class GMM

Public Types

• typedef enum GMM_Mode

Public Methods

•  GMM (int nb_gauss, int dim, Covariance *(*cov_new)(int)) : gaussians(vector<RCPtr<Gaussian> >(nb_gauss)) , apriori (vector<float>(nb_gauss,0.0)) , nb_gaussians (nb_gauss) , mode(accum) , nb_frames_aligned(0) , dimensions (dim) , using_gaussianIDs(false)
•  GMM () : gaussians(vector<RCPtr<Gaussian> >()) , nb_gaussians (0) , mode(accum) , nb_frames_aligned(0) , dimensions(1) , using_gaussianIDs(false)
• void  save (string file)
• int  get_nb_gaussians () const
• Gaussian & gaussian (int i) const
• void  accum_to_gaussian (int i, const float * fr)
• void  init (vector<float * > frames)
• void  kmeans1 (vector<float * > frames, int nb_iterations = 1)
• void  split1 ()
• void  kmeans2 (vector<float * > frames, GMM *gmm)
• void  adaptMAP (vector<float * > frame, GMM *gmm)
• void  to_real ()
• void  reset_to_accum_mode ()
• Score  minDistance (float * fr, Covariance *cov) const
• Score  score (float * fr) const
• void  binary_split ()
• vector<Score>  minDistance (vector <float *> fr) const
• vector<Score>  score (vector <float *> fr) const
• void  toIDsUsing (GaussianSet & gauss)
• void  toPtrsUsing (const GaussianSet & gauss)
• DiagGMM * createDiagGMM ()
• virtual void  printOn (ostream &out=cout) const
• void  readFrom (istream &in=cin)
• friend istream & operator >> (istream &in, GMM &gmm)
• istream & operator >> (istream &in, GMM &gmm)

Protected Members

• vector<RCPtr<Gaussian> > gaussians
• vector<float> apriori
• int nb_gaussians
• int mode
• int nb_frames_aligned
• int dimensions
• bool using_gaussianIDs
• vector<int> gaussianIDs

[protected]

STL vector containing all the gaussians in the GMM

[protected]

STL vector containing all the apriori weights of the gaussians

[protected]

Number of gaussians in the GMM

[protected]

Whether of not the GMM trained (like real/accum mode) (GMM_Mode)

[protected]

Number of frames aligned to (used to train) the GMM

[protected]

Number of dimensions

[protected]

Was the gaussian loaded using indexes for mean

[protected]

STL vector containing all the gaussian IDs in the GMM

Construct a GMM with nb_gauss gaussians, dim dimensions and a covariance pseudo-factory

[const]

Returns the number of gaussians in the GMM

[const]

Returns the i'th gaussian

Accumulates (adds) the frame to the i'th gaussian

Randomly init the GMM with a list (STL vector) of frames

Performs k-means training

splits the largest gaussian in two

Performs k-means training (using another GMM to score)

Perform MAP adaptation (using another GMM to score)

Converts the GMM from accum mode to real mode

Converts the GMM from real mode to accum mode and set everything to zero

[const]

Score a frame against the GMM without using the covariances (nearest euclidian distance)

[const]

Score a frame against the GMM

Double the number of gaussians

[const]

Score a list (STL vector) of frames against the GMM without using the covariances (nearest euclidian distance)

[const]

Score a list (STL vector) of frames against the GMM

Creates a DiagGMM object from a GMM

[const virtual]

print function used for operator <<

Reimplemented from Object.

Read function used for operator >>

Reimplemented from Object.

extractor for GMM