// gmm/diag-gmm-normal.h // Copyright 2009-2011 Saarland University Korbinian Riedhammer Yanmin Qian // // 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_GMM_DIAG_GMM_NORMAL_H_ #define KALDI_GMM_DIAG_GMM_NORMAL_H_ 1 #include #include "base/kaldi-common.h" #include "gmm/model-common.h" #include "gmm/diag-gmm.h" #include "matrix/matrix-lib.h" namespace kaldi { class DiagGmm; /** \class DiagGmmNormal * Definition for Gaussian Mixture Model with diagonal covariances in normal * mode: where the parameters are stored as means and variances (instead of * the exponential form that the DiagGmm class is stored as). This class will * be used in the update (since the update formulas are for the standard * parameterization) and then copied to the exponential form of the DiagGmm * class. The DiagGmmNormal class will not be used anywhere else, and should * not have any extra methods that are not needed. */ class DiagGmmNormal { public: /// Empty constructor. DiagGmmNormal() { } explicit DiagGmmNormal(const DiagGmm &gmm) { CopyFromDiagGmm(gmm); } /// Resizes arrays to this dim. Does not initialize data. void Resize(int32 nMix, int32 dim); /// Copies from given DiagGmm void CopyFromDiagGmm(const DiagGmm &diaggmm); /// Copies to DiagGmm the requested parameters void CopyToDiagGmm(DiagGmm *diaggmm, GmmFlagsType flags = kGmmAll) const; int32 NumGauss() { return weights_.Dim(); } int32 Dim() { return means_.NumCols(); } Vector weights_; ///< weights (not log). Matrix means_; ///< Means Matrix vars_; ///< diagonal variance KALDI_DISALLOW_COPY_AND_ASSIGN(DiagGmmNormal); }; } // End namespace kaldi #endif // KALDI_GMM_DIAG_GMM_NORMAL_H_