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// gmm/full-gmm-normal.h
// Copyright 2009-2011 Microsoft Corporation; Saarland University;
// Yanmin Qian
// Univ. Erlangen-Nuremberg, Korbinian Riedhammer
// 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_FULL_GMM_NORMAL_H_
#define KALDI_GMM_FULL_GMM_NORMAL_H_ 1
#include <vector>
#include "base/kaldi-common.h"
#include "gmm/model-common.h"
#include "gmm/full-gmm.h"
#include "matrix/matrix-lib.h"
namespace kaldi {
class FullGmm;
/** \class FullGmmNormal
* Definition for Gaussian Mixture Model with full covariances in normal
* mode: where the parameters are stored as means and variances (instead of
* the exponential form that the FullGmm 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 FullGmm
* class. The FullGmmNormal class will not be used anywhere else, and should
* not have any extra methods that are not needed.
*/
class FullGmmNormal {
public:
/// Empty constructor.
FullGmmNormal() { }
explicit FullGmmNormal(const FullGmm &gmm) {
CopyFromFullGmm(gmm);
}
/// Resizes arrays to this dim. Does not initialize data.
void Resize(int32 nMix, int32 dim);
/// Copies from given FullGmm
void CopyFromFullGmm(const FullGmm &fullgmm);
/// Copies to FullGmm
void CopyToFullGmm(FullGmm *fullgmm, GmmFlagsType flags = kGmmAll);
/// Generates random features from the model.
void Rand(MatrixBase<BaseFloat> *feats);
Vector<double> weights_; ///< weights (not log).
Matrix<double> means_; ///< Means
std::vector<SpMatrix<double> > vars_; ///< covariances
KALDI_DISALLOW_COPY_AND_ASSIGN(FullGmmNormal);
};
} // End namespace kaldi
#endif // KALDI_GMM_FULL_GMM_NORMAL_H_
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