// bin/acc-lda.cc
  
  // Copyright 2009-2011  Microsoft Corporation, Go-Vivace Inc.
  //                2014  Guoguo Chen
  
  // 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 "hmm/posterior.h"
  #include "transform/lda-estimate.h"
  
  /** @brief Accumulate LDA statistics based on pdf-ids. Inputs are the
  source models, that serve as the input (and may potentially contain
  the current transformation), the un-transformed features and state
  posterior probabilities */
  int main(int argc, char *argv[]) {
    using namespace kaldi;
    typedef kaldi::int32 int32;
    try {
      const char *usage =
          "Accumulate LDA statistics based on pdf-ids.
  "
          "Usage:  acc-lda [options] <transition-gmm/model> <features-rspecifier> <posteriors-rspecifier> <lda-acc-out>
  "
          "Typical usage:
  "
          " ali-to-post ark:1.ali ark:- | acc-lda 1.mdl \"ark:splice-feats scp:train.scp|\"  ark:- ldaacc.1
  ";
  
      bool binary = true;
      BaseFloat rand_prune = 0.0;
      ParseOptions po(usage);
      po.Register("binary", &binary, "Write accumulators in binary mode.");
      po.Register("rand-prune", &rand_prune,
                  "Randomized pruning threshold for posteriors");
      po.Read(argc, argv);
  
      if (po.NumArgs() != 4) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_rxfilename = po.GetArg(1);
      std::string features_rspecifier = po.GetArg(2);
      std::string posteriors_rspecifier = po.GetArg(3);
      std::string acc_wxfilename = po.GetArg(4);
  
      TransitionModel trans_model;
      {
        bool binary_read;
        Input ki(model_rxfilename, &binary_read);
        trans_model.Read(ki.Stream(), binary_read);
        // discard rest of file.
      }
  
      LdaEstimate lda;
  
      SequentialBaseFloatMatrixReader feature_reader(features_rspecifier);
      RandomAccessPosteriorReader posterior_reader(posteriors_rspecifier);
  
      int32 num_done = 0, num_fail = 0;
      for (;!feature_reader.Done(); feature_reader.Next()) {
        std::string utt = feature_reader.Key();
        if (!posterior_reader.HasKey(utt)) {
          KALDI_WARN << "No posteriors for utterance " << utt;
          num_fail++;
          continue;
        }
        const Posterior &post (posterior_reader.Value(utt));
        const Matrix<BaseFloat> &feats(feature_reader.Value());
  
        if (lda.Dim() == 0)
          lda.Init(trans_model.NumPdfs(), feats.NumCols());
  
        if (feats.NumRows() != static_cast<int32>(post.size())) {
          KALDI_WARN << "Posterior vs. feats size mismatch "
                     << post.size() << " vs. " << feats.NumRows();
          num_fail++;
          continue;
        }
        if (lda.Dim() != 0 && lda.Dim() != feats.NumCols()) {
          KALDI_WARN << "Feature dimension mismatch " << lda.Dim()
                     << " vs. " << feats.NumCols();
          num_fail++;
          continue;
        }
  
        Posterior pdf_post;
        ConvertPosteriorToPdfs(trans_model, post, &pdf_post);
        for (int32 i = 0; i < feats.NumRows(); i++) {
          SubVector<BaseFloat> feat(feats, i);
          for (size_t j = 0; j < pdf_post[i].size(); j++) {
            int32 pdf_id = pdf_post[i][j].first;
            BaseFloat weight = RandPrune(pdf_post[i][j].second, rand_prune);
            if (weight != 0.0) {
              lda.Accumulate(feat, pdf_id, weight);
            }
          }
        }
        num_done++;
        if (num_done % 100 == 0)
          KALDI_LOG << "Done " << num_done << " utterances.";
      }
  
      KALDI_LOG << "Done " << num_done << " files, failed for "
                << num_fail;
  
      Output ko(acc_wxfilename, binary);
      lda.Write(ko.Stream(), binary);
      KALDI_LOG << "Written statistics.";
      return (num_done != 0 ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what();
      return -1;
    }
  }