// nnet2bin/nnet-adjust-priors.cc
  
  // Copyright 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 "nnet2/am-nnet.h"
  #include "hmm/transition-model.h"
  #include "tree/context-dep.h"
  
  namespace kaldi {
  namespace nnet2 {
  
  
  // Computes one-sided K-L divergence from p to q.
  BaseFloat KlDivergence(const Vector<BaseFloat> &p,
                         const Vector<BaseFloat> &q) {
    BaseFloat sum_p = p.Sum(), sum_q = q.Sum();
    if (fabs(sum_p - 1.0) > 0.01 || fabs(sum_q - 1.0) > 0.01) {
      KALDI_WARN << "KlDivergence: vectors are not close to being normalized "
                 << sum_p << ", " << sum_q;
    }
    KALDI_ASSERT(p.Dim() == q.Dim());
    double ans = 0.0;
  
    for (int32 i = 0; i < p.Dim(); i++) {
      BaseFloat p_prob = p(i) / sum_p, q_prob = q(i) / sum_q;
      ans += p_prob * Log(p_prob / q_prob);
    }
    return ans;
  }
  
  void PrintPriorDiagnostics(const Vector<BaseFloat> &old_priors,
                             const Vector<BaseFloat> &new_priors) {
    if (old_priors.Dim() == 0) {
      KALDI_LOG << "Model did not previously have priors attached.";
    } else {
      Vector<BaseFloat> diff_prior(new_priors);
      diff_prior.AddVec(-1.0, old_priors);
      diff_prior.ApplyAbs();
      int32 max_index;
      diff_prior.Max(&max_index);
      KALDI_LOG << "Adjusting priors: largest absolute difference was for "
                << "pdf " << max_index << ", " << old_priors(max_index)
                << " -> " << new_priors(max_index);
      KALDI_LOG << "Adjusting priors: K-L divergence from old to new is "
                << KlDivergence(old_priors, new_priors);
    }
  }
  
  
  } // namespace nnet2
  } // namespace kaldi
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet2;
      typedef kaldi::int32 int32;
  
      const char *usage =
          "Set the priors of the neural net to the computed posterios from the net,
  "
          "on typical data (e.g. training data). This is correct under more general
  "
          "circumstances than using the priors of the class labels in the training data
  "
          "
  "
          "Typical usage of this program will involve computation of an average pdf-level
  "
          "posterior with nnet-compute or nnet-compute-from-egs, piped into matrix-sum-rows
  "
          "and then vector-sum, to compute the average posterior
  "
          "
  "
          "Usage: nnet-adjust-priors [options] <nnet-in> <summed-posterior-vector-in> <nnet-out>
  "
          "e.g.:
  "
          " nnet-adjust-priors final.mdl prior.vec final.mdl
  ";
      
      bool binary_write = true;
      BaseFloat prior_floor = 1.0e-15; // Have a very low prior floor, since this method
                                       // isn't likely to have a problem with very improbable
                                       // classes.
      
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      po.Register("prior-floor", &prior_floor, "When setting priors, floor for "
                  "priors (only used to avoid generating NaNs upon inversion)");
  
      po.Read(argc, argv);
      
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string nnet_rxfilename = po.GetArg(1),
          posterior_vec_rxfilename = po.GetArg(2),
          nnet_wxfilename = po.GetArg(3);
      
      TransitionModel trans_model;
      AmNnet 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<BaseFloat> posterior_vec;
      ReadKaldiObject(posterior_vec_rxfilename, &posterior_vec);
  
      KALDI_ASSERT(posterior_vec.Sum() > 0.0);
      posterior_vec.Scale(1.0 / posterior_vec.Sum()); // Renormalize
      
      Vector<BaseFloat> old_priors(am_nnet.Priors());
  
      PrintPriorDiagnostics(old_priors, posterior_vec);
      
      am_nnet.SetPriors(posterior_vec);
          
      {
        Output ko(nnet_wxfilename, binary_write);
        trans_model.Write(ko.Stream(), binary_write);
        am_nnet.Write(ko.Stream(), binary_write);
      }
      KALDI_LOG << "Modified priors of neural network model and wrote it to "
                << nnet_wxfilename;
      return 0;
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }