// sgmm2/decodable-am-sgmm2.cc
  
  // Copyright 2009-2012  Saarland University;  Lukas Burget;
  //                      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 <vector>
  using std::vector;
  
  #include "sgmm2/decodable-am-sgmm2.h"
  
  namespace kaldi {
  
  
  DecodableAmSgmm2::~DecodableAmSgmm2() {
    if (delete_vars_) {
      delete gselect_;
      delete feature_matrix_;
      delete spk_;
    }
  }
  
  BaseFloat DecodableAmSgmm2::LogLikelihoodForPdf(int32 frame, int32 pdf_id) {
    if (frame != cur_frame_) {
      cur_frame_ = frame;
      sgmm_cache_.NextFrame(); // it has a frame-index internally but it doesn't
      // have to match up with our index here, it just needs to be unique.
  
  
      SubVector<BaseFloat> data(*feature_matrix_, frame);
      
      sgmm_.ComputePerFrameVars(data, (*gselect_)[frame], *spk_,
                                &per_frame_vars_);
    }
    return sgmm_.LogLikelihood(per_frame_vars_, pdf_id, &sgmm_cache_, spk_,
                               log_prune_);  
  }
  
  
  }  // namespace kaldi