseFloat> pdf_counts(tmodel.NumPdfs());
    KALDI_ASSERT(transition_accs(0) == 0.0); // There is
    // no zero transition-id.
    for (int32 tid = 1; tid < transition_accs.Dim(); tid++) {
      int32 pdf = tmodel.TransitionIdToPdf(tid);
      pdf_counts(pdf) += transition_accs(tid);
    }
    BaseFloat sum = pdf_counts.Sum();
    KALDI_ASSERT(sum != 0.0);
    KALDI_ASSERT(prior_floor > 0.0 && prior_floor < 1.0);
    pdf_counts.Scale(1.0 / sum);
    pdf_counts.ApplyFloor(prior_floor);
    pdf_counts.Scale(1.0 / pdf_counts.Sum()); // normalize again.
    am_nnet->SetPriors(pdf_counts);
  }               
  
  
  } // namespace nnet3
  } // namespace kaldi
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
  
      const char *usage =
          "Train the transition probabilities of an nnet3 neural network acoustic model
  "
          "
  "
          "Usage:  nnet3-am-train-transitions [options] <nnet-in> <alignments-rspecifier> <nnet-out>
  "
          "e.g.:
  "
          " nnet3-am-train-transitions 1.nnet \"ark:gunzip -c ali.*.gz|\" 2.nnet
  ";
      
      bool binary_write = true;
      bool set_priors = true; // Also set the per-pdf priors in the model.
      BaseFloat prior_floor = 5.0e-06; // The default was previously 1e-8, but
                                       // once we had problems with a pdf-id that
                                       // was not being seen in training, being
                                       // recognized all the time.  This value
                                       // seemed to be the smallest prior of the
                                       // "seen" pdf-ids in one run.
      MleTransitionUpdateConfig transition_update_config;
      
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      po.Register("set-priors", &set_priors, "If true, also set priors in neural "
                  "net (we divide by these in test time)");
      po.Register("prior-floor", &prior_floor, "When setting priors, floor for "
                  "priors");
      transition_update_config.Register(&po);
      
      po.Read(argc, argv);
      
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string nnet_rxfilename = po.GetArg(1),
          ali_rspecifier = po.GetArg(2),
          nnet_wxfilename = po.GetArg(3);
      
      TransitionModel trans_model;
      AmNnetSimple am_nnet;
      {
        bool binary_read;
        Input ki(nnet_rxfilename, &binary_read);
        trans_model.Read(ki.Stream(), binary_read);
        am_nnet.Read(ki.Stream(), binary_read);
      }
      
      Vector<double> transition_accs;
      trans_model.InitStats(&transition_accs);
  
      int32 num_done = 0;
      SequentialInt32VectorReader ali_reader(ali_rspecifier);
      for (; ! ali_reader.Done(); ali_reader.Next()) {
        const std::vector<int32> alignment(ali_reader.Value());
        for (size_t i = 0; i < alignment.size(); i++) {
          int32 tid = alignment[i];
          BaseFloat weight = 1.0;
          trans_model.Accumulate(weight, tid, &transition_accs);
        }
        num_done++;
      }
      KALDI_LOG << "Accumulated transition stats from " << num_done
                << " utterances.";
  
      {
        BaseFloat objf_impr, count;
        trans_model.MleUpdate(transition_accs, transition_update_config,
                              &objf_impr, &count);
        KALDI_LOG << "Transition model update: average " << (objf_impr/count)
                  << " log-like improvement per frame over " << count
                  << " frames.";
      }
  
      if (set_priors) {
        KALDI_LOG << "Setting priors of pdfs in the model.";
        SetPriors(trans_model, transition_accs, prior_floor, &am_nnet);
      }
      
      {
        Output ko(nnet_wxfilename, binary_write);
        trans_model.Write(ko.Stream(), binary_write);
        am_nnet.Write(ko.Stream(), binary_write);
      }
      KALDI_LOG << "Trained transitions of neural network model and wrote it to "
                << nnet_wxfilename;
      return 0;
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }