// gmmbin/gmm-est.cc
  
  // Copyright 2009-2011  Microsoft Corporation
  
  // 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 "tree/context-dep.h"
  #include "hmm/transition-model.h"
  #include "gmm/mle-am-diag-gmm.h"
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      typedef kaldi::int32 int32;
  
      const char *usage =
          "Do Maximum Likelihood re-estimation of GMM-based acoustic model
  "
          "Usage:  gmm-est [options] <model-in> <stats-in> <model-out>
  "
          "e.g.: gmm-est 1.mdl 1.acc 2.mdl
  ";
  
      bool binary_write = true;
      MleTransitionUpdateConfig tcfg;
      MleDiagGmmOptions gmm_opts;
      int32 mixup = 0;
      int32 mixdown = 0;
      BaseFloat perturb_factor = 0.01;
      BaseFloat power = 0.2;
      BaseFloat min_count = 20.0;
      std::string update_flags_str = "mvwt";
      std::string occs_out_filename;
  
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      po.Register("mix-up", &mixup, "Increase number of mixture components to "
                  "this overall target.");
      po.Register("min-count", &min_count,
                  "Minimum per-Gaussian count enforced while mixing up and down.");
      po.Register("mix-down", &mixdown, "If nonzero, merge mixture components to this "
                  "target.");
      po.Register("power", &power, "If mixing up, power to allocate Gaussians to"
                  " states.");
      po.Register("update-flags", &update_flags_str, "Which GMM parameters to "
                  "update: subset of mvwt.");
      po.Register("perturb-factor", &perturb_factor, "While mixing up, perturb "
                  "means by standard deviation times this factor.");
      po.Register("write-occs", &occs_out_filename, "File to write pdf "
                  "occupation counts to.");
      tcfg.Register(&po);
      gmm_opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
      kaldi::GmmFlagsType update_flags =
          StringToGmmFlags(update_flags_str);
  
      std::string model_in_filename = po.GetArg(1),
          stats_filename = po.GetArg(2),
          model_out_filename = po.GetArg(3);
  
      AmDiagGmm am_gmm;
      TransitionModel trans_model;
      {
        bool binary_read;
        Input ki(model_in_filename, &binary_read);
        trans_model.Read(ki.Stream(), binary_read);
        am_gmm.Read(ki.Stream(), binary_read);
      }
  
      Vector<double> transition_accs;
      AccumAmDiagGmm gmm_accs;
      {
        bool binary;
        Input ki(stats_filename, &binary);
        transition_accs.Read(ki.Stream(), binary);
        gmm_accs.Read(ki.Stream(), binary, true);  // true == add; doesn't matter here.
      }
  
      if (update_flags & kGmmTransitions) {  // Update transition model.
        BaseFloat objf_impr, count;
        trans_model.MleUpdate(transition_accs, tcfg, &objf_impr, &count);
        KALDI_LOG << "Transition model update: Overall " << (objf_impr/count)
                  << " log-like improvement per frame over " << (count)
                  << " frames.";
      }
  
      {  // Update GMMs.
        BaseFloat objf_impr, count;
        BaseFloat tot_like = gmm_accs.TotLogLike(),
            tot_t = gmm_accs.TotCount();
        MleAmDiagGmmUpdate(gmm_opts, gmm_accs, update_flags, &am_gmm,
                           &objf_impr, &count);
        KALDI_LOG << "GMM update: Overall " << (objf_impr/count)
                  << " objective function improvement per frame over "
                  <<  count <<  " frames";
        KALDI_LOG << "GMM update: Overall avg like per frame = "
                  << (tot_like/tot_t) << " over " << tot_t << " frames.";
      }
  
      if (mixup != 0 || mixdown != 0 || !occs_out_filename.empty()) {
        // get pdf occupation counts
        Vector<BaseFloat> pdf_occs;
        pdf_occs.Resize(gmm_accs.NumAccs());
        for (int i = 0; i < gmm_accs.NumAccs(); i++)
          pdf_occs(i) = gmm_accs.GetAcc(i).occupancy().Sum();
  
        if (mixdown != 0)
          am_gmm.MergeByCount(pdf_occs, mixdown, power, min_count);
  
        if (mixup != 0)
          am_gmm.SplitByCount(pdf_occs, mixup, perturb_factor,
                              power, min_count);
  
        if (!occs_out_filename.empty()) {
          bool binary = false;
          WriteKaldiObject(pdf_occs, occs_out_filename, binary);
        }
      }
  
      {
        Output ko(model_out_filename, binary_write);
        trans_model.Write(ko.Stream(), binary_write);
        am_gmm.Write(ko.Stream(), binary_write);
      }
  
      KALDI_LOG << "Written model to " << model_out_filename;
      return 0;
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }