// transform/cmvn.h
  
  // Copyright 2009-2013 Microsoft Corporation
  //                     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.
  
  
  #ifndef KALDI_TRANSFORM_CMVN_H_
  #define KALDI_TRANSFORM_CMVN_H_
  
  #include "base/kaldi-common.h"
  #include "matrix/matrix-lib.h"
  
  namespace kaldi {
  
  /// This function initializes the matrix to dimension 2 by (dim+1);
  /// 1st "dim" elements of 1st row are mean stats, 1st "dim" elements
  /// of 2nd row are var stats, last element of 1st row is count,
  /// last element of 2nd row is zero.
  void InitCmvnStats(int32 dim, Matrix<double> *stats);
  
  /// Accumulation from a single frame (weighted).
  void AccCmvnStats(const VectorBase<BaseFloat> &feat,
                    BaseFloat weight,
                    MatrixBase<double> *stats);
  
  /// Accumulation from a feature file (possibly weighted-- useful in excluding silence).
  void AccCmvnStats(const MatrixBase<BaseFloat> &feats,
                    const VectorBase<BaseFloat> *weights,  // or NULL
                    MatrixBase<double> *stats);
  
  /// Apply cepstral mean and variance normalization to a matrix of features.
  /// If norm_vars == true, expects stats to be of dimension 2 by (dim+1), but
  /// if norm_vars == false, will accept stats of dimension 1 by (dim+1); these
  /// are produced by the balanced-cmvn code when it computes an offset and
  /// represents it as "fake stats".
  void ApplyCmvn(const MatrixBase<double> &stats,
                 bool norm_vars,
                 MatrixBase<BaseFloat> *feats);
  
  /// This is as ApplyCmvn, but does so in the reverse sense, i.e. applies a transform
  /// that would take zero-mean, unit-variance input and turn it into output with the
  /// stats of "stats".  This can be useful if you trained without CMVN but later want
  /// to correct a mismatch, so you would first apply CMVN and then do the "reverse"
  /// CMVN with the summed stats of your training data.
  void ApplyCmvnReverse(const MatrixBase<double> &stats,
                        bool norm_vars,
                        MatrixBase<BaseFloat> *feats);
  
  
  /// Modify the stats so that for some dimensions (specified in "dims"), we
  /// replace them with "fake" stats that have zero mean and unit variance; this
  /// is done to disable CMVN for those dimensions.
  void FakeStatsForSomeDims(const std::vector<int32> &dims,
                            MatrixBase<double> *stats);
                            
  
  
  }  // namespace kaldi
  
  #endif  // KALDI_TRANSFORM_CMVN_H_