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// 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 <vector>
#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<double> weights_; ///< weights (not log).
Matrix<double> means_; ///< Means
Matrix<double> vars_; ///< diagonal variance
KALDI_DISALLOW_COPY_AND_ASSIGN(DiagGmmNormal);
};
} // End namespace kaldi
#endif // KALDI_GMM_DIAG_GMM_NORMAL_H_
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